Oracle Database 11g SQL Tuning Workshop.pdf

Oracle Database 11g: SQL Tuning Workshop

Student Guide

D52163GC20

Edition 2.0

October 2010

D69160

Vijai Sahu (sahuvijay21@gmailฺcom) has a non-transferable license

to use this Student Guideฺ

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Copyright © 2010, Oracle and/or its affiliates. All rights reserved. Disclaimer This document contains proprietary information and is protected by copyright and other intellectual property laws. You may copy and print this document solely for your own use in an Oracle training course. The document may not be modified or altered in any way. Except where your use constitutes "fair use" under copyright law, you may not use, share, download, upload, copy, print, display, perform, reproduce, publish, license, post, transmit, or distribute this document in whole or in part without the express authorization of Oracle. The information contained in this document is subject to change without notice. If you find any problems in the document, please report them in writing to: Oracle University, 500 Oracle Parkway, Redwood Shores, California 94065 USA. This document is not warranted to be error-free. Restricted Rights Notice If this documentation is delivered to the United States Government or anyone using the documentation on behalf of the United States Government, the following notice is applicable: U.S. GOVERNMENT RIGHTS The U.S. Government’s rights to use, modify, reproduce, release, perform, display, or disclose these training materials are restricted by the terms of the applicable Oracle license agreement and/or the applicable U.S. Government contract. Trademark Notice Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners.

Author

James Spiller, Tulika Srivastava

Technical Contributors and Reviewers

Abhinav Gupta, Branislav Valny, Clinton Shaffer, Donna Keesling, Ira Singer, Howard Bradley, Sean Kim, Sue Harper, Teria Kidd

This book was published using: Oracle Tutor



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Copyright © 2010, Oracle and/or its affiliates. All rights reserved.

Oracle Database 11g: SQL Tuning Workshop Table of Contents

i

Table of Contents

Exploring the Oracle Database Architecture .................................................................................................1-1 Exploring the Oracle Database Architecture ..................................................................................................1-2 Objectives ......................................................................................................................................................1-3 Oracle Database Server Architecture: Overview ............................................................................................1-4 Connecting to the Database Instance ............................................................................................................1-5 Oracle Database Memory Structures: Overview ............................................................................................1-7 Database Buffer Cache ..................................................................................................................................1-8 Redo Log Buffer .............................................................................................................................................1-9 Shared Pool ...................................................................................................................................................1-10 Processing a DML Statement: Example .........................................................................................................1-11 COMMIT Processing: Example ......................................................................................................................1-13 Large Pool ......................................................................................................................................................1-14 Java Pool and Streams Pool ..........................................................................................................................1-16 Program Global Area (PGA) ..........................................................................................................................1-17 Background Process ......................................................................................................................................1-18 Automatic Shared Memory Management .......................................................................................................1-20 Automated SQL Execution Memory Management .........................................................................................1-21 Automatic Memory Management ...................................................................................................................1-22 Database Storage Architecture ......................................................................................................................1-23 Logical and Physical Database Structures .....................................................................................................1-25 Segments, Extents, and Blocks......................................................................................................................1-27 SYSTEM and SYSAUX Tablespaces .............................................................................................................1-28 Quiz ................................................................................................................................................................1-29 Summary ........................................................................................................................................................1-32 Practice 1: Overview ......................................................................................................................................1-33

Introduction to SQL Tuning .............................................................................................................................2-1 Introduction to SQL Tuning ............................................................................................................................2-2 Objectives ......................................................................................................................................................2-3 Reasons for Inefficient SQL Performance ......................................................................................................2-4 Inefficient SQL: Examples ..............................................................................................................................2-6 Performance Monitoring Solutions .................................................................................................................2-8 Monitoring and Tuning Tools: Overview .........................................................................................................2-10 EM Performance Pages for Reactive Tuning .................................................................................................2-11 Tuning Tools: Overview .................................................................................................................................2-12 SQL Tuning Tasks: Overview.........................................................................................................................2-14 CPU and Wait Time Tuning Dimensions ........................................................................................................2-15 Scalability with Application Design, Implementation, and Configuration ........................................................2-16 Common Mistakes on Customer Systems .....................................................................................................2-17 Proactive Tuning Methodology .......................................................................................................................2-19 Simplicity in Application Design ......................................................................................................................2-20 Data Modeling ................................................................................................................................................2-21 Table Design ..................................................................................................................................................2-22 Index Design ..................................................................................................................................................2-23 Using Views ...................................................................................................................................................2-24 SQL Execution Efficiency ...............................................................................................................................2-25 Writing SQL to Share Cursors ........................................................................................................................2-27 Performance Checklist ...................................................................................................................................2-29



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Development Environments: Overview ..........................................................................................................2-30 What Is Oracle SQL Developer? ....................................................................................................................2-31 Coding PL/SQL in SQL*Plus ..........................................................................................................................2-32 Quiz ................................................................................................................................................................2-34 Summary ........................................................................................................................................................2-38 Practice 2: Overview ......................................................................................................................................2-39

Introduction to the Optimizer ..........................................................................................................................3-1 Introduction to the Optimizer ..........................................................................................................................3-2 Objectives ......................................................................................................................................................3-3 Structured Query Language ...........................................................................................................................3-4 SQL Statement Representation .....................................................................................................................3-6 SQL Statement Implementation .....................................................................................................................3-7 SQL Statement Processing: Overview ...........................................................................................................3-8 SQL Statement Processing: Steps .................................................................................................................3-9 Step 1: Create a Cursor .................................................................................................................................3-10 Step 2: Parse the Statement ..........................................................................................................................3-11 Steps 3 and 4: Describe and Define ...............................................................................................................3-12 Steps 5 and 6: Bind and Parallelize ...............................................................................................................3-13 Steps 7 Through 9 ..........................................................................................................................................3-14 SQL Statement Processing PL/SQL: Example ..............................................................................................3-15 SQL Statement Parsing: Overview .................................................................................................................3-16 Why Do You Need an Optimizer? ..................................................................................................................3-18 Optimization During Hard Parse Operation ....................................................................................................3-20 Transformer: OR Expansion Example ............................................................................................................3-21 Transformer: Subquery Unnesting Example ..................................................................................................3-22 Transformer: View Merging Example .............................................................................................................3-23 Transformer: Predicate Pushing Example ......................................................................................................3-24 Transformer: Transitivity Example ..................................................................................................................3-25 Cost-Based Optimizer ....................................................................................................................................3-26 Estimator: Selectivity ......................................................................................................................................3-27 Estimator: Cardinality .....................................................................................................................................3-29 Estimator: Cost...............................................................................................................................................3-30 Plan Generator ...............................................................................................................................................3-31 Controlling the Behavior of the Optimizer .......................................................................................................3-32 Optimizer Features and Oracle Database Releases ......................................................................................3-37 Quiz ................................................................................................................................................................3-38 Summary ........................................................................................................................................................3-41 Practice 3: Overview ......................................................................................................................................3-42

Interpreting Execution Plans ...........................................................................................................................4-1 Interpreting Execution Plans ..........................................................................................................................4-2 Objectives ......................................................................................................................................................4-3 What Is an Execution Plan? ...........................................................................................................................4-4 Where to Find Execution Plans? ....................................................................................................................4-6 Viewing Execution Plans ................................................................................................................................4-8 The EXPLAIN PLAN Command .....................................................................................................................4-9 The EXPLAIN PLAN Command: Example .....................................................................................................4-11 PLAN_TABLE ................................................................................................................................................4-12 Displaying from PLAN_TABLE: Typical .........................................................................................................4-14 Displaying from PLAN_TABLE: ALL ..............................................................................................................4-16



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The EXPLAIN PLAN Command .....................................................................................................................4-18 Displaying from PLAN_TABLE: ADVANCED .................................................................................................4-19 Explain Plan Using SQL Developer ................................................................................................................4-20 AUTOTRACE .................................................................................................................................................4-21 The AUTOTRACE Syntax ..............................................................................................................................4-22 AUTOTRACE: Examples ...............................................................................................................................4-23 AUTOTRACE: Statistics .................................................................................................................................4-24 AUTOTRACE Using SQL Developer .............................................................................................................4-26 Using the V$SQL_PLAN View .......................................................................................................................4-27 The V$SQL_PLAN Columns ..........................................................................................................................4-28 The V$SQL_PLAN_STATISTICS View ..........................................................................................................4-29 Links Between Important Dynamic Performance Views .................................................................................4-30 Querying V$SQL_PLAN .................................................................................................................................4-32 Automatic Workload Repository (AWR) .........................................................................................................4-34 Managing AWR with PL/SQL .........................................................................................................................4-36 Important AWR Views ....................................................................................................................................4-38 Querying the AWR .........................................................................................................................................4-40 Generating SQL Reports from AWR Data ......................................................................................................4-42 SQL Monitoring: Overview .............................................................................................................................4-43 SQL Monitoring Report: Example ...................................................................................................................4-45 Interpreting an Execution Plan .......................................................................................................................4-49 Execution Plan Interpretation: Example 1 ......................................................................................................4-51 Execution Plan Interpretation: Example 2 ......................................................................................................4-55 Execution Plan Interpretation: Example 3 ......................................................................................................4-57 Reading More Complex Execution Plans .......................................................................................................4-59 Reviewing the Execution Plan ........................................................................................................................4-60 Looking Beyond Execution Plans ...................................................................................................................4-62 Quiz ................................................................................................................................................................4-63 Summary ........................................................................................................................................................4-67 Practice 4: Overview ......................................................................................................................................4-68

Application Tracing ..........................................................................................................................................5-1 Application Tracing .........................................................................................................................................5-2 Objectives ......................................................................................................................................................5-3 End-to-End Application Tracing Challenge ....................................................................................................5-4 End-to-End Application Tracing......................................................................................................................5-5 Location for Diagnostic Traces .......................................................................................................................5-6 What Is a Service? .........................................................................................................................................5-7 Using Services with Client Applications .........................................................................................................5-8 Tracing Services ............................................................................................................................................5-9 Use Enterprise Manager to Trace Services ...................................................................................................5-11 Service Tracing: Example ..............................................................................................................................5-12 Session Level Tracing: Example ....................................................................................................................5-14 Trace Your Own Session ...............................................................................................................................5-16 The trcsess Utility ...........................................................................................................................................5-17 Invoking the trcsess Utility ..............................................................................................................................5-18 The trcsess Utility: Example ...........................................................................................................................5-20 SQL Trace File Contents ................................................................................................................................5-21 SQL Trace File Contents: Example ................................................................................................................5-23 Formatting SQL Trace Files: Overview ..........................................................................................................5-24



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Invoking the tkprof Utility ................................................................................................................................5-26 tkprof Sorting Options ....................................................................................................................................5-28 Output of the tkprof Command .......................................................................................................................5-30 tkprof Output with No Index: Example ............................................................................................................5-35 tkprof Output with Index: Example .................................................................................................................5-36 Quiz ................................................................................................................................................................5-37 Summary ........................................................................................................................................................5-40 Practice 5: Overview ......................................................................................................................................5-41

Optimizer Operators ........................................................................................................................................6-1 Optimizer Operators .......................................................................................................................................6-2 Objectives ......................................................................................................................................................6-3 Row Source Operations .................................................................................................................................6-4 Main Structures and Access Paths ................................................................................................................6-5 Full Table Scan ..............................................................................................................................................6-6 Full Table Scans: Use Cases .........................................................................................................................6-7 ROWID Scan..................................................................................................................................................6-9 Sample Table Scans ......................................................................................................................................6-10 Indexes: Overview ..........................................................................................................................................6-12 Normal B*-tree Indexes ..................................................................................................................................6-14 Index Scans ...................................................................................................................................................6-15 Index Unique Scan .........................................................................................................................................6-16 Index Range Scan ..........................................................................................................................................6-17 Index Range Scan: Descending .....................................................................................................................6-19 Descending Index Range Scan ......................................................................................................................6-20 Index Range Scan: Function-Based ...............................................................................................................6-21 Index Full Scan ..............................................................................................................................................6-22 Index Fast Full Scan ......................................................................................................................................6-23 Index Skip Scan .............................................................................................................................................6-24 Index Skip Scan: Example .............................................................................................................................6-26 Index Join Scan ..............................................................................................................................................6-27 B*-tree Indexes and Nulls ..............................................................................................................................6-28 Using Indexes: Considering Nullable Columns ..............................................................................................6-29 Index-Organized Tables .................................................................................................................................6-30 Index-Organized Table Scans ........................................................................................................................6-32 Bitmap Indexes ..............................................................................................................................................6-33 Bitmap Index Access: Examples ....................................................................................................................6-35 Combining Bitmap Indexes: Examples ...........................................................................................................6-37 Combining Bitmap Index Access Paths .........................................................................................................6-38 Bitmap Operations .........................................................................................................................................6-39 Bitmap Join Index ...........................................................................................................................................6-40 Composite Indexes ........................................................................................................................................6-42 Invisible Index: Overview ...............................................................................................................................6-43 Invisible Indexes: Examples ...........................................................................................................................6-44 Guidelines for Managing Indexes ...................................................................................................................6-45 Investigating Index Usage ..............................................................................................................................6-47 Quiz ................................................................................................................................................................6-49 Summary ........................................................................................................................................................6-52 Practice 6: Overview ......................................................................................................................................6-53

Optimizer: Join Operators ...............................................................................................................................7-1



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Optimizer: Join Operators ..............................................................................................................................7-2 Objectives ......................................................................................................................................................7-3 Join Methods ..................................................................................................................................................7-4 Nested Loops Join .........................................................................................................................................7-6 Nested Loops Join: Prefetching .....................................................................................................................7-7 Nested Loops Join: 11g Implementation ........................................................................................................7-8 Sort Merge Join ..............................................................................................................................................7-9 Hash Join .......................................................................................................................................................7-11 Cartesian Join ................................................................................................................................................7-12 Join Types ......................................................................................................................................................7-13 Equijoins and Nonequijoins ............................................................................................................................7-14 Outer Joins .....................................................................................................................................................7-15 Semijoins .......................................................................................................................................................7-17 Antijoins .........................................................................................................................................................7-18 Quiz ................................................................................................................................................................7-19 Summary ........................................................................................................................................................7-23 Practice 7: Overview ......................................................................................................................................7-24

Other Optimizer Operators ..............................................................................................................................8-1 Other Optimizer Operators .............................................................................................................................8-2 Objectives ......................................................................................................................................................8-3 Clusters ..........................................................................................................................................................8-4 When Are Clusters Useful? ............................................................................................................................8-6 Cluster Access Path: Examples .....................................................................................................................8-8 Sorting Operators ...........................................................................................................................................8-9 Buffer Sort Operator .......................................................................................................................................8-11 Inlist Iterator ...................................................................................................................................................8-12 View Operator ................................................................................................................................................8-13 Count Stop Key Operator ...............................................................................................................................8-14 Min/Max and First Row Operators ..................................................................................................................8-15 Other N-Array Operations ..............................................................................................................................8-16 FILTER Operations ........................................................................................................................................8-17 Concatenation Operation ...............................................................................................................................8-18 UNION [ALL], INTERSECT, MINUS ..............................................................................................................8-19 Result Cache Operator ..................................................................................................................................8-20 Quiz ................................................................................................................................................................8-21 Summary ........................................................................................................................................................8-25 Practice 8: Overview ......................................................................................................................................8-26

Case Study: Star Transformation ...................................................................................................................9-1 Case Study: Star Transformation ...................................................................................................................9-2 Objectives ......................................................................................................................................................9-3 The Star Schema Model ................................................................................................................................9-4 The Snowflake Schema Model.......................................................................................................................9-5 Star Query: Example ......................................................................................................................................9-6 Execution Plan Without Star Transformation .................................................................................................9-7 Star Transformation .......................................................................................................................................9-8 Star Transformation: Considerations ..............................................................................................................9-10 Star Transformation: Rewrite Example ..........................................................................................................9-11 Retrieving Fact Rows from One Dimension ...................................................................................................9-12 Retrieving Fact Rows from All Dimensions ....................................................................................................9-13



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Joining the Intermediate Result Set with Dimensions ...................................................................................9-14 Star Transformation Plan: Example 1 ............................................................................................................9-15 Star Transformation: Further Optimization .....................................................................................................9-16 Using Bitmap Join Indexes .............................................................................................................................9-17 Star Transformation Plan: Example 2 ............................................................................................................9-18 Star Transformation Hints ..............................................................................................................................9-19 Bitmap Join Indexes: Join Model 1 .................................................................................................................9-20 Bitmap Join Indexes: Join Model 2 .................................................................................................................9-21 Bitmap Join Indexes: Join Model 3 .................................................................................................................9-22 Bitmap Join Indexes: Join Model 4 .................................................................................................................9-23 Quiz ................................................................................................................................................................9-24 Summary ........................................................................................................................................................9-27 Practice 9: Overview ......................................................................................................................................9-28

Optimizer Statistics ..........................................................................................................................................10-1 Optimizer Statistics ........................................................................................................................................10-2 Objectives ......................................................................................................................................................10-3 Optimizer Statistics ........................................................................................................................................10-4 Types of Optimizer Statistics ..........................................................................................................................10-5 Table Statistics (DBA_TAB_STATISTICS) ....................................................................................................10-6 Index Statistics (DBA_IND_STATISTICS) .....................................................................................................10-7 Index Clustering Factor ..................................................................................................................................10-9 Column Statistics (DBA_TAB_COL_STATISTICS) ........................................................................................10-11 Histograms .....................................................................................................................................................10-12 Frequency Histograms ...................................................................................................................................10-13 Viewing Frequency Histograms......................................................................................................................10-14 Height-Balanced Histograms .........................................................................................................................10-15 Viewing Height-Balanced Histograms ............................................................................................................10-16 Histogram Considerations ..............................................................................................................................10-17 Multicolumn Statistics: Overview ....................................................................................................................10-18 Expression Statistics: Overview .....................................................................................................................10-20 Gathering System Statistics ...........................................................................................................................10-21 Gathering System Statistics: Example ...........................................................................................................10-23 Mechanisms for Gathering Statistics ..............................................................................................................10-25 Statistic Preferences: Overview .....................................................................................................................10-26 When to Gather Statistics Manually ...............................................................................................................10-28 Manual Statistics Gathering ...........................................................................................................................10-29 Manual Statistics Collection: Factors .............................................................................................................10-30 Managing Statistics Collection: Example .......................................................................................................10-31 Optimizer Dynamic Sampling: Overview ........................................................................................................10-32 Optimizer Dynamic Sampling at Work ............................................................................................................10-33 OPTIMIZER_DYNAMIC_SAMPLING .............................................................................................................10-34 Locking Statistics ...........................................................................................................................................10-36 Restoring Statistics ........................................................................................................................................10-37 Export and Import Statistics ...........................................................................................................................10-38 Quiz ................................................................................................................................................................10-39 Summary ........................................................................................................................................................10-42 Practice 10: Overview ....................................................................................................................................10-43

Using Bind Variables .......................................................................................................................................11-1 Using Bind Variables ......................................................................................................................................11-2



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Objectives ......................................................................................................................................................11-3 Cursor Sharing and Different Literal Values ...................................................................................................11-4 Cursor Sharing and Bind Variables ................................................................................................................11-6 Bind Variables in SQL*Plus ............................................................................................................................11-7 Bind Variables in Enterprise Manager ............................................................................................................11-8 Bind Variables in SQL Developer ...................................................................................................................11-9 Bind Variable Peeking ....................................................................................................................................11-10 Cursor Sharing Enhancements ......................................................................................................................11-12 The CURSOR_SHARING Parameter ............................................................................................................11-14 Forcing Cursor Sharing: Example ..................................................................................................................11-15 Adaptive Cursor Sharing: Overview ...............................................................................................................11-16 Adaptive Cursor Sharing: Architecture ...........................................................................................................11-17 Adaptive Cursor Sharing: Views .....................................................................................................................11-19 Adaptive Cursor Sharing: Example ................................................................................................................11-21 Interacting with Adaptive Cursor Sharing .......................................................................................................11-22 Quiz ................................................................................................................................................................11-23 Summary ........................................................................................................................................................11-26 Practice 11: Overview ....................................................................................................................................11-27

SQL Tuning Advisor ........................................................................................................................................12-1 SQL Tuning Advisor .......................................................................................................................................12-2 Objectives ......................................................................................................................................................12-3 Tuning SQL Statements Automatically ...........................................................................................................12-4 Application Tuning Challenges .......................................................................................................................12-5 SQL Tuning Advisor: Overview ......................................................................................................................12-6 Stale or Missing Object Statistics ...................................................................................................................12-7 SQL Statement Profiling .................................................................................................................................12-8 Plan Tuning Flow and SQL Profile Creation ...................................................................................................12-9 SQL Tuning Loop ...........................................................................................................................................12-10 Access Path Analysis .....................................................................................................................................12-11 SQL Structure Analysis ..................................................................................................................................12-12 SQL Tuning Advisor: Usage Model ................................................................................................................12-13 Database Control and SQL Tuning Advisor ...................................................................................................12-14 Running SQL Tuning Advisor: Example .........................................................................................................12-15 Schedule SQL Tuning Advisor .......................................................................................................................12-16 Implementing Recommendations ...................................................................................................................12-17 Compare Explain Plan ...................................................................................................................................12-18 Quiz ................................................................................................................................................................12-19 Summary ........................................................................................................................................................12-21 Practice 12: Overview ....................................................................................................................................12-22

Using SQL Access Advisor .............................................................................................................................13-1 Using SQL Access Advisor ............................................................................................................................13-2 Objectives ......................................................................................................................................................13-3 SQL Access Advisor: Overview ......................................................................................................................13-4 SQL Access Advisor: Usage Model ...............................................................................................................13-6 Possible Recommendations ...........................................................................................................................13-8 SQL Access Advisor Session: Initial Options .................................................................................................13-10 SQL Access Advisor: Workload Source .........................................................................................................13-12 SQL Access Advisor: Recommendation Options ...........................................................................................13-13 SQL Access Advisor: Schedule and Review ..................................................................................................13-14



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SQL Access Advisor: Results ........................................................................................................................13-15 SQL Access Advisor: Results and Implementation ........................................................................................13-16 Quiz ................................................................................................................................................................13-18 Summary ........................................................................................................................................................13-20 Practice 13: Overview ....................................................................................................................................13-21

Automating SQL Tuning ..................................................................................................................................14-1 Automating SQL Tuning .................................................................................................................................14-2 Objectives ......................................................................................................................................................14-3 SQL Tuning Loop ...........................................................................................................................................14-4 Automatic SQL Tuning ...................................................................................................................................14-5 Automatic Tuning Process .............................................................................................................................14-6 Automatic SQL Tuning Controls .....................................................................................................................14-8 Automatic SQL Tuning Task ..........................................................................................................................14-9 Configuring Automatic SQL Tuning ................................................................................................................14-10 Automatic SQL Tuning: Result Summary .......................................................................................................14-11 Automatic SQL Tuning: Result Details ...........................................................................................................14-12 Automatic SQL Tuning Result Details: Drilldown ...........................................................................................14-13 Automatic SQL Tuning Considerations ..........................................................................................................14-14 Quiz ................................................................................................................................................................14-15 Summary ........................................................................................................................................................14-16 Practice 14: Overview ....................................................................................................................................14-17

SQL Plan Management ....................................................................................................................................15-1 SQL Plan Management ..................................................................................................................................15-2 Objectives ......................................................................................................................................................15-3 Maintaining SQL Performance .......................................................................................................................15-4 SQL Plan Management: Overview .................................................................................................................15-5 SQL Plan Baseline: Architecture ....................................................................................................................15-7 Loading SQL Plan Baselines .........................................................................................................................15-9 Evolving SQL Plan Baselines .........................................................................................................................15-11 Important Baseline SQL Plan Attributes .........................................................................................................15-12 SQL Plan Selection ........................................................................................................................................15-14 Possible SQL Plan Manageability Scenarios .................................................................................................15-16 SQL Performance Analyzer and SQL Plan Baseline Scenario .....................................................................15-17 Loading a SQL Plan Baseline Automatically ..................................................................................................15-18 Purging SQL Management Base Policy .........................................................................................................15-19 Enterprise Manager and SQL Plan Baselines ................................................................................................15-20 Quiz ................................................................................................................................................................15-21 Summary ........................................................................................................................................................15-22 Practice 15: Overview Using SQL Plan Management ....................................................................................15-23

Using Optimizer Hints ......................................................................................................................................16-1 Using Optimizer Hints ....................................................................................................................................16-2 Objectives ......................................................................................................................................................16-3 Optimizer Hints: Overview ..............................................................................................................................16-4 Types of Hints ................................................................................................................................................16-5 Specifying Hints .............................................................................................................................................16-6 Rules for Hints................................................................................................................................................16-7 Hint Recommendations ..................................................................................................................................16-8 Optimizer Hint Syntax: Example.....................................................................................................................16-9 Hint Categories ..............................................................................................................................................16-10



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Optimization Goals and Approaches ..............................................................................................................16-11 Hints for Access Paths ...................................................................................................................................16-13 The INDEX_COMBINE Hint: Example ...........................................................................................................16-17 Hints for Query Transformation ......................................................................................................................16-19 Hints for Join Orders ......................................................................................................................................16-22 Hints for Join Operations ................................................................................................................................16-23 Additional Hints ..............................................................................................................................................16-25 Hints and Views .............................................................................................................................................16-28 Global Table Hints ..........................................................................................................................................16-30 Specifying a Query Block in a Hint .................................................................................................................16-31 Specifying a Full Set of Hints .........................................................................................................................16-32 Summary ........................................................................................................................................................16-33 Practice Appendix B: Overview ......................................................................................................................16-34

Using SQL Developer ......................................................................................................................................17-1 Using SQL Developer ....................................................................................................................................17-2 Objectives ......................................................................................................................................................17-3 What Is Oracle SQL Developer? ....................................................................................................................17-4 Specifications of SQL Developer ....................................................................................................................17-5 SQL Developer 2.1 Interface ..........................................................................................................................17-6 Creating a Database Connection ...................................................................................................................17-8 Browsing Database Objects ...........................................................................................................................17-11 Displaying the Table Structure .......................................................................................................................17-12 Browsing Files ................................................................................................................................................17-13 Creating a Schema Object .............................................................................................................................17-14 Creating a New Table: Example .....................................................................................................................17-15 Using the SQL Worksheet ..............................................................................................................................17-16 Executing SQL Statements ............................................................................................................................17-20 Saving SQL Scripts ........................................................................................................................................17-21 Executing Saved Script Files: Method 1 .........................................................................................................17-22 Executing Saved Script Files: Method 2 .........................................................................................................17-23 Formatting the SQL Code ..............................................................................................................................17-24 Using Snippets ...............................................................................................................................................17-25 Using Snippets: Example ...............................................................................................................................17-26 Debugging Procedures and Functions ...........................................................................................................17-27 Database Reporting .......................................................................................................................................17-28 Creating a User-Defined Report .....................................................................................................................17-30 External Tools ................................................................................................................................................17-31 Setting Preferences ........................................................................................................................................17-32 Resetting the SQL Developer Layout .............................................................................................................17-33 Summary ........................................................................................................................................................17-34



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xi

Preface

Profile

Before You Begin This Course

Before you begin this course, you should be familiar with SQL Language statements, and have taken the Oracle Database 11g: Introduction to SQL course or have equivalent experience. It is also recommended that you have taken the Oracle Database 11g: SQL Fundamentals I course.

How This Course Is Organized

Oracle Database 11g: SQL Tuning Workshop is an instructor-led course featuring lectures and hands-on exercises. Online demonstrations and written practice sessions reinforce the concepts and skills that are introduced.

Related Publications

Oracle Publications

Title Part Number

Oracle Database SQL Reference 11g Release 2 (11.2) E10592-04

Oracle Database Performance Tuning Guide 11g Release 2 (11.2)E10821-05

Oracle SQL Developer User's Guide Release 2.1 E15222-02



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xii

Typographic Conventions The following two lists explain Oracle University typographical conventions for words that appear within regular text or within code samples.

1. Typographic Conventions for words within regular text Convention Object or Term Example

Courier new, User input; commands; column, table, and schema names; functions; PL/SQL objects; paths

Use the SELECT command to view information stored in the LAST_NAME column of the EMPLOYEES table. Enter 300. Log in as scott

Initial cap Triggers; user interface object names, such as button names

Assign a When-Validate-Item trigger to the ORD block. Click the Cancel button.

Italic Titles of courses and manuals; emphasized words or phrases; placeholders or variables

For more information on the subject see Oracle SQL Reference Manual Do not save changes to the database. Enter hostname, where hostname is the host on which the password is to be changed

Quotation marks

Lesson or module title referenced within a course

This subject is covered in Lesson 3, “Working with Objects.”

2. Typographic Conventions for words within code samples Convention Object or term Example Uppercase Commands,

functions SELECT employee_id FROM employees

Lowercase italic

Syntax variables CREATE ROLE role

Initial cap Forms triggers Form module: ORDTrigger level: S_ITEM.QUANTITY item Trigger name: When-Validate-Item . . .

Lowercase Column names, table names Filenames, PL/SQL objects

. . .OG_ACTIVATE_LAYER (OG_GET_LAYER ('prod_pie_layer')) . . . SELECT last_name FROM employees;

Bold Text that must be entered by a user

CREATE USER scott IDENTIFIED BY tiger;



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xiii

3. Typographic Conventions for Oracle Application Navigation Paths This course uses simplified navigation paths, such as the following example, to direct you through Oracle Applications.

(N) Invoice > Entry > Invoice Batches Summary (M) Query > Find (B) Approve

This simplified path translates to the following:

1. (N) From the Navigator window, select Invoice then Entry then Invoice Batches Summary.

2. (M) From the menu, select Query then Find.

3. (B) Click the Approve button.

Notations:

(N) = Navigator

(M) = Menu

(T) = Tab

(B) = Button

(I) = Icon

(H) = Hyperlink

(ST) = Sub Tab

4. Typographic Conventions for Oracle Application Help System Paths This course uses a “navigation path” convention to represent actions you perform to find pertinent information in the Oracle Applications Help System.

The following help navigation path, for example—

(Help) General Ledger > Journals > Enter Journals

—represents the following sequence of actions:

1. In the navigation frame of the help system window, expand the General Ledger entry.

2. Under the General Ledger entry, expand Journals.

3. Under Journals, select Enter Journals.

4. Review the Enter Journals topic that appears in the document frame of the help system window.



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xiv



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Exploring the Oracle Database Architecture

Chapter 1 - Page 1


Chapter 1



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Chapter 1 - Page 2





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Chapter 1 - Page 3

Objectives

Objectives

This lesson provides an overview of the Oracle Database server architecture. You learn about physical and logical structures and about the various components.

Objectives

After completing this lesson, you should be able to:

• List the major architectural components of Oracle Database server

• Explain memory structures

• Describe background processes

• Correlate logical and physical storage structures



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Chapter 1 - Page 4

Oracle Database Server Architecture: Overview


An Oracle Database server consists of an Oracle Database and one or more Oracle Database instances. An instance consists of memory structures and background processes. Every time an instance is started, a shared memory area called the System Global Area (SGA) is allocated and the background processes are started.

The SGA contains data and control information for one Oracle Database instance.

The background processes consolidate functions that would otherwise be handled by multiple Oracle Database server programs running for each user process. They may asynchronously perform input/output (I/O) and monitor other Oracle Database processes to provide increased parallelism for better performance and reliability.

The database consists of physical files and logical structures discussed later in this lesson. Because the physical and logical structures are separate, the physical storage of data can be managed without affecting access to the logical storage structures.

Note: Oracle Real Application Clusters (Oracle RAC) comprises two or more Oracle Database instances running on multiple clustered computers that communicates with each other by means of an interconnect and access the same Oracle Database.

Database


Instance

SGA

BGP2BGP1 BGPnBGP3



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Chapter 1 - Page 5

Connecting to the Database Instance


When users connect to an Oracle Database server, they are connected to an Oracle Database instance. The database instance services those users by allocating other memory areas in addition to the SGA, and starting other processes in addition to the Oracle Database background processes:

• User processes sometimes called client or foreground processes are created to run the software code of an application program. Most environments have separate machines for the client processes. A user process also manages communication with a corresponding server process through a program interface.

• Oracle Database server creates server processes to handle requests from connected user processes. A server process communicates with the user process and interacts with the instance and the database to carry out requests from the associated user process.

An Oracle Database instance can be configured to vary the number of user processes for each server process. In a dedicated server configuration, a server process handles requests for a single user process.

A shared server configuration enables many user processes to share a small number of shared server processes, minimizing the number of server processes and maximizing the use


• Connection: Bidirectional networkpathway between a user processon a client or middle tier and anoracle process on the server

• Session: Representation ofa specific login by a user

SQL> Select …

Connection

User

Userprocess

Serverprocess

Session (Specific connected database user)

S000

Userprocess

Userprocess

DedicatedServer

SharedServer

D000

Dispatcher

SGA

Listenerprocess

Server host



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Chapter 1 - Page 6

of available system resources. One or more dispatcher processes are then used to queue user process requests in the SGA and dequeue shared server responses.

The Oracle Database server runs a listener that is responsible for handling network connections. The application connects to the listener that creates a dedicated server process or handles the connection to a dispatcher.

Connections and sessions are closely related to user processes, but are very different in meaning:

• A connection is a communication pathway between a user process and an Oracle Database instance. A communication pathway is established by using available interprocess communication mechanisms (on a computer that runs both the user process and Oracle Database) or network software (when different computers run the database application and Oracle Database, and communicate through a network).

• A session represents the state of a current database user login to the database instance. For example, when a user starts SQL*Plus, the user must provide a valid database username and password, and then a session is established for that user. A session lasts from the time a user connects until the user disconnects or exits the database application.

Note: Multiple sessions can be created and exist concurrently for a single Oracle database user using the same username. For example, a user with the username/password of HR/HR can connect to the same Oracle Database instance several times.



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Chapter 1 - Page 7

Oracle Database Memory Structures: Overview


Oracle Database allocates memory structures for various purposes. For example, memory stores the program code that is run, data that is shared among users, and private data areas for each connected user. Two basic memory structures are associated with an instance:

• System Global Area (SGA): The SGA is shared by all server and background processes. The SGA includes the following data structures:

- Database buffer cache: Caches blocks of data retrieved from the database files

- Redo log buffer: Caches recovery information before writing it to the physical files

- Shared pool: Caches various constructs that can be shared among sessions

- Large pool: Optional area used for certain operations, such as Oracle backup and recovery operations, and I/O server processes

- Java pool: Used for session-specific Java code and data in the Java Virtual Machine (JVM)

- Streams pool: Used by Oracle Streams to store information about the capture and apply processes

• Program Global Areas (PGA): Memory regions that contain data and control information about a server or background process. A PGA is suballocated from the aggregated PGA area.


Backgroundprocess

Serverprocess

Serverprocess

SGA

Redo log buffer

Database buffercache

Java pool

Streams pool

Shared pool Large pool

AggregatedPGA

… …

…



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Chapter 1 - Page 8

Database Buffer Cache


The database buffer cache is the portion of the SGA that holds copies of data blocks that are read from data files. All users concurrently connected to the instance share access to the database buffer cache.

The first time an Oracle Database server process requires a particular piece of data, it searches for the data in the database buffer cache. If the process finds the data already in the cache (a cache hit), it can read the data directly from memory. If the process cannot find the data in the cache (a cache miss), it must copy the data block from a data file on disk into a buffer in the cache before accessing the data. Accessing data through a cache hit is faster than data access through a cache miss.

The buffers in the cache are managed by a complex algorithm that uses a combination of least recently used (LRU) lists and touch count. The DBWn (Database Writers) processes are responsible for writing modified (dirty) buffers in the database buffer cache to disk when necessary.


• Is a part of the SGA

• Holds copies of data blocks that are read from data files

• Is shared by all concurrent processes

Database writer process

Databasebuffercache

SGA

Data files

DBWn

Serverprocess



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Chapter 1 - Page 9

Redo Log Buffer

Redo Log Buffer

The redo log buffer is a circular buffer in the SGA that holds information about changes made to the database. This information is stored in redo entries. Redo entries contain the information necessary to reconstruct (or redo) changes that are made to the database by INSERT, UPDATE, DELETE, CREATE, ALTER, or DROP operations. Redo entries are used for database recovery, if necessary.

Redo entries are copied by Oracle Database server processes from the user’s memory space to the redo log buffer in the SGA. The redo entries take up continuous, sequential space in the buffer. The LGWR (log writer) background process writes the redo log buffer to the active redo log file (or group of files) on disk. LGWR is a background process that is capable of asynchronous I/O.

Note: Depending on the number of CPUs on your system, there may be more than one redo log buffer. They are automatically allocated.

Redo Log Buffer

• Is a circular buffer in the SGA (based on the number of CPUs)

• Contains redo entries that have the information to redo changes made by operations, such as DML and DDL

Log writer process

Redo logbuffer

SGA

Redo log files

LGWR

Serverprocess



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Chapter 1 - Page 10

Shared Pool

Shared Pool

The shared pool portion of the SGA contains the following main parts:

• The library cache includes the sharable parts of SQL statements, PL/SQL procedures and packages. It also contains control structures such as locks.

• The data dictionary is a collection of database tables containing reference information about the database. The data dictionary is accessed so often by Oracle Database that two special locations in memory are designated to hold dictionary data. One area is called the data dictionary cache, also known as the row cache, and the other area is called the library cache. All Oracle Database server processes share these two caches for access to data dictionary information.

• The result cache is composed of the SQL query result cache and the PL/SQL function result cache. This cache is used to store results of SQL queries or PL/SQL functions to speed up their future executions.

• Control structures are essentially lock structures.

Note: In general, any item in the shared pool remains until it is flushed according to a modified LRU algorithm.

SGA

Shared Pool

• Is part of the SGA

• Contains:– Library cache

— Shared parts of SQL andPL/SQL statements

– Data dictionary cache

– Result cache:— SQL queries

— PL/SQL functions

– Control structures— Locks

Library cache

Datadictionary

cache(row cache)

Control structures

Resultcache

Serverprocess

Shared pool



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Chapter 1 - Page 11

Processing a DML Statement: Example


The steps involved in executing a data manipulation language (DML) statement are:

1. The server process receives the statement and checks the library cache for any shared SQL area that contains a similar SQL statement. If a shared SQL area is found, the server process checks the user’s access privileges to the requested data, and the existing shared SQL area is used to process the statement. If not, a new shared SQL area is allocated for the statement, so that it can be parsed and processed.

2. If the data and undo segment blocks are not already in the buffer cache, the server process reads them from the data files into the buffer cache. The server process locks the rows that are to be modified.

3. The server process records the changes to be made to the data buffers as well as the undo changes. These changes are written to the redo log buffer before the in-memory data and undo buffers are modified. This is called write-ahead logging.

4. The undo segment buffers contain values of the data before it is modified. The undo buffers are used to store the before image of the data so that the DML statements can be rolled back, if necessary. The data buffers record the new values of the data.

5. The user gets the feedback from the DML operation (such as how many rows were affected by the operation).


Database

Data files

Control files

Redolog files

Userprocess

Shared pool

Redo logbuffer

Serverprocess 3

51 Library cache

24

Databasebuffer cache

DBWn SGA

2



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Chapter 1 - Page 12

Note: Any changed blocks in the buffer cache are marked as dirty buffers; that is, the buffers are not the same as the corresponding blocks on the disk. These buffers are not immediately written to disk by the DBWn processes.



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Chapter 1 - Page 13

COMMIT Processing: Example


When COMMIT is issued, the following steps are performed:

1. The server process places a commit record, along with the system change number (SCN), in the redo log buffer. The SCN is a number monotonically incremented and is unique within the database. It is used by Oracle Database as an internal time stamp to synchronize data and to provide read consistency when data is retrieved from the data files. Using the SCN enables Oracle Database to perform consistency checks without depending on the date and time of the operating system.

2. The LGWR background process performs a contiguous write of all the redo log buffer entries up to and including the commit record to the redo log files. After this point, Oracle Database can guarantee that the changes are not lost even if there is an instance failure.

3. If modified blocks are still in the SGA, and if no other session is modifying them, then the database removes lock-related transaction information from the blocks. This process is known as commit cleanout.

4. The server process provides feedback to the user process about the completion of the transaction.

Note: If not done already, DBWn eventually writes the actual changes back to disk based on its own internal timing mechanism.


Database

Data files

Control files

Redolog files

Userprocess

SGA

Shared pool

Redo logbuffer

Serverprocess 1

3Library cache


DBWn

2LGWR

SGA



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Chapter 1 - Page 14

Large Pool

Large Pool

You can configure an optional memory area called the large pool to provide large memory allocations for:

• Session memory for the shared server, the Oracle XA interface (used where transactions interact with more than one database), or parallel execution buffers

• I/O server processes

• Oracle Database backup and restore operations

By allocating the above memory components from the large pool, Oracle Database can use the shared pool primarily for caching the shared part of SQL and PL/SQL constructs. The shared pool was originally designed to store SQL and PL/SQL constructs. Using the large pool avoids fragmentation issues associated with having large and small allocations sharing the same memory area. Unlike the shared pool, the large pool does not have an LRU list.

You should consider configuring a large pool if your instance uses any of the following:

• Parallel execution: Parallel query uses shared pool memory to cache parallel execution message buffers.

• Recovery Manager: Recovery Manager uses the shared pool to cache I/O buffers during backup and restore operations.

Large Pool

• Provides large memory allocations for:– Session memory for the shared server and Oracle XA

interface

– Parallel execution buffers

– I/O server processes

– Oracle Database backupand restore operations

• Optional pool better suitedwhen using the following:– Parallel execution

– Recovery Manager

– Shared server

SGA

Serverprocess

Large pool

I/O bufferFree

memory

Response queue

Request queue



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Chapter 1 - Page 15

• Shared server: In a shared server architecture, the session memory for each client process is included in the shared pool.



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Chapter 1 - Page 16

Java Pool and Streams Pool


Java pool memory is used for all session-specific Java code and data in the JVM. Java pool memory is used in different ways, depending on the mode in which Oracle Database runs.

Oracle Streams enables the propagation and management of data, transactions and events in a data stream either within a database, or from one database to another. The Streams pool is used exclusively by Oracle Streams. The Streams pool stores buffered queue messages, and it provides memory for Oracle Streams capture and apply processes.

Note: A detailed discussion of Java programming and Oracle Streams is beyond the scope of this course.


• Java pool memory is used in server memory for all session-specific Java code and data in the JVM.

• Streams pool memory is used exclusively by Oracle Streams to:– Store buffered queue messages

– Provide memory for Oracle Streams processes

Java pool Streams pool



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Chapter 1 - Page 17

Program Global Area (PGA)


The PGA can be compared to a temporary countertop workspace used by a file clerk (the server process) to perform a function on behalf of a customer (client process). The clerk clears a section of the countertop, uses the workspace to store details about the customer’s request, and then gives up the space when the work is done.

Generally, the PGA memory is divided into the following areas:

• Session memory is the memory allocated to hold a session’s variables (logon information) and other information related to the session. For a shared server, the session memory is shared and not private.

• Cursors are handles to private memory structures of specific SQL statements

• SQL work areas are allocated to support memory-intensive operators, such as the ones listed in the slide. Generally, bigger work areas can significantly improve the performance of a particular operator at the cost of higher memory consumption.


• PGA is a memory area that contains:– Session information

– Cursor information

– SQL execution work areas:— Sort area

— Hash join area

— Bitmap merge area

— Bitmap create area

• Work area size influences SQL performance.

• Work areas can be automatically or manually managed.

StackSpace

User Global Area (UGA)

UserSession

Data

CursorStatus

SQLArea

Serverprocess



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Chapter 1 - Page 18

Background Process

Background Process

The background processes commonly seen in non-RAC, non-ASM environments can include the following:

• Database writer process (DBWn): Asynchronously writes modified (dirty) buffers in the database buffer cache to disk

• Log writer process (LGWR): Writes the recovery information called redo information in the log buffer to a redo log file on disk

• Checkpoint process (CKPT): Records checkpoint information in control files and each data file header

• System Monitor process (SMON): Performs recovery at instance startup and cleans up unused temporary segments

• Process monitor process (PMON): Performs process recovery when a user process fails

• Result cache background process (RCBG): Used to maintain the result cache in the shared pool

• Job queue process (CJQ0): Runs user jobs used in batch processing through the Scheduler

Background Process

PMON SMON ARCnDBWn LGWRCKPT

Databasebuffercache

Shared poolSGA Redo logbuffer

MMON CJQ0 QMNnRCBG MMAN



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Chapter 1 - Page 19

• Archiver processes (ARCn): Copies redo log files to a designated storage device after a log switch has occurred

• Queue monitor processes (QMNn): Monitors the Oracle Streams message queues

• Manageability monitoring process (MMON): Performs manageability-related background tasks

• Memory Manager background process (MMAN): Used to manage SGA and PGA memory components automatically



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Chapter 1 - Page 20

Automatic Shared Memory Management


You can use the Automatic Shared Memory Management (ASMM) feature to enable the database to automatically determine the size of each of these memory components within the limits of the total SGA size.

The system uses an SGA size parameter (SGA_TARGET) that includes all the memory in the SGA, including all the automatically sized components, manually sized components, and any internal allocations during startup. ASMM simplifies the configuration of the SGA by enabling you to specify a total memory amount to be used for all SGA components. The Oracle Database then periodically redistributes memory between the automatically tuned components, according to workload requirements.

Note: You must set STATISTICS_LEVEL to TYPICAL or ALL to use ASMM.

SGA

Javapool

Fixed SGA

Redo log buffer



Which size to choose?

Large poolShared pool

Streamspool

SGA_TARGET + STATISTICS_LEVEL

Automatically tuned SGA components



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Chapter 1 - Page 21

Automated SQL Execution Memory Management


This feature provides an automatic mode for allocating memory to working areas in the PGA. You can use the PGA_AGGREGATE_TARGET parameter to specify the total amount of memory that should be allocated to the PGA areas of the instance’s sessions. In automatic mode, working areas that are used by memory-intensive operators (sorts and hash joins) can be automatically and dynamically adjusted.

This feature offers several performance and scalability benefits for decision support system (DSS) workloads and mixed workloads with complex queries. The overall system performance is maximized, and the available memory is allocated more efficiently among queries to optimize both throughput and response time. In particular, the savings that are gained from improved use of memory translate to better throughput at high loads.

Note: In earlier releases of Oracle Database server, you had to manually specify the maximum work area size for each type of SQL operator, such as sort or hash join. This proved to be very difficult because the workload changes constantly. Although the current release of Oracle Database supports this manual PGA memory management method that might be useful for specific sessions, it is recommended that you leave automatic PGA memory management enabled.


Backgroundprocess

Serverprocess

Serverprocess

… …

…

PGA_AGGREGATE_TARGET

Which size to choose?

AggregatedPGA



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Chapter 1 - Page 22

Automatic Memory Management


As seen already, the size of the various memory areas of the instance directly impacts the speed of SQL processing. Depending on the database workload, it is difficult to size those components manually.

With Automatic Memory Management, the system automatically adapts the size of each memory’s components to your workload memory needs.

Set your MEMORY_TARGET initialization parameter for the database instance and the MMAN background process automatically tunes to the target memory size, redistributing memory as needed between the internal components of the SGA and between the SGA and the aggregated PGAs.

The Automatic Shared Memory Management feature uses the SGA memory broker that is implemented by two background processes Manageability Monitor (MMON) and Memory Manager (MMAN). Statistics and memory advisory data are periodically captured in memory by MMON. MMAN coordinates the sizing of the memory components according to MMON decisions.

Note: Currently, this mechanism is only implemented on Linux, Solaris, HP-UX, AIX, and Windows.


• Sizing of each memory component is vital for SQL execution performance.

• It is difficult to manually size each component.

• Automatic memory management automates memory allocation of each SGA component and aggregated PGA.

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Larg

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ared

po

ol

Java p

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s po

ol

Private

SQ

L areas

Oth

er SG

A

Un

tun

able

PG

A

Free

MEMORY_TARGET + STATISTICS_LEVEL

MMAN



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Chapter 1 - Page 23

Database Storage Architecture


The files that constitute an Oracle database are organized into the following:

• Control file: Contain data about the database itself (that is, physical database structure information). These files are critical to the database. Without them, you cannot open data files to access the data in the database.

• Data files: Contain the user or application data of the database, as well as metadata and the data dictionary

• Online redo log files: Allow for instance recovery of the database. If the database server crashes and does not lose any data files, the instance can recover the database with the information in these files.

The following additional files are important for the successful running of the database:

• Parameter file: Is used to define how the instance is configured when it starts up

• Password file: Allows sysdba, sysoper, and sysasm to connect remotely to the database and perform administrative tasks

• Backup files: Are used for database recovery. You typically restore a backup file when a media failure or user error has damaged or deleted the original file.


Online redo log files

Password file

Parameter file Archived redo log files

Control file Data files

Alert log and trace files

Backup files



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Chapter 1 - Page 24

• Archived redo log files: Contain an ongoing history of the data changes (redo) that are generated by the instance. Using these files and a backup of the database, you can recover a lost data file. That is, archive logs enable the recovery of restored data files.

• Trace files: Each server and background process can write to an associated trace file. When an internal error is detected by a process, the process dumps information about the error to its trace file. Some of the information written to a trace file is intended for the developer, whereas other information is for Oracle Support Services.

• Alert log file: These are special trace entries. The alert log of a database is a chronological log of messages and errors. Each instance has one alert log file. It is recommended that you review this periodically.



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Chapter 1 - Page 25

Logical and Physical Database Structures


The database has logical structures and physical structures.

Tablespaces

A database is divided into logical storage units called tablespaces, which group related logical structures together. For example, tablespaces commonly group all of an application’s objects to simplify some administrative operations. You may have a tablespace for different applications.

Databases, Tablespaces, and Data Files

The relationship among databases, tablespaces, and data files is illustrated in the slide. Each database is logically divided into one or more tablespaces. One or more data files are explicitly created for each tablespace to physically store the data of all logical structures in a tablespace. If it is a TEMPORARY tablespace instead of a tablespace containing data, the tablespace has a temporary file.

Schemas

A schema is a collection of database objects that are owned by a database user. Schema objects are the logical structures that directly refer to the database’s data. Schema objects include structures, such as tables, views, sequences, stored procedures, synonyms, indexes,


Database

Logical Physical

Tablespace Data file

OS block

Segment

Extent

Oracle datablock

Schema

0, 1, or many

Only 1 withbigfile

tablespaces

Undo tablespacesnever have 0



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Chapter 1 - Page 26

clusters, and database links. In general, schema objects include everything that your application creates in the database.

Data Blocks

At the finest level of granularity, an Oracle database’s data is stored in data blocks. One data block corresponds to a specific number of bytes of physical database space on the disk. A data block size is specified for each tablespace when it is created. A database uses and allocates free database space in Oracle data blocks.

Extents

The next level of logical database space is an extent. An extent is a specific number of contiguous data blocks (obtained in a single allocation) that are used to store a specific type of information.

Segments

The level of logical database storage above an extent is called a segment. A segment is a set of extents that are allocated for a certain logical structure. Different types of segments include:

• Data segments: Each nonclustered, non-index-organized table has a data segment, with the exception of external tables and global temporary tables that have no segments, and partitioned tables in which each table has one or more segments. All of the table’s data is stored in the extents of its data segment. For a partitioned table, each partition has a data segment. Each cluster has a data segment. The data of every table in the cluster is stored in the cluster’s data segment.

• Index segments: Each index has an index segment that stores all of its data. For a partitioned index, each partition has an index segment.

• Undo segments: One UNDO tablespace is created for each database instance. This tablespace contains numerous undo segments to temporarily store undo information. The information in an undo segment is used to generate read-consistent database information and, during database recovery, to roll back uncommitted transactions for users.

• Temporary segments: Temporary segments are created by the Oracle Database when a SQL statement needs a temporary work area to complete execution. When the statement finishes execution, the temporary segment’s extents are returned to the instance for future use. Specify either a default temporary tablespace for every user, or a default temporary tablespace that is used across the database.

The Oracle Database dynamically allocates space. When the existing extents of a segment are full, additional extents are added. Because extents are allocated as needed, the extents of a segment may or may not be contiguous on the disk.



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Chapter 1 - Page 27

Segments, Extents, and Blocks


Database objects, such as tables and indexes are stored as segments in tablespaces. Each segment contains one or more extents. An extent consists of contiguous data blocks, which means that each extent can exist only in one data file. Data blocks are the smallest unit of I/O in the database.

When the database requests a set of data blocks from the operating system (OS), the OS maps this to an actual file system or disk block on the storage device. Because of this, you do not need to know the physical address of any of the data in your database. This also means that a data file can be striped or mirrored on several disks.

The size of the data block can be set at the time of database creation. The default size of 8 KB is adequate for most databases. If your database supports a data warehouse application that has large tables and indexes, a larger block size may be beneficial.

If your database supports a transactional application in which reads and writes are random, specifying a smaller block size may be beneficial. The maximum block size depends on your OS. The minimum Oracle block size is 2 KB; it should rarely (if ever) be used.

You can have tablespaces with a nonstandard block size. For details, see the Oracle Database Administrator’s Guide.


• Segments exist in a tablespace.

• Segments are collections of extents.

• Extents are collections of data blocks.

• Data blocks are mapped to disk blocks.

Segment Extents Data blocks

Disk blocks



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Chapter 1 - Page 28

SYSTEM and SYSAUX Tablespaces


Each Oracle Database must contain a SYSTEM tablespace and a SYSAUX tablespace, which are automatically created when the database is created. The system default is to create a smallfile tablespace. You can also create bigfile tablespaces, which enable the Oracle database to manage ultra large files (up to 8 exabytes in size).

A tablespace can be online (accessible) or offline (not accessible). The SYSTEM tablespace is always online when the database is open. It stores tables that support the core functionality of the database, such as the data dictionary tables.

The SYSAUX tablespace is an auxiliary tablespace to the SYSTEM tablespace. The SYSAUX tablespace stores many database components, and it must be online for the correct functioning of all database components.

Note: The SYSAUX tablespace may be taken offline for performing tablespace recovery, whereas this is not possible in the case of the SYSTEM tablespace. Neither of them may be made read-only.


• The SYSTEM and SYSAUX tablespaces are mandatory tablespaces that are created at the time of database creation. They must be online.

• The SYSTEM tablespace is used for core functionality (for example, data dictionary tables).

• The auxiliary SYSAUX tablespace is used for additional database components (such as the Enterprise Manager Repository).



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Chapter 1 - Page 29

Quiz

Answer: a

Quiz

The first time an Oracle Database server process requires a particular piece of data, it searches for the data in the:

a. Database Buffer Cache

b. PGA

c. Redo Log Buffer

d. Shared Pool



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Chapter 1 - Page 30

Quiz

Answer: b

Quiz

Which of the following is not a database logical structure?

a. Tablespace

b. Data File

c. Schema

d. Segment



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Chapter 1 - Page 31

Quiz

Answer: b

Quiz

The SYSAUX tablespace is used for core functionality and the SYSTEM tablespace is used for additional database components such as the Enterprise Manager Repository.

a. True

b. False



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Chapter 1 - Page 32

Summary

Summary

In this lesson, you should have learned how to:

• List the major architectural components of the Oracle Database server

• Explain memory structures

• Describe background processes

• Correlate logical and physical storage structures



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Chapter 1 - Page 33

Practice 1: Overview


This practice covers the following topics:

• Listing the different components of an Oracle Database server

• Looking at some instance and database components directly on your machine



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Chapter 1 - Page 34



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Introduction to SQL Tuning

Chapter 2 - Page 1


Chapter 2



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Chapter 2 - Page 2





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Chapter 2 - Page 3

Objectives

Objectives

This lesson gives you an understanding of the tuning process and the different components of an Oracle Database that may require tuning.

Objectives


• Describe what attributes of a SQL statement can make it perform poorly

• List the Oracle tools that can be used to tune SQL

• List the tuning tasks



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Chapter 2 - Page 4

Reasons for Inefficient SQL Performance


SQL statements can perform poorly for a variety of reasons:

• Stale optimizer statistics: SQL execution plans are generated by the cost-based optimizer (CBO). For CBO to effectively choose the most efficient plan, it needs accurate information on the data volume and distribution of tables and indexes referenced in the queries. Without accurate optimizer statistics, the CBO can easily be mislead and generate suboptimal execution plans.

• Missing access structures: Absence of access structures, such as indexes, materialized views, and partitions is a common reason for poor SQL performance. The right set of access structures can improve SQL performance by several orders of magnitude.

• Suboptimal execution plan selection: The CBO can sometimes select a suboptimal execution plan for a SQL statement. This happens for most part because of incorrect estimates of some attributes of that SQL statement, such as its cost, cardinality, or predicate selectivity.

• Poorly constructed SQL: If the SQL statement is designed poorly, there is not much that the optimizer can do to improve its performance. A missing join condition leading to a Cartesian product, or the use of more expensive SQL constructs like UNION in place of UNION ALL, are just a couple of examples of inefficient SQL design.


• Stale or missing optimizer statistics

• Missing access structures

• Suboptimal execution plan selection

• Poorly constructed SQL



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Chapter 2 - Page 5

These four main causes of poor SQL optimization can have a drastic impact on performance.

Note: Additional reasons for poor performance might be connected with hardware-related issues, such as memory, I/Os, CPUs, and so on.



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Chapter 2 - Page 6

Inefficient SQL: Examples


The slide shows five examples of possibly poorly constructed SQL that could easily result in inefficient execution.

1. This is a common business question type. The query determines how many products have list prices less than 15% above the average cost of the product. This statement has a correlated subquery, which means that the subquery is run for every row found in the outer query. The query is better written as:

SELECT COUNT(*) FROM products p,

(SELECT prod_id, AVG(unit_cost) ac FROM costs

GROUP BY prod_id) c

WHERE p.prod_id = c.prod_id AND

p.prod_list_price < 1.15 * c.ac

2. This query applies functions to the join columns, restricting the conditions where indexes can be used. Use a simple equality, if you can. Otherwise, a function-based index may be necessary.

3. This query has a condition that forces implicit data type conversion; the ORDER_ID_CHAR column is a character type, and the constant is a numeric type. You should make the literal match the column type.


SELECT COUNT(*) FROM products pWHERE prod_list_price <1.15 * (SELECT avg(unit_cost) FROM costs c

WHERE c.prod_id = p.prod_id)

SELECT * FROM job_history jh, employees eWHERE substr(to_char(e.employee_id),2) =substr(to_char(jh.employee_id),2)

SELECT * FROM orders WHERE order_id_char = 1205

SELECT * FROM employeesWHERE to_char(salary) = :sal

1

2

3

4

SELECT * FROM parts_oldUNIONSELECT * FROM parts_new

5



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Chapter 2 - Page 7

4. The fourth query uses a data type conversion function in it to make the data types match in the comparison. The problem here is that the TO_CHAR function is applied to the column value, rather than to the constant. This means that the function is called for every row in the table. It would be better to convert the literal once, and not convert the column. This is better queried as:

SELECT * FROM employees WHERE salary = TO_NUMBER(:sal)

5. The UNION operator, as opposed to the UNION ALL operator, ensures that there are no duplicate rows in the result set. However, this requires an extra step, a unique sort, to eliminate any duplicates. If you know that there are no rows in common between the two UNIONed queries, use UNION ALL instead of UNION. This eliminates the unnecessary sort.



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Chapter 2 - Page 8

Performance Monitoring Solutions


Automatic Workload Repository (AWR): It collects, processes, and maintains performance statistics for problem detection and self-tuning purposes. This data is both in memory and stored in the database. The gathered data can be displayed in both reports and views.

Active Session History (ASH): It provides sampled session activity in the instance. Active sessions are sampled every second and are stored in a circular buffer in SGA.

Snapshots: Snapshots are sets of historical data for specific time periods that are used for performance comparisons by ADDM. AWR compares the difference between snapshots to determine which SQL statements to capture based on the effect on the system load. This reduces the number of SQL statements that must be captured over time.

Automatic Database Diagnostic Monitor (ADDM)

In addition to the classical reactive tuning capabilities of previous releases, such as Statspack, SQL trace files, and performance views, Oracle Database 10g introduced new methodologies to monitor your database in two categories:

• Proactive monitoring:

- Automatic Database Diagnostic Monitor (ADDM) automatically identifies bottlenecks within the Oracle Database. Additionally, working with other


Snapshots

In-memorystatistics

AWR report

SGA

60 min

ADDMresults

Snapshots

Statspack

Fore-ground

Automatic

ADDM

Alerts

ASH

AST

AWR

MMON

ASH: Active Session HistoryAWR: Automatic Workload RepositoryADDM: Automatic Database Diagnostic Monitor



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Chapter 2 - Page 9

manageability components, ADDM makes recommendations on the options available for fixing these bottlenecks.

- Oracle Database 11g further automates the SQL tuning process by identifying problematic SQL statements, running SQL Tuning Advisor on them, and implementing the resulting SQL profile recommendations to tune the statement without requiring user intervention. This automation uses the AUTOTASK framework through a new task called Automatic SQL Tuning Task that runs every night by default.

• Reactive monitoring:

- Server-generated alerts: The Oracle Database can automatically detect problematic situations. In reaction to a detected problem, the Oracle Database sends you an alert message with possible remedial action.

- The Oracle Database has powerful new data sources and performance-reporting capabilities. Enterprise Manager provides an integrated performance management console that uses all relevant data sources. Using a drill-down method, you can manually identify bottlenecks with just a few mouse clicks.

New data sources are introduced to capture important information about your database’s health—for example, new memory statistics (for current diagnostics) as well as statistics history stored in Automatic Workload Repository (AWR).

Note: Accessing Enterprise Manager or tools discussed here may require additional licenses and certain privileges generally reserved for database administrators.



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Chapter 2 - Page 10

Monitoring and Tuning Tools: Overview


Since Oracle Database 10g, Release 2, you can generate SQL reports from AWR data ($ORACLE_HOME/rdbms/admin/awrsqrpt.sql), basically, the equivalent to sqrepsql.sql in Statspack.

Note

• SPA stands for SQL Performance Analyzer.


Perfviews

SQLtraces

Statspack AWRreports

EMperf

pages

Services

Optimizerstatistics

SQLstatistics

Metrics ASH

Waitmodel

Timemodel

OSstatistics

SystemSessionstatistics

tkprof trcsess

Base/Segmentstatistics

AWRbaseline

Comparedperiods

ADDMand

advisors

Alertlog

Servicestatistics

ASHreport

Histograms

SPA

Alerts

Metricbaseline

Hanganalyzer

DirectSGA

monitor

SQLreport



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Chapter 2 - Page 11

EM Performance Pages for Reactive Tuning


There are cases where real-time problem diagnosis must be performed. An irate user calls you, or you see a sudden spike in the activity of the system on the monitor. The Enterprise Manager (EM) Performance pages use the same data sources as AWR and ADDM to display information about the running of the database and the host system in a manner that is easily absorbed and allows for rapid manual drilldown to the source of the problem.


Home

Performance

TopActivity

TopConsu-mers

DuplicateSQL

BlockingSessions

HangAnalysis

InstanceLocks

InstanceActivity

AWR

Baselines

Nonidlewait

classes

TopSessions

TopServices

TopModules

TopActions

TopFiles

TopObjects

TopSQL

Waitevent

histograms

ASHReport

SQLTuningAdvisor

SQLTuning

Sets

Enable/Disableaggregation

Enable/Disable SQL trace

ViewSQL trace file

Systemstatistics

RunADDM

SPA

Waitclass

details



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Chapter 2 - Page 12

Tuning Tools: Overview


Automatic Database Diagnostic Monitor: Continually analyzes the performance data that is collected from the database instance

SQL Tuning Advisor: Analyzes SQL statements that have been identified as problematic, in an effort to retune them. By default, this is an automated task. You can also, at any time, run the SQL Tuning Advisor on a specific SQL workload to look for ways to improve performance.

SQL Tuning Sets: Serve as a repository for sets of SQL statements. For example, the SQL Tuning Advisor can run against a workload that is represented by a SQL Tuning Set. They can even be transported from database to database, to perform analysis on different machines.

SQL Access Advisor: Analyzes a SQL statement, and provides advice on materialized views, indexes, materialized view logs, and partitions

SQL Performance Analyzer: Automates the process of assessing the overall effect of a change, such as upgrading a database or adding new indexes, on the full SQL workload by identifying performance divergence for each statement

SQL Monitoring: Enables you to monitor the performance of SQL statements while they execute

SQL Plan Management (SPM): Can be used to control execution plan evolution. By creating a SQL baseline, SPM will allow only approved execution plans to be used. Other plans


• Automatic Database Diagnostic Monitor (ADDM)

• SQL Tuning Advisor

• SQL Tuning Sets

• SQL Access Advisor

• SQL Performance Analyzer

• SQL Monitoring

• SQL Plan Management



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Chapter 2 - Page 13

discovered by the optimizer will be stored in the SQL plan history, but will not be used until they are approved.



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Chapter 2 - Page 14

SQL Tuning Tasks: Overview


Many SQL tuning tasks should be performed on a regular basis. You may see a way to rewrite a WHERE clause, but it may depend on a new index being built. This list of tasks gives you a background of some important tasks that must be performed, and gives you an idea of what dependencies you may have as you tune SQL:

• Identifying high-load SQL statements is one of the most important tasks you should perform. The ADDM is the ideal tool for this particular task.

• By default, the Oracle Database gathers optimizer statistics automatically. For this, a job is scheduled to run in the maintenance windows.

• Operating system statistics provide information about the usage and performance of the main hardware components as well as the performance of the operating system itself.

• Often, there is a beneficial impact on performance by rebuilding indexes. For example, removing nonselective indexes to speed the data manipulation language (DML), or adding columns to the index to improve selectivity.

• You can maintain the existing execution plan of SQL statements over time by using stored statistics or SQL plan baselines.


• Identifying high-load SQL

• Gathering statistics

• Generating system statistics

• Rebuilding existing indexes

• Maintaining execution plans

• Creating new index strategies



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Chapter 2 - Page 15

CPU and Wait Time Tuning Dimensions


When you tune your system, it is important that you compare the CPU time with the wait time of your system. By comparing CPU time with wait time, you can determine how much of the response time is spent on useful work and how much on waiting for resources potentially held by other processes.

As a general rule, the systems where CPU time is dominant usually need less tuning than the ones where wait time is dominant. On the other hand, high CPU usage can be caused by badly-written SQL statements.

Although the proportion of CPU time to wait time always tends to decrease as load on the system increases, steep increases in wait time are a sign of contention and must be addressed for good scalability.

Adding more CPUs to a node, or nodes to a cluster, would provide very limited benefit under contention. Conversely, a system where the proportion of CPU time does not decrease significantly as load increases can scale better, and would most likely benefit from adding CPUs or Real Application Clusters (RAC) instances if needed.

Note: AWR reports display CPU time together with wait time in the Top 5 Timed Events section, if the CPU time portion is among the top five events.


Scalability is a system’s ability to process more workload with a proportional increase in system resource use.

Scalableapplication

Scalableapplication

Possiblyneeds SQL

tuning

Needsinstance/RAC

tuning

CPUtime

Waittime

No gain achievedby adding

CPUs/nodes



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Chapter 2 - Page 16

Scalability with Application Design, Implementation, and Configuration


Poor application design, implementation, and configuration have a significant impact on scalability. This results in:

• Poor SQL and index design, resulting in a higher number of logical input/output (I/O) for the same number of rows returned

• Reduced availability because database objects take longer to maintain

However, design is not the only problem. The physical implementation of the application can be the weak link, as in the following examples:

• Systems can move to production environments with poorly written SQL that cause high I/O.

• Infrequent transaction COMMITs or ROLLBACKs can cause long locks on resources.

• The production environment can use different execution plans than those generated in testing.

• Memory-intensive applications that allocate a large amount of memory without much thought for freeing the memory can cause excessive memory fragmentation.

• Inefficient memory usage places high stress on the operating virtual memory subsystem. This affects performance and availability.


Applications have a significant impact on scalability.

• Poor schema design can cause expensive SQL that does not scale.

• Poor transaction design can cause locking and serialization problems.

• Poor connection management can cause unsatisfactory response times.



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Chapter 2 - Page 17

Common Mistakes on Customer Systems


1. Bad connection management: The application connects and disconnects for each database interaction. This problem is common with stateless middleware in application servers. It has over two orders of magnitude impact on performance, and is not scalable.

2. Bad use of cursors and the shared pool: Not using cursors results in repeated parses. If bind variables are not used, there may be hard parsing of all similar SQL statements. This has an order of magnitude impact on performance, and it is not scalable. Use cursors with bind variables that open the cursor and execute it many times. Be suspicious of applications generating dynamic SQL.

3. Bad SQL: Bad SQL is SQL that uses more resources than appropriate for the application. This can be a decision support system (DSS) query that runs for more than 24 hours or a query from an online application that takes more than a minute. SQL that consumes significant system resources should be investigated for potential improvement. ADDM identifies high-load SQL and the SQL Tuning Advisor can be used to provide recommendations for improvement.

4. Use of nonstandard initialization parameters: These might have been implemented based on poor advice or incorrect assumptions. Most systems give acceptable performance using only the set of basic parameters. In particular, undocumented optimizer features can cause a great deal of problems that may require considerable investigation. Likewise, optimizer parameters set in the initialization parameter file can


1. Bad connection management

2. Bad use of cursors and the shared pool

3. Excess of resources consuming SQL statements

4. Use of nonstandard initialization parameters

5. Poor database disk configuration

6. Redo log setup problems

7. Excessive serialization

8. Inappropriate full table scans

9. Large number of recursive SQL statements related to space management or parsing activity

10.Deployment and migration errors



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Chapter 2 - Page 18

override proven optimal execution plans. For these reasons, schemas, schema statistics, and optimizer settings should be managed together as a group to ensure consistency of performance.

5. Getting database I/O wrong: Many sites lay out their databases poorly over the available disks. Other sites specify the number of disks incorrectly because they configure disks by disk space and not by I/O bandwidth.

6. Redo log setup problems: Many sites run with too small redo log files. Small redo logs cause system checkpoints to continuously put a high load on the buffer cache and the I/O system. If there are very few redo logs, the archive cannot keep up, and the database waits for the archive process to catch up.

7. Excessive serialization: Serialization of data blocks in the buffer cache due to shortage of undo segments is particularly common in applications with large numbers of active users and a few undo segments. Use Automatic Segment Space Management (ASSM) and automatic undo management to solve this problem.

8. Long full table scans: Long full table scans for high-volume or interactive online operations could indicate poor transaction design, missing indexes, or poor SQL optimization. Long table scans, by nature, are I/O-intensive and not scalable.

9. High amounts of recursive (SYS) SQL: Large amounts of recursive SQL executed by SYS could indicate that space management activities, such as extent allocations, take place. This is not scalable and impacts user response time. Use locally managed tablespaces to reduce recursive SQL due to extent allocation. Recursive SQL executed under another user ID is probably SQL and PL/SQL, so this is not a problem.

10. Deployment and migration errors: In many cases, an application uses too many resources because the schema owning the tables has not been successfully migrated from the development environment or from an older implementation. Examples of this are missing indexes or incorrect statistics. These errors can lead to suboptimal execution plans and poor interactive user performance. When migrating applications of known performance, export the schema statistics to maintain plan stability using the DBMS_STATS package.

Although these errors are not directly detected by ADDM, ADDM highlights the resulting high-load SQL.



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Chapter 2 - Page 19

Proactive Tuning Methodology


Tuning usually implies fixing a performance problem. However, tuning should be part of the life cycle of an application, through the analysis, design, coding, production, and maintenance stages. The tuning phase is often left until the system is in production. At that time, tuning becomes a reactive exercise, where the most important bottleneck is identified and fixed.

The slide lists some of the issues that affect performance and that should be tuned proactively instead of reactively. These are discussed in more detail in the following slides.


• Simple design

• Data modeling

• Tables and indexes

• Using views

• Writing efficient SQL

• Cursor sharing

• Using bind variables



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Chapter 2 - Page 20

Simplicity in Application Design


Applications are no different from any other designed and engineered product. If the design looks right, it probably is right. This principle should always be kept in mind when building applications. Consider some of the following design issues:

• If the table design is so complicated that nobody can fully understand it, the table is probably designed badly.

• If SQL statements are so long and involved that it would be impossible for any optimizer to effectively optimize it in real time, there is probably a bad statement, underlying transaction, or table design.

• If there are many indexes on a table and the same columns are repeatedly indexed, there is probably a bad index design.

• If queries are submitted without suitable qualification (the WHERE clause) for rapid response for online users, there is probably a bad user interface or transaction design.


• Simple tables

• Well-written SQL

• Indexing only as required

• Retrieving only required information



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Chapter 2 - Page 21

Data Modeling

Data Modeling

Data modeling is important in successful relational application design. This should be done in a way that quickly and accurately represents the business practices. Apply your greatest modeling efforts to those entities affected by the most frequent business transactions. Use of modeling tools can then rapidly generate schema definitions and can be useful when a fast prototype is required.

Normalizing data prevents duplication. When data is normalized, you have a clear picture of the keys and relationships. It is then easier to perform the next step of creating tables, constraints, and indexes. A good data model ultimately means that your queries are written more efficiently.

Data Modeling

• Accurately represent business practices

• Focus on the most frequent and important business transactions

• Use modeling tools

• Appropriately normalize data (OLTP versus DW)



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Chapter 2 - Page 22

Table Design

Table Design

Table design is largely a compromise between flexibility and performance of core transactions. To keep the database flexible and able to accommodate unforeseen workloads, the table design should be very similar to the data model, and it should be normalized to at least third normal form. However, certain core transactions can require selective denormalization for performance purposes.

Use the features supplied with Oracle Database to simplify table design for performance, such as storing tables prejoined in clusters, adding derived columns and aggregate values, and using materialized views or partitioned tables. Additionally, create check constraints and columns with default values to prevent bad data from getting into the tables.

Design should be focused on business-critical tables so that good performance can be achieved in areas that are the most used. For noncritical tables, shortcuts in design can be adopted to enable a more rapid application development. If, however, a noncore table becomes a performance problem during prototyping and testing, remedial design efforts should be applied immediately.

Table Design

• Compromise between flexibility and performance:– Principally normalize

– Selectively denormalize

• Use Oracle performance and management features:– Default values

– Constraints

– Materialized views

– Clusters

– Partitioning

• Focus on business-critical tables



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Chapter 2 - Page 23

Index Design

Index Design

Index design is also a largely iterative process based on the SQL that is generated by application designers. However, it is possible to make a sensible start by building indexes that enforce foreign key constraints (to reduce response time on joins between primary key tables and foreign key tables) and creating indexes on frequently accessed data, such as a person’s name. Primary keys and unique keys are automatically indexed except for the DISABLE VALIDATE and DISABLE NOVALIDATE RELY constraints. As the application evolves and testing is performed on realistic sizes of data, certain queries need performance improvements, for which building a better index is a good solution.

The following indexing design ideas should be considered when building a new index.

Appending Columns to an Index or Using Index-Organized Tables

One of the easiest ways to speed up a query is to reduce the number of logical I/Os by eliminating a table scan from the execution plan. This can be done by creating an index over all the columns of the table referenced by the query. These columns are the select list columns, WHERE clause columns, and any required join or sort columns. This technique is particularly useful in speeding up an online application’s response times when time-consuming I/Os are reduced. This is best applied when testing the application with properly-sized data for the first time. The most aggressive form of this technique is to build an index-organized table (IOT).

Index Design

• Create indexes on the following:– Primary key (can be automatically created)

– Unique key (can be automatically created)

– Foreign keys (good candidates)

• Index data that is frequently queried (select list).

• Use SQL as a guide to index design.



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Chapter 2 - Page 24

Using Views

Using Views

Views can speed up and simplify application design. A simple view definition can mask data model complexity from the programmers whose priorities are to retrieve, display, collect, and store data. Views are often used to provide simple row and column-level access restrictions.

However, though views provide clean programming interfaces, they can cause suboptimal, resource-intensive queries when nested too deeply. The worst type of view use is creating joins on views that reference other views, which in turn reference other views. In many cases, developers can satisfy the query directly from the table without using a view. Because of their inherent properties, views usually make it difficult for the optimizer to generate the optimal execution plan.

Using Views

• Simplifies application design

• Is transparent to the developer

• Can cause suboptimal execution plans



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Chapter 2 - Page 25

SQL Execution Efficiency


An application that is designed for SQL execution efficiency must support the following characteristics:

Good database connection management: Connecting to the database is an expensive operation that is not scalable. Therefore, the number of concurrent connections to the database should be minimized as much as possible. A simple system, where a user connects at application initialization, is ideal. However, in a Web-based or multitiered application, where application servers are used to multiplex database connections to users, this can be difficult. With these types of applications, design efforts should ensure that database connections are pooled and not reestablished for each user request.

Good cursor usage and management: Maintaining user connections is equally important for minimizing the parsing activity on the system. Parsing is the process of interpreting a SQL statement and creating an execution plan for it. This process has many phases, including syntax checking, security checking, execution plan generation, and loading shared structures into the shared pool.

There are two types of parse operations:

• Hard parsing: A SQL statement is submitted for the first time, and no match is found in the shared pool. Hard parses are the most resource-intensive and are not scalable because they perform all the operations involved in a parse.


• Good database connectivity

• Minimizing parsing

• Share cursors

• Using bind variables



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Chapter 2 - Page 26

• Soft parsing: A SQL statement is submitted for the first time, and a match is found in the shared pool. The match can be the result of previous execution by another user. The SQL statement is shared, which is good for performance. However, soft parses are not ideal because they still require syntax and security checking, which consume system resources.

Because parsing should be minimized as much as possible, application developers should design their applications to parse SQL statements once and execute them many times. This is done through cursors. Experienced SQL programmers should be familiar with the concept of opening and reexecuting cursors.

Application developers must also ensure that SQL statements are shared within the shared pool. To do this, bind variables to represent the parts of the query that change from execution to execution. If this is not done, the SQL statement is likely to be parsed once and never reused by other users. To ensure that SQL is shared, use bind variables and do not use string literals with SQL statements.



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Chapter 2 - Page 27

Writing SQL to Share Cursors


Applications can share cursors when the code is written in the same way characterwise (which allows the system to recognize that two statements are the same and thus can be shared), even if you use some special initialization parameters, such as CURSOR_SHARING discussed later in the lesson titled “Using Bind Variables.” You should develop coding conventions for SQL statements in ad hoc queries, SQL scripts, and Oracle Call Interface (OCI) calls.

Use generic shared code:

• Write and store procedures that can be shared across applications.

• Use database triggers.

• Write referenced triggers and procedures when using application development tools.

• Write library routines and procedures in other environments.

Write to format standards:

• Develop format standards for all statements, including those in PL/SQL code.

• Develop rules for the use of uppercase and lowercase characters.

• Develop rules for the use of white space (spaces, tabs, returns).


• Create generic code using the following:– Stored procedures and packages

– Database triggers

– Any other library routines and procedures

• Write to format standards (improves readability):– Case

– White space

– Comments

– Object references

– Bind variables



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Chapter 2 - Page 28

• Develop rules for the use of comments (preferably keeping them out of the SQL statements themselves).

• Use the same names to refer to identical database objects. If possible, prefix each object with a schema name.



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Chapter 2 - Page 29

Performance Checklist


• Set the minimal number of initialization parameters. Ideally, most initialization parameters should be left at default. If there is more tuning to perform, this shows up when the system is under load. Set storage options for tables and indexes in appropriate tablespaces.

• Verify that all SQL statements are optimal and understand their resource usage.

• Validate that middleware and programs that connect to the database are efficient in their connection management and do not log on and log off repeatedly.

• Validate that the SQL statements use cursors efficiently. Each SQL statement should be parsed once and then executed multiple times. This happens mostly when bind variables are not used properly and the WHERE clause predicates are sent as string literals.

• Validate that all schema objects are correctly migrated from the development environment to the production database. This includes tables, indexes, sequences, triggers, packages, procedures, functions, Java objects, synonyms, grants, and views. Ensure that any modifications made in testing are made to the production system.

• As soon as the system is rolled out, establish a baseline set of statistics from the database and operating system. This first set of statistics validates or corrects any assumptions made in the design and rollout process.


• Set initialization parameters and storage options.

• Verify resource usage of SQL statements.

• Validate connections by middleware.

• Verify cursor sharing.

• Validate migration of all required objects.

• Verify validity and availability of optimizer statistics.



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Chapter 2 - Page 30

Development Environments: Overview

PL/SQL Development Environments

SQL Developer

This course has been developed using Oracle SQL Developer as the tool for running the SQL statements discussed in the examples in the slide and the practices.

• SQL Developer is shipped with Oracle Database 11g Release 2, and is the default tool for this class.

SQL*Plus

The SQL*Plus environment may also be used to run all SQL commands covered in this course.

Note: See Appendix C titled “Using SQL Developer” for information about using SQL Developer, including simple instructions on installing version 2.1.

Development Environments: Overview

• SQL Developer

• SQL*Plus

SQL Developer



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Chapter 2 - Page 31

What Is Oracle SQL Developer?


Oracle SQL Developer is a free graphical tool designed to improve your productivity and simplify the development of everyday database tasks. With just a few clicks, you can easily create and maintain stored procedures, test SQL statements, and view optimizer plans.

SQL Developer, the visual tool for database development, simplifies the following tasks:

• Browsing and managing database objects

• Executing SQL statements and scripts

• Editing and debugging PL/SQL statements

• Creating reports

You can connect to any target Oracle database schema using standard Oracle database authentication. When connected, you can perform operations on objects in the database.

Appendix C

Appendix C of this course provides an introduction on using the SQL Developer interface. It also provides information about creating a database connection, interacting with data using SQL and PL/SQL, and so on.


• Oracle SQL Developer is a free graphical tool that enhances productivity and simplifies database development tasks.

• You can connect to any target Oracle database schema using standard Oracle database authentication.

• You will use SQL Developer in this course.

• Appendix C contains details on using SQL Developer

SQL Developer



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Chapter 2 - Page 32

Coding PL/SQL in SQL*Plus


Oracle SQL*Plus is a command-line interface that enables you to submit SQL statements and PL/SQL blocks for execution and receive the results in an application or a command window.

SQL*Plus is:

• Shipped with the database

• Accessed from an icon or the command line

When coding PL/SQL subprograms using SQL*Plus, remember the following:

• You create subprograms by using the CREATE SQL statement.

• You execute subprograms by using either an anonymous PL/SQL block or the EXECUTE command.

• If you use the DBMS_OUTPUT package procedures to print text to the screen, you must first execute the SET SERVEROUTPUT ON command in your session.

Note

• To launch SQL*Plus in the Linux environment, open a Terminal window and enter the sqlplus command.




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Chapter 2 - Page 33

• For more information about how to use SQL*Plus, see the SQL*Plus User's Guide and Reference.



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Chapter 2 - Page 34

Quiz

Answer: c

Quiz

_________automatically identifies bottlenecks within Oracle Database and makes recommendations on the options available for fixing these bottlenecks.

a. ASH

b. AWR

c. ADDM

d. Snapshots



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Chapter 2 - Page 35

Quiz

Answer: a

Quiz

Normalizing data results in a good data model, which ultimately means that your queries are written more efficiently.

a. True

b. False



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Chapter 2 - Page 36

Quiz

Answer: a

Quiz

Views should be used carefully because, though they provide clean programming interfaces, they can cause suboptimal, resource-intensive queries when nested too deeply.

a. True

b. False



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Chapter 2 - Page 37

Quiz

Answer: b, c

Quiz

Identify the characteristics that must be supported by an application designed for SQL execution efficiency.

a. Use concurrent connections to the database.

b. Use cursors so that SQL statements are parsed once and executed multiple times.

c. Use bind variables.



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Chapter 2 - Page 38

Summary

Summary


• Describe what attributes of a SQL statement can make it perform poorly

• List the Oracle tools that can be used to tune SQL

• List the tuning tasks



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Chapter 2 - Page 39




• Rewriting queries for better performance

• Rewriting applications for better performance



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Chapter 2 - Page 40



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Introduction to the Optimizer

Chapter 3 - Page 1


Chapter 3



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Chapter 3 - Page 2





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Chapter 3 - Page 3

Objectives

Objectives


• Describe the execution steps of a SQL statement

• Discuss the need for an optimizer

• Explain the various phases of optimization

• Control the behavior of the optimizer



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Chapter 3 - Page 4

Structured Query Language


All programs and users access data in an Oracle Database with the language SQL Oracle tools and Application programs often allow users to access the database without using SQL directly, but then these applications must use SQL when executing user requests. Oracle Corp strives to comply with industry-accepted standards and participates in SQL standards committees (ANSI and ISO). You can categorize SQL statements into six main sets:

• Data manipulation language (DML) statements manipulate or query data in existing schema objects.

• Data definition language (DDL) statements define, alter the structure of, and drop schema objects.

• Transaction control statements (TCS) manage the changes made by DML statements and group DML statements into transactions.

• System Control statements change the properties of the Oracle Database instance.

• Session Control statements manage the properties of a particular user’s session.

• Embedded SQL statements incorporate DDL, DML, and TCS within a procedural language program, such as PL/SQL and Oracle precompilers. This incorporation is done using the statements listed in the slide under the ESS category.

DML

TCS

DDLINSERTUPDATEDELETEMERGE

SELECT

COMMITROLLBACKSAVEPOINT

SET TRANSACTION

CREATEDROPALTER RENAME

TRUNCATEGRANTREVOKEAUDIT

NOAUDITCOMMENT


SessionCS

ALTER SESSIONSET ROLESystemCS

ALTER SYSTEM

ESS

DECLARECONNECTOPENCLOSE

DESCRIBEWHENEVERPREPAREEXECUTEFETCH



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Chapter 3 - Page 5

Note: SELECT statements are the most used statements. While his course focuses mainly on queries, it is important to note that any type of SQL statement is subject to optimization.



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Chapter 3 - Page 6

SQL Statement Representation


Oracle Database represents each SQL statement it runs with a shared SQL area and a private SQL area. Oracle Database recognizes when two users execute the same SQL statement and reuses the shared SQL area for those users. However, each user must have a separate copy of the statement’s private SQL area.

A shared SQL area contains all optimization information necessary to execute the statement whereas a private SQL area contains all run-time information related to a particular execution of the statement.

Oracle Database saves memory by using one shared SQL area for SQL statements run multiple times, which often happens when many users run the same application.

Note: In evaluating whether statements are similar or identical, Oracle Database considers SQL statements issued directly by users and applications, as well as recursive SQL statements issued internally by a DDL statement.


Private SQL area

Private SQL area

Private SQL area

Shared SQL area

Private SQL area

Private SQL area

Shared SQL area



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Chapter 3 - Page 7

SQL Statement Implementation


Oracle Database creates and uses memory structures for various purposes. For example, memory stores program codes that are run, data that is shared among users, and private data areas for each connected user.

Oracle Database allocates memory from the shared pool when a new SQL statement is parsed, to store in the shared SQL area. The size of this memory depends on the complexity of the statement. If the entire shared pool has already been allocated, Oracle Database can deallocate items from the pool using a modified least recently used (LRU) algorithm until there is enough free space for the new statement’s shared SQL area. If Oracle Database deallocates a shared SQL area, the associated SQL statement must be reparsed and reassigned to another shared SQL area at its next execution.


SGA

Shared SQL area

Library cache

Data dictionary cache

Result cache

Shared SQL area

Other

SHARED_POOL

Userprocess

Serverprocess

Private SQL area

Userprocess

Serverprocess

Userprocess

Serverprocess

Userprocess

Serverprocess

Userprocess

Serverprocess

Private SQL area

Private SQL area

Private SQL area

Private SQL area

Java pool

Streams pool

Buffer cache

Redo logbuffer

Client

Server

AggregatedPGA



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Chapter 3 - Page 8

SQL Statement Processing: Overview


The graphic in the slide shows all the steps involved in query execution and these steps can be found in Oracle® Database Concepts 11g Release 1 (11.1).


OPEN

PARSE

describe? DESCRIBE

more?

DEFINE

query?

reparse? bind? BIND

FETCHquery?

PARALLELIZE

EXECUTE

execute others?

CLOSE

yes

no

yes

yesno no

yes

yes

yesmore?

more?

yes

nono

yes

no

no

no

no

yes

yes

nomore?



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Chapter 3 - Page 9

SQL Statement Processing: Steps


Note that not all statements require all these steps. For example, nonparallel DDL statements are required in only two steps: Create and Parse.

Parallelizing the statement involves deciding that it can be parallelized as opposed to actually building parallel execution structures.


1. Create a cursor.

2. Parse the statement.

3. Describe query results.

4. Define query output.

5. Bind variables.

6. Parallelize the statement.

7. Execute the statement.

8. Fetch rows of a query.

9. Close the cursor.



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Chapter 3 - Page 10

Step 1: Create a Cursor


A cursor can be thought of as an association between a cursor data area in a client program and Oracle server’s data structures. Most Oracle tools hide much of cursor handling from the user, but Oracle Call Interface (OCI) programs need the flexibility to be able to process each part of query execution separately. Therefore, precompilers allow explicit cursor declaration. Most of this can also be done using the DBMS_SQL package as well.

A handle is similar to the handle on a mug. When you have a hold of the handle, you have a hold of the cursor. It is a unique identifier for a particular cursor that can only be obtained by one process at a time.

Programs must have an open cursor to process a SQL statement. The cursor contains a pointer to the current row. The pointer moves as rows are fetched until there are no more rows left to process.

The following slides use the DBMS_SQL package to illustrate cursor management. This may be confusing to people unfamiliar with it; however, it is more friendly than PRO*C or OCI. It is slightly problematic in that it performs FETCH and EXECUTE together, so the execute phase cannot be separately identified in the trace.


• A cursor is a handle or name for a private SQL area.

• It contains information for statement processing.

• It is created by a program interface call in expectation of a SQL statement.

• The cursor structure is independent of the SQL statement that it contains.



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Chapter 3 - Page 11

Step 2: Parse the Statement


During parsing, the SQL statement is passed from the user process to the Oracle instance, and a parsed representation of the SQL statement is loaded into a shared SQL area.

Translation and verification involve checking if the statement already exists in the library cache.

For distributed statements, check for the existence of database links.

Typically, the parse phase is represented as the stage where the query plan is generated.

The parse step can be deferred by the client software to reduce network traffic. What this means is that the PARSE is bundled with the EXECUTE, so there are fewer round-trips to the server.

Note: When checking if statements are identical, they must be identical in every way including case and spacing.


• Statement passed from the user process to the Oracle instance

• Parsed representation of SQL created and moved into the shared SQL area if there is no identical SQL in the shared SQL area

• Can be reused if identical SQL exists



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Chapter 3 - Page 12

Steps 3 and 4: Describe and Define


Step 3: Describe

The describe stage is necessary only if the characteristics of a query’s result are not known, for example, when a query is entered interactively by a user. In this case, the describe stage determines the characteristics (data types, lengths, and names) of a query’s result. Describe tells the application what select list items are required. If, for example, you enter a query such as:

SQL> select * from employees;,

information about the columns in the employees table is required.

Step 4: Define

In the define stage, you specify the location, size, and data type of variables defined to receive each fetched value. These variables are called define variables. Oracle Database performs data type conversion, if necessary.

These two steps are generally hidden from users in tools such as SQL*Plus. However, with DBMS_SQL or OCI, it is necessary to tell the client what the output data is and which the setup areas are.


• The describe step provides information about the select list items; it is relevant when entering dynamic queries through an OCI application.

• The define step defines location, size, and data type information required to store fetched values in variables.



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Chapter 3 - Page 13

Steps 5 and 6: Bind and Parallelize


Step 5: Bind

At this point, Oracle Database knows the meaning of the SQL statement, but still does not have enough information to run the statement. Oracle Database needs values for any variables listed in the statement. The process of obtaining these values is called binding variables.

Step 6: Parallelize

Oracle Database can parallelize the execution of SQL statements (such as SELECT, INSERT, UPDATE, MERGE, DELETE), and some DDL operations, such as index creation, creating a table with a subquery, and operations on partitions. Parallelization causes multiple server processes to perform the work of the SQL statement, so it can complete faster.

Parallelization involves dividing the work of a statement among a number of slave processes.

Parsing has already identified if a statement can be parallelized or not and has built the appropriate parallel plan. At execution time, this plan is then implemented if sufficient resource is available.


• Bind any bind values:– Enables memory address to store data values

– Allows shared SQL even though bind values may change

• Parallelize the statement:– SELECT

– INSERT

– UPDATE

– MERGE

– DELETE

– CREATE

– ALTER



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Chapter 3 - Page 14

Steps 7 Through 9

Steps 7 Through 9

At this point, Oracle Database has all the necessary information and resources, so the statement is run. If the statement is a query (without the FOR UPDATE clause) statement, no rows need to be locked because no data is changed. If the statement is an UPDATE or a DELETE statement, however, all rows that the statement affects are locked until the next COMMIT, ROLLBACK, or SAVEPOINT for the transaction. This ensures data integrity.

For some statements, you can specify a number of executions to be performed. This is called array processing. Given n number of executions, the bind and define locations are assumed to be the beginning of an array of size n.

In the fetch stage, rows are selected and ordered (if requested by the query), and each successive fetch retrieves another row of the result until the last row has been fetched.

The final stage of processing a SQL statement is closing the cursor.

Steps 7 Through 9

• Execute:– Drives the SQL statement to produce the desired results

• Fetch rows:– Into defined output variables

– Query results returned in table format

– Array fetch mechanism

• Close the cursor.



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Chapter 3 - Page 15

SQL Statement Processing PL/SQL: Example


This example summarizes the various steps discussed previously.

Note: In this example, you do not show the fetch operation. It is also possible to combine both the EXECUTE and FETCH operations in EXECUTE_AND_FETCH to perform EXECUTE and FETCH together in one call. This may reduce the number of network round-trips when used against a remote database.


SQL> variable c1 numberSQL> execute :c1 := dbms_sql.open_cursor;

SQL> variable b1 varchar2SQL> execute dbms_sql.parse2 (:c13 ,'select null from dual where dummy = :b1'4 ,dbms_sql.native);

SQL> execute :b1:='Y';SQL> exec dbms_sql.bind_variable(:c1,':b1',:b1);

SQL> variable r numberSQL> execute :r := dbms_sql.execute(:c1);

SQL> variable r numberSQL> execute :r := dbms_sql.close_cursor(:c1);



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Chapter 3 - Page 16

SQL Statement Parsing: Overview


Parsing is one stage in the processing of a SQL statement. When an application issues a SQL statement, the application makes a parse call to Oracle Database. During the parse call, Oracle Database performs the following actions:

• Checks the statement for syntactic and semantic validity

• Determines whether the process issuing the statement has the privileges to run it

• Allocates a private SQL area for the statement

• Determines whether or not there is an existing shared SQL area containing the parsed representation of the statement in the library cache. If so, the user process uses this parsed representation and runs the statement immediately. If not, Oracle Database generates the parsed representation of the statement, and the user process allocates a shared SQL area for the statement in the library cache and stores its parsed representation there.

Note the difference between an application making a parse call for a SQL statement and Oracle Database actually parsing the statement.

• A parse call by the application associates a SQL statement with a private SQL area. After a statement has been associated with a private SQL area, it can be run repeatedly without your application making a parse call.

Syntactic and semantic check


Privileges check

Allocate private SQL Area

Existing sharedSQL area?

Allocate shared SQL area

Execute statement

No

(Hard parse)

Yes (Soft parse)

Parsecall

Parse operation(Optimization)

Private SQL area

Shared SQL area

Parsed representation



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Chapter 3 - Page 17

• A parse operation by Oracle Database allocates a shared SQL area for a SQL statement. After a shared SQL area has been allocated for a statement, it can be run repeatedly without being reparsed.

Both parse calls and parsing can be expensive relative to execution, so perform them as rarely as possible.

Note: Although parsing a SQL statement validates that statement, parsing only identifies errors that can be found before statement execution. Thus, some errors cannot be caught by parsing. For example, errors in data conversion or errors in data (such as an attempt to enter duplicate values in a primary key) and deadlocks are all errors or situations that can be encountered and reported only during the execution stage.



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Chapter 3 - Page 18

Why Do You Need an Optimizer?


The optimizer should always return the correct result as quickly as possible.

The query optimizer tries to determine which execution plan is most efficient by considering available access paths and by factoring in information based on statistics for the schema objects (tables or indexes) accessed by the SQL statement.

The query optimizer performs the following steps:

1. The optimizer generates a set of potential plans for the SQL statement based on available access paths.

2. The optimizer estimates the cost of each plan based on statistics in the data dictionary for the data distribution and storage characteristics of the tables, and indexes accessed by the statement.

3. The optimizer compares the costs of the plans and selects the one with the lowest cost.

Note: Because of the complexity of finding the best possible execution plan for a particular query, the optimizer’s goal is to find a “good” plan that is generally called the best cost plan.


SELECT * FROM emp WHERE job = 'MANAGER';

How to retrieve these rows?

Use theindex.

Read each row

and check.

Which one is faster?

Query to optimize

Only 1% of employees are managers

Statistics

Schemainformation

Use theindex

1

2

3

Possible access paths

I have a plan!



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Chapter 3 - Page 19


Why Do You Need an Optimizer? (continued)

The example in the slide shows you that if statistics change, the optimizer adapts its execution plan. In this case, statistics show that 80 percent of the employees are managers. In the hypothetical case, a full table scan is probably a better solution than using the index.


SELECT * FROM emp WHERE job = 'MANAGER';

How to retrieve these rows?

Use theindex.

Read each row

and check.

Which one is faster?

Query to optimize

80% of employees are managers

Statistics

Schemainformation

Use FullTable Scan

Possible access paths

Generate a plan

1

2

3



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Chapter 3 - Page 20

Optimization During Hard Parse Operation


The optimizer creates the execution plan for a SQL statement.

SQL queries submitted to the system first run through the parser, which checks syntax and analyzes semantics. The result of this phase is called a parsed representation of the statement, and is constituted by a set of query blocks. A query block is a self-contained DML against a table. A query block can be a top-level DML or a subquery. This parsed representation is then sent to the optimizer, which handles three main functionalities: Transformation, estimation, and execution plan generation.

Before performing any cost calculation, the system may transform your statement into an equivalent statement and calculate the cost of the equivalent statement. Depending on the version of Oracle Database, there are transformations that cannot be done, some that are always done, and some that are done, costed, and discarded.

The input to the query transformer is a parsed query, which is represented by a set of interrelated query blocks. The main objective of the query transformer is to determine if it is advantageous to change the structure of the query so that it enables generation of a better query plan. Several query transformation techniques are employed by the query transformer, such as transitivity, view merging, predicate pushing, subquery unnesting, query rewrite, star transformation, and OR expansion.


Statistics

Transformer

Dictionary

Optimizer

Estimator

Plan GeneratorCB

O

Execution Plan

Parsed representation(query blocks)

Shared SQL area



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Chapter 3 - Page 21

Transformer: OR Expansion Example


If a query contains a WHERE clause with multiple conditions combined with OR operators, the optimizer transforms it into an equivalent compound query that uses the UNION ALL set operator, if this makes the query execute more efficiently.

For example, if each condition individually makes an index access path available, the optimizer can make the transformation. The optimizer selects an execution plan for the resulting statement that accesses the table multiple times using the different indexes and then puts the results together. This transformation is done if the cost estimation is better than the cost of the original statement.

In the example in the slide, it is assumed that there are indexes on both the JOB and DEPTNO columns. Then, the optimizer might transform the original query into the equivalent transformed query shown in the slide. When the cost-based optimizer (CBO) decides whether to make a transformation, the optimizer compares the cost of executing the original query using a full table scan with that of executing the resulting query.


• Original query:

• Equivalent transformed query:

SELECT *

FROM emp

WHERE job = 'CLERK' OR deptno = 10;

SELECT *

FROM emp

WHERE job = 'CLERK'

UNION ALL

SELECT *

FROM emp

WHERE deptno = 10 AND job <> 'CLERK';

B*-tree Index



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Chapter 3 - Page 22

Transformer: Subquery Unnesting Example


To unnest a query, the optimizer may choose to transform the original query into an equivalent JOIN statement, and then optimize the JOIN statement.

The optimizer may do this transformation only if the resulting JOIN statement is guaranteed to return exactly the same rows as the original statement. This transformation allows the optimizer to take advantage of the join optimizer techniques.

In the example in the slide, if the CUSTNO column of the customers table is a primary key or has a UNIQUE constraint, the optimizer can transform the complex query into the shown JOIN statement that is guaranteed to return the same data.

If the optimizer cannot transform a complex statement into a JOIN statement, it selects execution plans for the parent statement and the subquery as though they were separate statements. The optimizer then executes the subquery and uses the rows returned to execute the parent query.

Note: Complex queries whose subqueries contain aggregate functions such as AVG cannot be transformed into JOIN statements.


• Original query:


SELECT *

FROM accounts

WHERE custno IN

(SELECT custno FROM customers);

SELECT accounts.*

FROM accounts, customers

WHERE accounts.custno = customers.custno;

Primary or unique key



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Chapter 3 - Page 23

Transformer: View Merging Example


To merge the view’s query into a referencing query block in the accessing statement, the optimizer replaces the name of the view with the names of its base tables in the query block and adds the condition of the view’s query’s WHERE clause to the accessing query block’s WHERE clause.

This optimization applies to select-project-join views, which contain only selections, projections, and joins. That is, views that do not contain set operators, aggregate functions, DISTINCT, GROUP BY, CONNECT BY, and so on.

The view in this example is of all employees who work in department 10.

The query that follows the view’s definition in the slide accesses the view. The query selects the IDs greater than 7800 of employees who work in department 10.

The optimizer may transform the query into the equivalent transformed query shown in the slide that accesses the view’s base table.

If there are indexes on the DEPTNO or EMPNO columns, the resulting WHERE clause makes them available.


• Original query:


CREATE VIEW emp_10 AS

SELECT empno, ename, job, sal, comm, deptno

FROM emp

WHERE deptno = 10;

SELECT empno FROM emp_10 WHERE empno > 7800;

SELECT empno

FROM emp

WHERE deptno = 10 AND empno > 7800;

Index



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Chapter 3 - Page 24

Transformer: Predicate Pushing Example


The optimizer can transform a query block that accesses a nonmergeable view by pushing the query block’s predicates inside the view’s query.

In the example in the slide, the two_emp_tables view is the union of two employee tables. The view is defined with a compound query that uses the UNION set operator.

The query that follows the view’s definition in the slide accesses the view. The query selects the IDs and names of all employees in either table who work in department 20.

Because the view is defined as a compound query, the optimizer cannot merge the view’s query into the accessing query block. Instead, the optimizer can transform the accessing statement by pushing its predicate, the WHERE clause condition deptno = 20, into the view’s compound query. The equivalent transformed query is shown in the slide.

If there is an index in the DEPTNO column of both tables, the resulting WHERE clauses make them available.


CREATE VIEW two_emp_tables AS

SELECT empno, ename, job, sal, comm, deptno FROM emp1

UNION

SELECT empno, ename, job, sal, comm, deptno FROM emp2;

SELECT ename FROM two_emp_tables WHERE deptno = 20;

SELECT ename

FROM ( SELECT empno, ename, job,sal, comm, deptno

FROM emp1 WHERE deptno = 20

UNION

SELECT empno, ename, job,sal, comm, deptno

FROM emp2 WHERE deptno = 20 );

• Original query:


Index



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Chapter 3 - Page 25

Transformer: Transitivity Example


If two conditions in the WHERE clause involve a common column, the optimizer sometimes can infer a third condition, using the transitivity principle. The optimizer can then use the inferred condition to optimize the statement.

The inferred condition can make available an index access path that was not made available by the original conditions.

This is demonstrated with the example in the slide. The WHERE clause of the original query contains two conditions, each of which uses the EMP.DEPTNO column. Using transitivity, the optimizer infers the following condition: dept.deptno = 20

If an index exists in the DEPT.DEPTNO column, this condition makes access paths available using that index.

Note: The optimizer only infers conditions that relate columns to constant expressions, rather than columns to other columns.


• Original query:


SELECT *

FROM emp, dept

WHERE emp.deptno = 20 AND emp.deptno = dept.deptno;

SELECT *

FROM emp, dept

WHERE emp.deptno = 20 AND emp.deptno = dept.deptno

AND dept.deptno = 20;

Index



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Chapter 3 - Page 26

Cost-Based Optimizer


The combination of the estimator and plan generator code is commonly called the cost-based optimizer (CBO).

The estimator generates three types of measures: selectivity, cardinality, and cost. These measures are related to each other. Cardinality is derived from selectivity and often the cost depends on cardinality. The end goal of the estimator is to estimate the overall cost of a given plan. If statistics are available, the estimator uses these to improve the degree of accuracy when computing the measures.

The main function of the plan generator is to try out different possible plans for a given query and pick the one that has the lowest cost. Many different plans are possible because of the various combinations of different access paths, join methods, and join orders that can be used to access and process data in different ways and produce the same result. The number of possible plans for a query block is proportional to the number of join items in the FROM clause. This number rises exponentially with the number of join items.

The optimizer uses various pieces of information to determine the best path: WHERE clause, statistics, initialization parameters, supplied hints, and schema information.


• Piece of code:– Estimator

– Plan generator

• Estimator determines cost of optimization suggestions made by the plan generator:– Cost: Optimizer’s best estimate of the number of

standardized I/Os made to execute a particular statement optimization

• Plan generator:– Tries out different statement optimization techniques

– Uses the estimator to cost each optimization suggestion

– Chooses the best optimization suggestion based on cost

– Generates an execution plan for best optimization



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Chapter 3 - Page 27

Estimator: Selectivity


Selectivity represents a fraction of rows from a row set. The row set can be a base table, a view, or the result of a join or a GROUP BY operator. The selectivity is tied to a query predicate, such as last_name = 'Smith', or a combination of predicates, such as last_name = 'Smith' AND job_type = 'Clerk'. A predicate acts as a filter that filters a certain number of rows from a row set. Therefore, the selectivity of a predicate indicates the percentage of rows from a row set that passes the predicate test. Selectivity lies in a value range from 0.0 to 1.0. A selectivity of 0.0 means that no rows are selected from a row set, and a selectivity of 1.0 means that all rows are selected.

If no statistics are available, the optimizer either uses dynamic sampling or an internal default value, depending on the value of the OPTIMIZER_DYNAMIC_SAMPLING initialization parameter. When statistics are available, the estimator uses them to estimate selectivity. For example, for an equality predicate (last_name = 'Smith'), selectivity is set to the reciprocal of the number n of distinct values of LAST_NAME because the query selects rows that contain one out of n distinct values. Thus, even distribution is assumed. If a histogram is available in the LAST_NAME column, the estimator uses it instead of the number of distinct values. The histogram captures the distribution of different values in a column, so it yields better selectivity estimates.


• Selectivity is the estimated proportion of a row set retrieved by a particular predicate or combination of predicates.

• It is expressed as a value between 0.0 and 1.0:

– High selectivity: Small proportion of rows

– Low selectivity: Big proportion of rows

• Selectivity computation:

– If no statistics: Use dynamic sampling

– If no histograms: Assume even distribution of rows

• Statistic information:– DBA_TABLES and DBA_TAB_STATISTICS (NUM_ROWS)

– DBA_TAB_COL_STATISTICS (NUM_DISTINCT, DENSITY, HIGH/LOW_VALUE,…)

Selectivity = Number of rows satisfying a condition

Total number of rows



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Chapter 3 - Page 28

Note: It is important to have histograms in columns that contain values with large variations in the number of duplicates (data skew).



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Chapter 3 - Page 29

Estimator: Cardinality


The cardinality of a particular operation in the execution plan of a query represents the estimated number of rows retrieved by that particular operation. Most of the time, the row source can be a base table, a view, or the result of a join or GROUP BY operator.

When costing a join operation, it is important to know the cardinality of the driving row source. With nested loops join, for example, the driving row source defines how often the system probes the inner row source.

Because sort costs are dependent on the size and number of rows to be sorted, cardinality figures are also vital for sort costing.

In the example in the slide, based on assumed statistics, the optimizer knows that there are 203 different values in the DEV_NAME column, and that the total number of rows in the COURSES table is 1018. Based on this assumption, the optimizer deduces that the selectivity of the DEV_NAME='ANGEL' predicate is 1/203 (assuming there are no histograms), and also deduces the cardinality of the query to be (1/203)*1018. This number is then rounded off to the nearest integer, 6.


• Expected number of rows retrieved by a particular operation in the execution plan

• Vital figure to determine join, filters, and sort costs

• Simple example:

– The number of distinct values in DEV_NAME is 203.

– The number of rows in COURSES (original cardinality) is 1018.

– Selectivity = 1/203 = 4.926*e-03

– Cardinality = (1/203)*1018 = 5.01 (rounded off to 6)

Cardinality = Selectivity * Total number of rows

SELECT days FROM courses WHERE dev_name = 'ANGEL';



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Chapter 3 - Page 30

Estimator: Cost

Estimator: Cost

The cost of a statement represents the optimizer’s best estimate of the number of standardized inputs/outputs (I/Os) it takes to execute that statement. Basically, the cost is a normalized value in terms of a number of single block random reads

The standard cost metric measured by the optimizer is in terms of number of single block random reads, so one cost unit corresponds to one single block random read. The formula shown in the slide combines three different cost units:

• Estimated time to do all the single-block random reads

• Estimated time to do all the multiblock reads

• Estimated time for the CPU to process the statement into one standard cost unit

The model includes CPU costing because in most cases CPU utilization is as important as I/O; often it is the only contribution to the cost (in cases of in-memory sort, hash, predicate evaluation, and cached I/O).

This model is straightforward for serial execution. For parallel execution, necessary adjustments are made while computing estimates for #SRds, #MRds, and #CPUCycles.

Note: #CPUCycles includes CPU cost of query processing (pure CPU cost) and CPU cost of data retrieval (CPU cost of the buffer cache get).

Estimator: Cost

• Cost is the optimizer’s best estimate of the number of standardized I/Os it takes to execute a particular statement.

• Cost unit is a standardized single block random read:– 1 cost unit = 1 SRds

• The cost formula combines three different costs units into standard cost units.

#SRds*sreadtim + #MRds*mreadtim + #CPUCycles/cpuspeed

sreadtimCost=

Single block I/O cost Multiblock I/O cost CPU cost

#SRds: Number of single block reads

#MRds: Number of multiblock reads

#CPUCycles: Number of CPU Cycles

Sreadtim: Single block read time

Mreadtim: Multiblock read time

Cpuspeed: Millions instructions per second



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Chapter 3 - Page 31

Plan Generator

Plan Generator

The plan generator explores various plans for a query block by trying out different access paths, join methods, and join orders. Ultimately, the plan generator delivers the best execution plan for your statement. The slide shows you an extract of an optimizer trace file generated for the select statement. As you can see from the trace, the plan generator has six possibilities, or six different plans to test: Two join orders, and for each, three different join methods. It is assumed that there are no indexes in this example.

To retrieve the rows, you can start to join the DEPARTMENTS table to the EMPLOYEES table. For that particular join order, you can use three possible join mechanisms that the optimizer knows: Nested Loop, Sort Merge, or Hash Join. For each possibility, you have the cost of the corresponding plan. The best plan is the one shown at the end of the trace.

The plan generator uses an internal cutoff to reduce the number of plans it tries when finding the one with the lowest cost. The cutoff is based on the cost of the current best plan. If the current best cost is large, the plan generator tries harder (in other words, explores more alternate plans) to find a better plan with lower cost. If the current best cost is small, the plan generator ends the search swiftly because further cost improvement is not significant. The cutoff works well if the plan generator starts with an initial join order that produces a plan with a cost close to optimal. Finding a good initial join order is a difficult problem.

Note: Access path, join methods, and plan are discussed in more detail in the lessons titled “Optimizer Operators” and “Interpreting Execution Plans.”

Plan Generator

select e.last_name, d.department_name

from employees e, departments d

where e.department_id = d.department_id;

Join order[1]: DEPARTMENTS[D]#0 EMPLOYEES[E]#1NL Join: Cost: 41.13 Resp: 41.13 Degree: 1SM cost: 8.01HA cost: 6.51Best:: JoinMethod: Hash Cost: 6.51 Degree: 1 Resp: 6.51 Card: 106.00Join order[2]: EMPLOYEES[E]#1 DEPARTMENTS[D]#0NL Join: Cost: 121.24 Resp: 121.24 Degree: 1SM cost: 8.01HA cost: 6.51Join order abortedFinal cost for query block SEL$1 (#0)All Rows Plan:Best join order: 1+----------------------------------------------------------------+| Id | Operation | Name | Rows | Bytes | Cost |+----------------------------------------------------------------+| 0 | SELECT STATEMENT | | | | 7 || 1 | HASH JOIN | | 106 | 6042 | 7 | | 2 | TABLE ACCESS FULL | DEPARTMENTS| 27 | 810 | 3 | | 3 | TABLE ACCESS FULL | EMPLOYEES | 107 | 2889 | 3 | +----------------------------------------------------------------+



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Chapter 3 - Page 32

Controlling the Behavior of the Optimizer


These parameters control the optimizer behavior:

• CURSOR_SHARING determines what kind of SQL statements can share the same cursors:

- FORCE: Forces statements that may differ in some literals, but are otherwise identical, to share a cursor, unless the literals affect the meaning of the statement

- SIMILAR: Causes statements that may differ in some literals, but are otherwise identical, to share a cursor, unless the literals affect either the meaning of the statement or the degree to which the plan is optimized. Forcing cursor sharing among similar (but not identical) statements can have unexpected results in some decision support system (DSS) applications, or applications that use stored outlines.

- EXACT: Only allows statements with identical text to share the same cursor. This is the default.

• DB_FILE_MULTIBLOCK_READ_COUNT is one of the parameters you can use to minimize I/O during table scans or index fast full scan. It specifies the maximum number of blocks read in one I/O operation during a sequential scan. The total number of I/Os needed to perform a full table scan or an index fast full scan depends on factors, such as the size of the segment, the multiblock read count, and whether parallel execution is


• CURSOR_SHARING: SIMILAR, EXACT, FORCE

• DB_FILE_MULTIBLOCK_READ_COUNT

• PGA_AGGREGATE_TARGET

• STAR_TRANSFORMATION_ENABLED

• RESULT_CACHE_MODE: MANUAL, FORCE

• RESULT_CACHE_MAX_SIZE

• RESULT_CACHE_MAX_RESULT

• RESULT_CACHE_REMOTE_EXPIRATION



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Chapter 3 - Page 33

being utilized for the operation. As of Oracle Database 10g, Release 2, the default value of this parameter is a value that corresponds to the maximum I/O size that can be performed efficiently. This value is platform-dependent and is calculated at instance startup for most platforms.

Because the parameter is expressed in blocks, it automatically computes a value that is equal to the maximum I/O size that can be performed efficiently divided by the standard block size. Note that if the number of sessions is extremely large, the multiblock read count value is decreased to avoid the buffer cache getting flooded with too many table scan buffers. Even though the default value may be a large value, the optimizer does not favor large plans if you do not set this parameter. It would do so only if you explicitly set this parameter to a large value. Basically, if this parameter is not set explicitly (or is set is 0), the optimizer uses a default value of 8 when costing full table scans and index fast full scans. Online transaction processing (OLTP) and batch environments typically have values in the range of 4 to 16 for this parameter. DSS and data warehouse environments tend to benefit most from maximizing the value of this parameter. The optimizer is more likely to select a full table scan over an index, if the value of this parameter is high.

• PGA_AGGREGATE_TARGET specifies the target aggregate PGA memory available to all server processes attached to the instance. Setting PGA_AGGREGATE_TARGET to a nonzero value has the effect of automatically setting the WORKAREA_SIZE_POLICY parameter to AUTO. This means that SQL working areas used by memory-intensive SQL operators (such as sort, group-by, hash-join, bitmap merge, and bitmap create) are automatically sized. A nonzero value for this parameter is the default because, unless you specify otherwise, the system sets it to 20% of the SGA or 10 MB, whichever is greater. Setting PGA_AGGREGATE_TARGET to 0 automatically sets the WORKAREA_SIZE_POLICY parameter to MANUAL. This means that SQL work areas are sized using the *_AREA_SIZE parameters. The system attempts to keep the amount of private memory below the target specified by this parameter by adapting the size of the work areas to private memory. When increasing the value of this parameter, you indirectly increase the memory allotted to work areas. Consequently, more memory-intensive operations are able to run fully in memory and a less number of them work their way over to disk. When setting this parameter, you should examine the total memory on your system that is available to the Oracle instance and subtract the SGA. You can assign the remaining memory to PGA_AGGREGATE_TARGET.

• STAR_TRANSFORMATION_ENABLED determines whether a cost-based query transformation is applied to star queries. This optimization is explained in the lesson titled “Case Study: Star Transformation.”

• The query optimizer manages the result cache mechanism depending on the settings of the RESULT_CACHE_MODE parameter in the initialization parameter file. You can use this parameter to determine whether or not the optimizer automatically sends the results of queries to the result cache. The possible parameter values are MANUAL, and FORCE:

- When set to MANUAL (the default), you must specify, by using the RESULT_CACHE hint, that a particular result is to be stored in the cache.

- When set to FORCE, all results are stored in the cache. For the FORCE setting, if the statement contains a [NO_]RESULT_CACHE hint, the hint takes precedence over the parameter setting.

• The memory size allocated to the result cache depends on the memory size of the SGA as well as the memory management system. You can change the memory allocated to the result cache by setting the RESULT_CACHE_MAX_SIZE parameter. The result cache



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Chapter 3 - Page 34

is disabled if you set its value to 0. The value of this parameter is rounded to the largest multiple of 32 KB that is not greater than the specified value. If the rounded value is 0, the feature is disabled.

• Use the RESULT_CACHE_MAX_RESULT parameter to specify the maximum amount of cache memory that can be used by any single result. The default value is 5%, but you can specify any percentage value between 1 and 100.

• Use the RESULT_CACHE_REMOTE_EXPIRATION parameter to specify the time (in number of minutes) for which a result that depends on remote database objects remains valid. The default value is 0, which implies that results using remote objects should not be cached. Setting this parameter to a nonzero value can produce stale answers, for example, if the remote table used by a result is modified at the remote database.



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Chapter 3 - Page 35


Controlling the Behavior of the Optimizer (continued)

• OPTIMIZER_INDEX_CACHING: This parameter controls the costing of an index probe in conjunction with a nested loop or an inlist iterator. The range of values 0 to 100 for OPTIMIZER_INDEX_CACHING indicates percentage of index blocks in the buffer cache, which modifies the optimizer’s assumptions about index caching for nested loops and inlist iterators. A value of 100 infers that 100% of the index blocks are likely to be found in the buffer cache and the optimizer adjusts the cost of an index probe or nested loop accordingly. The default for this parameter is 0, which results in default optimizer behavior. Use caution when using this parameter because execution plans can change in favor of index caching.

• OPTIMIZER_INDEX_COST_ADJ lets you tune optimizer behavior for access path selection to be more or less index friendly, that is, to make the optimizer more or less prone to selecting an index access path over a full table scan. The range of values is 1 to 10000. The default for this parameter is 100 percent, at which the optimizer evaluates index access paths at the regular cost. Any other value makes the optimizer evaluate the access path at that percentage of the regular cost. For example, a setting of 50 makes the index access path look half as expensive as normal.

• OPTIMIZER_FEATURES_ENABLED acts as an umbrella parameter for enabling a series of optimizer features based on an Oracle release number.


• OPTIMIZER_INDEX_CACHING

• OPTIMIZER_INDEX_COST_ADJ

• OPTIMIZER_FEATURES_ENABLED

• OPTIMIZER_MODE: ALL_ROWS, FIRST_ROWS, FIRST_ROWS_n

• OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES

• OPTIMIZER_USE_SQL_PLAN_BASELINES

• OPTIMIZER_DYNAMIC_SAMPLING

• OPTIMIZER_USE_INVISIBLE_INDEXES

• OPTIMIZER_USE_PENDING_STATISTICS



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Chapter 3 - Page 36

For example, if you upgrade your database from release 10.1 to release 11.1, but you want to keep the release 10.1 optimizer behavior, you can do so by setting this parameter to 10.1.0. At a later time, you can try the enhancements introduced in releases up to and including release 11.1 by setting the parameter to 11.1.0.6. However, it is not recommended to explicitly set the OPTIMIZER_FEATURES_ENABLE parameter to an earlier release. To avoid possible SQL performance regression that may result from execution plan changes, consider using SQL plan management instead.

• OPTIMIZER_MODE establishes the default behavior for selecting an optimization approach for either the instance or your session. The possible values are:

- ALL_ROWS: The optimizer uses a cost-based approach for all SQL statements in the session regardless of the presence of statistics and optimizes with a goal of best throughput (minimum resource use to complete the entire statement). This is the default value.

- FIRST_ROWS_n: The optimizer uses a cost-based approach, regardless of the presence of statistics, and optimizes with a goal of best response time to return the first n number of rows; n can equal 1, 10, 100, or 1000.

- FIRST_ROWS: The optimizer uses a mix of cost and heuristics to find a best plan for fast delivery of the first few rows. Using heuristics sometimes leads the query optimizer to generate a plan with a cost that is significantly larger than the cost of a plan without applying the heuristic. FIRST_ROWS is available for backward compatibility and plan stability; use FIRST_ROWS_n instead.

• OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES enables or disables the automatic recognition of repeatable SQL statements, as well as the generation of SQL plan baselines for such statements.

• OPTIMIZER_USE_SQL_PLAN_BASELINES enables or disables the use of SQL plan baselines stored in SQL Management Base. When enabled, the optimizer looks for a SQL plan baseline for the SQL statement being compiled. If one is found in SQL Management Base, the optimizer costs each of the baseline plans and picks one with the lowest cost.

• OPTIMIZER_DYNAMIC_SAMPLING controls the level of dynamic sampling performed by the optimizer. If OPTIMIZER_FEATURES_ENABLE is set to:

- 10.0.0 or later, the default value is 2

- 9.2.0, the default value is 1

- 9.0.1 or earlier, the default value is 0

• OPTIMIZER_USE_INVISIBLE_INDEXES enables or disables the use of invisible indexes.

• OPTIMIZER_USE_PENDING_STATISTICS specifies whether or not the optimizer uses pending statistics when compiling SQL statements.

Note: Invisible indexes, pending statistics, and dynamic sampling are discussed later in this course.



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Chapter 3 - Page 37

Optimizer Features and Oracle Database Releases


OPTIMIZER_FEATURES_ENABLED acts as an umbrella parameter for enabling a series of optimizer features based on an Oracle release number. The table in the slide describes some of the optimizer features that are enabled depending on the value specified for the OPTIMIZER_FEATURES_ENABLED parameter.


Features 9.0.0 to 9.2.0 10.1.0 to 10.1.0.5 10.2.0 to 10.2.0.2 11.1.0.6

Index fast full scan

Consideration of bitmap access to paths for tables with only B-tree indexes

Complex view merging

Peeking into user-defined bind variables

Index joins

Dynamic sampling

Query rewrite enables

Skip unusable indexes

Automatically compute index statistics as part of creation

Cost-based query transformations

Allow rewrites with multiple MVs and/or base tables

Adaptive cursor sharing

Use extended statistics to estimate selectivity

Use native implementation for full outer joins

Partition pruning using join filtering

Group by placement optimization

Null aware antijoins

OPTIMIZER_FEATURES_ENABLED



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Chapter 3 - Page 38

Quiz

Answer: c

Quiz

The _________step provides information about the select list items and is relevant when entering dynamic queries through an OCI application.

a. Parse

b. Define

c. Describe

d. Parallelize



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Chapter 3 - Page 39

Quiz

Answer: d

Quiz

Which of the following steps is performed by the query optimizer?

a. Generating a set of potential plans for the SQL statement based on available access paths

b. Estimating and comparing the cost of each plan

c. Selecting the plan with the lowest cost

d. All of the above



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Chapter 3 - Page 40

Quiz

Answer: b

Quiz

The expected number of rows retrieved by a particular operation in the execution plan is known as its:

a. Cost

b. Cardinality

c. Optimization quotient

d. Selectivity



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Chapter 3 - Page 41

Summary

Summary


• Describe the execution steps of a SQL statement

• Describe the need for an optimizer

• Explain the various phases of optimization

• Control the behavior of the optimizer



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Chapter 3 - Page 42



This practice covers exploring a trace file to understand the optimizer’s decisions.



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Interpreting Execution Plans

Chapter 4 - Page 1


Chapter 4



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Chapter 4 - Page 2





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Chapter 4 - Page 3

Objectives

Objectives


• Gather execution plans

• Display execution plans

• Interpret execution plans



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Chapter 4 - Page 4

What Is an Execution Plan?


An execution plan is the output of the optimizer and is presented to the execution engine for implementation. It instructs the execution engine about the operations it must perform for retrieving the data required by a query most efficiently.

The EXPLAIN PLAN statement gathers execution plans chosen by the Oracle optimizer for the SELECT, UPDATE, INSERT, and DELETE statements. The steps of the execution plan are not performed in the order in which they are numbered. There is a parent-child relationship between steps. The row source tree is the core of the execution plan. It shows the following information:

• An ordering of the tables referenced by the statement

• An access method for each table mentioned in the statement

• A join method for tables affected by join operations in the statement

• Data operations, such as filter, sort, or aggregation

In addition to the row source tree (or data flow tree for parallel operations), the plan table contains information about the following:

• Optimization, such as the cost and cardinality of each operation

• Partitioning, such as the set of accessed partitions

• Parallel execution, such as the distribution method of join inputs


• The execution plan of a SQL statement is composed of small building blocks called row sources for serial execution plans.

• The combination of row sources for a statement is called the execution plan.

• By using parent-child relationships, the execution plan can be displayed in a tree-like structure (text or graphical).



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Chapter 4 - Page 5

The EXPLAIN PLAN results help you determine whether the optimizer selects a particular execution plan, such as nested loops join.



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Chapter 4 - Page 6

Where to Find Execution Plans?


There are many ways to retrieve execution plans inside the database. The most well-known ones are listed in the slide:

• The EXPLAIN PLAN command enables you to view the execution plan that the optimizer might use to execute a SQL statement. This command is very useful because it outlines the plan that the optimizer may use and inserts it in a table called PLAN_TABLE without executing the SQL statement. This command is available from SQL*Plus or SQL Developer.

• V$SQL_PLAN provides a way to examine the execution plan for cursors that were recently executed. Information in V$SQL_PLAN is very similar to the output of an EXPLAIN PLAN statement. However, while EXPLAIN PLAN shows a theoretical plan that can be used if this statement was executed, V$SQL_PLAN contains the actual plan used.

• V$SQL_PLAN_MONITOR displays plan-level monitoring statistics for each SQL statement found in V$SQL_MONITOR. Each row in V$SQL_PLAN_MONITOR corresponds to an operation of the execution plan that is monitored.

• The Automatic Workload Repository (AWR) infrastructure and Statspack store execution plans of top SQL statements. Plans are recorded into DBA_HIST_SQL_PLAN or STATS$SQL_PLAN.


• PLAN_TABLE (SQL Developer or SQL*Plus)

• V$SQL_PLAN (Library Cache)

• V$SQL_PLAN_MONITOR (11g)

• DBA_HIST_SQL_PLAN (AWR)

• STATS$SQL_PLAN (Statspack)

• SQL management base (SQL plan baselines)

• SQL tuning set• Trace files generated by DBMS_MONITOR

• Event 10053 trace file

• Process state dump trace file since 10gR2



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Chapter 4 - Page 7

• Plan and row source operations are dumped in trace files generated by DBMS_MONITOR.

• The SQL management base (SMB) is a part of the data dictionary that resides in the SYSAUX tablespace. It stores statement log, plan histories, and SQL plan baselines, as well as SQL profiles.

• The event 10053, which is used to dump cost-based optimizer (CBO) computations may include a plan.

• Starting with Oracle Database 10g, Release 2, when you dump process state (or errorstack from a process), execution plans are included in the trace file that is generated.



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Chapter 4 - Page 8

Viewing Execution Plans


If you execute the EXPLAIN PLAN SQL*Plus command, you can then SELECT from the PLAN_TABLE to view the execution plan. There are several SQL*Plus scripts available to format the plan table output. The easiest way to view an execution plan is to use the DBMS_XPLAN package. The DBMS_XPLAN package supplies five table functions:

• DISPLAY: To format and display the contents of a plan table

• DISPLAY_AWR: To format and display the contents of the execution plan of a stored SQL statement in the AWR

• DISPLAY_CURSOR: To format and display the contents of the execution plan of any loaded cursor

• DISPLAY_SQL_PLAN_BASELINE: To display one or more execution plans for the SQL statement identified by SQL handle

• DISPLAY_SQLSET: To format and display the contents of the execution plan of statements stored in a SQL tuning set

An advantage of using the DBMS_XPLAN package table functions is that the output is formatted consistently without regard to the source.


• The EXPLAIN PLAN command followed by:– SELECT from PLAN_TABLE

– DBMS_XPLAN.DISPLAY()

• SQL*Plus Autotrace: SET AUTOTRACE ON

• DBMS_XPLAN.DISPLAY_CURSOR()

• DBMS_XPLAN.DISPLAY_AWR()

• DBMS_XPLAN.DISPLAY_SQLSET()

• DBMS_XPLAN.DISPLAY_SQL_PLAN_BASELINE()



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Chapter 4 - Page 9

The EXPLAIN PLAN Command


The EXPLAIN PLAN command is used to generate the execution plan that the optimizer uses to execute a SQL statement. It does not execute the statement, but simply produces the plan that may be used, and inserts this plan into a table. If you examine the plan, you can see how the Oracle Server executes the statement.

Using EXPLAIN PLAN

• First use the EXPLAIN PLAN command to explain a SQL statement.

• Then retrieve the plan steps by querying PLAN_TABLE.

PLAN_TABLE is automatically created as a global temporary table to hold the output of an EXPLAIN PLAN statement for all users. PLAN_TABLE is the default sample output table into which the EXPLAIN PLAN statement inserts rows describing execution plans.

Note: You can create your own PLAN_TABLE using the $ORACLE_HOME/rdbms/admin/utlxplan.sql script if you want to keep the execution plan information for a long term.


• Generates an optimizer execution plan• Stores the plan in PLAN_TABLE

• Does not execute the statement itself



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Chapter 4 - Page 10


The EXPLAIN PLAN Command (continued)

This command inserts a row in the plan table for each step of the execution plan. In the syntax diagram in the slide, the fields in italics have the following meanings:


SET STATEMENT_ID= 'text'

EXPLAIN PLAN

INTO your plan table

FOR statement



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Chapter 4 - Page 11

The EXPLAIN PLAN Command: Example


This command inserts the execution plan of the SQL statement in the plan table and adds the optional demo01 name tag for future reference. You can also use the following syntax:

EXPLAIN PLAN

FOR

SELECT e.last_name, d.department_name FROM hr.employees e, hr.departments d

WHERE e.department_id =d.department_id;


SQL> EXPLAIN PLAN

2 SET STATEMENT_ID = 'demo01' FOR

3 SELECT e.last_name, d.department_name

4 FROM hr.employees e, hr.departments d

5 WHERE e.department_id = d.department_id;

Explained.

SQL>

Note: The EXPLAIN PLAN command does not actually execute the statement.



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Chapter 4 - Page 12

PLAN_TABLE

PLAN_TABLE

There are various available methods to gather execution plans. Now, you are introduced only to the EXPLAIN PLAN statement. This SQL statement gathers the execution plan of a SQL statement without executing it, and outputs its result in the PLAN_TABLE table. Whatever the method to gather and display the explain plan, the basic format and goal are the same. However, PLAN_TABLE just shows you a plan that might not be the one chosen by the optimizer. PLAN_TABLE is automatically created as a global temporary table and is visible to all users. PLAN_TABLE is the default sample output table into which the EXPLAIN PLAN statement inserts rows describing execution plans. PLAN_TABLE is organized in a tree-like structure and you can retrieve that structure by using both the ID and PARENT_ID columns with a CONNECT BY clause in a SELECT statement. While a PLAN_TABLE table is automatically set up for each user, you can use the utlxplan.sql SQL script to manually create a local PLAN_TABLE in your schema and use it to store the results of EXPLAIN PLAN. The exact name and location of this script depends on your operating system. On UNIX, it is located in the $ORACLE_HOME/rdbms/admin directory. It is recommended that you drop and rebuild your local PLAN_TABLE table after upgrading the version of the database because the columns might change. This can cause scripts to fail or cause TKPROF to fail, if you are specifying the table.

PLAN_TABLE

• PLAN_TABLE:– Is automatically created to hold the EXPLAIN PLAN output.

– You can create your own using utlxplan.sql.

– Advantage: SQL is not executed

– Disadvantage: May not be the actual execution plan

• PLAN_TABLE is hierarchical.

• Hierarchy is established with the ID and PARENT_IDcolumns.



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Chapter 4 - Page 13

Note: If you want an output table with a different name, first create PLAN_TABLE manually with the utlxplan.sql script, and then rename the table with the RENAME SQL statement.



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Chapter 4 - Page 14

Displaying from PLAN_TABLE: Typical


In the example in the slide, the EXPLAIN PLAN command inserts the execution plan of the SQL statement in PLAN_TABLE and adds the optional demo01 name tag for future reference. The DISPLAY function of the DBMS_XPLAN package can be used to format and display the last statement stored in PLAN_TABLE. You can also use the following syntax to retrieve the same result: SELECT * FROM TABLE(dbms_xplan.display('plan_table','demo01','typical',null));

The output is the same as shown in the slide. In this example, you can substitute the name of another plan table instead of PLAN_TABLE and demo01 represents the statement ID. TYPICAL displays the most relevant information in the plan: operation ID, name and option, number of rows, bytes, and optimizer cost. The last parameter for the DISPLAY function is the one corresponding to filter_preds. This parameter represents a filter predicate or predicates to restrict the set of rows selected from the table where the plan is stored. When value is null (the default), the plan displayed corresponds to the last executed explain plan. This parameter can reference any column of the table where the plan is stored and can contain any SQL construct—for example, subquery or function calls.

Note: Alternatively, you can run the utlxpls.sql (or utlxplp.sql for parallel queries) script (located in the ORACLE_HOME/rdbms/admin/ directory) to display the execution plan


SQL> EXPLAIN PLAN SET STATEMENT_ID = 'demo01' FOR SELECT * FROM emp

2 WHERE ename = 'KING';

Explained.

SQL> SET LINESIZE 130

SQL> SET PAGESIZE 0

SQL> select * from table(DBMS_XPLAN.DISPLAY());

Plan hash value: 3956160932

--------------------------------------------------------------------------

| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |

--------------------------------------------------------------------------

| 0 | SELECT STATEMENT | | 1 | 37 | 3 (0)| 00:00:01 |

|* 1 | TABLE ACCESS FULL| EMP | 1 | 37 | 3 (0)| 00:00:01 |

--------------------------------------------------------------------------

Predicate Information (identified by operation id):

---------------------------------------------------

1 - filter("ENAME"='KING')



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Chapter 4 - Page 15

stored in PLAN_TABLE for the last statement explained. This script uses the DISPLAY table function from the DBMS_XPLAN package.



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Chapter 4 - Page 16

Displaying from PLAN_TABLE: ALL


Here you use the same EXPLAIN PLAN command example as in the previous slide. The ALL option used with the DISPLAY function allows you to output the maximum user level information. It includes information displayed with the TYPICAL level, with additional information such as PROJECTION, ALIAS, and information about REMOTE SQL, if the operation is distributed.

For finer control on the display output, the following keywords can be added to the format parameter to customize its default behavior. Each keyword either represents a logical group of plan table columns (such as PARTITION) or logical additions to the base plan table output (such as PREDICATE). Format keywords must be separated by either a comma or a space:

• ROWS: If relevant, shows the number of rows estimated by the optimizer

• BYTES: If relevant, shows the number of bytes estimated by the optimizer

• COST: If relevant, shows optimizer cost information

• PARTITION: If relevant, shows partition pruning information

• PARALLEL: If relevant, shows PX information (distribution method and table queue information)

• PREDICATE: If relevant, shows the predicate section


SQL> select * from table(DBMS_XPLAN.DISPLAY(null,null,'ALL'));


--------------------------------------------------------------------------


--------------------------------------------------------------------------


|* 1 | TABLE ACCESS FULL| EMP | 1 | 37 | 3 (0)| 00:00:01 |

--------------------------------------------------------------------------

Query Block Name / Object Alias (identified by operation id):

-------------------------------------------------------------

1 - SEL$1 / EMP@SEL$1


---------------------------------------------------

1 - filter("ENAME"='KING')

Column Projection Information (identified by operation id):

-----------------------------------------------------------

1 - "EMP"."EMPNO"[NUMBER,22], "ENAME"[VARCHAR2,10], "EMP"."JOB"[VARCHAR2,9],

"EMP"."MGR"[NUMBER,22], "EMP"."HIREDATE"[DATE,7], "EMP"."SAL"[NUMBER,22],

"EMP"."COMM"[NUMBER,22], "EMP"."DEPTNO"[NUMBER,22]



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Chapter 4 - Page 17

• PROJECTION: If relevant, shows the projection section



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Chapter 4 - Page 18


Displaying from PLAN_TABLE: ALL (continued)

• ALIAS: If relevant, shows the “Query Block Name/Object Alias” section

• REMOTE: If relevant, shows the information for the distributed query (for example, remote from serial distribution and remote SQL)

• NOTE: If relevant, shows the note section of the explain plan

If the target plan table also stores plan statistics columns (for example, it is a table used to capture the content of the fixed view V$SQL_PLAN_STATISTICS_ALL), additional format keywords can be used to specify which class of statistics to display when using the DISPLAY function. These additional format keywords are IOSTATS, MEMSTATS, ALLSTATS and LAST.

Note: Format keywords can be prefixed with the “-” sign to exclude the specified information. For example, “-PROJECTION” excludes projection information.

SET STATEMENT_ID= 'text'

EXPLAIN PLAN

INTO your plan table

FOR statement




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Chapter 4 - Page 19

Displaying from PLAN_TABLE: ADVANCED


The ADVANCED format is available only from Oracle Database 10g, Release 2 and later versions.

This output format includes all sections from the ALL format plus the outline data that represents a set of hints to reproduce that particular plan.

This section may be useful if you want to reproduce a particular execution plan in a different environment.

This is the same section, which is displayed in the trace file for event 10053.

Note: When the ADVANCED format is used with V$SQL_PLAN, there is one more section called Peeked Binds (identified by position).


select plan_table_output from table(DBMS_XPLAN.DISPLAY(null,null,'ADVANCED

-PROJECTION -PREDICATE -ALIAS'));


--------------------------------------------------------------------------


--------------------------------------------------------------------------


| 1 | TABLE ACCESS FULL| EMP | 1 | 37 | 3 (0)| 00:00:01 |

--------------------------------------------------------------------------

Outline Data

-------------

/*+

BEGIN_OUTLINE_DATA

FULL(@"SEL$1" "EMP"@"SEL$1")

OUTLINE_LEAF(@"SEL$1")

ALL_ROWS

DB_VERSION('11.1.0.6')

OPTIMIZER_FEATURES_ENABLE('11.1.0.6')

IGNORE_OPTIM_EMBEDDED_HINTS

END_OUTLINE_DATA

*/



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Chapter 4 - Page 20

Explain Plan Using SQL Developer


The Explain Plan icon generates the execution plan, which you can see in the Explain tab. An execution plan shows a row source tree with the hierarchy of operations that make up the statement. For each operation, it shows the ordering of the tables referenced by the statement, access method for each table mentioned in the statement, join method for tables affected by join operations in the statement, and data operations such as filter, sort, or aggregation. In addition to the row source tree, the plan table displays information about optimization (such as the cost and cardinality of each operation), partitioning (such as the set of accessed partitions), and parallel execution (such as the distribution method of join inputs).




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Chapter 4 - Page 21

AUTOTRACE

AUTOTRACE

When running SQL statements under SQL*Plus or SQL Developer, you can automatically get a report on the execution plan and the statement execution statistics. The report is generated after successful SQL DML (that is, SELECT, DELETE, UPDATE, and INSERT) statements. It is useful for monitoring and tuning the performance of these statements.

To use this feature, you must have a PLAN_TABLE available in your schema, and then have the PLUSTRACE role granted to you. The database administrator (DBA) privileges are required to grant the PLUSTRACE role. The PLUSTRACE role is created and granted to the DBA role by running the supplied $ORACLE_HOME/sqlplus/admin/plustrce.sql script.

On some versions and platforms, this is run by the database creation scripts. If this is not the case on your platform, connect as SYSDBA and run the plustrce.sql script.

The PLUSTRACE role contains the select privilege on three V$ views. These privileges are necessary to generate AUTOTRACE statistics.

AUTOTRACE is an excellent diagnostic tool for SQL statement tuning. Because it is purely declarative, it is easier to use than EXPLAIN PLAN.

Note: The system does not support EXPLAIN PLAN for statements performing implicit type conversion of date bind variables. With bind variables in general, the EXPLAIN PLAN output might not represent the real execution plan.

AUTOTRACE

• Is a SQL*Plus and SQL Developer facility

• Was introduced with Oracle 7.3• Needs a PLAN_TABLE

• Needs the PLUSTRACE role to retrieve statistics from some V$ views

• By default, produces the execution plan and statistics after running the query

• May not be the execution plan used by the optimizer when using bind peeking (recursive EXPLAIN PLAN)



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Chapter 4 - Page 22

The AUTOTRACE Syntax


You can enable AUTOTRACE in various ways using the syntax shown in the slide. The command options are as follows:

• OFF: Disables autotracing SQL statements

• ON: Enables autotracing SQL statements

• TRACE or TRACE[ONLY]: Enables autotracing SQL statements and suppresses statement output

• EXPLAIN: Displays execution plans, but does not display statistics

• STATISTICS: Displays statistics, but does not display execution plans

Note: If both the EXPLAIN and STATISTICS command options are omitted, execution plans and statistics are displayed by default.


OFF

TRACE[ONLY]

EXPLAINSTATISTICS

SHOW AUTOTRACE

SET AUTOTRACE ON



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Chapter 4 - Page 23

AUTOTRACE: Examples

AUTOTRACE: Examples

You can control the report by setting the AUTOTRACE system variable. The following are some examples:

• SET AUTOTRACE ON: The AUTOTRACE report includes both the optimizer execution plan and the SQL statement execution statistics.

• SET AUTOTRACE TRACEONLY EXPLAIN: The AUTOTRACE report shows only the optimizer execution path without executing the statement.

• SET AUTOTRACE ON STATISTICS: The AUTOTRACE report shows the SQL statement execution statistics and rows.

• SET AUTOTRACE TRACEONLY: This is similar to SET AUTOTRACE ON, but it suppresses the printing of the user’s query output, if any. If STATISTICS is enabled, the query data is still fetched, but not printed.

• SET AUTOTRACE OFF: No AUTOTRACE report is generated. This is the default.

AUTOTRACE: Examples

• To start tracing statements using AUTOTRACE:

• To display the execution plan only without execution:

• To display rows and statistics:

• To get the plan and the statistics only (suppress rows):

SQL> set autotrace on

SQL> set autotrace traceonly explain

SQL> set autotrace on statistics

SQL> set autotrace traceonly



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Chapter 4 - Page 24

AUTOTRACE: Statistics


The statistics are recorded by the server when your statement executes and indicate the system resources required to execute your statement. The results include the following statistics:

• recursive calls is the number of recursive calls generated at both the user and system level. Oracle Database maintains tables used for internal processing. When Oracle Database needs to make a change to these tables, it internally generates an internal SQL statement, which in turn generates a recursive call.

• db block gets is the number of times a CURRENT block was requested.

• consistent gets is the number of times a consistent read was requested for a block.

• physical reads is the total number of data blocks read from disk. This number equals the value of “physical reads direct” plus all reads into buffer cache.

• redo size is the total amount of redo generated in bytes.

• bytes sent via SQL*Net to client is the total number of bytes sent to the client from the foreground processes.

• bytes received via SQL*Net from client is the total number of bytes received from the client over Oracle Net.


SQL> show autotrace

autotrace OFF

SQL> set autotrace traceonly statistics

SQL> SELECT * FROM oe.products;

288 rows selected.

Statistics

--------------------------------------------------------

1334 recursive calls

0 db block gets

686 consistent gets

394 physical reads

0 redo size

103919 bytes sent via SQL*Net to client

629 bytes received via SQL*Net from client

21 SQL*Net roundtrips to/from client

22 sorts (memory)

0 sorts (disk)

288 rows processed



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Chapter 4 - Page 25

• SQL*Net roundtrips to/from client is the total number of Oracle Net messages sent to and received from the client.

Note: The statistics printed by AUTOTRACE are retrieved from V$SESSTAT.

• sorts (memory) is the number of sort operations that were performed completely in memory and did not require any disk writes.

• sorts (disk) is the number of sort operations that required at least one disk write.

• rows processed is the number of rows processed during the operation.

The client referred to in the statistics is SQL*Plus. Oracle Net refers to the generic process communication between SQL*Plus and the server, regardless of whether Oracle Net is installed. You cannot change the default format of the statistics report.

Note: db block gets indicates reads of the current block from the database. consistent gets are reads of blocks that must satisfy a particular system change number (SCN). physical reads indicates reads of blocks from disk. db block gets and consistent gets are the two statistics that are usually monitored. These should be low compared to the number of rows retrieved. Sorts should be performed in memory rather than on disk.



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Chapter 4 - Page 26

AUTOTRACE Using SQL Developer


The Autotrace pane displays trace-related information when you execute the SQL statement by clicking the Autotrace icon. This information can help you to identify SQL statements that will benefit from tuning.




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Chapter 4 - Page 27

Using the V$SQL_PLAN View


This view displays the execution plan for cursors that are still in the library cache. The information in this view is very similar to the information in PLAN_TABLE. However, V$SQL_PLAN contains the actual plan used. The execution plan obtained by the EXPLAIN PLAN statement can be different from the execution plan used to execute the cursor. This is because the cursor might have been compiled with different values of session parameters or bind variables..

V$SQL_PLAN shows the plan for a cursor rather than for all cursors associated with a SQL statement. The difference is that a SQL statement can have more than one cursor associated with it, with each cursor further identified by a CHILD_NUMBER. For example, the same statement executed by different users has different cursors associated with it if the object that is referenced is in a different schema. Similarly, different hints can cause different cursors. The V$SQL_PLAN table can be used to see the different plans for different child cursors of the same statement.

Note: Another useful view is V$SQL_PLAN_STATISTICS, which provides the execution statistics of each operation in the execution plan for each cached cursor. Also, the V$SQL_PLAN_STATISTICS_ALL view concatenates information from V$SQL_PLAN with execution statistics from V$SQL_PLAN_STATISTICS and V$SQL_WORKAREA.


• V$SQL_PLAN provides a way of examining the execution plan for cursors that are still in the library cache.

• V$SQL_PLAN is very similar to PLAN_TABLE:– PLAN_TABLE shows a theoretical plan that can be used if

this statement were to be executed.– V$SQL_PLAN contains the actual plan used.

• It contains the execution plan of every cursor in the library cache (including child).

• Link to V$SQL:– ADDRESS, HASH_VALUE, and CHILD_NUMBER



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Chapter 4 - Page 28

The V$SQL_PLAN Columns


The view contains many of the PLAN_TABLE columns, plus several others. The columns that are also present in PLAN_TABLE have the same values:

• ADDRESS

• HASH_VALUE

The ADDRESS and HASH_VALUE columns can be used to join with V$SQLAREA to add the cursor-specific information.

The ADDRESS, HASH_VALUE, and CHILD_NUMBER columns can be used to join with V$SQL to add the child cursor–specific information.

The PLAN_HASH VALUE column is a numerical representation of the SQL plan for the cursor. By comparing one PLAN_HASH_VALUE with another, you can easily identify whether the two plans are the same or not (rather than comparing the two plans line-by-line).

Note: Since Oracle Database 10g, SQL_HASH_VALUE in V$SESSION has been complemented with SQL_ID, which you retrieve in many other V$ views. SQL_HASH_VALUE is a 32-bit value and is not unique enough for large repositories of AWR data. SQL_ID is a 64-bit hash value, which is more unique, the bottom 32 bits of which are SQL_HASH_VALUE. It is normally represented as a character string to make it more manageable.


Note: This is only a partial listing of the columns.

HASH_VALUE Hash value of the parent statement in the

library cache

ADDRESS Address of the handle to the parent for this cursor

CHILD_NUMBER Child cursor number using this execution plan

POSITION Order of processing for all operations that have

the same PARENT_ID

PARENT_ID ID of the next execution step that operates on

the output of the current step

ID Number assigned to each step in the

execution plan

PLAN_HASH_VALUE Numerical representation of the SQL plan for the cursor



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Chapter 4 - Page 29

The V$SQL_PLAN_STATISTICS View


The V$SQL_PLAN_STATISTICS view provides the actual execution statistics for every operation in the plan, such as the number of output rows, and elapsed time. All statistics, except the number of output rows, are cumulative. For example, the statistics for a join operation also include the statistics for its two inputs. The statistics in V$SQL_PLAN_STATISTICS are available for cursors that have been compiled with the STATISTICS_LEVEL initialization parameter set to ALL or using the GATHER_PLAN_STATISTICS hint.

The V$SQL_PLAN_STATISTICS_ALL view contains memory-usage statistics for row sources that use SQL memory (sort or hash join). This view concatenates information in V$SQL_PLAN with execution statistics from V$SQL_PLAN_STATISTICS and V$SQL_WORKAREA.


• V$SQL_PLAN_STATISTICS provides actual execution statistics:– STATISTICS_LEVEL set to ALL

– The GATHER_PLAN_STATISTICS hint

• V$SQL_PLAN_STATISTICS_ALL enables side-by-side comparisons of the optimizer estimates with the actual execution statistics.



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Chapter 4 - Page 30

Links Between Important Dynamic Performance Views

Links Between Important Dynamic Performance Views

V$SQLAREA displays statistics on shared SQL areas and contains one row per SQL string. It provides statistics on SQL statements that are in memory, parsed, and ready for execution:

• SQL_ID is the SQL identifier of the parent cursor in the library cache.

• VERSION_COUNT is the number of child cursors that are present in the cache under this parent.

V$SQL lists statistics on shared SQL areas and contains one row for each child of the original SQL text entered:

• ADDRESS represents the address of the handle to the parent for this cursor.

• HASH_VALUE is the value of the parent statement in the library cache.

• SQL_ID is the SQL identifier of the parent cursor in the library cache.

• PLAN_HASH_VALUE is a numeric representation of the SQL plan for this cursor. By comparing one PLAN_HASH_VALUE with another, you can easily identify if the two plans are the same or not (rather than comparing the two plans line-by-line).

• CHILD_NUMBER is the number of this child cursor.

Links Between ImportantDynamic Performance Views

V$SQL

V$SQL_PLAN

V$SQL_PLAN_STATISTICS

V$SQLAREA V$SQL_WORKAREA

V$SQL_PLAN_STATISTICS_ALL

V$SQLSTATS

Execution statisticsfor each row source

Estimated statisticsfor each row source



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Chapter 4 - Page 31

Statistics displayed in V$SQL are normally updated at the end of query execution. However, for long-running queries, they are updated every five seconds. This makes it easy to see the impact of long-running SQL statements while they are still in progress.

V$SQL_PLAN contains the execution plan information for each child cursor loaded in the library cache. The ADDRESS, HASH_VALUE, and CHILD_NUMBER columns can be used to join with V$SQL to add the child cursor–specific information.

V$SQL_PLAN_STATISTICS provides execution statistics at the row source level for each child cursor. The ADDRESS and HASH_VALUE columns can be used to join with V$SQLAREA to locate the parent cursor. The ADDRESS, HASH_VALUE, and CHILD_NUMBER columns can be used to join with V$SQL to locate the child cursor using this area.

V$SQL_PLAN_STATISTICS_ALL contains memory usage statistics for row sources that use SQL memory (sort or hash join). This view concatenates information in V$SQL_PLAN with execution statistics from V$SQL_PLAN_STATISTICS and V$SQL_WORKAREA.

V$SQL_WORKAREA displays information about work areas used by SQL cursors. Each SQL statement stored in the shared pool has one or more child cursors that are listed in the V$SQL view. V$SQL_WORKAREA lists all work areas needed by these child cursors. V$SQL_WORKAREA can be joined with V$SQLAREA on (ADDRESS, HASH_VALUE) and with V$SQL on (ADDRESS, HASH_VALUE, CHILD_NUMBER).

You can use this view to find answers to the following questions:

• What are the top 10 work areas that require the most cache area?

• For work areas allocated in the AUTO mode, what percentage of work areas run using maximum memory?

V$SQLSTATS displays basic performance statistics for SQL cursors, with each row representing the data for a unique combination of SQL text and optimizer plan (that is, unique combination of SQL_ID and PLAN_HASH_VALUE). The column definitions for columns in V$SQLSTATS are identical to those in the V$SQL and V$SQLAREA views. However, the V$SQLSTATS view differs from V$SQL and V$SQLAREA in that it is faster, more scalable, and has a greater data retention (the statistics may still appear in this view, even after the cursor has been aged out of the shared pool). Note that V$SQLSTATS contains a subset of columns that appear in V$SQL and V$SQLAREA. Vijai Sahu (sahuvijay21@gmailฺcom) has a non-transferable license


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Chapter 4 - Page 32

Querying V$SQL_PLAN

Querying V$SQL_PLAN

You can query V$SQL_PLAN using the DBMS_XPLAN.DISPLAY_CURSOR() function to display the current or last executed statement (as shown in the example). You can pass the value of SQL_ID for the statement as a parameter to obtain the execution plan for a given statement. SQL_ID is the SQL_ID of the SQL statement in the cursor cache. You can retrieve the appropriate value by querying the SQL_ID column in V$SQL or V$SQLAREA. Alternatively, you could select the PREV_SQL_ID column for a specific session out of V$SESSION. This parameter defaults to null in which case the plan of the last cursor executed by the session is displayed. To obtain SQL_ID, execute the following query:

SELECT e.last_name, d.department_name

FROM hr.employees e, hr.departments d WHERE e.department_id =d.department_id;

SELECT SQL_ID, SQL_TEXT FROM V$SQL

WHERE SQL_TEXT LIKE '%SELECT e.last_name,%' ;

13saxr0mmz1s3 select SQL_id, sql_text from v$SQL …

47ju6102uvq5q SELECT e.last_name, d.department_name …

Querying V$SQL_PLAN

SQL_ID 47ju6102uvq5q, child number 0-------------------------------------SELECT e.last_name, d.department_nameFROM hr.employees e, hr.departments d WHEREe.department_id =d.department_id

Plan hash value: 2933537672--------------------------------------------------------------------------------| Id | Operation | Name | Rows | Bytes | Cost (%CPU|--------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | | | 6 (100|| 1 | MERGE JOIN | | 106 | 2862 | 6 (17|| 2 | TABLE ACCESS BY INDEX ROWID| DEPARTMENTS | 27 | 432 | 2 (0|| 3 | INDEX FULL SCAN | DEPT_ID_PK | 27 | | 1 (0||* 4 | SORT JOIN | | 107 | 1177 | 4 (25|| 5 | TABLE ACCESS FULL | EMPLOYEES | 107 | 1177 | 3 (0|--------------------------------------------------------------------------------Predicate Information (identified by operation id):---------------------------------------------------

4 - access("E"."DEPARTMENT_ID"="D"."DEPARTMENT_ID")filter("E"."DEPARTMENT_ID"="D"."DEPARTMENT_ID")

24 rows selected.

SELECT PLAN_TABLE_OUTPUT FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR('47ju6102uvq5q'));



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Chapter 4 - Page 33

CHILD_NUMBER is the child number of the cursor to display. If not supplied, the execution plan of all cursors matching the supplied SQL_ID parameter are displayed. CHILD_NUMBER can be specified only if SQL_ID is specified.

The FORMAT parameter controls the level of detail for the plan. In addition to the standard values (BASIC, TYPICAL, SERIAL, ALL, and ADVANCED), there are additional supported values to display run-time statistics for the cursor:

• IOSTATS: Assuming that the basic plan statistics are collected when SQL statements are executed (either by using the GATHER_PLAN_STATISTICS hint or by setting the statistics_level parameter to ALL), this format shows I/O statistics for ALL (or only for LAST) executions of the cursor.

• MEMSTATS: Assuming that the Program Global Area (PGA) memory management is enabled (that is, the pga_aggregate_target parameter is set to a nonzero value), this format allows to display memory management statistics (for example, execution mode of the operator, how much memory was used, number of bytes spilled to disk, and so on). These statistics only apply to memory-intensive operations, such as hash joins, sort or some bitmap operators.

• ALLSTATS: A shortcut for 'IOSTATS MEMSTATS'

• LAST: By default, plan statistics are shown for all executions of the cursor. The LAST keyword can be specified to see only the statistics for the last execution.



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Chapter 4 - Page 34

Automatic Workload Repository (AWR)


The AWR is part of the intelligent infrastructure introduced with Oracle Database 10g. This infrastructure is used by many components, such as Automatic Database Diagnostic Monitor (ADDM) for analysis. The AWR automatically collects, processes, and maintains system-performance statistics for problem-detection and self-tuning purposes and stores the statistics persistently in the database.

The statistics collected and processed by the AWR include:

• Object statistics that determine both access and usage statistics of database segments

• Time-model statistics based on time usage for activities, displayed in the V$SYS_TIME_MODEL and V$SESS_TIME_MODEL views

• Some of the system and session statistics collected in the V$SYSSTAT and V$SESSTAT views

• SQL statements that produce the highest load on the system, based on criteria, such as elapsed time, CPU time, buffer gets, and so on

• ASH statistics, representing the history of recent sessions

The database automatically generates snapshots of the performance data once every hour and collects the statistics in the workload repository. The data in the snapshot interval is then analyzed by ADDM. The ADDM compares the differences between snapshots to determine


• Collects, processes, and maintains performance statistics for problem-detection and self-tuning purposes

• Statistics include:– Object statistics

– Time-model statistics

– Some system and session statistics

– Active Session History (ASH) statistics

• Automatically generates snapshots of the performance data



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Chapter 4 - Page 35

which SQL statements to capture based on the effect on the system load. This reduces the number of SQL statements that need to be captured over time.

Note: By using PL/SQL packages, such as DBMS_WORKLOAD_REPOSITORY or Oracle Enterprise Manager, you can manage the frequency and retention period of SQL that is stored in the AWR.



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Chapter 4 - Page 36

Managing AWR with PL/SQL


Although the primary interface for managing the AWR is Enterprise Manager, monitoring functions can be managed with procedures in the DBMS_WORKLOAD_REPOSITORY package.

Snapshots are automatically generated for an Oracle Database; however, you can use DBMS_WORKLOAD_REPOSITORY procedures to manually create, drop, and modify the snapshots and baselines that are used by the ADDM. Snapshots and baselines are sets of historical data for specific time periods that are used for performance comparisons. To invoke these procedures, a user must be granted the DBA role.

Creating Snapshots

You can manually create snapshots with the CREATE_SNAPSHOT procedure if you want to capture statistics at times different than those of the automatically generated snapshots. Here is an example:

Exec DBMS_WORKLOAD_REPOSITORY.CREATE_SNAPSHOT ('ALL');

In this example, a snapshot for the instance is created immediately with the flush level specified to the default flush level of TYPICAL. You can view this snapshot in the DBA_HIST_SNAPSHOT view.

Dropping Snapshots


• Creating snapshots:

• Dropping snapshots:

• Managing snapshot settings:

SQL> exec DBMS_WORKLOAD_REPOSITORY.CREATE_SNAPSHOT ('ALL');

SQL> exec DBMS_WORKLOAD_REPOSITORY.DROP_SNAPSHOT_RANGE –(low_snap_id => 22, high_snap_id => 32, dbid => 3310949047);

SQL> exec DBMS_WORKLOAD_REPOSITORY.MODIFY_SNAPSHOT_SETTINGS –(retention => 43200, interval => 30, dbid => 3310949047);



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Chapter 4 - Page 37

You can drop a range of snapshots using the DROP_SNAPSHOT_RANGE procedure. To view a list of the snapshot IDs along with database IDs, check the DBA_HIST_SNAPSHOT view. For example, you can drop the following range of snapshots:

Exec DBMS_WORKLOAD_REPOSITORY.DROP_SNAPSHOT_RANGE - (low_snap_id => 22, high_snap_id => 32, dbid => 3310949047);

In the example, the range of snapshot IDs to drop is specified from 22 to 32. The optional database identifier is 3310949047. If you do not specify a value for dbid, the local database identifier is used as the default value.

ASH data that belongs to the time period specified by the snapshot range is also purged when the DROP_SNAPSHOT_RANGE procedure is called.

Modifying Snapshot Settings

You can adjust the interval and retention of snapshot generation for a specified database ID. However, note that this can affect the precision of the Oracle diagnostic tools.

The INTERVAL setting specifies how often (in minutes) snapshots are automatically generated. The RETENTION setting specifies how long (in minutes) snapshots are stored in the workload repository. To adjust the settings, use the MODIFY_SNAPSHOT_SETTINGS procedure, as in the following example:

Exec DBMS_WORKLOAD_REPOSITORY.MODIFY_SNAPSHOT_SETTINGS( -retention => 43200, interval => 30, dbid => 3310949047);

In this example, the retention period is specified as 43,200 minutes (30 days), and the interval between each snapshot is specified as 30 minutes. If NULL is specified, the existing value is preserved. The optional database identifier is 3310949047. If you do not specify a value for dbid, the local database identifier is used as the default value. You can check the current settings for your database instance with the DBA_HIST_WR_CONTROL view.



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Chapter 4 - Page 38

Important AWR Views

Important AWR Views

You can view the AWR data on Oracle Enterprise Manager screens or in AWR reports. However, you can also view the statistics directly from the following views:

V$ACTIVE_SESSION_HISTORY: This view displays active database session activity, sampled once every second.

V$ metric views provide metric data to track the performance of the system. The metric views are organized into various groups, such as event, event class, system, session, service, file, and tablespace metrics. These groups are identified in the V$METRICGROUP view.

The DBA_HIST views contain historical data stored in the database. This group of views includes:

• DBA_HIST_ACTIVE_SESS_HISTORY displays the history of the contents of the sampled in-memory active session history for recent system activity.

• DBA_HIST_BASELINE displays information about the baselines captured in the system.

• DBA_HIST_DATABASE_INSTANCE displays information about the database environment.

• DBA_HIST_SNAPSHOT displays information about snapshots in the system.

• DBA_HIST_SQL_PLAN displays SQL execution plans.

Important AWR Views

• V$ACTIVE_SESSION_HISTORY

• V$ metric views

• DBA_HIST views:– DBA_HIST_ACTIVE_SESS_HISTORY

– DBA_HIST_BASELINE DBA_HIST_DATABASE_INSTANCE

– DBA_HIST_SNAPSHOT

– DBA_HIST_SQL_PLAN

– DBA_HIST_WR_CONTROL



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Chapter 4 - Page 39

• DBA_HIST_WR_CONTROL displays the settings for controlling AWR.



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Chapter 4 - Page 40

Querying the AWR

Querying the AWR

You can use the DBMS_XPLAN.DISPLAY_AWR() function to display all stored plans in the AWR. In the example in the slide, you pass in a SQL_ID as an argument. SQL_ID is the SQL_ID of the SQL statement in the cursor cache. The DISPLAY_AWR() function also takes the PLAN_HASH_VALUE, DB_ID, and FORMAT parameters.

The steps to complete this example are as follows:

1. Execute the SQL statement:

SQL> select /* example */ * from hr.employees natural join hr.departments;

2. Query V$SQL_TEXT to obtain the SQL_ID:

SQL> select sql_id, sql_text from v$SQL

where sql_text

like '%example%';

SQL_ID SQL_TEXT

------------- -------------------------------------------

F8tc4anpz5cdb select sql_id, sql_text from v$SQL …

454rug2yva18w select /* example */ * from …

Querying the AWR

• Retrieve all execution plans stored for a particular SQL_ID.

• Display all execution plans of all statements containing “JF.”

SQL> SELECT PLAN_TABLE_OUTPUT FROM TABLE (DBMS_XPLAN.DISPLAY_AWR('454rug2yva18w'));

PLAN_TABLE_OUTPUT------------------------------------------------------------------------------------SQL_ID 454rug2yva18w --------------------select /* example */ * from hr.employees natural join hr.departments


----------------------------------------------------------------------------------| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |----------------------------------------------------------------------------------| 0 | SELECT STATEMENT | | | | 6 (100)| || 1 | HASH JOIN | | 11 | 968 | 6 (17)| 00:00:01 || 2 | TABLE ACCESS FULL| DEPARTMENTS | 11 | 220 | 2 (0)| 00:00:01 || 3 | TABLE ACCESS FULL| EMPLOYEES | 107 | 7276 | 3 (0)| 00:00:01 |----------------------------------------------------------------------------------

SELECT tf.* FROM DBA_HIST_SQLTEXT ht, table(DBMS_XPLAN.DISPLAY_AWR(ht.sql_id,null, null, 'ALL' )) tf

WHERE ht.sql_text like '%JF%';



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Chapter 4 - Page 41

3. Using the SQL_ID, verify that this statement has been captured in the DBA_HIST_SQLTEXT dictionary view. If the query does not return rows, it indicates that the statement has not yet been loaded in the AWR.

SQL> SELECT SQL_ID, SQL_TEXT FROM dba_hist_sqltext WHERE SQL_ID =' 454rug2yva18w';

no rows selected You can take a manual AWR snapshot rather than wait for the next snapshot (which occurs every hour). Then check to see if it has been captured in DBA_HIST_SQLTEXT:

SQL> exec dbms_workload_repository.create_snapshot;

PL/SQL procedure successfully completed.

SQL> SELECT SQL_ID, SQL_TEXT FROM dba_hist_sqltext WHERE SQL_ID =' 454rug2yva18w';

SQL_ID SQL_TEXT

-------------- -------------------------------

454rug2yva18w select /* example */ * from …

4. Use the DBMS_XPLAN.DISPLAY_AWR () function to retrieve the execution plan:

SQL>SELECT PLAN_TABLE_OUTPUT FROM TABLE (DBMS_XPLAN.DISPLAY_AWR('454rug2yva18w’));



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Chapter 4 - Page 42

Generating SQL Reports from AWR Data


Since Oracle Database 10g, Release 2, it is possible to generate SQL reports from AWR data, basically, the equivalent to sqrepsql.sql with Statspack. In 10.1.0.4.0, the equivalent to sprepsql.sql is not available in AWR. However, in 10gR2, the equivalent of sprepsql.sql is available. In 10gR2, the AWR SQL report can be generated by calling the $ORACLE_HOME/rdbms/admin/awrsqrpt.sql file.

You can display the plan information in AWR by using the display_awr table function in the dbms_xplan PL/SQL package.

For example, this displays the plan information for a SQL_ID in AWR: select * from table(dbms_xplan.display_awr('dvza55c7zu0yv'));

You can retrieve the appropriate value for the SQL statement of interest by querying SQL_ID in the DBA_HIST_SQLTEXT column.


SQL> @$ORACLE_HOME/rdbms/admin/awrsqrpt

Specify the Report Type …

Would you like an HTML report, or a plain text report?Specify the number of days of snapshots to choose from

Specify the Begin and End Snapshot Ids …

Specify the SQL Id …

Enter value for sql_id: dvza55c7zu0yvSpecify the Report Name …



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Chapter 4 - Page 43

SQL Monitoring: Overview


The SQL monitoring feature is enabled by default when the STATISTICS_LEVEL initialization parameter is either set to ALL or TYPICAL (the default value).

Additionally, the CONTROL_MANAGEMENT_PACK_ACCESS parameter must be set to DIAGNOSTIC+TUNING (the default value) because SQL monitoring is a feature of the Oracle Database Tuning Pack.

By default, SQL monitoring is automatically started when a SQL statement runs parallel, or when it has consumed at least five seconds of the CPU or I/O time in a single execution.

As mentioned, SQL monitoring is active by default. However, two statement-level hints are available to force or prevent a SQL statement from being monitored. To force SQL monitoring, use the MONITOR hint. To prevent the hinted SQL statement from being monitored, use the NO_MONITOR hint.

You can monitor the statistics for SQL statement execution using the V$SQL_MONITOR and V$SQL_PLAN_MONITOR views.

After monitoring is initiated, an entry is added to the dynamic performance V$SQL_MONITOR view. This entry tracks key performance metrics collected for the execution, including the elapsed time, CPU time, number of reads and writes, I/O wait time, and various other wait

STATISTICS_LEVEL=TYPICAL|ALL

CONTROL_MANAGEMENT_PACK_ACCESS=DIAGNOSTIC+TUNING

+

SQL monitoring

Everysecond V$SQL_MONITOR

V$SQL_PLAN_MONITOR

V$SQLV$SQL_PLAN

V$ACTIVE_SESSION_HISTORYV$SESSION_LONGOPS

V$SESSION

DBMS_SQLTUNE.REPORT_SQL_MONITOR




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Chapter 4 - Page 44

times. These statistics are refreshed in near real time as the statement executes, generally once every second.

After the execution ends, monitoring information is not deleted immediately, but is kept in the V$SQL_MONITOR view for at least one minute. The entry is eventually deleted so its space can be reclaimed as new statements are monitored.

The V$SQL_MONITOR and V$SQL_PLAN_MONITOR views can be used in conjunction with the following views to get additional information about the execution that is monitored:

V$SQL, V$SQL_PLAN, V$ACTIVE_SESSION_HISTORY, V$SESSION_LONGOPS, and V$SESSION

Instead, you can use the SQL monitoring report to view SQL monitoring data.

The SQL monitoring report is also available in a GUI version through Enterprise Manager and SQL Developer



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Chapter 4 - Page 45

SQL Monitoring Report: Example


In this example, it is assumed that you SELECT from SALES from a different session than the one used to print the SQL monitoring report.

The DBMS_SQLTUNE.REPORT_SQL_MONITOR function accepts several input parameters to specify the execution, the level of detail in the report, and the report type (TEXT, HTML, or XML). By default, a text report is generated for the last execution that was monitored if no parameters are specified as shown in the example in the slide.

After the SELECT statement is started, and while it executes, you print the SQL monitoring report from a second session.

From the report, you can see that the SELECT statement executes currently.

The Global Information section gives you some important information:

• To uniquely identify two executions of the same SQL statement, a composite key called an execution key is generated. This execution key consists of three attributes, each corresponding to a column in V$SQL_MONITOR:

- SQL identifier to identify the SQL statement (SQL_ID)

- An internally generated identifier to ensure that this primary key is truly unique (SQL_EXEC_ID)


SQL> set long 10000000SQL> set longchunksize 10000000SQL> set linesize 200SQL> select dbms_sqltune.report_sql_monitor from dual;

SQL Monitoring Report

SQL Text--------------------------select count(*) from sales

Global InformationStatus : EXECUTINGInstance ID : 1Session ID : 125SQL ID : fazrk33ng71kmSQL Execution ID : 16777216Plan Hash Value : 1047182207Execution Started : 02/19/2008 21:01:18First Refresh Time : 02/19/2008 21:01:22Last Refresh Time : 02/19/2008 21:01:42------------------------------------------------------------| Elapsed | Cpu | IO | Other | Buffer | Reads || Time(s) | Time(s) | Waits(s) | Waits(s) | Gets | |------------------------------------------------------------| 22 | 3.36 | 0.01 | 19 | 259K | 199K |------------------------------------------------------------

SQL> select count(*) from sales;

In a different session



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Chapter 4 - Page 46

- A start execution time stamp (SQL_EXEC_START)

The report also shows you some important statistics calculated so far.



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Chapter 4 - Page 47


SQL Monitoring Report: Example (continued)

The report then displays the execution path currently used by your statement. SQL monitoring gives you the display of the current operation that executes in the plan. This enables you to detect parts of the plan that are the most time consuming, so that you can focus your analysis on those parts. The running operation is marked by an arrow in the Id column of the report.

The Time Active(s) column shows how long the operation has been active (the delta in seconds between the first and the last active time).

The Start Active column shows, in seconds, when the operation in the execution plan started relative to the SQL statement execution start time. In this report, the table access full operation at Id 2 was the first to start (+1s Start Active) and ran for the first 23 seconds so far.

The Starts column shows the number of times the operation in the execution plan was executed.

The Rows (Actual) column indicates the number of rows produced, and the Rows (Estim) column shows the estimated cardinality from the optimizer.

The Activity (percent) and Activity Detail (sample #) columns are derived by joining the V$SQL_PLAN_MONITOR and V$ACTIVE_SESSION_HISTORY views. Activity (percent) shows the percentage of database time consumed by each operation of the execution plan. Activity Detail (sample#) shows the nature of that activity (such as CPU or wait event).


SQL Plan Monitoring Details====================================================================================| Id | Operation | Name | Rows | Cost | Time | Start || | | | (Estim) | | Active(s) | Active |====================================================================================| 0 | SELECT STATEMENT | | | 78139 | | || 1 | SORT AGGREGATE | | 1 | | | || -> 2 | TABLE ACCESS FULL | SALES | 53984K | 78139 | 23 | +1 || | | | | | | |====================================================================================

==================================================================================Starts | Rows | Activity | Activity Detail | Progress |

| (Actual) | (percent) | (sample #) | |1 | | | | |1 | | | | |1 | 42081K | 100.00 | Cpu (4) | 74% |

==================================================================================



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Chapter 4 - Page 48

In this report, the Activity Detail (sample #) column shows that most of the database time, 100%, is consumed by operation Id 2 (TABLE ACCESS FULL of SALES). So far, this activity consists of 4 samples, which are only attributed to CPU.

The last column, Progress, shows progress monitoring information for the operation from the V$SESSION_LONGOPS view. In this report, it shows that, so far, the TABLE ACCESS FULL operation is 74% complete. This column only appears in the report after a certain amount of time, and only for the instrumented row sources.

Note: Not shown by this particular report, the Memory and Temp columns indicate the amount of memory and temporary space consumed by corresponding operation of the execution plan.



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Chapter 4 - Page 49

Interpreting an Execution Plan


Explain plan output is a representation of a tree of row sources.

Each step (line in the execution plan or node in the tree) represents a row source.

The explain plan utility indents nodes to indicate that they are the children of the parent above it.

The order of the nodes under the parent indicates the order of execution of the nodes within that level. If two steps are indented at the same level, the first one is executed first.

In the tree format, the leaf at the left on each level of the tree is where the execution starts.

The steps of the execution plan are not performed in the order in which they are numbered. there is a parent–child relationship between steps.

In PLAN_TABLE and V$SQL_PLAN, the important elements to retrieve the tree structure are the ID, PARENT_ID, and POSITION columns. In a trace file, these columns correspond to the id, pid, and pos fields, respectively.

One way to read an execution plan is by converting it into a graph that has a tree structure. You can start from the top, with id=1, which is the root node in the tree. Next, you must find the operations that feed this root node. That is accomplished by operations, which have parent_id or pid with value 1.

Note: The course focuses on serial plans and does not discusses parallel execution plans.


Transform it into a tree.

Level 1

Level 2

Level 3

Level 4

Parent/Child

Child/Leaf

Parent/Child

Child/Leaf Parent/Child

Child/LeafChild/LeafChild/Leaf

id= 1 (pid= ) root/parentid= 2 (pid=1) (pos=1) parent/childid= 3 (pid=2) (pos=1) child/leafid= 4 (pid=2) (pos=2) parent/childid= 5 (pid=4) (pos=1) child/leafid= 6 (pid=4) (pos=2) child/leafid= 7 (pid=1) (pos=2) parent/childid= 8 (pid=7) (pos=1) child/leafid= 9 (pid=7) (pos=2) parent/childid=10 (pid=9) (pos=1) child/leaf

Fromtop/down

From left/right

Executed first

Executed next

Parent/Child

Root/Parent



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Chapter 4 - Page 50

To draw plan as a tree, do the following:

1. Take the ID with the lowest number and place it at the top.

2. Look for rows which have a PID (parent) equal to this value.

3. Place these in the tree below the Parent according to their POS values from the lowest to the highest, ordered from left to right.

4. After all the IDs for a parent have been found, move down to the next ID and repeat the process, finding new rows with the same PID.

The first thing to determine in an explain plan is which node is executed first. The method in the slide explains this, but sometimes with complicated plans it is difficult to do this and also difficult to follow the steps through to the end. Large plans are exactly the same as smaller ones, but with more entries. The same basic rules apply. You can always collapse the plan to hide a branch of the tree which does not consume much of the resources.

Standard explain plan interpretation:

1. Start at the top.

2. Move down the row sources until you get to one which produces data, but does not consume any. This is the start row source.

3. Look at the siblings of this row source. These row sources are executed next.

4. After the children are executed, the parent is executed next.

5. Now that this parent and its children are completed, work back up the tree, and look at the siblings of the parent row source and its parents. Execute as before.

6. Move back up the plan until all row sources are exhausted.

Standard tree interpretation:

1. Start at the top.

2. Move down the tree to the left until you reach the left node. This is executed first.

3. Look at the siblings of this row source. These row sources are executed next.

4. After the children are executed, the parent is executed next.

5. Now that this parent and its children are completed, work back up the tree, and look at the siblings of the parent row source and its parents. Execute as before.

6. Move back up the tree until all row sources are exhausted.

If you remember the few basic rules of explain plans and with some experience, you can read most plans easily.



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Chapter 4 - Page 51

Execution Plan Interpretation: Example 1


You start with an example query to illustrate how to interpret an execution plan. The slide shows a query with its associated execution plan and the same plan in the tree format.

The query tries to find employees who have salaries outside the range of salaries in the salary grade table. The query is a SELECT statement from two tables with a subquery based on another table to check the salary grades.

See the execution order for this query. Based on the example in the slide, and from the previous slide, the execution order is 3 – 5 – 4 – 2 – 6 – 1:

• 3: The plan starts with a full table scan of EMP (ID=3).

• 5: The rows are passed back to the controlling nested loops join step (ID=2), which uses them to execute the lookup of rows in the PK_DEPT index in ID=5.

• 4: The ROWIDs from the index are used to lookup the other information from the DEPT table in ID=4.

• 2: ID=2, the nested loops join step, is executed until completion.

• 6: After ID=2 has exhausted its row sources, a full table scan of SALGRADE in ID=6 (at the same level in the tree as ID=2, therefore, its sibling) is executed.

• 1: This is used to filter the rows from ID2 and ID6.


SELECT /*+ RULE */ ename,job,sal,dnameFROM emp,deptWHERE dept.deptno=emp.deptno and not exists(SELECT *

FROM salgradeWHERE emp.sal between losal and hisal);

--------------------------------------------------| Id | Operation | Name |--------------------------------------------------| 0 | SELECT STATEMENT | ||* 1 | FILTER | || 2 | NESTED LOOPS | || 3 | TABLE ACCESS FULL | EMP || 4 | TABLE ACCESS BY INDEX ROWID| DEPT ||* 5 | INDEX UNIQUE SCAN | PK_DEPT ||* 6 | TABLE ACCESS FULL | SALGRADE |--------------------------------------------------

Predicate Information (identified by operation id):---------------------------------------------------

1 - filter( NOT EXISTS(SELECT 0 FROM "SALGRADE" "SALGRADE" WHERE"HISAL">=:B1 AND "LOSAL"<=:B2))

5 - access("DEPT"."DEPTNO"="EMP"."DEPTNO")6 - filter("HISAL">=:B1 AND "LOSAL"<=:B2)

NESTEDLOOPS 2 6

3 4

5

1FILTER

TABLE ACCESS FULLSALGRADE

TABLE ACCESS FULLEMP

TABLE ACCESSBY ROWID

DEPT

INDEX UNIQUE SCANPK_DEPT



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Chapter 4 - Page 52

Note that children are executed before parents, so although structures for joins must be set up before the child execution, the children are notated as executed first. Probably, the easiest way is to consider it as the order in which execution completes, so for the NESTED LOOPS join at ID=2, the two children {ID=3 and ID=4 (together with its child)} must have completed their execution before ID=2 can be completed.



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Chapter 4 - Page 53


Execution Plan Interpretation: Example 1 (continued)

The example in the slide is a plan dump from V$SQL_PLAN with STATISTICS_LEVEL set to ALL. This report shows you some important additional information compared to the output of the EXPLAIN PLAN command:

• A-Rows corresponds to the number of rows produced by the corresponding row source.

• Buffers corresponds to the number of consistent reads done by the row source.

• Starts indicates how many times the corresponding operation was processed.

For each row from the EMP table, the system gets its ENAME, SAL, JOB, and DEPTNO.

Then the system accesses the DEPT table by its unique index (PK_DEPT) to get DNAME using DEPTNO from the previous result set.

If you observe the statistics closely, the TABLE ACCESS FULL operation on the EMP table (ID=3) is started once. However, operations from ID 5 and 4 are started 14 times; once for each EMP rows. At this step (ID=2), the system gets all ENAME, SAL, JOB, and DNAME.

The system now must filter out employees who have salaries outside the range of salaries in the salary grade table. To do that, for each row from ID=2, the system accesses the SALGRADE table using a FULL TABLE SCAN operation to check if the employee’s salary is outside the salary range. This operation only needs to be done 12 times in this case because

Execution Plan Interpretation: Example 1SQL> alter session set statistics_level=ALL;

Session altered.

SQL> select /*+ RULE to make sure it reproduces 100% */ ename,job,sal,dnamefrom emp,dept where dept.deptno = emp.deptno and not exists (select * from salgrade where emp.sal between losal and hisal);

no rows selected

SQL> select * from table(dbms_xplan.display_cursor(null,null,'TYPICAL IOSTATS LAST'));

SQL_ID 274019myw3vuf, child number 0-------------------------------------…Plan hash value: 1175760222--------------------------------------------------------------------------------| Id | Operation | Name | Starts | A-Rows | Buffers | --------------------------------------------------------------------------------|* 1 | FILTER | | 1 | 0 | 61 || 2 | NESTED LOOPS | | 1 | 14 | 25 || 3 | TABLE ACCESS FULL | EMP | 1 | 14 | 7 || 4 | TABLE ACCESS BY INDEX ROWID| DEPT | 14 | 14 | 18 ||* 5 | INDEX UNIQUE SCAN | PK_DEPT | 14 | 14 | 4 ||* 6 | TABLE ACCESS FULL | SALGRADE | 12 | 12 | 36 |--------------------------------------------------------------------------------…



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Chapter 4 - Page 54

at run time the system does the check for each distinct salary, and there are 12 distinct salaries in the EMP table.



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Chapter 4 - Page 55



This query retrieves names, department names, and addresses for employees whose departments are located in Seattle and who have managers.

For formatting reasons, the explain plan has the ID in the first column, and PID in the second column. The position is reflected by the indentation. The execution plan shows two nested loops join operations.

You follow the steps from the previous example:

1. Start at the top. ID=0

2. Move down the row sources until you get to the one, which produces data, but does not consume any. In this case, ID 0, 1, 2, and 3 consume data. ID=4 is the first row source that does not consume any. This is the start row source. ID=4 is executed first. The index range scan produces ROWIDs, which are used to lookup in the LOCATIONS table in ID=3.

3. Look at the siblings of this row source. These row sources are executed next. The sibling at the same level as ID=3 is ID=5. Node ID=5 has a child ID=6, which is executed before it. This is another index range scan producing ROWIDs, which are used to lookup in the DEPARTMENTS table in ID=5.


SQL> select /*+ USE_NL(d) use_nl(m) */ m.last_name as dept_manager2 , d.department_name3 , l.street_address4 from hr.employees m join5 hr.departments d on (d.manager_id = m.employee_id)6 natural join7 hr.locations l8 where l.city = 'Seattle';

0 SELECT STATEMENT 1 0 NESTED LOOPS 2 1 NESTED LOOPS 3 2 TABLE ACCESS BY INDEX ROWID LOCATIONS 4 3 INDEX RANGE SCAN LOC_CITY_IX 5 2 TABLE ACCESS BY INDEX ROWID DEPARTMENTS6 5 INDEX RANGE SCAN DEPT_LOCATION_IX 7 1 TABLE ACCESS BY INDEX ROWID EMPLOYEES 8 7 INDEX UNIQUE SCAN EMP_EMP_ID_PK

2 7

3 5

6

1

4

8



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Chapter 4 - Page 56

4. After the children operation, the parent operation is next. The NESTED LOOPS join at ID=2 is executed next bringing together the underlying data.

5. Now that this parent and its children are completed, walk back up the tree, and look at the siblings of the parent row source and its parents. Execute as before. The sibling of ID=2 at the same level in the plan is ID=7. This has a child ID=8, which is executed first. The index unique scan produces ROWIDs, which are used to lookup in the EMPLOYEES table in ID=7.

6. Move back up the plan until all row sources are exhausted. Finally this is brought together with the NESTED LOOPS at ID=1, which passes the results back to ID=0.

7. The execution order is: 4 – 3 – 6 – 5 – 2 – 8 – 7 – 1 – 0

Here is the complete description of this plan:

The inner nested loops is executed first using LOCATIONS as the driving table, using an index access on the CITY column. This is because you search for departments in Seattle only.

The result is joined with the DEPARTMENTS table, using the index on the LOCATION_ID join column; the result of this first join operation is the driving row source for the second nested loops join.

The second join probes the index on the EMPLOYEE_ID column of the EMPLOYEES table. The system can do that because it knows (from the first join) the employee ID of all managers of departments in Seattle. Note that this is a unique scan because it is based on the primary key.

Finally, the EMPLOYEES table is accessed to retrieve the last name.



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Chapter 4 - Page 57



See the execution plan in the slide. Try to find the order in which the plan is executed and deduce what is the join order (order in which the system joins tables). Again, ID is in the first column and PID in the second column. The position is reflected by the indentation. It is important to recognize what the join order of an execution plan is, to be able to find your plan in a 10053 event trace file.

Here is the interpretation of this plan:

• The system first hashes the T3 table (Operation ID=3) into memory.

• Then it hashes the T1 table (Operation ID=5) into memory.

• Then the scan of the T2 table begins (Operation ID=6).

• The system picks a row from T2 and probes T1 (T1.i=T2.i).

• If the row survives, the system probes T3 (T1.i=T3.i).

• If the row survives, the system sends it to next operation.

• The system outputs the maximum value from the previous result set.

In conclusion, the execution order is : 3 – 5 – 6 – 4 – 2 – 1

The join order is: T1 – T2 – T3


select /*+ ORDERED USE_HASH(b) SWAP_JOIN_INPUTS(c) */ max(a.i)from t1 a, t2 b, t3 cwhere a.i = b.i and a.i = c.i;

0 SELECT STATEMENT1 SORT AGGREGATE2 1 HASH JOIN3 2 TABLE ACCESS FULL T34 2 HASH JOIN5 4 TABLE ACCESS FULL T16 4 TABLE ACCESS FULL T2

43

5 6

2

1

Join order is: T1 - T2 - T3



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Chapter 4 - Page 58

You can also use Enterprise Manager to understand execution plans, especially because it displays the Order column.

Note: A special hint was used to make sure T3 would be first in the plan.



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Chapter 4 - Page 59

Reading More Complex Execution Plans


The plan at the left comes from the query (in the slide) on the data dictionary. It is so long that it is very difficult to apply the previous method to interpret it and locate the first operation.

You can always collapse a plan to make it readable. This is illustrated at the right where you can see the same plan collapsed. As shown, this is easy to do when using the Enterprise Manager or SQL Developer graphical interface. You can clearly see that this plan is a UNION ALL of two branches. Your knowledge about the data dictionary enables you to understand that the two branches correspond to dictionary-managed tablespaces and locally-managed ones. Your knowledge about your database enables you to know that there are no dictionary-managed tablespaces. So, if there is a problem, it must be on the second branch. To get confirmation, you must look at the plan information and execution statistics of each row source to locate the part of the plan that consumes most resources. Then, you just need to expand the branch you want to investigate (where time is being spent). To use this method, you must look at the execution statistics that are generally found in V$SQL_PLAN_STATISTICS or in the tkprof reports generated from trace files. For example, tkprof cumulates for each parent operation the time it takes to execute itself plus the sum of all its child operation time.


SELECT owner , segment_name , segment_type FROM dba_extents WHERE file_id = 1 AND 123213 BETWEEN block_id AND block_id + blocks -1;

Collapse using indentationand

focus on operations consuming most resources.



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Chapter 4 - Page 60

Reviewing the Execution Plan


When you tune a SQL statement in an online transaction processing (OLTP) environment, the goal is to drive from the table that has the most selective filter. This means that there are fewer rows passed to the next step. If the next step is a join, this means fewer rows are joined. Check to see whether the access paths are optimal. When you examine the optimizer execution plan, look for the following:

• The plan is such that the driving table has the best filter.

• The join order in each step means that the fewest number of rows are returned to the next step (that is, the join order should reflect going to the best not-yet-used filters).

• The join method is appropriate for the number of rows being returned. For example, nested loop joins through indexes may not be optimal when many rows are returned.

• Views are used efficiently. Look at the SELECT list to see whether access to the view is necessary.

• There are any unintentional Cartesian products (even with small tables).

• Each table is being accessed efficiently: Consider the predicates in the SQL statement and the number of rows in the table. Look for suspicious activity, such as a full table scans on tables with large number of rows, which have predicates in the WHERE clause.


• Drive from the table that has most selective filter.

• Look for the following:– Driving table has the best filter

– Fewest number of rows are returned to the next step

– The join method is appropriate for the number of rows returned

– Views are correctly used

– Unintentional Cartesian products

– Tables accessed efficiently



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Chapter 4 - Page 61

Also, a full table scan might be more efficient on a small table, or to leverage a better join method (for example, hash join) for the number of rows returned.

If any of these conditions are not optimal, consider restructuring the SQL statement or the indexes available on the tables.



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Chapter 4 - Page 62

Looking Beyond Execution Plans


The execution plan alone cannot differentiate between well-tuned statements and those that perform poorly. For example, an EXPLAIN PLAN output that shows that a statement uses an index does not necessarily mean that the statement runs efficiently. Sometimes indexes can be extremely inefficient.

It is best to use EXPLAIN PLAN to determine an access plan, and then later prove that it is the optimal plan through testing. When evaluating a plan, you should examine the statement’s actual resource consumption.

The rest of this course is intended to show you various methods to achieve this.


• An execution plan alone cannot tell you whether a plan is good or not.

• May need additional testing and tuning:– SQL Tuning Advisor

– SQL Access Advisor

– SQL Performance Analyzer

– SQL Monitoring

– Tracing



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Chapter 4 - Page 63

Quiz

Answer: a

Quiz

A user needs to be granted some specialized privileges to generate AUTOTRACE statistics.

a. True

b. False



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Chapter 4 - Page 64

Quiz

Answer: b

Quiz

An EXPLAIN PLAN command executes the statement and inserts the plan used by the optimizer into a table.

a. True

b. False



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Chapter 4 - Page 65

Quiz

Answer: b

Quiz

Which of the following is not true about a PLAN_TABLE?

a. The PLAN_TABLE is automatically created to hold the EXPLAIN PLAN output.

b. You cannot create your own PLAN_TABLE.

c. The actual SQL command is not executed.d. The plan in the PLAN_TABLE may not be the actual

execution plan.



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Chapter 4 - Page 66

Quiz

Answer: b

Quiz

After monitoring is initiated, an entry is added to the _______view. This entry tracks key performance metrics collected for the execution.a. V$SQL_MONITOR

b. V$PLAN_MONITOR

c. ALL_SQL_MONITOR

d. ALL_SQL_PLAN_MONITOR



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Chapter 4 - Page 67

Summary

Summary


• Gather execution plans

• Display execution plans

• Interpret execution plans



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Chapter 4 - Page 68




• Using different techniques to extract execution plans

• Using SQL monitoring



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Application Tracing

Chapter 5 - Page 1

Application Tracing

Chapter 5



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Application Tracing

Chapter 5 - Page 2

Application Tracing

Application Tracing



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Application Tracing

Chapter 5 - Page 3

Objectives

Objectives

After completing this lesson, you should be able to do the following:

• Configure the SQL Trace facility to collect session statistics

• Use the trcsess utility to consolidate SQL trace files

• Format trace files using the tkprof utility

• Interpret the output of the tkprof command



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Application Tracing

Chapter 5 - Page 4

End-to-End Application Tracing Challenge


Oracle Database implements tracing by generating a trace file for each server process when you enable the tracing mechanism.

Tracing a specific client is usually not a problem in the dedicated server model as a single dedicated process serves a session during its lifetime. All the trace information for the session can be seen from the trace file belonging to the dedicated server serving it. However, in a shared server configuration, a client is serviced by different processes from time-to-time. The trace pertaining to the user session is scattered across different trace files belonging to different processes. This makes it difficult for you to get a complete picture of the life cycle of a session.

Moreover, what if you want to consolidate trace information for a particular service for performance or debugging purposes? This is also difficult because you have multiple clients using the same service and each generating trace files belonging to the server process serving it.


• I want to retrieve traces from CRM service.

• I want to retrieve traces from client C4.

• I want to retrieve traces from session 6.

Client

Dedicatedserver

Tracefile

Clients

Sharedserver

Tracefile

Sharedserver

Tracefile

Sharedserver

Tracefile

Client

Dedicatedserver

Tracefile

Client

Dedicatedserver

Tracefile

CRM ERP CRM CRM ERP CRM

Client C4Client OE Client JF/Session 6Client OE



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Application Tracing

Chapter 5 - Page 5

End-to-End Application Tracing


End-to-end application tracing simplifies the diagnosis of performance problems in multitier environments. In multitier environments, a request from an end client is routed to different database sessions by the middle tier, making it difficult to track a specific client. A client identifier is used to uniquely trace a specific end client through all tiers to the database server.

You can used end-to-end application tracing to identify the source of an excessive workload, such as a high-load SQL statement. Also, you can identify what a user’s session does at the database level to resolve that user's performance problems.

End-to-end application tracing also simplifies management of application workloads by tracking specific modules and actions in a service. Workload problems can be identified by:

• Client identifier: Specifies an end user based on the logon ID, such as HR

• Service: Specifies a group of applications with common attributes, service-level thresholds, and priorities; or a single application

• Module: Specifies a functional block within an application

• Action: Specifies an action, such as an INSERT or an UPDATE operation, in a module

• Session: Specifies a session based on a given database session identifier (SID)

The primary interface for end-to-end application tracing is Enterprise Manager. Other tools listed in the slide are discussed later in this lesson.


• Simplifies the process of diagnosing performance problems in multitier environments by allowing application workloads to be seen by:– Service– Module– Action– Session– Client

• End-to-end application tracing tools:– Enterprise Manager– DBMS_APPICATION_INFO, DBMS_SERVICE,

DBMS_MONITOR, DBMS_SESSION– SQL Trace and trcsess utility– tkprof



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Application Tracing

Chapter 5 - Page 6

Location for Diagnostic Traces


Starting with Oracle Database 11g, Release 1, Automatic Diagnostic Repository (ADR) is a file-based repository for database diagnostic data such as traces, incident dumps, packages, the alert log, Health Monitor reports, core dumps, and so on. The traditional …_DUMP_DEST initialization parameters are ignored. The ADR root directory is known as the ADR base. Its location is set by the DIAGNOSTIC_DEST initialization parameter. In the slide, this location is denoted by $ADR_HOME. However, there is no official environment variable called ADR_HOME. The table shown in the slide describes the different classes of trace data and dumps that reside both in Oracle Database 10g (and earlier releases) and in Oracle Database 11g. With Oracle Database 11g, there is no distinction between foreground and background trace files. Both types of files go into the $ADR_HOME/trace directory. You can use V$DIAG_INFO to list some important ADR locations.

All nonincident traces are stored inside the TRACE subdirectory. Starting with Oracle Database 11g, critical error information is dumped into the corresponding process trace files instead of incident dumps. Incident dumps are placed in files separated from the normal process trace files.

Note: The main difference between a trace and a dump is that a trace is a continuous output, such as when SQL tracing is turned on, and a dump is a one-time output in response to an event, such as an incident. Also, a core dump is a binary memory dump that is port specific.


Diagnostic Data Previous Location ADR Location

Foreground process traces

USER_DUMP_DEST $ADR_HOME/trace

Background process traces

BACKGROUND_DUMP_DEST $ADR_HOME/trace

Alert log data BACKGROUND_DUMP_DEST $ADR_HOME/alert

$ADR_HOME/trace

Core dumps CORE_DUMP_DEST $ADR_HOME/cdump

Incident dumps USER_DUMP_DEST

BACKGROUND_DUMP_DEST

$ADR_HOME/incident/incdir_n

$ADR_HOME/trace Oracle Database 11g trace – critical error trace<=

V$DIAG_INFO

DIAGNOSTIC_DEST



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Application Tracing

Chapter 5 - Page 7

What Is a Service?

What Is a Service?

The concept of a service was first introduced in Oracle8i as a means for the listener to perform connection load balancing between nodes and instances of a cluster. However, the concept, definition, and implementation of services have been dramatically expanded. A service organizes work execution within the database to make it more manageable, measurable, tunable, and recoverable. A service is a grouping of related tasks within the database with common functionality, quality expectations, and priority relative to other services. A service provides a single-system image for managing competing applications that run within a single instance and across multiple instances and databases.

Services can be configured, administered, enabled, disabled, and measured as a single entity using standard interfaces, Enterprise Manager, and SRVCTL,.

Services provide availability. Following outages, a service is recovered quickly and automatically at surviving instances.

Services provide an additional dimension to performance tuning. With services, workloads are visible and measurable. Tuning by “service and SQL” replaces tuning by “session and SQL” in the majority of systems where sessions are anonymous and shared.

From a tracing point of view, a service provides a handle that permits capturing trace information by service name regardless of the session.

What Is a Service?

• Is a means of grouping sessions that perform the same kind of work

• Provides a single-system image instead of a multiple-instances image

• Is a part of the regular administration tasks that provide dynamic service-to-instance allocation

• Is the base for high availability of connections

• Provides a performance-tuning dimension

• Is a handle for capturing trace information



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Application Tracing

Chapter 5 - Page 8

Using Services with Client Applications


A service name is used by any client connecting to the database server. That service name is automatically applied to the client actions. Applications can be grouped by services by simply using a different service name for each application to connect.

Applications and middle-tier connection pools select a service by using the Transparent Network Substrate (TNS) connection descriptor.

The selected service must match the service that has been created.

The first example in the slide shows the TNS connect descriptor that can be used to access the ERP service.

The second example shows the thick Java Database Connectivity (JDBC) connection description using the previously defined TNS connect descriptor.

The third example shows the thin JDBC connection description using the same TNS connect descriptor.


ERP=(DESCRIPTION=(ADDRESS=(PROTOCOL=TCP)(HOST=mynode)(PORT=1521))

(CONNECT_DATA=(SERVICE_NAME=ERP)))

url="jdbc:oracle:oci:@ERP"

url="jdbc:oracle:thin:@(DESCRIPTION= (ADDRESS=(PROTOCOL=TCP)(HOST=mynode)(PORT=1521))

(CONNECT_DATA=(SERVICE_NAME=ERP)))"



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Application Tracing

Chapter 5 - Page 9

Tracing Services

Tracing Services

An application can qualify a service by MODULE and ACTION names to identify the important transactions within the service. This enables you to locate the poorly performing transactions for categorized workloads. This is important when you monitor performance in systems using connection pools or transaction processing monitors. For these systems, the sessions are shared, which makes accountability difficult. SERVICE_NAME, MODULE, ACTION, CLIENT_IDENTIFIER, and SESSION_ID are actual columns in V$SESSION. SERVICE_NAME is set automatically at login time based on the connect descriptor, and SESSION_ID is automatically set by the database when a session is created. MODULE and ACTION names are set by the application by using the DBMS_APPLICATION_INFO PL/SQL package or special Oracle Call Interface (OCI) calls. MODULE should be set to a name that is recognizable by the user for the program that currently executes. Likewise, ACTION should be set to a specific action or task that a user performs within a module (for example, entering a new customer). CLIENT_IDENTIFIER can be set using the DBMS_SESSION.SET_IDENTIFIER procedure.

The traditional method of tracing each session produces trace files with SQL commands that may contain the trace information for multiple end users or applications. Unless all database sessions are being traced, some information from the end user sessions may be missed. This results in a hit-or-miss approach to diagnose problematic SQL.

Tracing Services

• Applications using services can be further qualified by:– MODULE– ACTION– CLIENT_IDENTIFIER

• Set using the following PL/SQL packages:– DBMS_APPLICATION_INFO– DBMS_SESSION

• Tracing can be done at all levels:– CLIENT_IDENTIFIER– SESSION_ID– SERVICE_NAMES– MODULE– ACTION

– Combination of SERVICE_NAME, MODULE, ACTION



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Application Tracing

Chapter 5 - Page 10

With the criteria that you provide (SERVICE_NAME, MODULE, or ACTION), specific trace information is captured in a set of trace files and combined into a single output trace file. This enables you to produce trace files that contain SQL that is relevant to a specific workload. It is also possible to do the same for CLIENT_IDs and SESSION_IDs.

Note: DBA_ENABLED_TRACES displays information about enabled traces.



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Application Tracing

Chapter 5 - Page 11

Use Enterprise Manager to Trace Services


On the Performance page, you can click the Top Consumers link. The Top Consumers page is displayed.

The Top Consumers page has several tabs for displaying your database as a single-system image. The Overview tabbed page contains four pie charts: Top Clients, Top Services, Top Modules, and Top Actions. Each chart provides a different perspective about the top resource consumers in your database.

The Top Services tabbed page displays performance-related information for the services that are defined in your database. On this page, you can enable or disable tracing at the service level.




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Application Tracing

Chapter 5 - Page 12

Service Tracing: Example


In the first code box, all sessions that log in under the AP service are traced. A trace file is created for each session that uses the service, regardless of the module and action. You can also enable tracing for specific tasks within a service. This is illustrated in the second example, where all sessions of the AP service that execute the QUERY_DELINQUENT action within the PAYMENTS module are traced.

Tracing by service, module, and action enable you to focus your tuning efforts on specific SQL, rather than sifting through trace files with SQL from different programs. Only the SQL statements that are identified with this MODULE and ACTION are recorded in the trace file. With this feature, relevant wait events for a specific action can be identified.

You can also start tracing for a particular client identifier as shown by the third example. In this example, C4 is the client identifier for which SQL tracing is to be enabled. The TRUE argument specifies that wait information is present in the trace file. The FALSE argument specifies that bind information is not present in the trace file.

Although not shown in the slide, you can use the CLIENT_ID_TRACE_DISABLE procedure to disable tracing globally for the database for a given client identifier. To disable tracing, for the previous example, execute the following command:

EXECUTE DBMS_MONITOR.CLIENT_ID_TRACE_DISABLE(client_id => 'C4');


• Trace on service, module, and action:

• Trace a particular client identifier:

exec DBMS_MONITOR.SERV_MOD_ACT_TRACE_ENABLE('AP');

exec DBMS_MONITOR.SERV_MOD_ACT_TRACE_ENABLE(-'AP', 'PAYMENTS', 'QUERY_DELINQUENT');

exec DBMS_MONITOR.CLIENT_ID_TRACE_ENABLE

(client_id=>'C4', waits => TRUE, binds => FALSE);



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Application Tracing

Chapter 5 - Page 13

Note: CLIENT_IDENTIFIER can be set using the DBMS_SESSION.SET_IDENTIFIER procedure.



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Application Tracing

Chapter 5 - Page 14

Session Level Tracing: Example


You can use tracing to debug performance problems. Trace-enabling procedures have been implemented as part of the DBMS_MONITOR package. These procedures enable tracing globally for a database.

You can use the DATABASE_TRACE_ENABLE procedure to enable session level SQL tracing instance-wide. The procedure has the following parameters:

• WAITS: Specifies whether wait information is to be traced

• BINDS: Specifies whether bind information is to be traced

• INSTANCE_NAME: Specifies the instance for which tracing is to be enabled. Omitting INSTANCE_NAME means that the session-level tracing is enabled for the whole database.

Use the DATABASE_TRACE_DISABLE procedure to disable SQL tracing for the whole database or a specific instance.

Similarly, you can use the SESSION_TRACE_ENABLE procedure to enable tracing for a given database session identifier on the local instance. The SID and SERIAL# information can be found from V$SESSION.


• For all sessions in the database:

• For a particular session:

EXEC dbms_monitor.DATABASE_TRACE_ENABLE(TRUE,TRUE);

EXEC dbms_monitor.DATABASE_TRACE_DISABLE();

EXEC dbms_monitor.SESSION_TRACE_ENABLE(session_id=> 27, serial_num=>60, waits=>TRUE, binds=>FALSE);

EXEC dbms_monitor.SESSION_TRACE_DISABLE(session_id =>27, serial_num=>60);



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Application Tracing

Chapter 5 - Page 15

Use the SESSION_TRACE_DISABLE procedure to disable the trace for a given database session identifier and serial number.

Note: SQL Trace involves some overhead, so you usually do not want to enable SQL Trace at the instance level.



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Application Tracing

Chapter 5 - Page 16

Trace Your Own Session


Although the DBMS_MONITOR package can be invoked only by a user with the DBA role, any user can enable SQL tracing for his or her own session by using the DBMS_SESSION package. The SESSION_TRACE_ENABLE procedure can be invoked by any user to enable session level SQL tracing for his or her own session. An example is shown in the slide.

You can then use the DBMS_SESSION.SESSION_TRACE_DISABLE procedure to stop dumping to your trace file.

The TRACEFILE_IDENTIFIER initialization parameter specifies a custom identifier that becomes part of the Oracle trace file name. You can use such a custom identifier to identify a trace file simply from its name and without opening it or view its contents. Each time this parameter is dynamically modified at the session level, the next trace dump written to a trace file will have the new parameter value embedded in its name. This parameter can only be used to change the name of the foreground process trace file; the background processes continue to have their trace files named in the regular format. For foreground processes, the TRACEID column of the V$PROCESS view contains the current value of this parameter. When this parameter value is set, the trace file name has the following format: sid_ora_pid_traceid.trc.


• Enabling trace:

• Disabling trace:

• Easily identifying your trace files:

EXEC DBMS_SESSION.SESSION_TRACE_DISABLE();

EXEC DBMS_SESSION.SESSION_TRACE_ENABLE(waits => TRUE, binds => FALSE);

alter session set tracefile_identifier='mytraceid';



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Application Tracing

Chapter 5 - Page 17

The trcsess Utility

The trcsess Utility

The trcsess utility consolidates trace output from selected trace files on the basis of several criteria: session ID, client identifier, service name, action name, and module name. After trcsess merges the trace information into a single output file, the output file can be processed by tkprof.

When using the DBMS_MONITOR.SERV_MOD_ACT_TRACE_ENABLE procedure, tracing information is present in multiple trace files and you must use the trcsess tool to collect it into a single file.

The trcsess utility is useful for consolidating the tracing of a particular session or service for performance or debugging purposes.

Tracing a specific session is usually not a problem in the dedicated server model because a single dedicated process serves a session during its lifetime. All the trace information for the session can be seen from the trace file belonging to the dedicated server that serves it. However, tracing a service might become a complex task even in the dedicated server model.

Moreover, in a shared-server configuration, a user session is serviced by different processes from time-to-time. The trace pertaining to the user session is scattered across different trace files belonging to different processes. This makes it difficult to get a complete picture of the life cycle of a session.

The trcsess Utility

TRCSESS

Trace filefor one clienttkprof

Reportfile

trcsess

Trace filefor CRM service

Client

Dedicatedserver

Clients

Sharedserver

Sharedserver

Tracefile

Sharedserver

Client

Dedicatedserver

Tracefile

Client

Dedicatedserver

CRM ERP CRM CRM ERP CRM

Tracefile

Tracefile

Tracefile

Tracefile



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Application Tracing

Chapter 5 - Page 18

Invoking the trcsess Utility


The syntax for the trcsess utility is shown in the slide, where:

• output specifies the file where the output is generated. If this option is not specified, standard output is used for the output.

• session consolidates the trace information for the session specified. The session identifier is a combination of session index and session serial number, such as 21.2371. You can locate these values in the V$SESSION view.

• clientid consolidates the trace information for the given client identifier.

• service consolidates the trace information for the given service name.

• action consolidates the trace information for the given action name.

• module consolidates the trace information for the given module name.

• <trace file names> is a list of all the trace file names, separated by spaces, in which trcsess should look for trace information. The wildcard character “*” can be used to specify the trace file names. If trace files are not specified, all the files in the current directory are taken as input to trcsess. You can find trace files in ADR.


trcsess [output=output_file_name][session=session_id][clientid=client_identifier][service=service_name][action=action_name][module=module_name][<trace file names>]

Tracefile

Tracefile

TRCSESS

Consolidatedtrace file

Tracefile



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Application Tracing

Chapter 5 - Page 19

Note: One of the session, clientid, service, action, or module options must be specified. If there is more than one option specified, the trace files, which satisfy all the criteria specified are consolidated into the output file.



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Application Tracing

Chapter 5 - Page 20

The trcsess Utility: Example


The example in the slide illustrates a possible use of the trcsess utility. The example assumes that you have three different sessions: Two sessions that are traced (left and right), and one session (center) that enables or disables tracing and concatenates trace information from the previous two sessions.

The first and second session set their client identifier to the ‘HR session’ value. This is done using the DBMS_SESSION package. Then, the third session enables tracing for these two sessions using the DBMS_MONITOR package.

At that point, two new trace files are generated in ADR; one for each session that is identified with the ‘HR session’ client identifier.

Each traced session now executes its SQL statements. Every statement generates trace information in its own trace file in ADR.

Then, the third session stops trace generation using the DBMS_MONITOR package, and consolidates trace information for the ‘HR session’ client identifier in the mytrace.trc file. The example assumes that all trace files are generated in the $ORACLE_BASE/diag/rdbms/orcl/orcl/trace directory, which is the default in most cases.


exec dbms_session.set_identifier('HR session');

exec dbms_session.set_identifier('HR session');

exec DBMS_MONITOR.CLIENT_ID_TRACE_ENABLE( -client_id=>'HR session', waits => FALSE, -binds => FALSE);

select * from employees;

select * from departments;

exec DBMS_MONITOR.CLIENT_ID_TRACE_DISABLE( -client_id => 'HR session');

trcsess output=mytrace.trc clientid='HR session' $ORACLE_BASE/diag/rdbms/orcl/orcl/trace/*.trc

…

…

First session Second session

Third session



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Application Tracing

Chapter 5 - Page 21

SQL Trace File Contents


As seen already, a SQL trace file provides performance information on individual SQL statements. It generates the following statistics for each statement:

• Parse, execute, and fetch counts

• CPU and elapsed times

• Physical reads and logical reads

• Number of rows processed

• Misses on the library cache

• Username under which each parse occurred

• Each commit and rollback

• Wait event data for each SQL statement, and a summary for each trace file

If the cursor for the SQL statement is closed, SQL Trace also provides row source information that includes:

• Row operations showing the actual execution plan of each SQL statement

• Number of rows, number of consistent reads, number of physical reads, number of physical writes, and time elapsed for each operation. This is possible only when the STATISTICS_LEVEL initialization parameter is set to ALL.


• Parse, execute, and fetch counts

• CPU and elapsed times

• Physical reads and logical reads

• Number of rows processed

• Misses on the library cache

• Username under which each parse occurred

• Each commit and rollback

• Wait event and bind data for each SQL statement

• Row operations showing the actual execution plan of each SQL statement

• Number of consistent reads, physical reads, physical writes, and time elapsed for each operation on a row



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Application Tracing

Chapter 5 - Page 22

Note: Using the SQL Trace facility can have a severe performance impact and may result in increased system overhead, excessive CPU usage, and inadequate disk space.



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Application Tracing

Chapter 5 - Page 23

SQL Trace File Contents: Example


There are multiple types of trace files that can be generated by the Oracle Database. The one that is referred to in this lesson is generally called a SQL trace file. The slide shows you a sample output from the mytrace.trc SQL trace file generated by the previous example.

In this type of trace file, you can find (for each statement that was traced) the statement itself, with some corresponding cursor details. You can see statistic details for each phase of the statement’s execution: PARSE, EXEC, and FETCH. As you can see, you can have multiple FETCH for one EXEC depending on the number of rows returned by your query.

Last part of the trace is the execution plan with some cumulated statistics for each row source.

Depending on the way you enabled tracing, you can also obtain information about wait events and bind variables in the generated trace files.

Generally, you do not try to interpret the trace file itself. This is because you do not get an overall idea of what your sessions did. For example, one session could have executed the same statement multiple times at different moments. The corresponding traces are then scattered across the entire trace file, which makes them hard to find.

Instead, you use another tool, such as tkprof to interpret the contents of the raw trace information.


*** [ Unix process pid: 15911 ]*** 2010-07-29 13:43:11.327*** 2010-07-29 13:43:11.327*** 2010-07-29 13:43:11.327*** 2010-07-29 13:43:11.327…====================PARSING IN CURSOR #2 len=23 dep=0 uid=85 oct=3 lid=85 tim=1280410994003145 hv=4069246757 ad='4cd57ac0' sqlid='f34thrbt8rjt5'select * from employeesEND OF STMTPARSE #2:c=3000,e=2264,p=0,cr=0,cu=0,mis=1,r=0,dep=0,og=1,plh=1445457117, tim=1280410994003139EXEC #2:c=0,e=36,p=0,cr=0,cu=0,mis=0,r=0,dep=0,og=1,plh=1445457117, tim=1280410994003312FETCH #2:c=0,e=215,p=0,cr=3,cu=0,mis=0,r=1,dep=0,og=1,plh=1445457117, tim=1280410994003628FETCH #2:c=0,e=89,p=0,cr=5,cu=0,mis=0,r=15,dep=0,og=1,plh=1445457117, tim=1280410994004232…FETCH #2:c=0,e=60,p=0,cr=1,cu=0,mis=0,r=1,dep=0,og=1,plh=1445457117, tim=1280410994107857STAT #2 id=1 cnt=107 pid=0 pos=1 obj=73933 op='TABLE ACCESS FULL EMPLOYEES (cr=15 pr=0 pw=0 time=0 us cost=3 size=7383 card=107)'XCTEND rlbk=0, rd_only=1, tim=1280410994108875=====================



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Application Tracing

Chapter 5 - Page 24

Formatting SQL Trace Files: Overview


The tkprof utility parses SQL trace files to produce more readable output. Remember that all the information in tkprof is available from the raw trace file. There is a huge number of sort options that you can invoke with tkprof at the command prompt. A useful starting point is the fchela sort option, which orders the output by elapsed time fetching. The resultant file contains the most time-consuming SQL statement at the start of the file. Another useful parameter is SYS=NO. This can be used to prevent SQL statements run as the SYS user from being displayed. This can make the output file much shorter and easier to manage.

After a number of SQL trace files have been generated, you can perform any of the following:

• Run tkprof on each individual trace file, producing a number of formatted output files, one for each session.

• Concatenate the trace files, and then run tkprof on the result to produce a formatted output file for the entire instance.

• Run the trcsess command-line utility to consolidate tracing information from several trace files, then run tkprof on the result.

tkprof does not report COMMITs and ROLLBACKs that are recorded in the trace file.


Use the tkprof utility to format your SQL trace files:

• Sort raw trace file to exhibit top SQL statements

• Filter dictionary statements

Tracefile

TracefileTrace

file

tkprof

Reportfile

trcsess

Consolidatedtrace file

Tracefile

TracefileTrace

fileTracefile

Tracefile

Tracefile

Concatenatedtrace file



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Application Tracing

Chapter 5 - Page 25

Note: Set the TIMED_STATISTICS parameter to TRUE when tracing sessions because no time-based comparisons can be made without this. TRUE is the default value with Oracle Database 11g.



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Application Tracing

Chapter 5 - Page 26

Invoking the tkprof Utility


When you enter the tkprof command without any arguments, it generates a usage message together with a description of all tkprof options. The various arguments are shown in the slide:

• inputfile: Specifies the SQL trace input file

• outputfile: Specifies the file to which tkprof writes its formatted output

• waits: Specifies whether to record the summary for any wait events found in the trace file. Values are YES or NO. The default is YES.

• sorts: Sorts traced SQL statements in the descending order of specified sort option before listing them into the output file. If more than one option is specified, the output is sorted in the descending order by the sum of the values specified in the sort options. If you omit this parameter, tkprof lists statements into the output file in the order of first use.

• print: Lists only the first integer sorted SQL statements from the output file. If you omit this parameter, tkprof lists all traced SQL statements. This parameter does not affect the optional SQL script. The SQL script always generates insert data for all traced SQL statements.


tkprof inputfile outputfile [waits=yes|no]

[sort=option]

[print=n]

[aggregate=yes|no]

[insert=sqlscriptfile]

[sys=yes|no]

[table=schema.table]

[explain=user/password]

[record=statementfile]

[width=n]



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Application Tracing

Chapter 5 - Page 27

• aggregate: If set to NO, tkprof does not aggregate multiple users of the same SQL text.

• insert: Creates a SQL script to store the trace file statistics in the database. tkprof creates this script with the name you specify for sqlscriptfile. This script creates a table and inserts a row of statistics for each traced SQL statement into the table.

• sys: Enables and disables the listing of SQL statements issued by the SYS user, or recursive SQL statements, into the output file. The default value of YES causes tkprof to list these statements. The value of NO causes tkprof to omit them. This parameter does not affect the optional SQL script. The SQL script always inserts statistics for all traced SQL statements, including recursive SQL statements.

• table: Specifies the schema and name of the table into which tkprof temporarily places execution plans before writing them to the output file. If the specified table already exists, tkprof deletes all rows in the table, uses it for the EXPLAIN PLAN statement (which writes more rows into the table), and then deletes those rows. If this table does not exist, tkprof creates it, uses it, and then drops it. The specified user must be able to issue INSERT, SELECT, and DELETE statements against the table. If the table does not already exist, the user must also be able to issue the CREATE TABLE and DROP TABLE statements. This option allows multiple individuals to run tkprof concurrently with the same user in the EXPLAIN value. These individuals can specify different TABLE values and avoid destructively interfering with each other’s processing on the temporary plan table. If you use the EXPLAIN parameter without the TABLE parameter, tkprof uses the PROF$PLAN_TABLE table in the schema of the user specified by the EXPLAIN parameter. If you use the TABLE parameter without the EXPLAIN parameter, tkprof ignores the TABLE parameter. If no plan table exists, tkprof creates the PROF$PLAN_TABLE table and then drops it at the end.

• explain: Determines the execution plan for each SQL statement in the trace file and writes these execution plans to the output file. tkprof determines execution plans by issuing the EXPLAIN PLAN statement after connecting to the system with the user and password specified in this parameter. The specified user must have CREATE SESSION system privileges. tkprof takes longer to process a large trace file if the EXPLAIN option is used.

• record: Creates a SQL script with the specified file name statementfile with all the nonrecursive SQL statements in the trace file. This can be used to replay the user events from the trace file.

• width: An integer that controls the output line width of some tkprof output, such as the explain plan. This parameter is useful for post-processing of tkprof output.

The input and output files are the only required arguments.



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Application Tracing

Chapter 5 - Page 28

tkprof Sorting Options


The table lists all the sort options you can use with the sort argument of tkprof.


Sort Option Description

prscnt Number of times parse was called

prscpu CPU time parsing

prsela Elapsed time parsing

prsdsk Number of disk reads during parse

prsqry Number of buffers for consistent read during parse

prscu Number of buffers for current read during parse

prsmis Number of misses in the library cache during parse

execnt Number of executes that were called

execpu CPU time spent executing

exeela Elapsed time executing

exedsk Number of disk reads during execute

exeqry Number of buffers for consistent read during execute

execu Number of buffers for current read during execute



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Application Tracing

Chapter 5 - Page 29



Sort Option Description

exerow Number of rows processed during execute

exemis Number of library cache misses during execute

fchcnt Number of times fetch was called

fchcpu CPU time spent fetching

fchela Elapsed time fetching

fchdsk Number of disk reads during fetch

fchqry Number of buffers for consistent read during fetch

fchcu Number of buffers for current read during fetch

fchrow Number of rows fetched

userid User ID of user that parsed the cursor



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Application Tracing

Chapter 5 - Page 30

Output of the tkprof Command


The tkprof output file lists the statistics for a SQL statement by the SQL processing step. The step for each row that contains statistics is identified by the value of the call column.

PARSE This step translates the SQL statement into an execution plan and includes checks for proper security authorization and checks for the existence of tables, columns, and other referenced objects.

EXECUTE This step is the actual execution of the statement by the Oracle server. For the INSERT, UPDATE, and DELETE statements, this step modifies the data (including sorts when needed). For the SELECT statements, this step identifies the selected rows.

FETCH This step retrieves rows returned by a query and sorts them when needed. Fetches are performed only for the SELECT statements.

Note: The PARSE value includes both hard and soft parses. A hard parse refers to the development of the execution plan (including optimization); it is subsequently stored in the library cache. A soft parse means that a SQL statement is sent for parsing to the database, but the database finds it in the library cache and only needs to verify things, such as access rights. Hard parses can be expensive, particularly due to the optimization. A soft parse is mostly expensive in terms of library cache activity 25


• Text of the SQL statement

• Trace statistics (for statement and recursive calls) separated into three SQL processing steps:

PARSE Translates the SQL statement into an execution plan

EXECUTE Executes the statement(This step modifies the data for the INSERT, UPDATE, and DELETE statements.)

FETCH Retrieves the rows returned by a query

(Fetches are performed only for the SELECT

statements.)



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Application Tracing

Chapter 5 - Page 31


Output of the tkprof Command (continued)

The output is explained on the following page.

Sample output is as follows: call count cpu elapsed disk query current rows ------- ------ -------- ---------- ---------- ---------- ---------- ---

Parse 1 0.03 0.06 0 0 0 0 Execute 1 0.06 0.30 1 3 0 0 Fetch 2 0.00 0.46 0 0 0 1 ------- ------ -------- ---------- ---------- ---------- ---------- ---

total 4 0.09 0.83 1 3 0 1

Next to the CALL column, tkprof displays the following statistics for each statement:

• Count: Number of times a statement was parsed, executed, or fetched (Check this column for values greater than 1 before interpreting the statistics in the other columns. Unless the AGGREGATE = NO option is used, tkprof aggregates identical statement executions into one summary table.)

• CPU: Total CPU time in seconds for all parse, execute, or fetch calls

• Elapsed: Total elapsed time in seconds for all parse, execute, or fetch calls


There are seven categories of trace statistics:

Count Number of times the procedure was executed

CPU Number of seconds to process

Elapsed Total number of seconds to execute

Disk Number of physical blocks read

Query Number of logical buffers read for consistent read

Current Number of logical buffers read in current mode

Rows Number of rows processed by the fetch or execute



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Application Tracing

Chapter 5 - Page 32

• Disk: Total number of data blocks physically read from the data files on disk for all parse, execute, or fetch calls

• Query: Total number of buffers retrieved in consistent mode for all parse, execute, or fetch calls (Buffers are usually retrieved in consistent mode for queries.)

• Current: Total number of buffers retrieved in current mode (Buffers typically are retrieved in current mode for data manipulation language statements. However, segment header blocks are always retrieved in current mode.)

• Rows: Total number of rows processed by the SQL statement (This total does not include rows processed by subqueries of the SQL statement. For SELECT statements, the number of rows returned appears for the fetch step. For the UPDATE, DELETE, and INSERT statements, the number of rows processed appears for the execute step.)

Note

• DISK is equivalent to physical reads from v$sysstat or AUTOTRACE.

• QUERY is equivalent to consistent gets from v$sysstat or AUTOTRACE.

• CURRENT is equivalent to db block gets from v$sysstat or AUTOTRACE.



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Application Tracing

Chapter 5 - Page 33


Output of the tkprof Command (continued)

Recursive Calls

To execute a SQL statement issued by a user, the Oracle server must occasionally issue additional statements. Such statements are called recursive SQL statements. For example, if you insert a row in a table that does not have enough space to hold that row, the Oracle server makes recursive calls to allocate the space dynamically. Recursive calls are also generated when data dictionary information is not available in the data dictionary cache and must be retrieved from disk.

If recursive calls occur while the SQL Trace facility is enabled, tkprof marks them clearly as recursive SQL statements in the output file. You can suppress the listing of recursive calls in the output file by setting the SYS=NO command-line parameter. Note that the statistics for recursive SQL statements are always included in the listing for the SQL statement that caused the recursive call.

Library Cache Misses

tkprof also lists the number of library cache misses resulting from parse and execute steps for each SQL statement. These statistics appear on separate lines following the tabular statistics.


The tkprof output also includes the following:

• Recursive SQL statements

• Library cache misses

• Parsing user ID

• Execution plan

• Optimizer mode or hint

• Row source operation...Misses in library cache during parse: 1Optimizer mode: ALL_ROWSParsing user id: 85

Rows Row Source Operation------- ---------------------------------------------------

5 TABLE ACCESS BY INDEX ROWID EMPLOYEES (cr=4 pr=1 pw=0 time=0 us …5 INDEX RANGE SCAN EMP_NAME_IX (cr=2 pr=1 pw=0 time=80 us cost=1 …

…



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Application Tracing

Chapter 5 - Page 34

Row Source Operations

These provide the number of rows processed for each operation executed on the rows and additional row source information, such as physical reads and writes; cr = consistent reads, w = physical writes, r = physical reads, time = time (in microseconds).

Parsing User ID

This is the ID of the last user to parse the statement.

Row Source Operation

The row source operation shows the data sources for execution of the SQL statement. This is included only if the cursor has been closed during tracing. If the row source operation does not appear in the trace file, you may then want to view the output of the EXPLAIN PLAN.

Execution Plan

If you specify the EXPLAIN parameter on the tkprof command line, tkprof uses the EXPLAIN PLAN command to generate the execution plan of each SQL statement traced. tkprof also displays the number of rows processed by each step of the execution plan.

Note: Be aware that the execution plan is generated at the time that the tkprof command is run and not at the time the trace file was produced. This could make a difference if, for example, an index has been created or dropped since tracing the statements.

Optimizer Mode or Hint

This indicates the optimizer hint that is used during the execution of the statement. If there is no hint, it shows the optimizer mode that is used.



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Application Tracing

Chapter 5 - Page 35

tkprof Output with No Index: Example


The example in the slide shows that the aggregation of results across several executions (rows) is being fetched from the CUSTOMERS table. It requires 0.12 second of CPU fetch time. The statement is executed through a full table scan of the CUSTOMERS table, as you can see in the row source operation of the output.

The statement must be optimized.

Note: If CPU or elapsed values are 0, timed_statistics is not set.


...select max(cust_credit_limit) from customers where cust_city ='Paris'

call count cpu elapsed disk query current rows------- ------ ------- --------- -------- -------- --------- ---------Parse 1 0.00 0.00 0 0 0 0Execute 1 0.00 0.00 0 0 0 0Fetch 2 0.02 0.10 72 1459 0 1------- ------ ------- --------- -------- -------- --------- ---------total 4 0.02 0.10 72 1459 0 1

Misses in library cache during parse: 1Optimizer mode: ALL_ROWSParsing user id: 88


1 SORT AGGREGATE (cr=1459 pr=72 pw=0 time=0 us)77 TABLE ACCESS FULL CUSTOMERS (cr=1459 pr=72 pw=0 time=4104 us

cost=405 size=1260 card=90)...



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Application Tracing

Chapter 5 - Page 36

tkprof Output with Index: Example


The results shown in the slide indicate that CPU time was reduced to 0.01 second when an index was created on the CUST_CITY column. These results may have been achieved because the statement uses the index to retrieve the data. Additionally, because this example reexecutes the same statement, most of the data blocks are already in memory. You can achieve significant improvements in performance by indexing sensibly. Identify areas for potential improvement using the SQL Trace facility.

Note: Indexes should not be built unless required. Indexes do slow down the processing of the INSERT, UPDATE, and DELETE commands because references to rows must be added, changed, or removed. Unused indexes should be removed. However, instead of processing all the application SQL through EXPLAIN PLAN, you can use index monitoring to identify and remove any indexes that are not used.


...select max(cust_credit_limit) from customers where cust_city ='Paris'

call count cpu elapsed disk query current rows------- ------ -------- ---------- --------- --------- ---------- ----------Parse 1 0.00 0.00 0 0 0 0Execute 1 0.00 0.00 0 0 0 0Fetch 2 0.00 0.00 1 77 0 1------- ------ -------- ---------- --------- --------- ---------- ----------total 4 0.00 0.00 1 77 0 1

Misses in library cache during parse: 1Optimizer mode: ALL_ROWSParsing user id: 88


1 SORT AGGREGATE (cr=77 pr=1 pw=0 time=0 us)77 TABLE ACCESS BY INDEX ROWID CUSTOMERS (cr=77 pr=1 pw=0 time=760 us

cost=85 size=1260 card=90)77 INDEX RANGE SCAN CUST_CUST_CITY_IDX (cr=2 pr=1 pw=0 time=152 us

cost=1 size=0 card=90)(object id 78183)



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Application Tracing

Chapter 5 - Page 37

Quiz

Answer: b

Quiz

Which command would you use to create a trace file of your SQL*Plus session in a dedicated server environment?a. alter session set

tracefile_identifier='mytraceid';

b. EXEC DBMS_SESSION.SESSION_TRACE_ENABLE(waits => TRUE, binds => FALSE);

c. trcsess output=mytrace.trc clientid='HR session' $ORACLE_BASE/diag/rdbms/orcl/orcl/trace/*.trc

d. tkprof *mytrace*.trc mytrace.txt SYS=NO



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Application Tracing

Chapter 5 - Page 38

Quiz

Answer: b, c

Quiz

The _____ utility formats the trace file into a readable format.a. trcsess

b. tkprof

c. SQL Developer

d. SQL*Plus Autotrace



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Application Tracing

Chapter 5 - Page 39

Quiz

Answer: d

Quiz

In an environment with an applications server that uses a connection pool, you will use ________ to identify which trace files need to be combined to get an overall trace of the application. a. trcsess

b. tkprof

c. SQL Developer

d. DBMS_APPLICATION_INFO



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Application Tracing

Chapter 5 - Page 40

Summary

Summary


• Configure the SQL Trace facility to collect session statistics

• Use the trcsess utility to consolidate SQL trace files

• Format trace files using the tkprof utility

• Interpret the output of the tkprof command



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Application Tracing

Chapter 5 - Page 41




• Creating a service

• Tracing your application using services• Interpreting trace information using trcsess and tkprof



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Application Tracing

Chapter 5 - Page 42



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Optimizer Operators

Chapter 6 - Page 1

Optimizer Operators

Chapter 6



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Optimizer Operators

Chapter 6 - Page 2

Optimizer Operators

Optimizer Operators



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Optimizer Operators

Chapter 6 - Page 3

Objectives

Objectives

This lesson helps you understand the execution plans that use operators related to table and index access methods.

Objectives


• Describe the SQL operators for tables and indexes

• List the possible access paths



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Optimizer Operators

Chapter 6 - Page 4



A row source is a set of rows returned by a step in the execution plan. The row source can be a table, view, or result of a join or grouping operation.

You can classify row sources as follows:

• Unary operations: Operations that act on only one input, such as an access path

• Binary operations: Operations that act on two inputs, such as joins

• N-ary operations: Operations that act on several inputs, such as a relational operator

Access paths are ways in which data is retrieved from the database. In general, index access paths should be used for statements that retrieve a small subset of table rows, while full scans are more efficient when accessing a large portion of the table. Online transaction processing (OLTP) applications, which consist of short-running SQL statements with high selectivity, are often characterized by the use of index access paths. Decision support systems (DSS), on the other hand, tend to use partitioned tables and perform full scans of the relevant partitions.


• Unary operations– Access Path

• Binary operations– Joins

• N-ary operations

Ams row source is a set of rows returned by a step in the execution plan .



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Optimizer Operators

Chapter 6 - Page 5

Main Structures and Access Paths


Any row can be located and retrieved with one of the methods mentioned in the slide.

In general, index access paths should be used for statements that retrieve a small subset of table rows, while full scans are more efficient when accessing a large portion of the table. To decide on the alternative, the optimizer gives each alternative (execution plan) a cost. The one with the lower cost is elected.

There are special types of table access paths including clusters, index-organized tables, and partitions, which have not been mentioned in the slide.

Clusters are an optional method of storing table data. A cluster is a group of tables that share the same data blocks because they share common columns and are often used together. For example, the EMP and DEPT table share the DEPTNO column. When you cluster the EMP and DEPT tables, Oracle physically stores all rows for each department from both the EMP and DEPT tables in the same data blocks.

Hash clusters are single-table clusters in which rows with the same hash-key values are stored together. A mathematical hash function is used to select the location of a row within the cluster. All rows with the same key value are stored together on disk.

The special types of access paths are discussed later in this course.


Access Paths1. Full Table Scan

2. Rowid Scan

3. Sample Table Scan

4. Index Scan (Unique)

5. Index Scan (Range)

6. Index Scan (Full)

7. Index Scan (Fast Full)

8. Index Scan (Skip)

9. Index Scan (Index Join)

10. Using Bitmap Indexes

11. Combining Bitmap Indexes

Structures

Tables

Indexes



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Optimizer Operators

Chapter 6 - Page 6

Full Table Scan

Full Table Scan

A full table scan sequentially reads all rows from a table and filters out those that do not meet the selection criteria. During a full table scan, all formatted blocks in the table that are under the high-water mark are scanned even if all the rows have been deleted from the table. Each block is read only once. The high-water mark indicates the amount of used space, or space that was formatted to receive data. Each row is examined to determine whether it satisfies the statement’s WHERE clause using the applicable filter conditions specified in the query.

You can see the filter conditions in the “Predicate Information” section of the explain plan. The filter to be applied returns only rows where EMP.ENAME='King'. Because a full table scan reads all the formatted blocks in a table, it reads blocks that are physically adjacent to each other. This means that performance benefits can be reaped by utilizing input/output (I/O) calls that read multiple blocks at the same time. The size of the read call can range from a single block to any number of blocks up to the DB_FILE_MULTIBLOCK_READ_COUNT init parameter.

Note: In Oracle 6, a full table scan (FTS) could flood the buffer cache because there was no difference in the way blocks were handled between FTS and other reads. Since Oracle V7, blocks read by FTS are allowed to occupy only a small percentage of the buffer cache. Currently, FTS are read into the PGA with direct reads bypassing the buffer cache in most cases.

Full Table Scan

• Performs multiblock reads(here DB_FILE_MULTIBLOCK_READ_COUNT = 4)

• Reads all formatted blocks below the high-water mark

• May filter rows

• Is faster than index range scans for large amount of data

B B B B B B B B B...

HWM



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Optimizer Operators

Chapter 6 - Page 7

Full Table Scans: Use Cases


The optimizer uses a full table scan in any of the following cases:

• Lack of index: If the query is unable to use any existing indexes, it uses a full table scan (unless a ROWID filter or a cluster access path is available). For example, if there is a function used on the indexed column in the query, the optimizer cannot use the index and instead uses a full table scan. If you need to use the index for case-independent searches, either do not permit mixed-case data in the search columns or create a function-based index, such as UPPER(last_name) on the search column.

• Large amount of data (low selectivity): If the optimizer thinks that the query accesses enough blocks in the table, it may use a full table scan even though indexes might be available.

• Small table: If a table contains less than DB_FILE_MULTIBLOCK_READ_COUNT blocks under the high-water mark, a full table scan might be cheaper than an index range scan, regardless of the fraction of tables being accessed or indexes present.

• High degree of parallelism: A high degree of parallelism for a table skews the optimizer towards full table scans over range scans. Examine the DEGREE column in ALL_TABLES for the table to determine the degree of parallelism.


• No suitable index

• Low selectivity filters (or no filters)

• Small table

• High degree of parallelism• Full table scan hint: FULL (<table name>)



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Optimizer Operators

Chapter 6 - Page 8

• Full table scan hints: Use the FULL(table alias) hint to instruct the optimizer to use a full table scan.



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Optimizer Operators

Chapter 6 - Page 9

ROWID Scan

ROWID Scan

The rowid of a row specifies the data file and data block containing the row and the location of the row in that block. Locating a row by specifying its rowid is the fastest way to retrieve a single row because the exact location of the row in the database is specified.

To access a table by rowid, the system first obtains the rowids of the selected rows, either from the statement’s WHERE clause or through an index scan of one or more of the table’s indexes. The system then locates each selected row in the table based on its rowid.

Mostly, the optimizer uses rowids after retrieving them from an index (See the “Index Scans” slides.). The table access might be required for columns in the statement that are not present in the index. A table access by rowid does not need to follow every index scan. If the index contains all the columns needed for the statement, table access by rowid might not occur.

Rowids are the system’s internal representation of where data is stored. Accessing data based on position is not recommended because rows can move around due to row migration and chaining, and also after export and import.

Note: Due to row migration, a rowid can sometimes point to an address different from the actual row location, resulting in more than one block being accessed to locate a row. For example, an update to a row may cause the row to be placed in another block with a pointer in the original block. The rowid, however, still has only the address of the original block.

ROWID Scan

select * from scott.emp where rowid='AAAQ+LAAEAAAAAfAAJ';

------------------------------------------------------------------| Id | Operation | Name | Rows | Bytes | Cost |------------------------------------------------------------------| 0 | SELECT STATEMENT | | 1 | 37 | 1|| 1 | TABLE ACCESS BY USER ROWID| EMP | 1 | 37 | 1|------------------------------------------------------------------

B BBB B

Block 6959–Row 2

.Row migration



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Optimizer Operators

Chapter 6 - Page 10

Sample Table Scans

Sample Table Scans

A sample table scan retrieves a random sample of data from a simple table or a complex SELECT statement, such as a statement involving joins and views. This access path is used when a statement’s FROM clause includes the SAMPLE clause or the SAMPLE BLOCK clause. To perform a sample table scan when sampling by rows with the SAMPLE clause, the system reads a specified percentage of rows in the table. To perform a sample table scan when sampling by blocks with the SAMPLE BLOCK clause, the system reads a specified percentage of table blocks.

• SAMPLE option: To perform a sample table scan when sampling by rows, the system reads a specified percentage of rows in the table and examines each of these rows to determine whether it satisfies the statement’s WHERE clause.

• SAMPLE BLOCK option: To perform a sample table scan when sampling by blocks, the system reads a specified percentage of the table’s blocks and examines each row in the sampled blocks to determine whether it satisfies the statement’s WHERE clause.

The sample percent is a number specifying the percentage of the total row or block count to be included in the sample. The sample value must be in the [0.000001 , 99.999999] range.

This percentage indicates the probability of each row, or each cluster of rows in the case of block sampling, being selected as part of the sample. It does not mean that the database retrieves exactly sample_percent of the rows of table.

Sample Table Scans

SELECT * FROM emp SAMPLE BLOCK (10) SEED (1);

---------------------------------------------------------------------| Id | Operation | Name | Rows | Bytes | Cost (%CPU)|---------------------------------------------------------------------| 0 | SELECT STATEMENT | | 4 | 99 | 2 (0)| | 1 | TABLE ACCESS SAMPLE| EMP | 4 | 99 | 2 (0)| ---------------------------------------------------------------------

B BBB B



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Chapter 6 - Page 11

• SEED seed_value: Specify this clause to instruct the database to attempt to return the same sample from one execution to the next. seed_value must be an integer between 0 and 4294967295. If you omit this clause, the resulting sample changes from one execution to the next.

In row sampling, more blocks need to be accessed given a particular sample size, but the results are usually more accurate. Block samples are less costly, but may be inaccurate; more so with smaller samples.

Note: Block sampling is possible only during full table scans or index fast full scans. If a more efficient execution path exists, Oracle Database does not perform block sampling. If you want to guarantee block sampling for a particular table or index, use the FULL or INDEX_FFS hint.



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Chapter 6 - Page 12

Indexes: Overview

Indexes: Overview

An index is an optional database object that is logically and physically independent of the table data. Being independent structures, they require storage space. Just as the index of a book helps you locate information fast, an Oracle Database index provides a faster access path to table data. The Oracle Database may use an index to access data that is required by a SQL statement, or it may use indexes to enforce integrity constraints. The system automatically maintains indexes when the related data changes. You can create and drop indexes at any time. If you drop an index, all applications continue to work. However, access to previously indexed data might be slower. Indexes can be unique or nonunique.

A composite index, also called a concatenated index, is an index that you create on multiple columns (up to 32) in a table. Columns in a composite index can appear in any order and need not be adjacent in the table. For standard indexes, the database uses B*-tree indexes that are balanced to equalize access times. B*-tree indexes can be normal, reverse key, descending, or function based.

• B*-tree indexes: They are by far the most common indexes. Similar in construct to a binary tree, B*-tree indexes provide fast access, by key, to an individual row or range of rows, normally requiring few reads to find the correct row. However, the “B” in “B*-tree” does not stand for “binary,” but rather for “balanced.”

Indexes: Overview

Indexes

• Storage techniques:– B*-tree indexes: The default and the most common

— Normal

— Function based: Precomputed value of a function or expression

— Index-organized table (IOT)

– Bitmap indexes

– Cluster indexes: Defined specifically for cluster

• Index attributes:– Key compression

– Reverse key

– Ascending, descending

• Domain indexes: Specific to an application or cartridge



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Optimizer Operators

Chapter 6 - Page 13

• Descending indexes: Descending indexes allow for data to be sorted from “big to small” (descending) instead of from “small to big” (ascending) in the index structure.

• Reverse key indexes: These are B*-tree indexes whereby the bytes in the key are reversed. Reverse key indexes can be used to obtain a more even distribution of index entries throughout an index that is populated with increasing values. For example, if you use a sequence to generate a primary key, the sequence generates values such as 987500, 987501, 987502, and so on. With a reverse key index, the database logically indexes 005789, 105789, 205789, and so on, instead of 987500, 987501, and 987502. Because these reverse keys are now likely to be placed in different locations, this can reduce contention for particular blocks that may otherwise be targets for contention. However, only equality predicates can benefit from these indexes.

• Index key compression: The basic concept behind a compressed key index is that every entry is broken into two—a prefix and a suffix component. The prefix is built on the leading columns of the concatenated index and has many repeating values. The suffix is built on the trailing columns in the index key and is the unique component of the index entry within the prefix. This is not compression in the same manner that ZIP files are compressed; rather, this is an optional compression that removes redundancies from concatenated (multicolumn) indexes.

• Function-based indexes: These are B*-tree or bitmap indexes that store the computed result of a function on a row’s column or columns, and not the column data itself. You can consider them as indexes on a virtual (derived or hidden) column. In other words, it is a column that is not physically stored in the table. You can gather statistics on this virtual column.

• Index-organized tables: These are tables stored in a B*-tree structure. While rows of data in a heap organized table are stored in an unorganized fashion (data goes wherever there is available space), data in an IOT is stored and sorted by a primary key. IOTs behave like regular tables as far as your application is concerned.

• Bitmap indexes: In a normal B*-tree, there is a one-to-one relationship between an index entry and a row, that is, an index entry points to a row. With bitmap indexes, a single index entry uses a bitmap to point to many rows simultaneously. They are appropriate for repetitive data (data with few distinct values relative to the total number of rows in the table) that is mostly read-only. Bitmap indexes should never be considered in an OLTP database for concurrency-related issues.

• Bitmap join indexes: A bitmap join index is a bitmap index for the join of two or more tables. A bitmap join index can be used to avoid actual joins of tables, or to greatly reduce the volume of data that must be joined, by performing restrictions in advance. Queries using bitmap join indexes can be sped up using bit-wise operations.

• Application domain indexes: These are indexes you build with packages and store, either in the database or even outside the database. You tell the optimizer how selective your index is and how costly it is to execute, and the optimizer decides whether or not to use your index based on that information.



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Optimizer Operators

Chapter 6 - Page 14

Normal B*-tree Indexes


Each B*-tree index has a root block as a starting point. Depending on the number of entries, there are multiple branch blocks that can have multiple leaf blocks. The leaf blocks contain all values of the index plus ROWIDs that point to the rows in the associated data segment.

Previous and next block pointers connect the leaf blocks so that they can be traversed from left to right (and vice versa). Indexes are always balanced, and they grow from the top down. In certain situations, the balancing algorithm can cause the B*-tree height to increase unnecessarily. It is possible to reorganize indexes. This is done by the ALTER INDEX … REBUILD | COALESCE command.

The internal structure of a B*-tree index allows rapid access to the indexed values. The system can directly access rows after it has retrieved the address (the ROWID) from the index leaf blocks.

Note: The maximum size of a single index entry is approximately one-half of the data block size.


Index entry header

Key column length

Key column value

rowid

Root

Branch

Leaf

Index entry

Table data retrieved by using rowid



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Optimizer Operators

Chapter 6 - Page 15

Index Scans

Index Scans

An index scan can be one of the following types:

A row is retrieved by traversing the index, using the indexed column values specified by the statement’s WHERE clause. An index scan retrieves data from an index based on the value of one or more columns in the index. To perform an index scan, the system searches the index for the indexed column values accessed by the statement. If the statement accesses only columns of the index, the system reads the indexed column values directly from the index, rather than from the table.

The index contains not only the indexed value, but also the rowids of rows in the table that have the value. Therefore, if the statement accesses other columns in addition to the indexed columns, the system can find the rows in the table by using either a table access by rowid or a cluster scan.

Note: The graphic shows a case where four rows are retrieved from the table using their rowids obtained by the index scan.

Index Scans

Types of index scans:

• Unique

• Min/Max

• Range (Descending)

• Skip

• Full and fast full

• Index join

B-Tree index IX_EMP

B BBBB B

Table EMP

B : block



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Chapter 6 - Page 16

Index Unique Scan

Index Unique Scan

An index unique scan returns, at most, a single ROWID. The system performs a unique scan if a statement contains a UNIQUE or a PRIMARY KEY constraint that guarantees that only a single row is accessed. This access path is used when all the columns of a unique (B*-tree) index are specified with equality conditions.

Key values and ROWIDs are obtained from the index, and table rows are obtained using ROWIDs.

You can look for access conditions in the “Predicate Information” section of the execution plan (The execution plan is dealt with in detail in the lesson titled “Interpreting Execution Plans.”). Here the system accesses only matching rows for which EMPNO=9999.

Note: Filter conditions filter rows after the fetch operation and output the filtered rows.

Index Unique Scan

create unique index PK_EMP on EMP(empno)

select * from emp where empno = 9999;

index UNIQUE Scan PK_EMP



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Optimizer Operators

Chapter 6 - Page 17

Index Range Scan

Index Range Scan

An index range scan is a common operation for accessing selective data. It can be bounded (on both sides) or unbounded (on one or both sides). Data is returned in the ascending order of index columns. Multiple rows with identical values are sorted in the ascending order by ROWID.

The optimizer uses a range scan when it finds one or more leading columns of an index specified in conditions (the WHERE clause), such as col1 = :b1, col1 < :b1, col1 > :b1, and any combination of the preceding conditions.

Wildcard searches (col1 like '%ASD') should not be in a leading position, as this does not result in a range scan.

Range scans can use unique or nonunique indexes. Range scans can avoid sorting when index columns constitute the ORDER BY/GROUP BY clause and the indexed columns are NOT NULL as otherwise they are not considered.

An index range scan descending is identical to an index range scan, except that the data is returned in the descending order. The optimizer uses index range scan descending when an order by descending clause can be satisfied by an index.

Index Range Scan

create index I_DEPTNO on EMP(deptno);

select /*+ INDEX(EMP I_DEPTNO) */ *

from emp where deptno = 10 and sal > 1000;

Index Range SCAN I_DEPTNO



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Optimizer Operators

Chapter 6 - Page 18

In the example in the slide, using index I_DEPTNO, the system accesses rows for which EMP.DEPTNO=10. It gets their ROWIDs, fetches other columns from the EMP table, and finally, applies the EMP.SAL >1000 filter from these fetched rows to output the final result.



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Optimizer Operators

Chapter 6 - Page 19

Index Range Scan: Descending


In addition to index range scans in ascending order, which are described in the previous slide, the system is also able to scan indexes in the reverse order as illustrated by the graphic in the slide.

The example retrieves rows from the EMP table by descending order on the DEPTNO column. You can see the DESCENDING operation row source for ID 2 in the execution plan that materialized this type of index scans.

Note: By default an index range scan is done in the ascending order.


select * from emp where deptno>20 order by deptno desc;

Index Range SCAN IDX



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Optimizer Operators

Chapter 6 - Page 20

Descending Index Range Scan


A descending index range scan is identical to an index range scan, except that the data is returned in descending order. Descending indexes allow for data to be sorted from “big to small” (descending) instead of “small to big” (ascending) in the index structure. Usually, this scan is used when ordering data in a descending order to return the most recent data first, or when seeking a value less than a specified value as in the example in the slide.

The optimizer uses descending index range scan when an order by descending clause can be satisfied by a descending index.

The INDEX_DESC(table_alias index_name) hint can be used to force this access path if possible.

Note: The system treats descending indexes as function-based indexes. The columns marked DESC are stored in a special descending order in the index structure that is reversed again using the SYS_OP_UNDESCEND function.


drop index I_Deptno;

create index IX_D on EMP(deptno desc);

select * from emp where deptno <30;

Index Range SCAN IX_D



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Optimizer Operators

Chapter 6 - Page 21

Index Range Scan: Function-Based


A function-based index can be stored as B*-tree or bitmap structures. These indexes include columns that are either transformed by a function, such as the UPPER function, or included in an expression, such as col1 + col2. With a function-based index, you can store computation-intensive expressions in the index.

Defining a function-based index on the transformed column or expression allows that data to be returned using the index when that function or expression is used in a WHERE clause or an ORDER BY clause. This allows the system to bypass computing the value of the expression when processing SELECT and DELETE statements. Therefore, a function-based index can be beneficial when frequently-executed SQL statements include transformed columns, or columns in expressions, in a WHERE or ORDER BY clause.

For example, function-based indexes defined with the UPPER(column_name) or LOWER(column_name) keywords allow non-case-sensitive searches, such as shown in the slide.


create index IX_FBI on EMP(UPPER(ename));

select * from emp where upper(ENAME) like 'A%';

Index Range SCAN IX_FBI



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Optimizer Operators

Chapter 6 - Page 22

Index Full Scan

Index Full Scan

A full scan is available if a predicate references one of the columns in the index. The predicate does not need to be an index driver (leading column). A full scan is also available when there is no predicate, if both the conditions are met:

• All the columns in the table referenced in the query are included in the index.

• At least one of the index columns is not null.

A full scan can be used to eliminate a sort operation because the data is ordered by the index key.

Note: An index full scan reads index using single-block input/output (I/O) (unlike a fast full index scan).

Index Full Scan

create index I_DEPTNO on EMP(deptno);

select *from empwhere sal > 1000 and deptno is not nullorder by deptno; index Full Scan I_DEPTNO



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Optimizer Operators

Chapter 6 - Page 23

Index Fast Full Scan


Index fast full scans are an alternative to full table scans when the index contains all the columns that are needed for the query and at least one column in the index key has a NOT NULL constraint. A fast full scan accesses the data in the index itself without accessing the table.

It cannot be used to eliminate a sort operation because the data is not ordered by the index key. It can be used for the min/avg/sum aggregate functions. In this case, the optimizer must know that all table rows are represented in the index; at least one NOT NULL column.

This operation reads the entire index using multiblock reads (unlike a full index scan). Fast full index scans cannot be performed against bitmap indexes. A fast full scan is faster than a normal full index scan because it can use multiblock I/O just as a table scan.

You can specify fast full index scans with the OPTIMIZER_FEATURES_ENABLE initialization parameter or the INDEX_FFS hint as shown in the slide example.

Note: Index fast full scans are used against an index when it is rebuilt offline.


LEGEND:SH=segment headerR=root blockB=branch blockL=leaf block

L...R B BL L L L LSH

multiblock read

discard discard discard

db_file_multiblock_read_count = 4

multiblock read

select /*+ INDEX_FFS(EMP I_DEPTNO) */ deptno from empwhere deptno is not null;



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Optimizer Operators

Chapter 6 - Page 24

Index Skip Scan

Index Skip Scan

Index skip scans improve index scans by skipping blocks that could never contain keys matching the filter column values. Scanning index blocks is often faster than scanning table data blocks. Skip scanning can happen when the initial (leading) column of the composite index is not specified in a query. Suppose that there is a concatenated index on the GENDER and AGE columns in the EMPLOYEES table. This example illustrates how skip scanning is processed to answer the query in the slide.

The system starts from the root of the index [R] and proceeds to the left branch block [B1]. From there, the system identifies a first entry to be F16, goes to the left leaf [L1], and starts to scan it because it could contain A25 (that is, where the “gender” is before “F” in the alphabet). The server identifies that this is not possible because the first entry is F10. It is thus not possible to find an entry such as A25 in this leaf, so it can be skipped.

Backtracking to the first branch block [B1], the server identifies that the next subtree (F16) does not need to be scanned because the next entry in [B1] is F20. Because the server is certain that it is not possible to find a 25 between F16 and F20, the second leaf block [L2] can be skipped.

Returning to [B1], the server finds that the next two entries have a common prefix of F2. This identifies possible subtrees to scan. The system knows that these subtrees are ordered by age.

Index Skip Scan

F10F11F12F13F14F15

Min F16 F20 F26 F30 M10 M16 M20 M26 M30

Min M10

F16F17F18F19

F20F21F22F23F24F25

F26F27F28F29

F30F31F32F33F34F35

M10M11M12M13M14M15

M16M17M18M19

M20M21M22M23M24M25

M26M27M28M29

M30M31M32M33M34M35

Index on (GENDER, AGE)SELECT * FROM employees WHERE age BETWEEN 20 AND 29

B1 B2

L1 L3L2 L4 L8L5 L6 L7 L9

R

L10



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Chapter 6 - Page 25

So the third and fourth leaf blocks [L3–L4] are scanned and some values are retrieved. By looking at the fourth entry in the first branch block [B1], the system determines that it is no longer possible to find an F2x entry. Thus, it is not necessary to scan that subtree [L5].

The same process continues with the right part of this index. Note that out of a total of 10 leaf blocks, only five are scanned.



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Chapter 6 - Page 26

Index Skip Scan: Example


The example in the slide finds employees who have salary less than 1500 using an index skip scan.

It is assumed that there is a concatenated index on the DEPTNO and SAL columns.

As you can see, the query does not have a predicate on the DEPTNO leading column. This leading column only has some discrete values, that is, 10, 20 and 30.

Skip scanning lets a composite index be split logically into smaller subindexes. The number of logical subindexes is determined by the number of distinct values in the initial column.

The system pretends that the index is really three little index structures hidden inside one big one. In the example, it is three index structures:

• where deptno = 10



The output is ordered by DEPTNO.

Note: Skip scanning is advantageous if there are few distinct values in the leading column of the composite index and many distinct values in the nonleading key of the index.


create index IX_SS on EMP(DEPTNO,SAL);

select /*+ index_ss(EMP IX_SS) */ * from emp where SAL < 1500;

Index on (DEPTNO, SAL)



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Chapter 6 - Page 27

Index Join Scan

Index Join Scan

An index join is a hash join of several indexes that together contain all the table columns that are referenced in the query. If an index join is used, no table access is needed because all the relevant column values can be retrieved from the indexes. An index join cannot be used to eliminate a sort operation.

The index join is not a real join operation (note that the example is a single table query), but is built using index accesses followed by a join operation on rowid. The example in the slide assumes that you have two separate indexes on the ENAME and SAL columns of the EMP table.

Note: You can specify an index join with the INDEX_JOIN hint as shown in the example.

Index Join Scan

alter table emp modify (SAL not null, ENAME not null);

create index I_ENAME on EMP(ename);

create index I_SAL on EMP(sal);



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Optimizer Operators

Chapter 6 - Page 28

B*-tree Indexes and Nulls

B*-tree Indexes and Nulls

It is a common mistake to forget about nulls when dealing with B*-tree indexes. Single-column

B*-tree indexes do not store null values and so indexes on nullable columns cannot be used to drive queries unless there is something that eliminates the null values from the query.

In the slide example, you create a table containing a nullable column called COL1, and COL2, which cannot have null values. One index is built on top of each column.

The first query, retrieves all COL1 values. Because COL1 is nullable, the index cannot be used without a predicate. Hinting the index on COL1 (nullind1) to force index utilization makes no difference because COL1 is nullable. Because you only search for COL1 values, there is no need to read the table itself.

However, with the second query, the effect of the predicate against COL1 is to eliminate nulls from the data returned from the column. This allows the index to be used.

The third query can directly use the index because the corresponding column is declared NOT NULL at table-creation time.

Note: The index could also be used by forcing the column to return only NOT NULL values using the COL1 IS NOT NULL predicate.

B*-tree Indexes and Nullscreate table nulltest ( col1 number, col2 number not null);create index nullind1 on nulltest (col1);create index notnullind2 on nulltest (col2);



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Optimizer Operators

Chapter 6 - Page 29

Using Indexes: Considering Nullable Columns


Some queries look as if they should use an index to compute a simple count of rows in the table. This is typically more efficient that scanning the table. But the index to be used must not be built on a column that can contain null values. Single-column B*-tree indexes never store null values, so the rows are not represented in the index, and thus, do not contribute to the COUNT being computed, for example.

In the example in the slide, there is a unique index on the SSN column of the PERSON table. The SSN column is defined as allowing null values, and creating a unique index on it does nothing to change that. This index is not used when executing the count query in the slide. Any rows with null for SSN are not represented in the index, so the count across the index is not necessarily accurate. This is one reason why it is better to create a primary key rather than a unique index. A primary key column cannot contain null values. In the slide, after the unique index is dropped in the place of designating a primary key, the index is used to compute the row count.

Note: The PRIMARY KEY constraints combine a NOT NULL constraint and a unique constraint in a single declaration.


SELECT COUNT(*) FROM person;

SELECT STATEMENT |SORT AGGREGATE | TABLE ACCESS FULL| PERSON

SSNFNAMELNAME

PERSON

CREATE UNIQUE INDEX person_ssn_ixON person(ssn);

DROP INDEX person_ssn_ix;

Column Null?

YYN

ALTER TABLE person ADD CONSTRAINT pk_ssn PRIMARY KEY (ssn);

SELECT /*+ INDEX(person) */ COUNT(*) FROM person;

SELECT STATEMENT |SORT AGGREGATE | INDEX FAST FULL SCAN| PK_SSN

SSNFNAMELNAME

PERSON

Column Null?

NYN



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Optimizer Operators

Chapter 6 - Page 30

Index-Organized Tables


An index-organized table (IOT) is a table physically stored in a concatenated index structure. The key values (for the table and the B*-tree index) are stored in the same segment. An IOT contains:

• Primary key values

• Other (non-key) column values for the row

The B*-tree structure, which is based on the primary key of the table, is organized in the same way as an index. The leaf blocks in this structure contain the rows instead of the ROWIDs. This means that the rows in the IOT are always maintained in the order of the primary key.

You can create additional indexes on IOTs. The primary key can be a composite key. Because large rows of an IOT can destroy the dense and efficient storage of the B*-tree structure, you can store part of the row in another segment, which is called an overflow area.

Index-organized tables provide fast key-based access to table data for queries involving exact match and range searches. Changes to the table data result only in updating the index structure. Also, storage requirements are reduced because key columns are not duplicated in the table and index. The remaining non-key columns are stored in the index structure. IOTs are particularly useful when you use applications that must retrieve data based on a primary key and have only a few, relatively short non-key columns.


Indexed access on table

ROWID

Accessing index-organized table

Row header

Non-key columns

Key column



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Optimizer Operators

Chapter 6 - Page 31

Note: The descriptions mentioned here are correct if no overflow segments exist. Overflow segments should be used with long rows.



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Optimizer Operators

Chapter 6 - Page 32

Index-Organized Table Scans


Index-organized tables are just like indexes. They use the same access paths that you saw for normal indexes.

The major difference from a heap-organized table is that there is no need to access both an index and a table to retrieve indexed data.

Note: SYS_IOT_TOP_75664 is the system-generated name of the segment used to store the IOT structure. You can retrieve the link between the table name and the segment from USER_INDEXES with these columns: INDEX_NAME, INDEX_TYPE, TABLE_NAME.


select * from iotemp where empno=9999;

select * from iotemp where sal>1000;

create table iotemp( empno number(4) primary key, ename varchar2(10) not null, job varchar2(9), mgr number(4), hiredate date,sal number(7,2) not null, comm number(7,2), deptno number(2))

organization index;



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Optimizer Operators

Chapter 6 - Page 33

Bitmap Indexes

Bitmap Indexes

In a B*-tree, there is a one-to-one relationship between an index entry and a row; an index entry points to a row. A bitmap index is organized as a B*-tree index but, with bitmap indexes, a single index entry uses a bitmap to point to many rows simultaneously. If a bitmap index involves more than one column, there is a bitmap for every possible combination. Each bitmap header stores start and end ROWIDs. Based on these values, the system uses an internal algorithm to map bitmaps onto ROWIDs. This is possible because the system knows the maximum possible number of rows that can be stored in a system block. Each position in a bitmap maps to a potential row in the table even if that row does not exist. The contents of that position in the bitmap for a particular value indicate whether that row has that value in the bitmap columns. The value stored is 1 if the row values match the bitmap condition; otherwise it is 0. Bitmap indexes are widely used in data warehousing environments. These environments typically have large amounts of data and ad hoc queries, but no concurrent data manipulation language (DML) transactions because when locking a bitmap, you lock many rows in the table at the same time. For such applications, bitmap indexing provides reduced response time for large classes of ad hoc queries, reduced storage requirements compared to other indexing techniques, dramatic performance gains even on hardware with a relatively small number of CPUs or a small amount of memory, and efficient maintenance during parallel DML and loads.

Bitmap Indexes

<Blue, 10.0.3, 12.8.3, 100010000 010000000 010010100>

<Green, 10.0.3, 12.8.3, 000101000 000000000 100100000>

<Red, 10.0.3, 12.8.3, 010000000 001100000 000001001>

<Yellow, 10.0.3, 12.8.3, 001000000 100000000 001000010>

Key StartROWID

EndROWID

Bitmap

Table

IndexBlock 10

Block 11

Block 12

File 3



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Optimizer Operators

Chapter 6 - Page 34

Note: Unlike most other types of indexes, bitmap indexes include rows that have NULL values. Indexing of nulls can be useful for some types of SQL statements, such as queries with the aggregate function COUNT. The IS NOT NULL predicate can also benefit from bitmap indexes. Although bitmaps are compressed internally, they are split in multiple leaves if the number of rows increases.



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Optimizer Operators

Chapter 6 - Page 35

Bitmap Index Access: Examples


The examples in the slide illustrate two possible access paths for bitmap indexes—BITMAP INDEX SINGLE VALUE and BITMAP INDEX RANGE SCAN—depending on the type of predicate you use in the queries.

The first query scans the bitmap for country “FR” for positions containing a “1.” Positions with a “1” are converted into ROWIDs and have their corresponding rows returned for the query.

In some cases (such as a query counting the number of rows with COUNTRY FR), the query might simply use the bitmap itself and count the number of 1s (not needing the actual rows).

This is illustrated in the following example: SELECT count(*) FROM PERF_TEAM WHERE country>'FR';

----------------------------------------------------------------------------

| Id | Operation | Name | Rows | Bytes | Cost (%CPU)|

----------------------------------------------------------------------------

| 0 | SELECT STATEMENT | | 1 | 3 | 2 (0)|

| 1 | SORT AGGREGATE | | 1 | 3 | |

| 2 | BITMAP CONVERSION COUNT | | 1 | 3 | 2 (0)|

| 3 | BITMAP INDEX RANGE SCAN| IX_B2 | | | |


SELECT * FROM PERF_TEAM WHERE country='FR';

---------------------------------------------------------------------

| Id | Operation | Name | Rows | Bytes |

---------------------------------------------------------------------

| 0 | SELECT STATEMENT | | 1 | 45 |

| 1 | TABLE ACCESS BY INDEX ROWID | PERF_TEAM | 1 | 45 |

| 2 | BITMAP CONVERSION TO ROWIDS| | | |

| 3 | BITMAP INDEX SINGLE VALUE | IX_B2 | | |

---------------------------------------------------------------------

Predicate: 3 - access("COUNTRY"='FR')

SELECT * FROM PERF_TEAM WHERE country>'FR';

---------------------------------------------------------------------


---------------------------------------------------------------------

| 0 | SELECT STATEMENT | | 1 | 45 | | 1 | TABLE ACCESS BY INDEX ROWID | PERF_TEAM | 1 | 45 |


| 3 | BITMAP INDEX RANGE SCAN | IX_B2 | | | ---------------------------------------------------------------------Predicate: 3 - access("COUNTRY">'FR') filter("COUNTRY">'FR')



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Optimizer Operators

Chapter 6 - Page 36

----------------------------------------------------------------------------

Predicate: 3 - access("COUNTRY">'FR') filter("COUNTRY">'FR')



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Optimizer Operators

Chapter 6 - Page 37

Combining Bitmap Indexes: Examples


Bitmap indexes are the most effective for queries that contain multiple conditions in the WHERE clause. Rows that satisfy some, but not all, conditions are filtered out before the table itself is accessed. This improves response time, often dramatically. As the bitmaps from bitmap indexes can be combined quickly, it is usually best to use single-column bitmap indexes.

Due to fast bit-and, bit-minus, and bit-or operations, bitmap indexes are efficient when:

• Using IN (value_list)

• Predicates are combined with AND or OR


SELECT * FROM PERF_TEAM WHERE country in('FR','DE');

FR 0 0 1 1 1 1 0 0 0 0 0 0

DE 0 1 0 0 0 0 0 0 0 0 0 0

0 1 1 1 1 1 0 0 0 0 0OR

SELECT * FROM EMEA_PERF_TEAM T WHERE country='FR' and gender='M';

F 0 0 1 1 1 1 0 0 0 0 0 0

M 1 1 1 0 1 1 0 1 0 1 1 1

0 0 1 0 1 1 0 0 0 0 0AND



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Optimizer Operators

Chapter 6 - Page 38

Combining Bitmap Index Access Paths


Bitmap indexes can be used efficiently when a query combines several possible values for a column or when two separately-indexed columns are used.

In some cases, a WHERE clause might reference several separately indexed columns as in the examples shown in the slide.

If both the COUNTRY and GENDER columns have bitmap indexes, a bit-and operation on the two bitmaps quickly locates the rows that you look for. The more complex the compound WHERE clauses become, the more benefit you get from bitmap indexing.


SELECT * FROM PERF_TEAM WHERE country in ('FR','DE');

---------------------------------------------------------------------| Id | Operation | Name | Rows | Bytes |


| 1 | INLIST ITERATOR | | | |



| 4 | BITMAP INDEX SINGLE VALUE | IX_B2 | | | Predicate: 4 - access("COUNTRY"='DE' OR "COUNTRY"='FR')

SELECT * FROM PERF_TEAM WHERE country='FR' and gender='M';

---------------------------------------------------------------------| Id | Operation | Name | Rows | Bytes |




| 3 | BITMAP AND | | | |

| 4 | BITMAP INDEX SINGLE VALUE| IX_B1 | | |

| 5 | BITMAP INDEX SINGLE VALUE| IX_B2 | | |

Predicate: 4 - access("GENDER"='M') 5 - access("COUNTRY"='FR')



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Optimizer Operators

Chapter 6 - Page 39

Bitmap Operations

Bitmap Operations

The slide summarizes all the possible bitmap operations. The following operations have not been explained so far:

• BITMAP CONVERSION FROM ROWID: B*-tree index converted by the optimizer into BITMAP (cost is lower than other methods) to make these efficient bitmaps comparison operations available. After the bitmap comparison has been done, the resultant bitmap is converted back into ROWIDs (BITMAP CONVERSION TO ROWIDS) to perform the data lookup.

• BITMAP MERGE merges several bitmaps resulting from a range scan into one bitmap.

• BITMAP MINUS is a dual operator that takes the second bitmap operation and negates it by changing ones to zeros, and zeros to ones. The bitmap minus operation can then be performed as a BITMAP AND operation using this negated bitmap. This would typically be the case with the following combination of predicates: C1=2 and C2<>6.

• BITMAP KEY ITERATION takes each row from a table row source and finds the corresponding bitmap from a bitmap index. This set of bitmaps is then merged into one bitmap in a BITMAP MERGE operation.

Bitmap Operations

• BITMAP CONVERSION:– TO ROWIDS

– FROM ROWIDS

– COUNT

• BITMAP INDEX:– SINGLE VALUE

– RANGE SCAN

– FULL SCAN

• BITMAP MERGE

• BITMAP AND/OR

• BITMAP MINUS

• BITMAP KEY ITERATION



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Optimizer Operators

Chapter 6 - Page 40

Bitmap Join Index

Bitmap Join Index

In addition to a bitmap index on a single table, you can create a bitmap join index. A bitmap join index is a bitmap index for the join of two or more tables. A bitmap join index is a space-efficient way of reducing the volume of data that must be joined by performing the join in advance. Note: Bitmap join indexes are much more efficient in storage than materialized join views. For a row source example, see the lesson titled “Case Study: Star Transformation.”

Here, you create a new bitmap join index named cust_sales_bji on the SALES table. The key of this index is the CUST_CITY column of the CUSTOMERS table. This example assumes that there is an enforced primary key constraint on CUSTOMERS to ensure that what is stored in the bitmap reflects the reality of the data in the tables. The CUST_ID column is the primary key of CUSTOMERS, but is also a foreign key inside SALES, although not required.

The FROM and WHERE clause in the CREATE statement allow the system to make the link between the two tables. They represent the join condition between the two tables. The middle part of the graphic shows you a theoretical implementation of this bitmap join index. Each entry or key in the index represents a possible city found in the CUSTOMERS table. A bitmap is then associated to one particular key. Each bit in a bitmap corresponds to one row in the SALES table. In the first key in the slide (Rognes), you see that the first row in the SALES table corresponds to a product sold to a Rognes customer, while the second bit is not a product

Bitmap Join Index

SalesCustomers

CREATE BITMAP INDEX cust_sales_bji ON sales(c.cust_city) FROM sales s, customers cWHERE c.cust_id = s.cust_id;

<Rognes, 1.2.3, 10.8000.3, 100010010010100…><Aix-en-Provence, 1.2.3, 10.8000.3, 000101000100000…>

<Marseille, 1.2.3, 10.8000.3, 010000001000001…>

1.2.3

10.8000.3



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Optimizer Operators

Chapter 6 - Page 41

sold to a Rognes customer. By storing the result of a join, the join can be avoided completely for SQL statements using a bitmap join index.



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Optimizer Operators

Chapter 6 - Page 42

Composite Indexes

Composite Indexes

A composite index is also referred to as a concatenated index because it concatenates column values together to form the index key value. In the illustration in the slide, the MAKE and MODEL columns are concatenated together to form the index. It is not required that the columns in the index are adjacent. And, you can include up to 32 columns in the index, unless it is a bitmap composite index, in which case the limit is 30.

Composite indexes can provide additional advantages over single-column indexes:

• Improved selectivity: Sometimes two or more columns or expressions, each with poor selectivity, can be combined to form a composite index with higher selectivity.

• Reduced I/O: If all columns selected by a query are in a composite index, the system can return these values from the index without accessing the table.

A composite index is mainly useful when you often have a WHERE clause that references all, or the leading portion of the columns in the index. If some keys are used in WHERE clauses more frequently, and you decided to create a composite index, be sure to create the index so that the more frequently selected keys constitute a leading portion for allowing the statements that use only these keys to use the index.

Note: It is also possible for the optimizer to use a concatenated index even though your query does not reference a leading part of that index. This is possible since index skip scans and fast full scans were implemented.

Composite Indexes

Index columns

CARS

create index cars_make_model_idx on cars(make, model);

select *from carswhere make = 'CITROËN' and model = '2CV';

MAKE MODEL

-----------------------------------------------------------------| Id | Operation | Name |-----------------------------------------------------------------| 0 | SELECT STATEMENT | || 1 | TABLE ACCESS BY INDEX ROWID| CUSTOMERS ||* 2 | INDEX RANGE SCAN | CARS_MAKE_MODEL_IDX |-----------------------------------------------------------------



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Optimizer Operators

Chapter 6 - Page 43

Invisible Index: Overview


An invisible index is an index that is ignored by the optimizer unless you explicitly set the OPTIMIZER_USE_INVISIBLE_INDEXES initialization parameter to TRUE at the session or system level. The default value for this parameter is FALSE.

Making an index invisible is an alternative to making it unusable or dropping it. Using invisible indexes, you can perform the following actions:

• Test the removal of an index before dropping it.

• Use temporary index structures for certain operations or modules of an application without affecting the overall application.

Unlike unusable indexes, an invisible index is maintained during DML statements.


INVISIBLEIndex

VISIBLEIndex

Optimizer view point

Data view point

Use index. Do not use index.

Update index. Update index.Update table. Update table.

OPTIMIZER_USE_INVISIBLE_INDEXES=FALSE



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Optimizer Operators

Chapter 6 - Page 44

Invisible Indexes: Examples


When an index is invisible, the optimizer selects plans that do not use the index. If there is no discernible drop in performance, you can drop the index. You can also create an index initially as invisible, perform testing, and then determine whether to make the index visible.

You can query the VISIBILITY column of the *_INDEXES data dictionary views to determine whether the index is VISIBLE or INVISIBLE.

Note: For all the statements given in the slide, it is assumed that OPTIMIZER_USE_INVISIBLE_INDEXES is set to FALSE.


• Index is altered as not visible to the optimizer:

• Optimizer does not consider this index:

• Optimizer considers this index:

• Create an index as invisible initially:

ALTER INDEX ind1 INVISIBLE;

SELECT /*+ index(TAB1 IND1) */ COL1 FROM TAB1 WHERE …;

ALTER INDEX ind1 VISIBLE;

CREATE INDEX IND1 ON TAB1(COL1) INVISIBLE;



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Optimizer Operators

Chapter 6 - Page 45

Guidelines for Managing Indexes


• Create indexes after inserting table data: Data is often inserted or loaded into a table using either the SQL*Loader or an import utility. It is more efficient to create an index for a table after inserting or loading the data.

• Index the correct tables and columns: Use the following guidelines for determining when to create an index:

- Create an index if you frequently want to retrieve less than 15% of the rows in a large table.

- To improve performance on joins of multiple tables, index the columns used for joins.

- Small tables do not require indexes.

• Columns suitable for indexing: Some columns are strong candidates for indexing:

- Values are relatively unique in the column.

- There is a wide range of values (good for regular indexes).

- There is a small range of values (good for bitmap indexes).

- The column contains many nulls, but queries often select all rows having a value.

• Columns not suitable for indexing:


• Create indexes after inserting table data.

• Index the correct tables and columns.

• Order index columns for performance.

• Limit the number of indexes for each table.

• Drop indexes that are no longer required.

• Specify the tablespace for each index.

• Consider parallelizing index creation.• Consider creating indexes with NOLOGGING.

• Consider costs and benefits of coalescing or rebuilding indexes.

• Consider cost before disabling or dropping constraints.



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Optimizer Operators

Chapter 6 - Page 46

- There are many nulls in the column and you do not search on the not null values.

- The LONG and LONG RAW columns cannot be indexed.

- Virtual columns: You can create unique or nonunique indexes on virtual columns.

• Order index columns for performance: The order of columns in the CREATE INDEX statement can affect query performance. In general, specify the most frequently used columns first.

• Limit the number of indexes for each table: A table can have any number of indexes. However, the more indexes there are, the more overhead is incurred as the table is modified. Thus, there is a trade-off between the speed of retrieving data from a table and the speed of updating the table.

• Drop indexes that are no longer required.

• Specify the tablespace for each index: If you use the same tablespace for a table and its index, it can be more convenient to perform database maintenance, such as tablespace backup.

• Consider parallelizing index creation: You can parallelize index creation, just as you can parallelize table creation. This speeds up index creation. However, an index created with an INITIAL value of 5M and a parallel degree of 12 consumes at least 60 MB of storage during index creation.

• Consider creating indexes with NOLOGGING: You can create an index and generate minimal redo log records by specifying NOLOGGING in the CREATE INDEX statement. Because indexes created using NOLOGGING are not archived, perform a backup after you create the index. Note that NOLOGGING is the default in a NOARCHIVELOG database.

• Consider costs and benefits of coalescing or rebuilding indexes: Improper sizing or increased growth can produce index fragmentation. To eliminate or reduce fragmentation, you can rebuild or coalesce the index. But before you perform either task, weigh the costs and benefits of each option, and select the one that works best for your situation.

• Consider cost before disabling or dropping constraints: Because unique and primary keys have associated indexes, you should factor in the cost of dropping and creating indexes when considering whether to disable or drop a UNIQUE or PRIMARY KEY constraint. If the associated index for a UNIQUE key or PRIMARY KEY constraint is extremely large, you can save time by leaving the constraint enabled rather than dropping and re-creating the large index. You also have the option of explicitly specifying that you want to keep or drop the index when dropping or disabling a UNIQUE or PRIMARY KEY constraint.



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Optimizer Operators

Chapter 6 - Page 47

Investigating Index Usage

Investigating Index Usage You may often run a SQL statement expecting a particular index to be used, and it is not. This can be because the optimizer is unaware of some information, or because it should not use the index.

Functions

If you apply a function to the indexed column in the WHERE clause, the index cannot be used; the index is based on column values without the effect of the function. For example, the following statement does not use an index on the salary column:

SELECT * FROM employees WHERE 1.10*salary > 10000

If you want an index to be used in this case, you can create a function-based index. Function-based indexes were covered under “Index Range Scan: Function-Based” earlier in this lesson.

Data Type Mismatch

If there is a data type mismatch between the indexed column and the compared value, the index is not used. This is due to the implicit data type conversion. For example, if the SSN column is of the VARCHAR2 type, the following does not use the index on SSN:

SELECT * FROM person WHERE SSN = 123456789

Investigating Index Usage

An index may not be used for one of many reasons:

• There are functions being applied to the predicate.

• There is a data type mismatch.

• Statistics are old.

• The column can contain null.

• Using the index would actually be slower than not using it.



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Optimizer Operators

Chapter 6 - Page 48

Old Statistics

The statistics are considered by the optimizer when deciding whether to use an index. If they are outdated, they may influence the optimizer to make poor decisions about indexes.

Null Columns

If a column can contain nulls, it may prevent the use of an index on that column. This is covered later in this lesson.

Slower Index

Sometimes the use of an index is not efficient. This is covered later in this lesson.



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Optimizer Operators

Chapter 6 - Page 49

Quiz

Answer: a

Quiz

A full table scan sequentially reads all rows from a table and filters out those that do not meet the selection criteria.

a. True

b. False



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Optimizer Operators

Chapter 6 - Page 50

Quiz

Answer: b

Quiz

Assuming that the column email has an index, the following query will result in an index range scan:Select employee_name from employees

where email like '%A';

a. True

b. False



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Optimizer Operators

Chapter 6 - Page 51

Quiz

Answer: c

Quiz

To get optimum result from indexes:

a. Create indexes before inserting table data

b. Do not order index columns

c. Limit the number of indexes for each table

d. Do not specify the tablespace for each index



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Optimizer Operators

Chapter 6 - Page 52

Summary

Summary

In this lesson, you should have learned to:

• Describe the SQL operators for tables and indexes




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Optimizer Operators

Chapter 6 - Page 53



This practice covers using different access paths for better optimization.

• Case 1 through case 13



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Optimizer Operators

Chapter 6 - Page 54



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Optimizer: Join Operators

Chapter 7 - Page 1


Chapter 7



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Chapter 7 - Page 2





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Chapter 7 - Page 3

Objectives

Objectives

This lesson helps you understand the execution plans related to join operations.

Objectives


• Describe the SQL operators for joins




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Chapter 7 - Page 4

Join Methods

Join Methods

A row source is a set of data that can be accessed in a query. It can be a table, an index, a nonmergeable view, or even the result set of a join tree consisting of many different objects.

A join predicate is a predicate in the WHERE clause that combines the columns of two of the tables in the join.

A nonjoin predicate is a predicate in the WHERE clause that references only one table.

A join operation combines the output from two row sources (such as tables or views) and returns one resulting row source (data set). The optimizer supports different join methods such as the following:

• Nested loop join: Useful when small subsets of data are being joined and if the join condition is an efficient way of accessing the second table

• Sort-merge join: Can be used to join rows from two independent sources. Hash joins generally perform better than sort-merge joins. On the other hand, sort-merge joins can perform better than hash joins if one or two row sources are already sorted.

• Hash join: Used for joining large data sets. The optimizer uses the smaller of two tables or data sources to build a hash table on the join key in memory. It then scans the larger table, probing the hash table to find the joined rows. This method is best used when the

Join Methods

A join:

• Defines the relationship between two row sources• Is a method of combining data from two data sources• Is controlled by join predicates, which define how the

objects are related• Join methods:

– Nested loops– Sort-merge join– Hash join

SELECT e.ename,d.dnameFROM emp e, dept dWHERE e.deptno = d.deptno AND

(e.job = 'ANALYST' OR e.empno = 9999);Join predicateNonjoin predicate

SELECT e.ename, d.dnameFROM dept d JOIN emp e USING (deptno)WHERE e.job = 'ANALYST' OR e.empno = 9999;

Join predicateNonjoin predicate



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Chapter 7 - Page 5

smaller table fits in the available memory. The cost is then limited to a single read pass over the data for the two tables.

Note: The slide shows you the same query using both the American National Standards Institute (ANSI) and non-ANSI join syntax. The ANSI syntax is the first example.



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Chapter 7 - Page 6

Nested Loops Join

Nested Loops Join

In the general form of the nested loops join, one of the two tables is defined as the outer table, or the driving table. The other table is called the inner table, or the right-hand side.

For each row in the outer (driving) table that matches the single table predicates, all rows in the inner table that satisfy the join predicate (matching rows) are retrieved. If an index is available, it can be used to access the inner table by rowid.

Any nonjoin predicates on the inner table are considered after this initial retrieval, unless a composite index combining both the join and the nonjoin predicate is used.

The code to emulate a nested loop join might look as follows: for r1 in (select rows from EMP that match single table predicate) loop for r2 in (select rows from DEPT that match current row from EMP) loop

output values from current row of EMP and current row of DEPT

end loop

end loop

The optimizer uses nested loop joins when joining small number of rows, with a good driving condition between the two tables. You drive from the outer loop to the inner loop, so the order of tables in the execution plan is important. Therefore, you should use other join methods when two independent row sources are joined.

Nested Loops Join

• Driving row source is scanned.

• Each row returned drives a lookup ininner row source.

• Joining rows are then returned.

select ename, e.deptno, d.deptno, d.dname

from emp e, dept d

where e.deptno = d.deptno and ename like 'A%';

---------------------------------------------------------------------| Id | Operation | Name | Rows |Cost |---------------------------------------------------------------------| 0 | SELECT STATEMENT | | 2 | 4 || 1 | NESTED LOOPS | | 2 | 4 || 2 | TABLE ACCESS FULL | EMP | 2 | 2 || 3 | TABLE ACCESS BY INDEX ROWID| DEPT | 1 | 1 || 4 | INDEX UNIQUE SCAN | PK_DEPT | 1 | |---------------------------------------------------------------------

2 - filter("E"."ENAME" LIKE 'A%')4 - access("E"."DEPTNO"="D"."DEPTNO")

NL

TAF TAR

IS

Driving

InnerFor each



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Chapter 7 - Page 7

Nested Loops Join: Prefetching


Oracle 9iR2 introduced a mechanism called nested loops prefetching. The idea is to improve I/O utilization, therefore response time, of index access with table lookup by batching rowid lookups into parallel block reads.

This change to the plan output is not considered a different execution plan. It does not affect the join order, join method, access method, or parallelization scheme.

This optimization is only available when the inner access path is index range scan and not if the inner access path is index unique scan.

The prefetching mechanism is used by table lookup. When an index access path is chosen and the query cannot be satisfied by the index alone, the data rows indicated by the ROWID also must be fetched. This ROWID to data row access (table lookup) is improved using data block prefetching, which involves reading an array of blocks which are pointed at by an array of qualifying ROWIDs.

Without data block prefetching, accessing a large number of rows using a poorly clustered B*-tree index could be expensive. Each row accessed by the index would likely be in a separate data block and thus would require a separate I/O operation.

With data block prefetching, the system delays data blocks reads until multiple rows specified by the underlying index are ready to be accessed and then retrieves multiple data blocks simultaneously, rather than reading a single data block at a time.



from emp e, dept d


---------------------------------------------------------------------| 0 | SELECT STATEMENT | | 2 | 84 | 5 | 1 | TABLE ACCESS BY INDEX ROWID| DEPT | 1 | 22 | 1 | 2 | NESTED LOOPS | | 2 | 84 | 5 |* 3 | TABLE ACCESS FULL | EMP | 2 | 40 | 3 |* 4 | INDEX RANGE SCAN | IDEPT | 1 | | 0 ---------------------------------------------------------------------

3 - filter("E"."ENAME" LIKE 'A%')4 - access("E"."DEPTNO"="D"."DEPTNO")

NL

TAF TAR

IRS

Driving

Inner



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Chapter 7 - Page 8

Nested Loops Join: 11g Implementation


Oracle Database 11g introduces a new way of performing joins with NESTED LOOPS operators. With this NESTED LOOPS implementation, the system first performs a NESTED LOOPS join between the other table and the index. This produces a set of ROWIDs that you can use to look up the corresponding rows from the table with the index. Instead of going to the table for each ROWID produced by the first NESTED LOOPS join, the system batches up the ROWIDs and performs a second NESTED LOOPS join between the ROWIDs and the table. This ROWID batching technique improves performance as the system only reads each block in the inner table once.



from emp e, dept d


NL

NL TAR

Driving InnerTAF IRS



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Chapter 7 - Page 9

Sort Merge Join

Sort Merge Join

In a sort merge join, there is no concept of a driving table. A sort merge join is executed as follows:

1. Get the first data set, using any access and filter predicates, and sort it on the join columns.

2. Get the second data set, using any access and filter predicates, and sort it on the join columns.

3. For each row in the first data set, find the start point in the second data set and scan until you find a row that does not join.

The merge operation combines the two sorted row sources to retrieve every pair of rows that contain matching values for the columns used in the join predicate.

If one row source has already been sorted in a previous operation (there is an index on the join column, for example), the sort merge operation skips the sort on that row source. When you perform a merge join, you must fetch all rows from the two row sources before to return the first row to the next operation. Sorting could make this join technique expensive, especially if sorting cannot be performed in memory.

The optimizer can select a sort merge join over a hash join for joining large amounts of data if any of the following conditions are true:

Sort Merge Join

• First and second row sources are sortedby the same sort key.

• Sorted rows from both tables are merged.select /*+ USE_MERGE(d e) NO_INDEX(d) */

ename, e.deptno, d.deptno, dname

from emp e, dept d

where e.deptno = d.deptno and ename > 'A'

Independent

Sorted

Merged

MJ

SJ SJ

TAF IRS

Sorted



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Chapter 7 - Page 10

• The join condition between two tables is not an equijoin.

• Sorts already required by previous operations.

Note: Sort merge joins are useful when the join condition between two tables is an inequality condition (but not a nonequality), such as <, <=, >, or >=.



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Chapter 7 - Page 11

Hash Join

Hash Join

To perform a hash join between two row sources, the system reads the first data set and builds an array of hash buckets in memory. A hash bucket is little more than a location that acts as the starting point for a linked list of rows from the build table. A row belongs to a hash bucket if the bucket number matches the result that the system gets by applying an internal hashing function to the join column or columns of the row.

The system starts to read the second set of rows, using whatever access mechanism is most appropriate for acquiring the rows, and uses the same hash function on the join column or columns to calculate the number of the relevant hash bucket. The system then checks to see if there are any rows in that bucket. This is known as probing the hash table.

If there are no rows in the relevant bucket, the system can immediately discard the row from the probe table.

If there are some rows in the relevant bucket, the system does an exact check on the join column or columns to see if there is a proper match. Any rows that survive the exact check can immediately be reported (or passed on to the next step in the execution plan). So, when you perform a hash join, you must fetch all rows from the smallest row source to return the first row to next operation.

Note: Hash joins are performed only for equijoins, and are most useful when joining large amount of data.

Hash Join

• The smallest row source is usedto build a hash table.

• The second row source is hashedand checked against the hash table.

select /*+ USE_HASH(e d) */ename, e.deptno, d.deptno, dnamefrom emp e, dept dwhere e.deptno = d.deptno and ename like 'A%’

Driving

Probe

Build hashtable inmemory

HJ

TAF TAR

IS



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Chapter 7 - Page 12

Cartesian Join

Cartesian Join

A Cartesian join is used when one or more of the tables does not have any join conditions to any other tables in the statement. The optimizer joins every row from one data source with every row from the other data source, creating the Cartesian product of the two sets.

A Cartesian join can be seen as a nested loop with no elimination; the first row source is read and then for every row, all the rows are returned from the other row source.

Note: Cartesian join is generally not desirable. However, it is perfectly acceptable to have one with single-row row source (guaranteed by a unique index, for example) joined to some other table.

Cartesian Join

select ename, e.deptno, d.deptno, dname

from emp e, dept d where ename like 'A%';



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Chapter 7 - Page 13

Join Types

Join Types

Join operation types include the following:

• Join (equijoin and nonequijoin): Returns rows that match predicate join

• Outer join: Returns rows that match predicate join and row when no match is found

• Semi join: Returns rows that match the EXISTS subquery. Find one match in the inner table, then stop search.

• Anti join: Returns rows with no match in the NOT IN subquery. Stop as soon as one match is found.

• Star join: This is not a join type, but just a name for an implementation of a performance optimization to better handle the fact and dimension model.

Antijoin and semijoin are considered to be join types, even though the SQL constructs that cause them are subqueries. Antijoin and semijoin are internal optimizations algorithms used to flatten subquery constructs in such a way that they can be resolved in a join-like way.

Join Types

• A join operation combines the output from two row sources and returns one resulting row source.

• Join operation types include the following :– Join (Equijoin/Natural – Nonequijoin)

– Outer join (Full, Left, and Right)– Semi join: EXISTS subquery

– Anti join: NOT IN subquery

– Star join (Optimization)



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Chapter 7 - Page 14

Equijoins and Nonequijoins


The join condition determines whether a join is an equijoin or a nonequijoin. An equijoin is a join with a join condition containing an equality operator. When a join condition relates two tables by an operator other than equality, it is a nonequijoin.

Equijoins are the most commonly used. An example each of an equijoin and a nonequijoin are shown in the slide. Nonequijoins are less frequently used.

To improve SQL efficiency, use equijoins whenever possible. Statements that perform equijoins on untransformed column values are the easiest to tune.

Note: If you have a nonequijoin, a hash join is not possible.


SELECT e.ename, e.sal, s.gradeFROM emp e ,salgrade sWHERE e.sal = s.hisal;

SELECT e.ename, e.sal, s.gradeFROM emp e ,salgrade s

WHERE e.sal between s.hisal and s.hisal;

Equijoin

Nonequijoin



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Chapter 7 - Page 15

Outer Joins

Outer Joins

The simple join is the most commonly used within the system. Other joins open up extra functionality, but have much more specialized uses. The outer join operator is placed on the deficient side of the query. In other words, it is placed against the table that has the missing join information. Consider EMP and DEPT. There may be a department that has no employees. If EMP and DEPT are joined together, this particular department would not appear in the output because there is no row that matches the join condition for that department. By using the outer join, the missing department can be displayed.

1. Merge Outer joins: By default, the optimizer uses MERGE OUTER JOIN.

2. Outer join with nested loops: The left/driving table is always the table whose rows are being preserved (DEPT in the example). For each row from DEPT, look for all matching rows in EMP. If none is found, output DEPT values with null values for the EMP columns. If rows are found, output DEPT values with these EMP values.

3. Hash Outer joins: The left/outer table whose rows are being preserved is used to build the hash table, and the right/inner table is used to probe the hash table. When a match is found, the row is output and the entry in the hash table is marked as matched to a row. After the inner table is exhausted, the hash table is read over once again, and any rows that are not marked as matched are output with null values for the EMP columns. The system hashes the table whose rows are not being preserved, and then reads the

Outer Joins

An outer join returns a row even if no match is found.

SELECT /*+ USE_NL(e d) */

d.deptno,d.dname,e.empno,e.ename

FROM emp e, dept d

WHERE e.deptno(+)=d.deptno;

SELECT d.deptno,d.dname,e.empno,e.enameFROM emp e, dept dWHERE e.deptno(+)=d.deptno;

SELECT /*+ USE_HASH(e d) */ d.deptno,d.dname,e.empno,e.enameFROM emp e, dept dWHERE e.deptno(+)=d.deptno;

1

2

3

10

20

30

40

20

10

20

30

10

DEPT EMP



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Chapter 7 - Page 16

table whose rows are being preserved, probing the hash table to see whether there was a row to join to.

Note: You can also use the ANSI syntax for full, left, and right outer joins (not shown in the slide).



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Chapter 7 - Page 17

Semijoins

Semijoins

Semijoins return a result when you hit the first joining record. A semijoin is an internal way of transforming an EXISTS subquery into a join. However, you cannot see this occur anywhere.

Semijoins return rows that match an EXISTS subquery without duplicating rows from the left side of the predicate when multiple rows on the right side satisfy the criteria of the subquery.

In the above diagram, for each DEPT record, only the first matching EMP record is returned as a join result. This prevents scanning huge numbers of duplicate rows in a table when all you are interested in is if there are any matches.

When the subquery is not unnested, a similar result could be achieved by using a FILTER operation and scanning a row source until a match is found, then returning it.

Note: A semijoin can always use a Merge join. The optimizer may choose nested-loop, or hash joins methods to perform semijoins as well.

Semijoins

Semijoins look only for the first match.

SELECT deptno, dname

FROM dept

WHERE EXISTS (SELECT 1 FROM emp WHERE emp.deptno=dept.deptno);

10

20

30

40

20

10

20

30

10

DEPT EMP



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Chapter 7 - Page 18

Antijoins

Antijoins

Antijoins return rows that fail to match (NOT IN) the subquery at the right side. For example, an antijoin can select a list of departments which do not have any employee.

The optimizer uses a merge antijoin algorithm for NOT IN subqueries by default. However, if the HASH_AJ or NL_AJ hints are used and various required conditions are met, the NOT IN uncorrelated subquery can be changed. Although antijoins are mostly transparent to the user, it is useful to know that these join types exist and could help explain unexpected performance changes between releases.

Antijoins

Reverse of what would have been returned by a join

SELECT deptno, dname

FROM dept

WHERE deptno not in (SELECT deptno FROM emp);

SELECT deptno, dname FROM dept

WHERE deptno IS NOT NULL AND

deptno NOT IN

(SELECT /*+ HASH_AJ */ deptno FROM emp WHERE deptno IS NOT NULL);

10

20

30

40

20

10

20

3010

DEPT EMP

EMP DEPT



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Chapter 7 - Page 19

Quiz

Answer: b

Quiz

The _______ join is used when one or more of the tables do not have any join conditions to any other tables in the statement.

a. Hash

b. Cartesian

c. Non-equi

d. Outer



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Chapter 7 - Page 20

Quiz

Answer: d

Quiz

The _______ join returns a row even if no match is found.

a. Hash

b. Cartesian

c. Semi

d. Outer



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Chapter 7 - Page 21

Quiz

Answer: c

Quiz

The _______ join looks only for the first match.

a. Hash

b. Cartesian

c. Semi

d. Outer



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Chapter 7 - Page 22

Quiz

Answer: b

Quiz

In a hash join, the _______row source is used to build a hash table.

a. Biggest

b. Smallest

c. Sorted

d. Unsorted



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Chapter 7 - Page 23

Summary

Summary


• Describe the SQL operators for joins




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Chapter 7 - Page 24




• Using different access paths for better optimization

• Using the result cache



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Other Optimizer Operators

Chapter 8 - Page 1


Chapter 8



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Chapter 8 - Page 2





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Chapter 8 - Page 3

Objectives

Objectives

This lesson helps you understand the execution plans that use common operators of other access methods.

Objectives


• Describe SQL operators for:– Clusters

– In-List

– Sorts

– Filters

– Set Operations

• Result Cache operators



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Chapter 8 - Page 4

Clusters

Clusters

Clusters are an optional method for storing table data. A cluster is a group of tables that share the same data blocks because they share common columns and are often used together. For example, the ORDERS and ORDER_ITEMS table share the ORDER_ID column. When you cluster the ORDERS and ORDER_ITEMS tables, the system physically stores all rows for each order from both the ORDERS and ORDER_ITEMS tables in the same data blocks.

Cluster index: A cluster index is an index defined specifically for a cluster. Such an index contains an entry for each cluster key value. To locate a row in a cluster, the cluster index is used to find the cluster key value, which points to the data block associated with that cluster key value. Therefore, the system accesses a given row with a minimum of two I/Os.

Hash clusters: Hashing is an optional way of storing table data to improve the performance of data retrieval. To use hashing, you create a hash cluster and load tables into the cluster. The system physically stores the rows of a table in a hash cluster and retrieves them according to the results of a hash function. The key of a hash cluster (just as the key of an index cluster) can be a single column or composite key. To find or store a row in a hash cluster, the system applies the hash function to the row’s cluster key value; the resulting hash value corresponds to a data block in the cluster, which the system then reads or writes on behalf of the issued statement.

Clusters

Clustered ORDERS and ORDER_ITEMS tables

Unclustered ORDERS and ORDER_ITEMS tables

ORD_NO PROD QTY ... ------------ ------

101 A4102 20102 A2091 11102 G7830 20102 N9587 26101 A5675 19101 W0824 10

ORD_NO ORD_DT CUST_CD------ ------ ------

101 05-JAN-97 R01102 07-JAN-97 N45

Cluster Key(ORD_NO)

101 ORD_DT CUST_CD05-JAN-97 R01

PROD QTYA4102 20A5675 19 W0824 10

102 ORD_DT CUST_CD07-JAN-97 N45

PROD QTYA2091 11G7830 20 N9587 26



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Chapter 8 - Page 5

Note: Hash clusters are a better choice than using an indexed table or index cluster when a table is queried frequently with equality queries.



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Chapter 8 - Page 6

When Are Clusters Useful?


• Index clusters allow row data from one or more tables that share a cluster key value to be stored in same block. You can locate these rows using a cluster index, which has one entry per cluster key value and not for each row. Therefore, the index is smaller and less costly to access for finding multiple rows. The rows with the same key are in a small group of blocks. This means that in an index cluster the clustering factor is very good and provides clustering for data from multiple tables sharing the same join key. The smaller index and smaller group of blocks reduce the cost of access by reducing block visits to the buffer cache. Index clusters are useful when the size of the tables is not known in advance (For example: Creating a new table rather than converting an existing one whose size is stable) because a cluster bucket is only created after a cluster key value is used. They are also useful for all filter operations or searches. Note that full table scans do not perform well on a table in a multiple table cluster as it has more blocks than the table would have if created as a heap table.

• Hash clusters allow row data from one or more tables that share a cluster key value to be stored in same block. You can locate these rows using a system-provided or user-provided hashing function or using the cluster key value assuming that this is already evenly distributed making the access to a row faster than using index clusters. Table rows with the same cluster key values hash into the same cluster buckets and can be stored in the same block or small group of blocks.


• Index cluster:– Tables always joined on the same keys

– The size of the table is not known

– In any type of searches

• Hash cluster:– Tables always joined on the same keys

– Storage for all cluster keys allocated initially

– In either equality (=) or nonequality (<>) searches



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Chapter 8 - Page 7


When Are Clusters Useful? (continued)

• This means that in a hash cluster the clustering factor is also very good and a row may be accessed by its key with one block visit only and without needing an index. Hash clusters allocate all the storage for all the hash buckets when the cluster is created, so they may waste space. They also do not perform well other than on equality searches or nonequality searches. Like index clusters if they contain multiple tables, full scans are more expensive for the same reason.

• Single-table hash clusters are similar to a hash cluster, but are optimized in the block structures for access to a single table, thereby providing the fastest possible access to a row other than by using a rowid filter. As they only have one table, full scans, if they happen, cost as much as they would in a heap table.

• Sorted hash clusters are designed to reduce costs of accessing ordered data by using a hashing algorithm on the hash key. Accessing the first row matching the hash key may be less costly than using an IOT for a large table because it saves the cost of a B*-tree probe. All the rows that match on a particular hash key (For example: Account number) are stored in the cluster in the order of the sort key or keys (For example: Phone calls), thereby, eliminating the need for a sort to process the order by clause. These clusters are very good for batch reporting, billing, and so on.


• Single-table hash cluster:– Fastest way to access a large table with an equality search

• Sorted hash cluster:– Only used for equality search

– Avoid sorts on batch reporting

– Avoid overhead probe on the branch blocks of an IOT



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Chapter 8 - Page 8

Cluster Access Path: Examples


The example in the slide shows you two different cluster access paths.

In the top one, a hash scan is used to locate rows in a hash cluster, based on a hash value. In a hash cluster, all rows with the same hash value are stored in the same data block. To perform a hash scan, the system first obtains the hash value by applying a hash function to a cluster key value specified by the statement. The system then scans the data blocks containing rows with that hash value.

The second one assumes that a cluster index was used to cluster both the EMP and DEPT tables. In this case, a cluster scan is used to retrieve, from a table stored in an indexed cluster, all rows that have the same cluster key value. In an indexed cluster, all rows with the same cluster key value are stored in the same data block. To perform a cluster scan, the system first obtains the ROWID of one of the selected rows by scanning the cluster index. The system then locates the rows based on this ROWID.

Note: You see examples of how to create clusters in the labs for this lesson.




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Chapter 8 - Page 9

Sorting Operators

Sorting Operators

Sort operations result when users specify an operation that requires a sort. Commonly encountered operations include the following:

• SORT AGGREGATE does not involve a sort. It retrieves a single row that is the result of applying a group function to a group of selected rows. Operations such as COUNT and MIN are shown as SORT AGGREGATE.

• SORT UNIQUE sorts output rows to remove duplicates. It occurs if a user specifies a DISTINCT clause or if an operation requires unique values for the next step.

• SORT JOIN happens during a sort-merge join, if the rows need to be sorted by the join key.

• SORT GROUP BY is used when aggregates are computed for different groups in the data. The sort is required to separate the rows into different groups.

• SORT ORDER BY is required when the statement specifies an ORDER BY that cannot be satisfied by one of the indexes.

• HASH GROUP BY hashes a set of rows into groups for a query with a GROUP BY clause.

Sorting Operators

• SORT operator:– AGGREGATE: Single row from group function

– UNIQUE: To eliminate duplicates

– JOIN: Precedes a merge join

– GROUP BY, ORDER BY: For these operators

• HASH operator:– GROUP BY: For this operator

– UNIQUE: Equivalent to SORT UNIQUE

• If you want ordered results, always use ORDER BY.



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Chapter 8 - Page 10

• HASH UNIQUE hashes a set of rows to eliminate duplicates. It occurs if a user specifies a DISTINCT clause or if an operation requires unique values for the next step. This is similar to SORT UNIQUE.

Note: Several SQL operators cause implicit sorts (or hashes since Oracle Database 10g, Release 2), such as DISTINCT, GROUP BY, UNION, MINUS, and INTERSECT. However, do not rely on these SQL operators to return ordered rows. If you want to have rows ordered, use the ORDER BY clause.



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Chapter 8 - Page 11

Buffer Sort Operator


The BUFFER SORT operator uses a temporary table or a sort area in memory to store intermediate data. However, the data is not necessarily sorted.

The BUFFER SORT operator is needed if there is an operation that needs all the input data before it can start. (See “Cartesian Join.”)

So BUFFER SORT uses the buffering mechanism of a traditional sort, but it does not do the sort itself. The system simply buffers the data, in the User Global Area (UGA) or Program Global Area (PGA), to avoid multiple table scans against real data blocks.

The whole sort mechanism is reused, including the swap to disk when not enough sort area memory is available, but without sorting the data.

The difference between a temporary table and a buffer sort is as follows:

• A temporary table uses System Global Area (SGA).

• A buffer sort uses UGA.


SELECT ename, emp.deptno, dept.deptno, dname

FROM emp, dept

WHERE ename like 'A%';



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Chapter 8 - Page 12

Inlist Iterator

Inlist Iterator

It is used when a query contains an IN clause with values or multiple equality predicates on the same column linked with ORs.

The INLIST ITERATOR operator iterates over the enumerated value list, and every value is executed separately.

The execution plan is identical to the result of a statement with an equality clause instead of IN, except for one additional step. The extra step occurs when INLIST ITERATOR feeds the equality clause with unique values from the list.

You can view this operator as a FOR LOOP statement in PL/SQL. In the example in the slide, you iterate the index probe over two values: 1 and 2.

Also, it is a function that uses an index, which is scanned for each value in the list. An alternative handling is UNION ALL of each value or a FILTER of the values against all the rows, this is significantly more efficient.

The optimizer uses an INLIST ITERATOR when an IN clause is specified with values, and the optimizer finds a selective index for that column. If there are multiple OR clauses using the same index, the optimizer selects this operation rather than CONCATENATION or UNION ALL, because it is more efficient.

Inlist Iterator

SELECT * FROM emp WHERE empno IN (7876, 7900, 7902);

Every value executed separately

deptno=1 deptno=2



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Chapter 8 - Page 13

View Operator

View Operator

Each query produces a variable set of data in the form of a table. A view simply gives a name to this set of data.

When views are referenced in a query, the system can handle them in two ways. If a number of conditions are met, they can be merged into the main query. This means that the view text is rewritten as a join with the other tables in the query. Views can also be left as standalone views and selected from directly as in the case of a table. Predicates can also be pushed into or pulled out of the views as long as certain conditions are met.

When a view is not merged, you can see the VIEW operator. The view operation is executed separately. All rows from the view are returned, and the next operation can be done.

Sometimes a view cannot be merged and must be executed independently in a separate query block. In this case, you can also see the VIEW operator in the explain plan. The VIEW keyword indicates that the view is executed as a separate query block. For example, views containing GROUP BY functions cannot be merged.

The second example in the slide shows a nonmergeable inline view. An inline view is basically a query within the FROM clause of your statement.

Basically, this operator collects all rows from a query block before they can be processed by higher operations in the plan.

View Operator

create view V as select /*+ NO_MERGE */ DEPTNO, sal from emp ;

select * from V;

select v.*,d.dname from (select DEPTNO, sum(sal) SUM_SAL

from emp group by deptno) v, dept d where v.deptno=d.deptno;



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Chapter 8 - Page 14

Count Stop Key Operator


COUNT STOPKEY limits the number of rows returned. The limitation is expressed by the ROWNUM expression in the WHERE clause. It terminates the current operation when the count is reached.

Note: The cost of this operator depends on the number of occurrences of the values you try to retrieve. If the value appears very frequently in the table, the count is reached quickly. If the value is very infrequent, and there are no indexes, the system has to read most of the table’s blocks before reaching the count.


SELECT count(*)FROM (SELECT /*+ NO_MERGE */ *

FROM emp WHERE empno ='1' and rownum < 10);



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Chapter 8 - Page 15

Min/Max and First Row Operators


FIRST ROW retrieves only the first row selected by a query. It stops accessing the data after the first value is returned. This is an optimization introduced in Oracle 8i and it works with the index range scan and the index full scan.

In the example in the slide, it is assumed that there is an index on the quantity_on_hand column.


SELECT MIN(quantity_on_hand)

FROM INVENTORIES

WHERE quantity_on_hand < 500;



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Chapter 8 - Page 16

Other N-Array Operations

Other N-Array Operations

• FILTER

• CONCATENATION

• UNION ALL/UNION

• INTERSECT

• MINUS



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Chapter 8 - Page 17

FILTER Operations

FILTER Operations

A FILTER operation is any operation that discards rows returned by another step, but is not involved in retrieving the rows itself. All sorts of operations can be filters, including subqueries and single table predicates.

In the example 1, FILTER applies to the groups that are created by the GROUP BY operation.

In the example 2, FILTER is almost used in the same way as NESTED LOOPS. DEPT is accessed once, and for each row from DEPT, EMP is accessed by its index on DEPTNO. This operation is done as many times as the number of rows in DEPT.

The FILTER operation is applied, for each row, after DEPT rows are fetched. The FILTER discards rows for the inner query returned at least one row (select 1 from emp e where e.deptno=d.deptno) is TRUE.

FILTER Operations

• Accepts a set of rows• Eliminates some of them• Returns the rest

SELECT deptno, sum(sal) SUM_SAL FROM emp

GROUP BY deptno HAVING sum(sal) > 9000;

1

2

SELECT deptno, dname FROM dept d WHERE NOT EXISTS (select 1 from emp e where e.deptno=d.deptno);



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Chapter 8 - Page 18

Concatenation Operation


CONCATENATION concatenates the rows returned by two or more row sets. This works like UNION ALL and does not remove duplicate rows.

It is used with OR expansions. However, OR does not return duplicate rows, so for each component after the first, it appends a negation of the previous components (LNNVL):

CONCATENATION

- BRANCH 1 - SAL=2

- BRANCH 2 - DEPTNO = 1 AND NOT row in Branch 1

The LNNVL function is generated by the OR clause to process this negation.

The LNNVL() function returns TRUE, if the predicate is NULL or FALSE.

So filter (LNNVL(SAL=2)) returns all rows for which SAL != 2 or SAL is NULL.

Note: The explain plan in the slide is from Oracle Database 11g Release 1.


SELECT * FROM emp WHERE deptno=1 or sal=2;

--------------------------------------------------------------------


--------------------------------------------------------------------


| 1 | CONCATENATION | | | |

| 2 | TABLE ACCESS BY INDEX ROWID| EMP | 4 | 348 |

| 3 | INDEX RANGE SCAN | I_SAL | 2 | |

| 4 | TABLE ACCESS BY INDEX ROWID| EMP | 4 | 348 |

| 5 | INDEX RANGE SCAN | I_DEPTNO | 2 | |

--------------------------------------------------------------------


---------------------------------------------------

3 - access("SAL"=2)

4 - filter(LNNVL("SAL"=2))

5 - access("DEPTNO"=1)



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Chapter 8 - Page 19

UNION [ALL], INTERSECT, MINUS


SQL handles duplicate rows with an ALL or DISTINCT modifier in different places in the language. ALL preserves duplicates and DISTINCT removes them. Here is a quick description of the possible SQL set operations:

• INTERSECTION: Operation accepting two sets of rows and returning the intersection of the sets, eliminating duplicates. Subrow sources are executed or optimized individually. This is very similar to sort-merge-join processing: Full rows are sorted and matched.

• MINUS: Operation accepting two sets of rows and returning rows appearing in the first set, but not in the second, eliminating duplicates. Subrow sources are executed or optimized individually. Similar to INTERSECT processing. However, instead of match-and-return, it is match-and-exclude.

• UNION: Operation accepting two sets of rows and returning the union of the sets, eliminating duplicates. Subrow sources are executed or optimized individually. Rows retrieved are concatenated and sorted to eliminate duplicate rows.

• UNION ALL: Operation accepting two sets of rows and returning the union of the sets, and not eliminating duplicates. The expensive sort operation is not necessary. Use UNION ALL if you know you do not have to deal with duplicates.


3

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1

MINUS

SORT UNIQUE NOSORT

INDEX FULL SCAN

SORT UNIQUE

INDEX FAST FULL SCAN

INTERSECTION

SORT UNIQUE NOSORT

INDEX FULL SCAN

SORT UNIQUE

INDEX FAST FULL SCAN

SORT UNIQUE

UNION-ALL

INDEX FULL SCAN

INDEX FAST FULL SCANUNION ALLUNION

INTERSECT

MINUS



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Chapter 8 - Page 20

Result Cache Operator


The SQL query result cache enables explicit caching of query result sets and query fragments in database memory. A dedicated memory buffer stored in the shared pool can be used for storing and retrieving the cached results. The query results stored in this cache become invalid when data in the database objects that are accessed by the query is modified. Although the SQL query cache can be used for any query, good candidate statements are the ones that need to access a very high number of rows to return only a fraction of them. This is mostly the case for data warehousing applications.

If you want to use the query result cache and the RESULT_CACHE_MODE initialization parameter is set to MANUAL, you must explicitly specify the RESULT_CACHE hint in your query. This introduces the ResultCache operator into the execution plan for the query. When you execute the query, the ResultCache operator looks up the result cache memory to check whether the result for the query already exists in the cache. If it exists, the result is retrieved directly out of the cache. If it does not yet exist in the cache, the query is executed, the result is returned as output, and is also stored in the result cache memory.

If the RESULT_CACHE_MODE initialization parameter is set to FORCE, and you do not want to store the result of a query in the result cache, you must then use the NO_RESULT_CACHE hint in your query.


SELECT /*+ RESULT_CACHE */ deptno, AVG(sal)

FROM emp

GROUP BY deptno;



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Chapter 8 - Page 21

Quiz

Answer: a

Quiz

Hash clusters are a better choice than using an indexed table or index cluster when a table is queried frequently with equality queries.

a. True

b. False



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Chapter 8 - Page 22

Quiz

Answer: a

Quiz

The ________ operator uses a temporary table to store intermediate data.

a. Buffer Sort Operator

b. Inlist

c. Min/Max

d. N-Array



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Chapter 8 - Page 23

Quiz

Answer: b

Quiz

The following query uses the ________ operator:SELECT * FROM emp WHERE empno IN (7876, 7900, 7902);

a. Buffer Sort Operator

b. Inlist

c. Min/Max

d. N-Array



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Chapter 8 - Page 24

Quiz

Answer: b

Quiz

A FILTER operation retrieves rows returned by another statement.

a. True

b. False



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Chapter 8 - Page 25

Summary

Summary


• Describe SQL operators for:– Clusters

– In-List

– Sorts

– Filters

– Set Operations

• Result Cache operators



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Chapter 8 - Page 26




• Using different access paths for better optimization– Case 14 to case 16

• Using the result cache



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Case Study: Star Transformation

Chapter 9 - Page 1


Chapter 9



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Chapter 9 - Page 2


Case Study:Star Transformation



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Chapter 9 - Page 3

Objectives

Objectives


• Define a star schema

• Show a star query plan without transformation

• Define the star transformation requirements

• Show a star query plan after transformation



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Chapter 9 - Page 4

The Star Schema Model


The star schema is the simplest data warehouse schema. It is called a star schema because the entity-relationship diagram of this schema resembles a star, with points radiating from a central table. The center of the star consists of one or more fact tables and the points of the star are the dimension tables. A star schema is characterized by one or more very large fact tables that contain the primary information in the data warehouse and a number of much smaller dimension tables (or lookup tables), each of which contains information about the entries for a particular attribute in the fact table. A star query is a join between a fact table and a number of dimension tables. Each dimension table is joined to the fact table using a primary key to foreign key join, but the dimension tables are not joined to each other. The cost-based optimizer (CBO) recognizes star queries and generates efficient execution plans for them. A typical fact table contains keys and measures. For example, in the Sales History schema, the sales fact table contains the quantity_sold, amount, and cost measures, and the cust_id, time_id, prod_id, channel_id, and promo_id keys. The dimension tables are customers, times, products, channels, and promotions. The products dimension table, for example, contains information about each product number that appears in the fact table.

Note: It is easy to generalize this model to include more than one fact table.


PRODUCTS

PROD_ID

PROD_NAMEPROD_DESC PROD_ID

TIME_IDCHANNEL_ID

SALES

AMOUNT_SOLDQUANTITY_SOLD

Fact table

Dimension/Lookup table

Fact >> Dimension

Keys

TIMES

TIME_ID

DAY_NAMECALENDAR_YEAR

CHANNELS

CHANNEL_ID

CHANNEL_DESCCHANNEL_CLASS

CUSTOMERS

CUST_ID

CUST_GENDERCUST_CITY

Measures



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Chapter 9 - Page 5

The Snowflake Schema Model


The snowflake schema is a more complex data warehouse model than a star schema, and is a type of star schema. It is called a snowflake schema because the diagram of the schema resembles a snowflake. Snowflake schemas normalize dimensions to eliminate redundancy. That is, the dimension data has been grouped into multiple tables instead of one large table. For example, a product dimension table in a star schema might be normalized into a products table, a product_category table, and a product_manufacturer table in a snowflake schema or, as shown in the slide, you can normalize the customers table using the countries table. While this saves space, it increases the number of dimension tables and requires more foreign key joins. The result is more complex queries and reduced query performance.

Note: It is suggested that you select a star schema over a snowflake schema unless you have a clear reason to choice the snowflake schema.


PRODUCTS

SALES

CHANNELS

TIMES

COUNTRIES

CUSTOMERS



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Chapter 9 - Page 6

Star Query: Example

Star Query: Example

Consider the star query in the slide. For the star transformation to operate, it is supposed that the sales table of the Sales History schema has bitmap indexes on the time_id, channel_id, and cust_id columns.

Star Query: Example

SELECT ch.channel_class, c.cust_city, t.calendar_quarter_desc,SUM(s.amount_sold) sales_amount

FROM sales s,times t,customers c,channels ch

WHERE s.time_id = t.time_id AND

s.cust_id = c.cust_id AND

s.channel_id = ch.channel_id AND

c.cust_state_province = 'CA' AND ch.channel_desc IN ('Internet','Catalog') AND t.calendar_quarter_desc IN ('1999-Q1','1999-Q2')

GROUP BY ch.channel_class, c.cust_city, t.calendar_quarter_desc;

0110

0110

0110



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Chapter 9 - Page 7

Execution Plan Without Star Transformation


Before you see the benefits of a star transformation, you should review how a join on a star schema is processed without their benefits.

The fundamental issue with the plan in the slide is that the query always starts joining the SALES table to a dimension table. This results in a very large number of rows that can only be trimmed down by the other parent joins in the execution plan.

Predicate Information (by operation id):--------------------------------------------2 - access("S"."CHANNEL_ID"="CH"."CHANNEL_ID")3 - filter("CH"."CHANNEL_DESC"='Catalog' OR

"CH"."CHANNEL_DESC"='Internet')4 - access("S"."TIME_ID"="T"."TIME_ID")5 - filter("T"."CALENDAR_QUARTER_DESC"='1999-Q1'

OR "T"."CALENDAR_QUARTER_DESC"='1999-Q2')6 - access("S"."CUST_ID"="C"."CUST_ID")7 - filter("C"."CUST_STATE_PROVINCE"='CA')


SALESCUSTOMERS

Hash JoinTIMES

Hash JoinCHANNELS

Hash Join

-------------------------------------------| Id | Operation | Name |-------------------------------------------| 0 | SELECT STATEMENT | | | 1 | HASH GROUP BY | | |* 2 | HASH JOIN | | |* 3 | TABLE ACCESS FULL | CHANNELS ||* 4 | HASH JOIN | ||* 5 | TABLE ACCESS FULL | TIMES ||* 6 | HASH JOIN | ||* 7 | TABLE ACCESS FULL| CUSTOMERS | | 8 | TABLE ACCESS FULL| SALES |-------------------------------------------



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Chapter 9 - Page 8

Star Transformation

Star Transformation

To get the best possible performance for star queries, it is important to follow some basic guidelines:

• A bitmap index should be built on each of the foreign key columns of the fact table or tables.

• The STAR_TRANSFORMATION_ENABLED initialization parameter should be set to TRUE. This enables an important optimizer feature for star queries. It is set to FALSE by default for backwards compatibility.

When a data warehouse satisfies these conditions, the majority of the star queries that run in the data warehouse use a query execution strategy known as star transformation. Star transformation provides very efficient query performance for star queries.

Star transformation is a powerful optimization technique that relies on implicitly rewriting (or transforming) the SQL of the original star query. The end user never needs to know any of the details about the star transformation. The system’s CBO automatically selects star transformation where appropriate. The optimizer creates an execution plan that processes a star query using two basic phases:

• The first phase retrieves exactly the necessary rows from the fact table (the result set). Because this retrieval utilizes bitmap indexes, it is very efficient.

Star Transformation

• Create bitmap indexes on fact tables foreign keys.• Set STAR_TRANSFORMATION_ENABLED to TRUE.

• Requires at least two dimensions and one fact table

• Gather statistics on all corresponding objects.

• Carried out in two phases:– First, identify interesting fact rows using bitmap indexes

based on dimensional filters.

– Join them to the dimension tables.



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Chapter 9 - Page 9

• The second phase joins this result set to the dimension tables. This operation is called a semijoin.

Note: At least three tables are used in the query (two dimensions and one fact).



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Chapter 9 - Page 10

Star Transformation: Considerations


Star transformation is not supported for tables with any of the following characteristics:

• Queries with a table hint that is incompatible with a bitmap access path

• Queries that contain bind variables

• Tables with too few bitmap indexes. There must be a bitmap index on a fact table column for the optimizer to generate a subquery for it.

• Remote fact tables. However, remote dimension tables are allowed in the subqueries that are generated.

• Antijoined tables

• Tables that are already used as a dimension table in a subquery

• Tables that are really unmerged views, which are not view partitions


• Queries containing bind variables are not transformed.

• Queries referring to remote fact tables are not transformed.

• Queries containing antijoined tables are not transformed.

• Queries referring to unmerged nonpartitioned views are not transformed.



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Chapter 9 - Page 11

Star Transformation: Rewrite Example


The system processes the query seen earlier in two phases. In the first phase, the system uses the filters against the dimensional tables to retrieve the dimensional primary keys, which match those filters. Then the system uses those primary keys to probe the bitmap indexes on the foreign key columns of the fact table to identify and retrieve only the necessary rows from the fact table. That is, the system retrieves the result set from the sales table by using essentially the rewritten query in the slide.

Note: The SQL in the slide is a theoretical SQL statement that represents what goes on in phase I.


SELECT s.amount_soldFROM sales s

WHERE time_id IN (SELECT time_id FROM timesWHERE calendar_quarter_desc

IN('1999-Q1','1999-Q2'))

AND cust_id IN (SELECT cust_id FROM customers WHERE cust_state_province = 'CA')

AND channel_id IN(SELECT channel_id FROM channels WHERE channel_desc IN

('Internet','Catalog'));

0110

0110

0110

Phase 1



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Chapter 9 - Page 12

Retrieving Fact Rows from One Dimension


The slide shows retrieval of fact table rows using only one dimension table. Based on the corresponding dimension filter predicates, like in t.calendar_quarter_desc IN ('1999-Q1','1999-Q2') from the example in the previous slide, the system scans the dimension table, and for each corresponding row, it probes the corresponding fact bitmap index and fetches the corresponding bitmap.

BITMAP KEY ITERATION makes each key coming from its left input a lookup key for the index on its right input, and returns all bitmaps fetched by that index. Note that its left input supplies join keys from the dimension table in this case.

The last step in this tree merges all fetched bitmaps from the previous steps. This merge operation produces one bitmap that can be described as representing the rows of the fact table that join with the rows of interest from the dimension table.

Note: BITMAP_MERGE_AREA_SIZE plays an important role in tuning the performance of this operation when using the shared server mode. The system does not recommend using the BITMAP_MERGE_AREA_SIZE parameter unless the instance is configured with the shared server option. The system recommends that you enable automatic sizing of SQL working areas by setting PGA_AGGREGATE_TARGET instead. BITMAP_MERGE_AREA_SIZE is retained for backward compatibility.


BITMAP KEY

ITERATION

Dimension Table

Access

Fact Table Bitmap Access

BITMAPMERGE One bitmap

is Produced.

Phase 1



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Chapter 9 - Page 13

Retrieving Fact Rows from All Dimensions


During the first phase, the steps mentioned in the previous slide are repeated for each dimension table. So each BITMAP MERGE in the plan generates a bitmap for a single dimension table. To identify all rows from the fact table that are of interest, the system must intersect all generated bitmaps. This is to eliminate fact rows that join with one dimension, but not with all of them. This is achieved by performing a very efficient BITMAP AND operation on all the bitmaps generated for each dimension. The resulting bitmap can be described as representing the rows from the fact table that are known to join with all the qualified dimension rows.

Note: Until now, only fact bitmap indexes and dimension tables were used. To further access the fact table, the system must convert the generated bitmap to a rowids set.


…

Multiple bitmaps are

ANDed together.

IntermediateResult Set

(IRS)

MERGE …

Phase 1

BITMAP AND

MERGE 1 MERGE n

BITMAPConversionTo Rowids



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Chapter 9 - Page 14

Joining the Intermediate Result Set with Dimensions


After the result set is determined, the system enters phase 2 of the star transformation algorithm. In this phase, it is needed to join the sales data, corresponding to the result set, with the dimension tables data used to group the rows and pertaining to the query’s select list.

Note that the graphic in the slide shows that a hash join is performed between the fact table and its dimensions. Although a hash join is statistically the most-used technique to join rows in a star query, this might not be always true, as this is evaluated by the CBO.


Phase 2

Fact Table Access

From IRS

Dimension 1 Table

Access

Hash JoinDimension i

Table Access

Hash JoinDimension n

Table Access

Hash Join



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Chapter 9 - Page 15

Star Transformation Plan: Example 1


This is a possible plan to answer the query shown in the “Execution Plan Without Star Transformation” section. Note that for formatting purposes, only the channels and times dimensions are shown. It is easy to generalize the case for n dimensions.

Note: It is supposed that sales is not partitioned.


SORT GROUP BYHASH JOIN

HASH JOINTABLE ACCESS BY INDEX ROWID SALESBITMAP CONVERSION TO ROWIDSBITMAP ANDBITMAP MERGEBITMAP KEY ITERATIONBUFFER SORTTABLE ACCESS FULL CHANNELS

BITMAP INDEX RANGE SCAN SALES_CHANNELS_BXBITMAP MERGEBITMAP KEY ITERATIONBUFFER SORTTABLE ACCESS FULL TIMES

BITMAP INDEX RANGE SCAN SALES_TIMES_BX

…TABLE ACCESS FULL CHANNELS

TABLE ACCESS FULL TIMES



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Chapter 9 - Page 16

Star Transformation: Further Optimization


When you look at the previous execution plan, you see that each dimension table is accessed twice—once during the first phase, where the system determines the necessary fact table rows, and once when joining the fact rows to each dimension table during the second phase. This might be a performance issue if the dimension tables are big, and there is no fast access path to them for solving the problem. In such cases, the system might decide to create temporary tables containing information needed for both phases. This decision is made if the cost for creating a temporary table, consisting of the result set for both the predicate and the join columns on the dimension table, is cheaper than accessing the dimension table twice. In the previous execution plan example, the TIMES and CHANNELS tables are very small, and accessing them using a full table scan has a very small cost.

The creation of these temporary tables and the data insertion are shown in the execution plan. The name of those temporary tables is system-generated and varies. In the slide, you see an extract from an execution plan using temporary tables for the CUSTOMERS table.

Note: Temporary tables are not used by star transformation under the following conditions:

• The database is in read-only mode.

• The star query is part of a transaction that is in serializable mode.

• STAR_TRANSFORMATION_ENABLED is set to TEMP_DISABLE.


• In a star transformation execution plan, dimension tables are accessed twice; once for each phase.

• This might be a performance issue in the case of big dimension tables and low selectivity.

• If the cost is lower, the system might decide to create a temporary table and use it instead of accessing the same dimension table twice.

• Temporary table’s creation in the plan:

LOAD AS SELECT SYS_TEMP_0FD9D6720_BEBDCTABLE ACCESS FULL CUSTOMERS

…filter("C"."CUST_STATE_PROVINCE"='CA')



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Chapter 9 - Page 17

Using Bitmap Join Indexes


The volume of data that must be joined can be reduced if the join indexes used have already been precalculated.

In addition, the join indexes, which contain multiple dimension tables can eliminate bitwise operations, which are necessary in the star transformation with existing bitmap indexes.

Finally, bitmap join indexes are much more efficient in storage than materialized join views (MJVs), which do not compress rowids of the fact tables.

Assume that you have created the additional index structure mentioned in the slide.

Note: Since the SALES table is partitioned the bitmap join index will also be partitioned therefore the LOCAL keyword is required.


• Volume of data to be joined is reduced

• Can be used to eliminate bitwise operations

• More efficient in storage than MJVs

CREATE BITMAP INDEX sales_q_bjxON sales(times.calendar_quarter_desc)FROM sales, timesWHERE sales.time_id = times.time_id LOCAL;



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Chapter 9 - Page 18



The processing of the same star query using the bitmap join index is similar to the previous example. The only difference is that the system uses the join index instead of a single-table bitmap index to access the times data in the first phase of the star query.

The difference between this plan as compared to the previous one is that the inner part of the bitmap index scan for the times dimension has no subselect in the rewritten query for phase 1. This is because the join predicate information on times.calendar_quarter_desc can be satisfied with the sales_q_bjx bitmap join index.

Note that access to the join index is done twice because the corresponding query’s predicate is t.calendar_quarter_desc IN ('1999-Q1','1999-Q2')


SORT GROUP BYHASH JOIN

HASH JOINTABLE ACCESS BY INDEX ROWID SALES

BITMAP CONVERSION TO ROWIDSBITMAP ANDBITMAP MERGEBITMAP KEY ITERATIONBUFFER SORTTABLE ACCESS FULL CHANNELS

BITMAP INDEX RANGE SCAN SALES_CHANNELS_BXBITMAP ORBITMAP INDEX SINGLE VALUE SALES_Q_BJXBITMAP INDEX SINGLE VALUE SALES_Q_BJX

TABLE ACCESS FULL CHANNELSTABLE ACCESS FULL TIMES



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Chapter 9 - Page 19

Star Transformation Hints


• The STAR_TRANSFORMATION hint makes the optimizer use the best plan in which the transformation has been used. Without the hint, the optimizer could make a cost-based decision to use the best plan generated without the transformation, instead of the best plan for the transformed query. Even if the hint is given, there is no guarantee that the transformation takes place. The optimizer only generates the subqueries if it seems reasonable to do so. If no subqueries are generated, there is no transformed query, and the best plan for the untransformed query is used, regardless of the hint.

• The FACT hint is used in the context of the star transformation to indicate to the transformation that the hinted table should be considered as a fact table and all other tables regardless of their size are considered as dimensions.

• The NO_FACT hint is used in the context of the star transformation to indicate to the transformation that the hinted table should not be considered as a fact table.

Note: The FACT and NO_FACT hints might be useful only in case there are more than one fact table accessed in the star query.


• The STAR_TRANSFORMATION hint: Use best plan containing a star transformation, if there is one.

• The FACT(<table_name>) hint: The hinted table should be considered as the fact table in the context of a star transformation.

• The NO_FACT (<table_name>) hint: The hinted table should not be considered as the fact table in the context of a star transformation.

• The FACT and NO_FACT hints are useful for star queries containing more than one fact table.



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Chapter 9 - Page 20

Bitmap Join Indexes: Join Model 1


In the following three slides, F represents the fact table, D, the dimension table, PK a primary key, and FK a foreign key.

A bitmap join index can be used in the SELECT statement in the slide to avoid the join operation.

Similar to the materialized join view, a bitmap join index precomputes the join and stores it as a database object. The difference is that a materialized join view materializes the join into a table while a bitmap join index materializes the join into a bitmap index.

Note: C1 is the indexed column in the dimension table.


pk

d f

CREATE BITMAP INDEX bji ON f(d.c1) FROM f, d WHERE d.pk = f.fk;

c1 fk

SELECT sum(f.facts)FROM d, fWHERE d.pk = f.fk AND d.c1 = 1;



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Chapter 9 - Page 21



The model in the slide is an extension of model 1, requiring a concatenated bitmap join index to represent it.

Note that BJX, in this case, can also be used to answer the following select statement:

select sum(f.facts) from d,f where d.pk=f.fk and d.c1=1

This is due to the fact that D.C1 is the leading part of the BJX.

pk

d f

c1 fk


c2

CREATE BITMAP INDEX bjx ON f(d.c1,d.c2) FROM f, d WHERE d.pk = f.fk;

SELECT sum(f.facts)FROM d, fWHERE d.pk = f.fk AND d.c1 = 1 AND d.c2 = 1;



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Chapter 9 - Page 22



This model also requires the concatenated bitmap join index shown in the slide. In this case, two dimension tables are used.


pk

d1 f

CREATE BITMAP INDEX bjx ON f(d1.c1,d2.c1)FROM f, d1, d2WHERE d1.pk = f.fk1 AND d2.pk = f.fk2;

c1 fk1

SELECT sum(f.sales)FROM d1, f, d2WHERE d1.pk = f.fk1 AND d2.pk = f.fk2 AND

d1.c1 = 1 AND d2.c1 = 2;

d1 d2

fk2 pk c1



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Chapter 9 - Page 23



The slide shows a snowflake model that involves joins between two or more dimension tables. It can be expressed by a bitmap join index. The bitmap join index can be either single or concatenated depending on the number of columns in the dimension tables to be indexed. A bitmap join index on D1.C1 with a join between D1 and D2 and a join between D2 and F can be created as shown in the slide with BJX.


pk

d1 d2

CREATE BITMAP INDEX bjx ON f(d1.c1)FROM f, d1, d2WHERE d1.pk = d2.c2 AND d2.pk = f.fk;

c1 c2

SELECT sum(f.sales)FROM d1, d2, fWHERE d1.pk = d2.c2 AND d2.pk = f.fk AND

d1.c1 = 1;

d1 f

pk fk



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Chapter 9 - Page 24

Quiz

Answer: c

Quiz

If the star_transformation_enabled parameter is set to true, the optimizer will:

a. Always use a star transformation

b. Always use a temporary table

c. Always consider a star transformation

d. Never use a temporary table

e. Always use a hash join



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Chapter 9 - Page 25

Quiz

Answer: a, c

Quiz

Which two of the following properties of the schema structure are required by the optimizer to consider using the star transformation?

a. At least one fact table

b. At least one bitmap join index

c. At least two dimension tables

d. At least one bind variable

e. At least one histogram on a join column



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Chapter 9 - Page 26

Quiz

Answer: b

Quiz

Assuming that the START_TRANSFORMATION_ENABLEDparameter is set to TRUE, the star transformation is chosen by the optimizer when:

a. All the conditions are met

b. The cost is lower than a tradition access path

c. The bitmap join indexes eliminate additional joins

d. Temporary tables can be used to reduce table accesses



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Chapter 9 - Page 27

Summary

Summary


• Define a star schema

• Show a star query plan without transformation

• Define the star transformation requirements

• Show a star query plan after transformation



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Chapter 9 - Page 28



This practice covers using the star transformation technique to optimize your query.



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Optimizer Statistics

Chapter 10 - Page 1


Chapter 10



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Chapter 10 - Page 2





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Chapter 10 - Page 3

Objectives

Objectives


• Gather optimizer statistics

• Gather system statistics

• Set statistic preferences

• Use dynamic sampling

• Manipulate optimizer statistics



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Chapter 10 - Page 4



Optimizer statistics describe details about the database and the objects in the database. These statistics are used by the query optimizer to select the best execution plan for each SQL statement.

Because the objects in a database change constantly, statistics must be regularly updated so that they accurately describe these database objects. Statistics are maintained automatically by Oracle Database, or you can maintain the optimizer statistics manually using the DBMS_STATS package.


• Describe the database and the objects in the database

• Information used by the query optimizer to estimate:– Selectivity of predicates

– Cost of each execution plan

– Access method, join order, and join method

– CPU and input/output (I/O) costs

• Refreshing optimizer statistics whenever they are stale is as important as gathering them:– Automatically gathered by the system– Manually gathered by the user with DBMS_STATS



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Chapter 10 - Page 5

Types of Optimizer Statistics


Most of the optimizer statistics are listed in the slide.

Starting with Oracle Database 10g, index statistics are automatically gathered when the index is created or rebuilt.

Note: The statistics mentioned in this slide are optimizer statistics, which are created for query optimization and are stored in the data dictionary. These statistics should not be confused with performance statistics visible through V$ views.


• Table statistics:– Number of rows

– Number of blocks

– Average row length

• Index Statistics:– B*-tree level

– Distinct keys

– Number of leaf blocks

– Clustering factor

• System statistics – I/O performance and utilization

– CPU performance and utilization

• Column statistics– Basic: Number of distinct

values, number of nulls, average length, min, max

– Histograms (data distributionwhen the column data is skewed)

– Extended statistics



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Chapter 10 - Page 6

Table Statistics (DBA_TAB_STATISTICS)


NUM_ROWS This is the basis for cardinality computations. Row count is especially important if the table is the driving table of a nested loops join, as it defines how many times the inner table is probed.

BLOCKS This is the number of used data blocks. Block count in combination with DB_FILE_MULTIBLOCK_READ_COUNT gives the base table access cost.

AVG_ROW_LEN This is the average length of a row in the table in bytes.

STALE_STATS

This tells you if statistics are valid on the corresponding table.

Note: There are three other statistics: EMPTY_BLOCKS, AVE_ROW_LEN, and CHAIN_CNT that are not used by the optimizer, and not gathered by the DBMS_STATS procedures. If these are required the ANALYZE command must be used.


• Table statistics are used to determine:– Table access cost

– Join cardinality

– Join order

• Some of the table statistics gathered are:– Row count (NUM_ROWS)

– Block count (BLOCKS) Exact– Average row length (AVG_ROW_LEN)

– Statistics status (STALE_STATS)



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Chapter 10 - Page 7

Index Statistics (DBA_IND_STATISTICS)


In general, to select an index access, the optimizer requires a predicate on the prefix of the index columns. However, in case there is no predicate and all the columns referenced in the query are present in an index, the optimizer considers using a full index scan versus a full table scan.

BLEVEL This is used to calculate the cost of leaf block lookups. It indicates the depth of the index from its root block to its leaf blocks. A depth of "0" indicates that the root block and leaf block are the same.

LEAF_BLOCKS This is used to calculate the cost of a full index scan.

CLUSTERING_FACTOR This measures the order of the rows in the table based on the values of the index. If the value is near the number of blocks, the table is very well ordered. In this case, the index entries in a single leaf block tend to point to the rows in the same data blocks. If the value is near the number of rows, the table is very randomly ordered. In this case, it is unlikely that the index entries in the same leaf block point to rows in the same data blocks.


• Used to decide:– Full table scan versus index scan

• Statistics gathered are:– B*-tree level (BLEVEL) Exact– Leaf block count (LEAF_BLOCKS)

– Clustering factor (CLUSTERING_FACTOR)

– Distinct keys (DISTINCT_KEYS)

– Average number of leaf blocks in which each distinct value in the index appears (AVG_LEAF_BLOCKS_PER_KEY)

– Average number of data blocks in the table pointed to by a distinct value in the index (AVG_DATA_BLOCKS_PER_KEY)

– Number of rows in the index (NUM_ROWS)



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Chapter 10 - Page 8

STALE_STATS

This tells you if the statistics are valid in the corresponding index.

DISTINCT_KEYS This is the number of distinct indexed values. For indexes that enforce UNIQUE and PRIMARY KEY constraints, this value is the same as the number of rows in the table.

AVG_LEAF_BLOCKS_PER_KEY This is the average number of leaf blocks in which each distinct value in the index appears, rounded to the nearest integer. For indexes that enforce UNIQUE and PRIMARY KEY constraints, this value is always 1 (one).

AVG_DATA_BLOCKS_PER_KEY This is the average number of data blocks in the table that are pointed to by a distinct value in the index rounded to the nearest integer. This statistic is the average number of data blocks that contain rows that contain a given value for the indexed columns.

NUM_ROWS It is the number of rows in the index.



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Chapter 10 - Page 9

Index Clustering Factor


The system performs input/output (I/O) by blocks. Therefore, the optimizer’s decision to use full table scans is influenced by the percentage of blocks accessed, not rows. When an index range scan is used, each index entry selected points to a block in the table. If each entry points to a different block, the rows accessed and blocks accessed are the same. Consequently, the desired number of rows could be clustered together in a few blocks, or they could be spread out over a larger number of blocks. This is called the index clustering factor.

The cost formula of an index range scan uses the level of the B*-tree, the number of leaf blocks, the index selectivity, and the clustering factor. A clustering factor indicates that the individual rows are concentrated within fewer blocks in the table. A high clustering factor indicates that the individual rows are scattered more randomly across the blocks in the table. Therefore, a high clustering factor means that it costs more to use an index range scan to fetch rows by ROWID because more blocks in the table need to be visited to return the data. In real-life scenarios, it appears that the clustering factor plays an important role in determining the cost of an index range scan simply because the number of leaf blocks and the height of the B*-tree are relatively small compared to the clustering factor and table’s selectivity.

Note: If you have more than one index on a table, the clustering factor for one index might be small while at the same time the clustering factor for another index might be large. An attempt to reorganize the table to improve the clustering factor for one index can cause degradation of the clustering factor of the other index.


A B C A B C A B C

Block 1 Block 2 Block 3

A A A B B B C C C

Block 1 Block 2 Block 3

Low clustering factor:Favor the

index range scan path

High clustering factor:Favor alternative paths

DBA_IND_STATISTICS.CLUSTERING_FACTOR

Number of blocks (3)

Number of rows (9)

Only need to read one block to retrieve all As

Must read all blocks to retrieve all As

ABC

A B C



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Chapter 10 - Page 10

The clustering factor is computed and stored in the CLUSTERING_FACTOR column of the DBA_INDEXES view when you gather statistics on the index. The way it is computed is relatively easy. You read the index from left to right, and for each indexed entry, you add one to the clustering factor if the corresponding row is located in a different block than the one from the previous row. Based on this algorithm, the smallest possible value for the clustering factor is the number of blocks, and the highest possible value is the number of rows.

The example in the slide shows how the clustering factor can affect cost. Assume the following situation: There is a table with 9 rows, there is a nonunique index on col1 for table, the c1 column currently stores the values A, B, and C, and the table only has three data blocks.

• Case 1: If the same rows in the table are arranged so that the index values are scattered across the table blocks (rather than collocated), the index clustering factor is high.

• Case 2: The index clustering factor is low for the rows as they are collocated in the same block for the same value.

Note: For bitmap indexes, the clustering factor is not applicable and is not used.



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Column Statistics (DBA_TAB_COL_STATISTICS)


NUM_DISTINCT is used in selectivity calculations, for example, 1/Number of Distinct Values

LOW_VALUE and HIGH_VALUE: The cost-based optimizer (CBO) assumes uniform distribution of values between low and high values for all data types. These values are used to determine range selectivity.

NUM_NULLS helps with selectivity of nullable columns and the IS NULL and IS NOT NULL predicates.

DENSITY is only relevant for histograms. It is used as the selectivity estimate for nonpopular values. It can be thought of as the probability of finding one particular value in this column. The calculation depends on the histogram type.

NUM_BUCKETS is the number of buckets in histogram for the column.

HISTOGRAM indicates the existence or type of the histogram: NONE, FREQUENCY, HEIGHT BALANCED


• Count of distinct values of the column (NUM_DISTINCT)

• Low value (LOW_VALUE) Exact• High value (HIGH_VALUE) Exact• Number of nulls (NUM_NULLS)

• Selectivity estimate for nonpopular values (DENSITY)

• Number of histogram buckets (NUM_BUCKETS)

• Type of histogram (HISTOGRAM)



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Histograms

Histograms

A histogram captures the distribution of different values in a column, so it yields better selectivity estimates. Having histograms on columns that contain skewed data or values with large variations in the number of duplicates help the query optimizer generate good selectivity estimates and make better decisions regarding index usage, join orders, and join methods.

Without histograms, a uniform distribution is assumed. If a histogram is available on a column, the estimator uses it instead of the number of distinct values.

When creating histograms, Oracle Database uses two different types of histogram representations depending on the number of distinct values found in the corresponding column. When you have a data set with less than 254 distinct values, and the number of histogram buckets is not specified, the system creates a frequency histogram. If the number of distinct values is greater than the required number of histogram buckets, the system creates a height-balanced histogram.

You can find information about histograms in these dictionary views: DBA_TAB_HISTOGRAMS, DBA_PART_HISTOGRAMS, and DBA_SUBPART_HISTOGRAMS

Note: Gathering histogram statistics is the most resource-consuming operation in gathering statistics.

Histograms

• The optimizer assumes uniform distributions; this may lead to suboptimal access plans in the case of data skew.

• Histograms:– Store additional column distribution information

– Give better selectivity estimates in the case of nonuniform distributions

• With unlimited resources you could store each different value and the number of rows for that value.

• This becomes unmanageable for a large number of distinct values and a different approach is used:– Frequency histogram (#distinct values ≤ #buckets)

– Height-balanced histogram (#buckets < #distinct values)

• They are stored in DBA_TAB_HISTOGRAMS.



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Frequency Histograms


For the example in the slide, assume that you have a column that is populated with 40,001 numbers. You only have ten distinct values: 1, 3, 5, 7, 10, 16, 27, 32, 39, and 49. Value 10 is the most popular value with 16,293 occurrences.

When the requested number of buckets equals (or is greater than) the number of distinct values, you can store each different value and record exact cardinality statistics. In this case, in DBA_TAB_HISTOGRAMS, the ENDPOINT_VALUE column stores the column value and the ENDPOINT_NUMBER column stores the cumulative row count including that column value, because this can avoid some calculation for range scans. The actual row counts are derived from the endpoint values if needed. The actual number of row counts is shown by the curve in the slide for clarity; only the ENDPOINT_VALUE and ENDPOINT_NUMBER columns are stored in the data dictionary.


10 buckets, 10 distinct values

0

10000

20000

30000

40000

1 3 5 7 10 16 27 32 39 49

ENDPOINT VALUE: Column value

ENDPOINT

NUMBER

Cumulative cardinality

# rows for column value

Distinct values: 1, 3, 5, 7, 10, 16, 27, 32, 39, 49

Number of rows: 40001



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Viewing Frequency Histograms


The example in the slide shows you how to view a frequency histogram. Because the number of distinct values in the WAREHOUSE_ID column of the INVENTORIES table is 9, and the number of requested buckets is 20, the system automatically creates a frequency histogram with 9 buckets. You can view this information in the USER_TAB_COL_STATISTICS view.

To view the histogram itself, you can query the USER_HISTOGRAMS view. You can see both ENDPOINT_NUMBER that corresponds to the cumulative frequency of the corresponding ENDPOINT_VALUE, which represents, in this case, the actual value of the column data.

In this case, the warehouse_id is 1 and there are 36 rows with warehouse_id = 1. There are 177 rows with warehouse_id = 2 so the sum of rows so far (36+177) is the cumulative frequency of 213.

Note: The DBMS_STATS package is dealt with later in the lesson.


BEGINDBMS_STATS.gather_table_STATS (OWNNAME=>'OE', TABNAME=>'INVENTORIES',

METHOD_OPT => 'FOR COLUMNS SIZE 20 warehouse_id');END;

SELECT column_name, num_distinct, num_buckets, histogramFROM USER_TAB_COL_STATISTICSWHERE table_name = 'INVENTORIES' AND

column_name = 'WAREHOUSE_ID';

COLUMN_NAME NUM_DISTINCT NUM_BUCKETS HISTOGRAM------------ ------------ ----------- ---------WAREHOUSE_ID 9 9 FREQUENCY

SELECT endpoint_number, endpoint_valueFROM USER_HISTOGRAMSWHERE table_name = 'INVENTORIES' and column_name = 'WAREHOUSE_ID'ORDER BY endpoint_number;

ENDPOINT_NUMBER ENDPOINT_VALUE--------------- --------------36 1213 2261 3…



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Height-Balanced Histograms


In a height-balanced histogram, the ordered column values are divided into bands so that each band contains approximately the same number of rows. The histogram tells you values of the endpoints of each band. In the example in the slide, assume that you have a column that is populated with 40,001 numbers. There will be 8,000 values in each band. You only have ten distinct values: 1, 3, 5, 7, 10, 16, 27, 32, 39, and 49. Value 10 is the most popular value with 16,293 occurrences. When the number of buckets is less than the number of distinct values, ENDPOINT_NUMBER records the bucket number and ENDPOINT_VALUE records the column value that corresponds to this endpoint. In the example, the number of rows per bucket is one-fifth of the total number of rows, that is 8000. Based on this assumption, value 10 appears between 8000 and 24000 times. So you are sure that value 10 is a popular value.

This type of histogram is good for equality predicates on popular value, and range predicates.

The number of rows per bucket is not recorded because this can be derived from the total number of values and the fact that all the buckets contain an equal number of values. In this example, value 10 is a popular value because it spans multiple endpoint values. To save space, the histogram does not actually store duplicated buckets. In the example in the slide, bucket 2 (with endpoint value 10) would not be recorded in DBA_TAB_HISTOGRAMS for that reason.


5 buckets, 10 distinct values(8000 rows per bucket)

0 1 3 4 5

ENDPOINT NUMBER: Bucket number

ENDPOINT VALUE

2

Same numberof rows per bucket

1 7 10 10 32 49

Distinct values: 1, 3, 5, 7, 10, 16, 27, 32, 39, 49

Number of rows: 40001

Popular value



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Viewing Height-Balanced Histograms


The example in the slide shows you how to view a height-balanced histogram. Because the number of distinct values in the QUANTITY_ON_HAND column of the INVENTORIES table is 237, and the number of requested buckets is 10, the system automatically creates a height-balanced histogram with 10 buckets. You can view this information in the USER_TAB_COL_STATISTICS view.

To view the histogram itself, you can query the USER_HISTOGRAMS view. You can see that the ENDPOINT_NUMBER corresponds to the bucket number, and ENDPOINT_VALUE corresponds to values of the endpoints end.

Note: The DBMS_STATS package is dealt with later in the lesson.


BEGINDBMS_STATS.gather_table_STATS(OWNNAME =>'OE', TABNAME=>'INVENTORIES', METHOD_OPT => 'FOR COLUMNS SIZE 10 quantity_on_hand');

END;

SELECT column_name, num_distinct, num_buckets, histogram FROM USER_TAB_COL_STATISTICSWHERE table_name = 'INVENTORIES' AND column_name = 'QUANTITY_ON_HAND';

COLUMN_NAME NUM_DISTINCT NUM_BUCKETS HISTOGRAM------------------------------ ------------ ----------- ---------------QUANTITY_ON_HAND 237 10 HEIGHT BALANCED

SELECT endpoint_number, endpoint_value FROM USER_HISTOGRAMSWHERE table_name = 'INVENTORIES' and column_name = 'QUANTITY_ON_HAND'ORDER BY endpoint_number;

ENDPOINT_NUMBER ENDPOINT_VALUE--------------- --------------

0 01 272 423 57

…



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Histogram Considerations


Histograms are useful only when they reflect the current data distribution of a given column. The data in the column can change as long as the distribution remains constant. If the data distribution of a column changes frequently, you must recompute its histogram frequently.

Histograms are useful when you have a high degree of data skew in the columns for which you want to create histograms.

However, there is no need to create histograms for columns which do not appear in a WHERE clause of a SQL statement. Similarly, there is no need to create histograms for columns with uniform distribution.

In addition, for columns declared as UNIQUE, histograms are useless because the selectivity is obvious. Also, the maximum number of buckets is 254, which can be lower depending on the actual number of distinct column values. Histograms can affect performance and should be used only when they substantially improve query plans. For uniformly distributed data, the optimizer can make fairly accurate guesses about the cost of executing a particular statement without the use of histograms.

Note: Character columns have some exceptional behavior as histogram data is stored only for the first 32 bytes of any string.


• Histograms are useful when you have a high degree of skew in the column distribution.

• Histograms are not useful for:– Columns which do not appear in the WHERE or JOIN clauses

– Columns with uniform distributions

– Equality predicates with unique columns

• The maximum number of buckets is the least (254,# distinct values).

• Do not use histograms unless they substantially improve performance.



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Multicolumn Statistics: Overview


With Oracle Database 10g, the query optimizer takes into account the correlation between columns when computing the selectivity of multiple predicates in the following limited cases:

• If all the columns of a conjunctive predicate match all the columns of a concatenated index key, and the predicates are equalities used in equijoins, then the optimizer uses the number of distinct keys (NDK) in the index for estimating selectivity, as 1/NDK.

• When DYNAMIC_SAMPLING is set to level 4, the query optimizer uses dynamic sampling to estimate the selectivity of complex predicates involving several columns from the same table. However, the sample size is very small and increases parsing time. As a result, the sample is likely to be statistically inaccurate and may cause more harm than good.

In all other cases, the optimizer assumes that the values of columns used in a complex predicate are independent of each other. It estimates the selectivity of a conjunctive predicate by multiplying the selectivity of individual predicates. This approach results in underestimation of the selectivity if there is a correlation between the columns. To circumvent this issue, Oracle Database 11g allows you to collect, store, and use the following statistics to capture functional dependency between two or more columns (also called groups of columns): number of distinct values, number of nulls, frequency histograms, and density.


VEHICLE

MAKE MODEL

S(MAKE Λ MODEL)=S(MAKE)xS(MODEL)

select dbms_stats.create_extended_stats('jfv','vehicle','(make,model)')

from dual;

exec dbms_stats.gather_table_stats('jfv','vehicle',-

method_opt=>'for all columns size 1 for columns (make,model) size 3');

VEHICLE

MAKE MODEL

S(MAKE Λ MODEL)=S(MAKE,MODEL)

DBA_STAT_EXTENSIONS

SYS_STUF3GLKIOP5F4B0BTTCFTMX0W

1

2

3

4



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For example, consider a VEHICLE table in which you store information about cars. The MAKE and MODEL columns are highly correlated, in that MODEL determines MAKE. This is a strong dependency, and both columns should be considered by the optimizer as highly correlated. You can signal that correlation to the optimizer by using the CREATE_EXTENDED_STATS function as shown in the example in the slide, and then compute the statistics for all columns (including the ones for the correlated groups that you created).

The optimizer only uses multicolumn statistics with equality predicates.

Note

• The CREATE_EXTENDED_STATS function returns a virtual hidden column name, such as SYS_STUW_5RHLX443AN1ZCLPE_GLE4.

• Based on the example in the slide, the name can be determined by using the following SQL: select dbms_stats.show_extended_stats_name('jfv','vehicle','(make,model)') from dual

• After you create the statistics extensions, you can retrieve them by using the ALL|DBA|USER_STAT_EXTENSIONS views.



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Expression Statistics: Overview


Predicates involving expressions on columns are a significant issue for the query optimizer. When computing selectivity on predicates of the form function(Column) = constant, the optimizer assumes a static selectivity value of 1 percent. This approach almost never has the correct selectivity and it may cause the optimizer to produce suboptimal plans.

The query optimizer has been extended to better handle such predicates in limited cases where functions preserve the data distribution characteristics of the column and thus allow the optimizer to use the columns statistics. An example of such a function is TO_NUMBER.

Further enhancements have been made to evaluate built-in functions during query optimization to derive better selectivity using dynamic sampling. Finally, the optimizer collects statistics on virtual columns created to support function-based indexes.

However, these solutions are either limited to a certain class of functions or work only for expressions used to create function-based indexes. By using expression statistics in Oracle Database 11g, you can use a more general solution that includes arbitrary user-defined functions and does not depend on the presence of function-based indexes. As shown in the example in the slide, this feature relies on the virtual column infrastructure to create statistics on expressions of columns.


S(upper( MODEL))=0.01

VEHICLEMODEL

VEHICLEMODEL

CREATE INDEX upperidx ON VEHICLE(upper(MODEL))

VEHICLEMODELselect

dbms_stats.create_extended_stats('jfv','vehicle','(upper(model))') from dual;

SYS_STU3FOQ$BDH0S_14NGXFJ3TQ50

DBA_STAT_EXTENSIONS

Recommended

Still possible

exec dbms_stats.gather_table_stats('jfv','vehicle',-method_opt=>'for all columns size 1 for columns (upper(model)) size 3');



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Gathering System Statistics


System statistics allow the optimizer to consider a system’s I/O and CPU performance, and utilization. For each candidate plan, the optimizer computes estimates for I/O and CPU costs. It is important to know the system characteristics to select the most efficient plan with optimal proportion between I/O and CPU cost. System CPU and I/O characteristics depend on many factors and do not stay constant all the time. Using system statistics management routines, you can capture statistics in the interval of time when the system has the most common workload. For example, database applications can process online transaction processing (OLTP) transactions during the day and run OLAP reports at night. You can gather statistics for both states and activate appropriate OLTP or OLAP statistics when needed. This allows the optimizer to generate relevant costs with respect to the available system resource plans. When the system generates system statistics, it analyzes system activity in a specified period of time. Unlike the table, index, or column statistics, the system does not invalidate already parsed SQL statements when system statistics get updated. All new SQL statements are parsed using new statistics.

It is highly recommended that you gather system statistics. System statistics are gathered in a user-defined time frame with the DBMS_STATS.GATHER_SYSTEM_STATS routine. You can also set system statistics values explicitly using DBMS_STATS.SET_SYSTEM_STATS. Use DBMS_STATS.GET_SYSTEM_STATS to verify system statistics.


• System statistics enable the CBO to use CPU and I/O characteristics.

• System statistics must be gathered on a regular basis; this does not invalidate cached plans.

• Gathering system statistics equals analyzing system activity for a specified period of time:

• Procedures:– DBMS_STATS.GATHER_SYSTEM_STATS

– DBMS_STATS.SET_SYSTEM_STATS

– DBMS_STATS.GET_SYSTEM_STATS

• GATHERING_MODE:– NOWORKLOAD|INTERVAL

– START|STOP



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When you use the GATHER_SYSTEM_STATS procedure, you should specify the GATHERING_MODE parameter:

• NOWORKLOAD: This is the default. This mode captures characteristics of the I/O system. Gathering may take a few minutes and depends on the size of the database. During this period the system estimates the average read seek time and transfer speed for the I/O system. This mode is suitable for all workloads. It is recommended that you run GATHER_SYSTEM_STATS ('noworkload') after you create the database and tablespaces.

• INTERVAL: Captures system activity during a specified interval. This works in combination with the interval parameter that specifies the amount of time for the capture. You should provide an interval value in minutes, after which system statistics are created or updated in the dictionary or a staging table. You can use GATHER_SYSTEM_STATS (gathering_mode=>'STOP') to stop gathering earlier than scheduled.

• START | STOP: Captures system activity during specified start and stop times, and refreshes the dictionary or a staging table with statistics for the elapsed period.

Note: Since Oracle Database 10g, Release 2, the system automatically gathers essential parts of system statistics at startup.



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Gathering System Statistics: Example


The example in the slide shows database applications processing OLTP transactions during the day and running reports at night.

First, system statistics must be collected during the day. In this example, gathering ends after 120 minutes and is stored in the mystats table.

Then, system statistics are collected during the night. Gathering ends after 120 minutes and is stored in the mystats table.

Generally, the syntax in the slide is used to gather system statistics. Before invoking the GATHER_SYSTEM_STATS procedure with the INTERVAL parameter specified, you must activate job processes using a command, such as SQL> alter system set job_queue_processes = 1;. Note: In Oracle Database 11g Release 2, the default value of job_queue_processes is 1000. You can also invoke the same procedure with different arguments to enable manual gathering instead of using jobs. If appropriate, you can switch between the statistics gathered. Note that it is possible to automate this process by submitting a job to update the dictionary with appropriate statistics. During the day, a job may import the OLTP statistics for the daytime run, and during the night, another job imports the online analytical processing (OLAP) statistics for the nighttime run.


EXECUTE DBMS_STATS.GATHER_SYSTEM_STATS(interval => 120,stattab => 'mystats', statid => 'OLTP');

EXECUTE DBMS_STATS.GATHER_SYSTEM_STATS(interval => 120,stattab => 'mystats', statid => 'OLAP');

EXECUTE DBMS_STATS.IMPORT_SYSTEM_STATS(stattab => 'mystats', statid => 'OLTP');

EXECUTE DBMS_STATS.IMPORT_SYSTEM_STATS(stattab => 'mystats', statid => 'OLAP');

Next days

Next nights

First day

First night



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Gathering System Statistics: Example (continued)

The example in the previous slide shows how to collect system statistics with jobs by using the internal parameter of the DBMS_STATS.GATHER_SYSTEM_STATS procedure. To collect system statistics manually, another parameter of this procedure can be used as shown in the slide.

First, you must start the system statistics collection, and then you can end the collection process at any time after you are certain that a representative workload has been generated on the instance.

The example collects system statistics and stores them directly in the data dictionary.


• Start manual system statistics collection in the data dictionary:

• Generate the workload.

• End the collection of system statistics:

SQL> EXECUTE DBMS_STATS.GATHER_SYSTEM_STATS( -2 gathering_mode => 'STOP');

SQL> EXECUTE DBMS_STATS.GATHER_SYSTEM_STATS( -2 gathering_mode => 'START');



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Mechanisms for Gathering Statistics


Oracle Database provides several mechanisms to gather statistics. These are discussed in more detail in the subsequent slides. It is recommended that you use automatic statistics gathering for objects.

Note: When the system encounters a table with missing statistics, it dynamically gathers the necessary statistics needed by the optimizer. However, for certain types of tables, it does not perform dynamic sampling. These include remote tables and external tables. In those cases and also when dynamic sampling has been disabled, the optimizer uses default values for its statistics.


• Automatic statistics gathering– gather_stats_prog automated task

• Manual statistics gathering– DBMS_STATS package

• Dynamic sampling

• When statistics are missing:

Selectivity:

Equality 1%

Inequality 5%

Other predicates 5%

Table row length 20

# of index leaf blocks 25

# of distinct values 100

Table cardinality 100

Remote table cardinality 2000



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Statistic Preferences: Overview


The automated statistics-gathering feature was introduced in Oracle Database 10g, Release 1 to reduce the burden of maintaining optimizer statistics. However, there were cases where you had to disable it and run your own scripts instead. One reason was the lack of object-level control. Whenever you found a small subset of objects for which the default gather statistics options did not work well, you had to lock the statistics and analyze them separately by using your own options. For example, the feature that automatically tries to determine adequate sample size (ESTIMATE_PERCENT=AUTO_SAMPLE_SIZE) does not work well against columns that contain data with very high frequency skews. The only way to get around this issue was to manually specify the sample size in your own script.

The Statistic Preferences feature in Oracle Database 11g introduces flexibility so that you can rely more on the automated statistics-gathering feature to maintain the optimizer statistics when some objects require settings that are different from the database default.

This feature allows you to associate the statistics-gathering options that override the default behavior of the GATHER_*_STATS procedures and the automated Optimizer Statistics Gathering task at the object or schema level. You can use the DBMS_STATS package to manage the gathering statistics options shown in the slide.

You can set, get, delete, export, and import those preferences at the table, schema, database, and global levels. Global preferences are used for tables that do not have preferences,


Database level

Schema level

Table level

Statement levelOptimizerstatisticsgatheringtask

set_database_prefs

set_schema_prefs

set_table_prefs

gather_*_stats

exec dbms_stats.set_table_prefs('SH','SALES','STALE_PERCENT','13');

DBA_TAB_STAT_PREFS

CASCADE DEGREE

ESTIMATE_PERCENT METHOD_OPT

NO_INVALIDATE GRANULARITY

PUBLISH INCREMENTAL

STALE_PERCENT

Global level

set_global_prefs



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whereas database preferences are used to set preferences on all tables. The preference values that are specified in various ways take precedence from the outer circles to the inner ones (as shown in the slide).

In the graphic in the slide, the last three highlighted options are new in Oracle Database 11g,

Release 1:

• CASCADE gathers statistics on the indexes as well. Index statistics gathering is not parallelized.

• ESTIMATE_PERCENT is the estimated percentage of rows used to compute statistics (Null means all rows): The valid range is [0.000001,100]. Use the constant DBMS_STATS.AUTO_SAMPLE_SIZE to have the system determine the appropriate sample size for good statistics. This is the recommended default.

• NO_INVALIDATE controls the invalidation of dependent cursors of the tables for which statistics are being gathered. It does not invalidate the dependent cursors if set to TRUE. The procedure invalidates the dependent cursors immediately if set to FALSE. Use DBMS_STATS.AUTO_INVALIDATE to have the system decide when to invalidate dependent cursors. This is the default.

• PUBLISH is used to decide whether to publish the statistics to the dictionary or to store them in a pending area before.

• STALE_PERCENT is used to determine the threshold level at which an object is considered to have stale statistics. The value is a percentage of rows modified since the last statistics gathering. The example changes the 10 percent default to 13 percent for SH.SALES only.

• DEGREE determines the degree of parallelism used to compute statistics. The default for degree is null, which means use the table default value specified by the DEGREE clause in the CREATE TABLE or ALTER TABLE statement. Use the constant DBMS_STATS.DEFAULT_DEGREE to specify the default value based on the initialization parameters. The AUTO_DEGREE value determines the degree of parallelism automatically. This is either 1 (serial execution) or DEFAULT_DEGREE (the system default value based on the number of CPUs and initialization parameters), depending on the size of the object.

• METHOD_OPT is a SQL string used to collect histogram statistics. The default value is FOR ALL COLUMNS SIZE AUTO.

• GRANULARITY is the granularity of statistics to collect for partitioned tables.

• INCREMENTAL is used to gather global statistics on partitioned tables in an incremental way.

It is important to note that you can change default values for the above parameters using the DBMS_STATS.SET_GLOBAL_PREFS procedure.

Note: You can describe all the effective statistics preference settings for all relevant tables by using the DBA_TAB_STAT_PREFS view.



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When to Gather Statistics Manually


The automatic statistics gathering mechanism gather statistics on schema objects in the database for which statistics are absent or stale. It is important to determine when and how often to gather new statistics. The default gathering interval is nightly, but you can change this interval to suit your business needs. You can do so by changing the characteristics of your maintenance windows. Some cases may require manual statistics gathering. For example, the statistics on tables that are significantly modified during the day may become stale. There are typically two types of such objects:

• Volatile tables that are modified significantly during the course of the day

• Objects that are the target of large bulk loads that add 10% or more to the object’s total size between statistics-gathering intervals

For external tables, statistics are only collected manually using GATHER_TABLE_STATS. Sampling on external tables is not supported, so the ESTIMATE_PERCENT option should be explicitly set to null. Because data manipulation is not allowed against external tables, it is sufficient to analyze external tables when the corresponding file changes. Other areas in which statistics need to be manually gathered are the system statistics and fixed objects, such as the dynamic performance tables. These statistics are not automatically gathered.


• Rely mostly on automatic statistics collection:– Change the frequency of automatic statistics collection to

meet your needs.– Remember that STATISTICS_LEVEL should be set to

TYPICAL or ALL for automatic statistics collection to work properly.

• Gather statistics manually for:– Objects that are volatile

– Objects modified in batch operations: Gather statistics as part of the batch operation.

– External tables, system statistics, fixed objects

– New objects: Gather statistics right after object creation.



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Manual Statistics Gathering


Both Enterprise Manager and the DBMS_STATS package enable you to manually generate and manage statistics for the optimizer. You can use the DBMS_STATS package to gather, modify, view, export, import, lock, and delete statistics. You can also use this package to identify or name gathered statistics. You can gather statistics on indexes, tables, columns, and partitions at various granularity: object, schema, and database level.

DBMS_STATS gathers only statistics needed for optimization; it does not gather other statistics. For example, the table statistics gathered by DBMS_STATS include the number of rows, number of blocks currently containing data, and average row length, but not the number of chained rows, average free space, or number of unused data blocks.

Note: Do not use the COMPUTE and ESTIMATE clauses of the ANALYZE statement to collect optimizer statistics. These clauses are supported solely for backward compatibility and may be removed in a future release. The DBMS_STATS package collects a broader, more accurate set of statistics, and gathers statistics more efficiently. You may continue to use the ANALYZE statement for other purposes not related to the optimizer statistics collection:

• To use the VALIDATE or LIST CHAINED ROWS clauses

• To collect information on free list blocks


You can use Enterprise Manager and the DBMS_STATSpackage to:

• Generate and manage statistics for use by the optimizer:– Gather/Modify

– View/Name

– Export/Import

– Delete/Lock

• Gather statistics on:– Indexes, tables, columns, partitions

– Object, schema, or database

• Gather statistics either serially or in parallel

• Gather/Set system statistics (currently not possible in EM)



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Manual Statistics Collection: Factors


When you manually gather optimizer statistics, you must pay special attention to the following factors:

• Monitoring objects for mass data manipulation language (DML) operations and gathering statistics if necessary

• Determining the correct sample sizes

• Determining the degree of parallelism to speed up queries on large objects

• Determining if histograms should be created on columns with skewed data

• Determining whether changes on objects cascade to any dependent indexes


• Monitor objects for DMLs.

• Determine the correct sample sizes.

• Determine the degree of parallelism.

• Determine if histograms should be used.

• Determine the cascading effects on indexes.• Procedures to use in DBMS_STATS:

– GATHER_INDEX_STATS

– GATHER_TABLE_STATS

– GATHER_SCHEMA_STATS

– GATHER_DICTIONARY_STATS

– GATHER_DATABASE_STATS

– GATHER_SYSTEM_STATS



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Managing Statistics Collection: Example


The first example uses the DBMS_STATS package to gather statistics on the CUSTOMERS table of the SH schema. It uses some of the options discussed in the previous slides.

Setting Parameter Defaults

You can use the SET_PARAM procedure in DBMS_STATS to set default values for parameters of all DBMS_STATS procedures. The second example in the slide shows this usage. You can also use the GET_PARAM function to get the current default value of a parameter.

Note: Granularity of statistics to collect is pertinent only if the table is partitioned. This parameter determines at which level statistics should be gathered. This can be at the partition, subpartition, or table level.


dbms_stats.gather_table_stats('sh' -- schema,'customers' -- table, null -- partition, 20 -- sample size(%), false -- block sample?,'for all columns' -- column spec, 4 -- degree of parallelism,'default' -- granularity , true ); -- cascade to indexes

dbms_stats.set_param('CASCADE','DBMS_STATS.AUTO_CASCADE');

dbms_stats.set_param('ESTIMATE_PERCENT','5'); dbms_stats.set_param('DEGREE','NULL');



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Optimizer Dynamic Sampling: Overview


Dynamic sampling improves server performance by determining more accurate selectivity and cardinality estimates that allow the optimizer to produce better performing plans. For example, although it is recommended that you collect statistics on all of your tables for use by the CBO, you may not gather statistics for your temporary tables and working tables used for intermediate data manipulation. In these cases, the CBO provides a value through a simple algorithm that can lead to a suboptimal execution plan. You can use dynamic sampling to:

• Estimate single-table predicate selectivities when collected statistics cannot be used or are likely to lead to significant errors in estimation

• Estimate table cardinality for tables and relevant indexes without statistics or for tables whose statistics are too outdated to be reliable

You control dynamic sampling with the OPTIMIZER_DYNAMIC_SAMPLING initialization parameter. The DYNAMIC_SAMPLING and DYNAMIC_SAMPLING_EST_CDN hints can be used to further control dynamic sampling.

Note: The OPTIMIZER_FEATURES_ENABLE initialization parameter turns off dynamic sampling if set to a version prior to 9.2.


• Dynamic sampling can be done for tables and indexes:– Without statistics

– Whose statistics cannot be trusted

• Used to determine more accurate statistics when estimating:– Table cardinality

– Predicate selectivity

• Feature controlled by:– The OPTIMIZER_DYNAMIC_SAMPLING parameter

– The OPTIMIZER_FEATURES_ENABLE parameter

– The DYNAMIC_SAMPLING hint

– The DYNAMIC_SAMPLING_EST_CDN hint



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Optimizer Dynamic Sampling at Work


The primary performance attribute is compile time. The system determines at compile time whether a query would benefit from dynamic sampling. If so, a recursive SQL statement is issued to scan a small random sample of the table’s blocks, and to apply the relevant single table predicates to estimate predicate selectivities.

Depending on the value of the OPTIMIZER_DYNAMIC_SAMPLING initialization parameter, a certain number of blocks is read by the dynamic sampling query.

For a query that normally completes quickly (in less than a few seconds), you do not want to incur the cost of dynamic sampling. However, dynamic sampling can be beneficial under any of the following conditions:

• A better plan can be found using dynamic sampling.

• The sampling time is a small fraction of total execution time for the query.

• The query is executed many times.

Note: Dynamic sampling can be applied to a subset of a single table’s predicates and combined with standard selectivity estimates of predicates for which dynamic sampling is not done.


• Sampling is done at compile time.

• If a query benefits from dynamic sampling:– A recursive SQL statement is executed to sample data

– The number of blocks sampled depends on the OPTIMIZER_DYNAMIC_SAMPLING initialization parameter

• During dynamic sampling, predicates are applied to the sample to determine selectivity.

• Use dynamic sampling when:– Sampling time is a small fraction of the execution time

– Query is executed many times

– You believe a better plan can be found



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OPTIMIZER_DYNAMIC_SAMPLING


You control dynamic sampling with the OPTIMIZER_DYNAMIC_SAMPLING parameter, which can be set to a value from "0" to "10." A value of "0" means dynamic sampling is not done.

A value of "1" means dynamic sampling is performed on all unanalyzed tables if the following criteria are met:

• There is at least one unanalyzed table in the query.

• This unanalyzed table is joined to another table or appears in a subquery or nonmergeable view.

• This unanalyzed table has no indexes.

• This unanalyzed table has more blocks than the default number of blocks that would be used for dynamic sampling of this table. This default number is 32.

The default value is "2" if OPTIMIZER_FEATURES_ENABLE is set to 10.0.0 or higher. At this level, the system applies dynamic sampling to all unanalyzed tables. The number of blocks sampled is two times the default number of dynamic sampling blocks (32).

Increasing the value of the parameter results in more aggressive application of dynamic sampling, in terms of both the type of tables sampled (analyzed or unanalyzed) and the amount of I/O spent on sampling.


• Dynamic session or system parameter

• Can be set to a value from "0" to "10"

• "0" turns off dynamic sampling

• "1" samples all unanalyzed tables, if an unanalyzed table:– Is joined to another table or appears in a subquery or

nonmergeable view

– Has no indexes

– Has more than 32 blocks

• "2" samples all unanalyzed tables

• The higher the value the more aggressive application of sampling

• Dynamic sampling is repeatable if no update activity



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Note: Dynamic sampling is repeatable if no rows have been inserted, deleted, or updated in the table being sampled since the previous sample operation.



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Locking Statistics

Locking Statistics

Starting with Oracle Database 10g, you can lock statistics on a specified table with the LOCK_TABLE_STATS procedure of the DBMS_STATS package. You can lock statistics on a table without statistics or set them to NULL using the DELETE_*_STATS procedures to prevent automatic statistics collection so that you can use dynamic sampling on a volatile table with no statistic. You can also lock statistics on a volatile table at a point when it is fully populated so that the table statistics are more representative of the table population.

You can also lock statistics at the schema level by using the LOCK_SCHEMA_STATS procedure. You can query the STATTYPE_LOCKED column in the {USER | ALL | DBA}_TAB_STATISTICS view to determine whether the statistics on the table are locked.

You can use the UNLOCK_TABLE_STATS procedure to unlock the statistics on a specified table.

You can set the value of the FORCE parameter to TRUE to overwrite the statistics even if they are locked. The FORCE argument is found in the following DBMS_STATS procedures: DELETE_*_STATS, IMPORT_*_STATS, RESTORE_*_STATS, and SET_*_STATS.

Note: When you lock the statistics on a table, all the dependent statistics are considered locked. This includes table statistics, column statistics, histograms, and dependent index statistics.

Locking Statistics

• Prevents automatic gathering

• Is mainly used for volatile tables:– Lock without statistics implies dynamic sampling.

– Lock with statistics for representative values.

• The FORCE argument overrides statistics locking.

BEGIN DBMS_STATS.DELETE_TABLE_STATS('OE','ORDERS'); DBMS_STATS.LOCK_TABLE_STATS('OE','ORDERS');

END;

SELECT stattype_locked FROM dba_tab_statistics;

BEGINDBMS_STATS.GATHER_TABLE_STATS('OE','ORDERS');DBMS_STATS.LOCK_TABLE_STATS('OE','ORDERS');

END;



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Restoring Statistics


Old versions of statistics are saved automatically whenever statistics in dictionary are modified with the DBMS_STATS procedures. You can restore statistics using RESTORE procedures of DBMS_STATS package. These procedures use a time stamp as an argument and restore statistics as of that time stamp. This is useful when newly collected statistics lead to sub-optimal execution plans and the administrator wants to revert to the previous set of statistics. Note: the ANALYZE command does not store old statistics.

There are dictionary views that can be used to determine the time stamp for restoration of statistics. The views *_TAB_STATS_HISTORY views (ALL, DBA, or USER) contain a history of table statistics modifications. For the example in the slide the timestamp was determined by:

select stats_update_time from dba_tab_stats_history

where table_name = 'INVENTORIES'

The database purges old statistics automatically at regular intervals based on the statistics history retention setting and the time of the recent analysis of the system. You can configure retention using the DBMS_STATS .ALTER_STATS_HISTORY_RETENTION procedure. The default value is 31 days, which means that you would be able to restore the optimizer statistics to any time in last 31 days.


• Past Statistics may be restored with DBMS_STATS.RESTORE_*_STATS procedures

• Statistics are automatically stored– With the timestamp in DBA_TAB_STATS_HISTORY

– When collected with DBMS_STATS procedures

• Statistics are purged– When STATISTICS_LEVEL is set to TYPICAL or ALL

automatically

– After 31 days or time defined by DBMS_STATS.ALTER_STATS_HISTORY_RETENTION

BEGINDBMS_STATS.RESTORE_TABLE_STATS(

OWNNAME=>'OE', TABNAME=>'INVENTORIES',AS_OF_TIMESTAMP=>'15-JUL-10 09.28.01.597526000 AM -05:00');

END;



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Export and Import Statistics


You can export and import statistics from the data dictionary to user-owned tables, enabling you to create multiple versions of statistics for the same schema. You can also copy statistics from one database to another database. You may want to do this to copy the statistics from a production database to a scaled-down test database.

Before exporting statistics, you first need to create a table for holding the statistics. The procedure DBMS_STATS.CREATE_STAT_TABLE creates the statistics table. After table creation, you can export statistics from the data dictionary into the statistics table using the DBMS_STATS.EXPORT_*_STATS procedures. You can then import statistics using the DBMS_STATS.IMPORT_*_STATS procedures.

The optimizer does not use statistics stored in a user-owned table. The only statistics used by the optimizer are the statistics stored in the data dictionary. To have the optimizer use the statistics in a user-owned tables, you must import those statistics into the data dictionary using the statistics import procedures.

To move statistics from one database to another, you must first export the statistics on the first database, then copy the statistics table to the second database, using the Data Pump Export and Import utilities or other mechanisms, and finally import the statistics into the second database.


Use DBMS_STATS procedures:

• CREATE_STAT_TABLE creates the statistics table.

• EXPORT_*_STATS moves the statistics to the statistics table.

• Use Data Pump to move the statistics table.• IMPORT_*_STATS moves the statistics to data dictionary.

expdp impdp

EXPORT_*_STATS IMPORT_*_STATS



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Quiz

Answer: b, c

Quiz

When there are no statistics for an object being used in a SQL statement, the optimizer uses:

a. Rule-based optimization

b. Dynamic sampling

c. Fixed values

d. Statistics gathered during parse phase

e. Random values



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Quiz

Answer: a, c, e

Quiz

The optimizer depends on accurate statistics to produce the best execution plans. The automatic statistics gathering (AGS) task does not gather statistics on everything. Which objects require you to gather statistics manually?

a. External tables

b. Data dictionary

c. Fixed objects

d. Volatile tables

e. System statistics



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Quiz

Answer: a, b, f

Quiz

There is a very volatile table in the database. The size of the table changes by more than 50% daily. What steps are part of the procedure to force dynamic sampling?

a. Delete statistics.b. Lock statistics.

c. Gather statistics when the table is at its largest.

d. Set DYNAMIC_SAMPLING=9.

e. Set DYNAMIC_SAMPLING=0.

f. Allow the DYNAMIC_SAMPLING parameter to default.



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Summary

Summary


• Collect optimizer statistics

• Collect system statistics

• Set statistic preferences

• Use dynamic sampling

• Manipulate optimizer statistics



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• Using system statistics

• Using automatic statistics gathering



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Using Bind Variables

Chapter 11 - Page 1


Chapter 11



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Chapter 11 - Page 2





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Chapter 11 - Page 3

Objectives

Objectives


• List the benefits of using bind variables

• Use bind peeking

• Use adaptive cursor sharing



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Chapter 11 - Page 4

Cursor Sharing and Different Literal Values


If your SQL statements use literal values for the WHERE clause conditions, there will be many versions of almost identical SQL stored in the library cache. For each different SQL statement, the optimizer must perform all the steps for processing a new SQL statement. This may also cause the library cache to fill up quickly because of all the different statements stored in it.

When coded this way, you are not taking advantage of cursor sharing. If the cursor is shared using a bind variable rather than a literal, there will be one shared cursor, with one execution plan.

However, depending on the literal value provided, different execution plans might be generated by the optimizer. For example, there might be several JOBS, where MIN_SALARY is greater than 12000. Alternatively, there might be very few JOBS that have a MIN_SALARY greater than 18000. This difference in data distribution could justify the addition of an index so that different plans can be used depending on the value provided in the query. This is illustrated in the slide. As you can see, the first and third queries use the same execution plan, but the second query uses a different one.

From a performance perspective, it is good to have separate cursors. However, this is not very economic because you could have shared cursors for the first and last queries in this example.


SELECT * FROM jobs WHERE min_salary > 12000;

Library cache



Cursor sharing



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Chapter 11 - Page 5

Note: In the case of the example in the slide, V$SQL.PLAN_HASH_VALUE is identical for the first and third query.



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Chapter 11 - Page 6

Cursor Sharing and Bind Variables


If, instead of issuing different statements for each literal, you use a bind variable, then that extra parse activity is eliminated (in theory). This is because the optimizer recognizes that the statement is already parsed and decides to reuse the same execution plan even though you specified different bind values the next time you execute the same statement.

In the example in the slide, the bind variable is called min_sal. It is to be compared with the MIN_SALARY column of the JOBS table. Instead of issuing three different statements, issue a single statement that uses a bind variable. At execution time, the same execution plan is used, the given value is substituted for the variable.

However, from a performance perspective, this is not the best situation because you get best performance two times out of three. On the other hand, this is very economic because you just need one shared cursor in the library cache to execute all the three statements.


SELECT * FROM jobs WHERE min_salary > :min_sal;



12000

18000

17500

Library cache

Cursor sharing



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Chapter 11 - Page 7

Bind Variables in SQL*Plus


Bind variables can be used in SQL*Plus sessions. In SQL*Plus, use the VARIABLE command to define a bind variable. Then, you can assign values to the variable by executing an assignment statement with the EXEC[UTE] command. Any references to that variable from then on use the value you assigned.

In the example in the slide, the first count is selected while SA_REP is assigned to the variable. The result is 30. Then, AD_VP is assigned to the variable, and the resulting count is 2.


SQL> variable job_id varchar2(10)SQL> exec :job_id := 'SA_REP';


SQL> select count(*) from employees where job_id = :job_id;

COUNT(*)----------

30

SQL> exec :job_id := 'AD_VP';


SQL> select count(*) from employees where job_id = :job_id;

COUNT(*)----------

2



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Chapter 11 - Page 8

Bind Variables in Enterprise Manager


On the SQL Worksheet page of Enterprise Manager (see the SQL Worksheet link in the Related Links region of the Database Home page), you can specify that a SQL statement should use bind variables. You can do this by selecting the "Use bind variables for execution" check box. When you select that, several fields are generated, where you can enter bind variable values. Refer to these values in the SQL statement using variable names that begin with a colon. The order in which variables are referred to defines which variable gets which value. The first variable referred to gets the first value, the second variable gets the second value, and so on. If you change the order in which variables are referenced in the statement, you may need to change the value list to match that order.




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Chapter 11 - Page 9

Bind Variables in SQL Developer


On the SQL Worksheet pane of SQL, you can specify that a SQL statement that uses bind variables. When you execute the statement , the Enter Binds dialog appears where you can enter bind variable values. Refer to these values in the SQL statement using variable names that begin with a colon. Select each bind variable in turn to enter a value for that variable.




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Bind Variable Peeking


When literals are used in a query, those literal values can be used by the optimizer to decide on the best plan. However, when bind variables are used, the optimizer still needs to select the best plan based on the values of the conditions in the query, but cannot see those values readily in the SQL text. That means, as a SQL statement is parsed, the system needs to be able to see the value of the bind variables, to ensure that a good plan that would suit those values is selected. The optimizer does this by peeking at the value in the bind variable. When the SQL statement is hard parsed, the optimizer evaluates the value for each bind variable, and uses that as input in determining the best plan. After the execution is determined the first time you parsed the query, it is reused when you execute the same statement regardless of the bind values used.

This feature was introduced in Oracle9i Database, Release 2. Oracle Database 11g changes this behavior.


SELECT * FROM jobs WHERE min_salary > :min_sal

min_sal=12000

Plan A

First timeyou execute

12000

SELECT * FROM jobs WHERE min_salary > :min_sal 18000

Next timeyou execute



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Bind Variable Peeking (continued)

Under some conditions, bind variable peeking can cause the optimizer to select the suboptimal plan. This occurs because the first value of the bind variable is used to determine the plan for all subsequent executions of the query. Therefore, even though subsequent executions provide different bind values, the same plan is used. It is possible that a different plan would be better for executions that have different bind variable values. An example is where the selectivity of a particular index varies extremely depending on the column value. For low selectivity, a full table scan may be faster. For high selectivity, an index range scan may be more appropriate. As shown in the slide, plan A may be good for the first and third values of min_sal, but it may not be the best for the second one. Suppose there are very few MIN_SALARY values that are above 18000, and plan A is a full table scan. It is probable that a full table scan is not a good plan for the second execution, in that case.

So bind variables are beneficial in that they cause more cursor sharing to happen, and thus reduce parsing of SQL. But, as in this case, it is possible that they cause a suboptimal plan to be chosen for some of the bind variable values. This is a good reason for not using bind variables for decision support system (DSS) environments, where the parsing of the query is a very small percentage of the work done when submitting a query. The parsing may take fractions of a second, but the execution may take minutes or hours. To execute with a slower plan is not worth the savings gained in parse time.


SELECT * FROM jobs WHERE min_salary > :min_sal

1

2

3

Plan A

min_sal=12000

min_sal=18000

min_sal=7500

One plan not always appropriate for all bind values



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Cursor Sharing Enhancements


Oracle8i introduced the possibility of sharing SQL statements that differ only in literal values. Rather than developing an execution plan each time the same statement—with a different literal value—is executed, the optimizer generates a common execution plan used for all subsequent executions of the statement.

Because only one execution plan is used instead of potential different ones, this feature should be tested against your applications before you decide to enable it or not. That is why Oracle9i extends this feature by sharing only statements considered as similar. That is, only when the optimizer has the guarantee that the execution plan is independent of the literal value used. For example, consider a query, where EMPLOYEE_ID is the primary key: SQL> SELECT * FROM employees WHERE employee_id = 153;

The substitution of any value would produce the same execution plan. It would, therefore, be safe for the optimizer to generate only one plan for different occurrences of the same statement executed with different literal values.

On the other hand, assume that the same EMPLOYEES table has a wide range of values in its DEPARTMENT_ID column. For example, department 50 could contain over one third of all employees and department 70 could contain just one or two.

See the two queries: SQL> SELECT * FROM employees WHERE department_id = 50;


• Oracle8i introduced the possibility of sharing SQL statements that differ only in literal values.

• Oracle9i extends this feature by limiting it to similar statements only, instead of forcing it.

• Similar: Regardless of the literal value, same execution plan

• Not similar: Possible different execution plans for different literal values

SQL> SELECT * FROM employees2 WHERE employee_id = 153;

SQL> SELECT * FROM employees2 WHERE department_id = 50;



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SQL> SELECT * FROM employees WHERE department_id = 70;

Using only one execution plan for sharing the same cursor would not be safe if you have histogram statistics (and there is skew in the data) on the DEPARTMENT_ID column. In this case, depending on which statement was executed first, the execution plan could contain a full table (or fast full index) scan, or it could use a simple index range scan.



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The CURSOR_SHARING Parameter


The value of the CURSOR_SHARING initialization parameter determines how the optimizer processes statements with bind variables:

• EXACT: Literal replacement disabled completely

• FORCE: Causes sharing for all literals

• SIMILAR: Causes sharing for safe literals only

In earlier releases, you could select only the EXACT or the FORCE option. Setting the value to SIMILAR causes the optimizer to examine the statement to ensure that replacement occurs only for safe literals. It can then use information about the nature of any available index (unique or nonunique) and statistics collected on the index or underlying table, including histograms.

The value of CURSOR_SHARING in the initialization file can be overridden with an ALTER SYSTEM SET CURSOR_SHARING or an ALTER SESSION SET CURSOR_SHARING command.

The CURSOR_SHARING_EXACT hint causes the system to execute the SQL statement without any attempt to replace literals by bind variables.


• The CURSOR_SHARING parameter values:– FORCE

– EXACT (default)

– SIMILAR

• CURSOR_SHARING can be changed using:– ALTER SYSTEM

– ALTER SESSION

– Initialization parameter files

• The CURSOR_SHARING_EXACT hint



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Forcing Cursor Sharing: Example


Because you forced cursor sharing with the ALTER SESSION command, all your queries that differ only with literal values are automatically rewritten to use the same system-generated bind variable called SYS_B_0 in the example in the slide. As a result, you end up with only one child cursor instead of three.

Note: Adaptive cursor sharing may also apply, and might generate a second child cursor in this case.


SELECT * FROM jobs WHERE min_salary > :"SYS_B_0"

SELECT * FROM jobs WHERE min_salary > 12000;SELECT * FROM jobs WHERE min_salary > 18000;SELECT * FROM jobs WHERE min_salary > 7500;

SQL> alter session set cursor_sharing = FORCE;

System-generatedbind variable



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Adaptive Cursor Sharing: Overview


Bind variables were designed to allow the Oracle Database to share a single cursor for multiple SQL statements to reduce the amount of shared memory used to parse SQL statements. However, cursor sharing and SQL optimization are conflicting goals. Writing a SQL statement with literals provides more information for the optimizer and naturally leads to better execution plans, while increasing memory and CPU overhead caused by excessive hard parses. Oracle9i Database was the first attempt to introduce a compromise solution by allowing similar SQL statements using different literal values to be shared. For statements using bind variables, Oracle9i also introduced the concept of bind peeking. To benefit from bind peeking, it is assumed that cursor sharing is intended and that different invocations of the statement are supposed to use the same execution plan. If different invocations of the statement would significantly benefit from different execution plans, bind peeking is of no use in generating good execution plans.

To address this issue as much as possible, Oracle Database 11g introduces adaptive cursor sharing. This feature is a more sophisticated strategy designed to not share the cursor blindly, but generate multiple plans per SQL statement with bind variables if the benefit of using multiple execution plans outweighs the parse time and memory usage overhead. However, because the purpose of using bind variables is to share cursors in memory, a compromise must be found regarding the number of child cursors that need to be generated.


Adaptive cursor sharing:

• Allows for intelligent cursor sharing for statements that use bind variables

• Is used to compromise between cursor sharing and optimization

• Has the following benefits:– Automatically detects when different executions would

benefit from different execution plans

– Limits the number of generated child cursors to a minimum

– Provides an automated mechanism that cannot be turned off



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Adaptive Cursor Sharing: Architecture


When you use adaptive cursor sharing, the following steps take place in the scenario illustrated in the slide:

1. The cursor starts its life with a hard parse, as usual. If bind peeking takes place, and a histogram is used to compute selectivity of the predicate containing the bind variable, then the cursor is marked as a bind-sensitive cursor. In addition, some information is stored about the predicate containing the bind variables, including the predicate selectivity. In the slide example, the predicate selectivity that would be stored is a cube centered around (0.15,0.0025). Because of the initial hard parse, an initial execution plan is determined using the peeked binds. After the cursor is executed, the bind values and the execution statistics of the cursor are stored in that cursor.

During the next execution of the statement when a new set of bind values is used, the system performs a usual soft parse, and finds the matching cursor for execution. At the end of execution, execution statistics are compared with the ones currently stored in the cursor. The system then observes the pattern of the statistics over all the previous runs (see V$SQL_CS_… views in the slide that follows) and decides whether or not to mark the cursor as bind aware.

2. On the next soft parse of this query, if the cursor is now bind aware, bind-aware cursor matching is used. Suppose the selectivity of the predicate with the new set of bind values is now (0.18,0.003). Because selectivity is used as part of bind-aware cursor


Initial selectivity cube

Bind-sensitive cursor

SELECT * FROM emp WHERE sal = :1 and dept = :2

.

0.15

0.0025

:1=A & :2=B S(:1)=0.15 ∧ S(:2)=0.0025

.

0.18

0.003

:1=C & :2=D S(:1)=0.18 ∧ S(:2)=0.003

HJ

GB

Initial plan

HJ

GB

No need for new plan

.

0.3

0.009

:1=E & :2=F S(:1)=0.3 ∧ S(:2)=0.009

HJ

GB

HJ

HJ

GB

HJ.

0.28

0.004

:1=G & :2=H S(:1)=0.28 ∧ S(:2)=0.004

HJ

GB

HJ

GB

Same selectivity cube

Second selectivity cube Need new planMerged selectivity cubes

Cubes merged

No need for new plan

Bind-aware cursor

Soft

Parse

Hard

arse

P

Hard

arse

P

1Systemobservesstatementfor a while.

1

2

34

HJHJ

HJHJ



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matching, and because the selectivity is within an existing cube, the statement uses the existing child cursor’s execution plan to run.

3. On the next soft parse of this query, suppose that the selectivity of the predicate with the new set of bind values is now (0.3,0.009). Because that selectivity is not within an existing cube, no child cursor match is found. So the system does a hard parse, which generates a new child cursor with a second execution plan in that case. In addition, the new selectivity cube is stored as part of the new child cursor. After the new child cursor executes, the system stores the bind values and execution statistics in the cursor.

4. On the next soft parse of this query, suppose the selectivity of the predicate with the new set of bind values is now (0.28,0.004). Because that selectivity is not within one of the existing cubes, the system does a hard parse. Suppose that this time, the hard parse generates the same execution plan as the first one. Because the plan is the same as the first child cursor, both child cursors are merged. That is, both cubes are merged into a new bigger cube, and one of the child cursors is deleted. The next time there is a soft parse, if the selectivity falls within the new cube, the child cursor matches.



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Adaptive Cursor Sharing: Views


These views determine whether a query is bind aware or not, and is handled automatically, without any user input. However, information about what goes on is exposed through V$ views so that you can diagnose problems, if any. New columns have been added to V$SQL:

• IS_BIND_SENSITIVE: Indicates if a cursor is bind sensitive; value YES | NO. A query for which the optimizer peeked at bind variable values when computing predicate selectivities and where a change in a bind variable value may lead to a different plan is called bind sensitive.

• IS_BIND_AWARE: Indicates if a cursor is bind aware; value YES | NO. A cursor in the cursor cache that has been marked to use bind-aware cursor sharing is called bind aware.

• V$SQL_CS_HISTOGRAM: Shows the distribution of the execution count across a three-bucket execution history histogram.

• V$SQL_CS_SELECTIVITY: Shows the selectivity cubes or ranges stored in a cursor for every predicate containing a bind variable and whose selectivity is used in the cursor sharing checks. It contains the text of the predicates and the selectivity range low and high values.


The following views provide information about adaptive cursor sharing usage:

V$SQL Two new columns show whether a cursor is bind sensitive or bind aware.

V$SQL_CS_HISTOGRAM Shows the distribution of the execution count across the execution history histogram

V$SQL_CS_SELECTIVITY Shows the selectivity cubes stored for every predicate containing a bind variable and whose selectivity is used in the cursor sharing checks

V$SQL_CS_STATISTICS Shows execution statistics of a cursor using different bind sets



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• V$SQL_CS_STATISTICS: Adaptive cursor sharing monitors execution of a query and collects information about it for a while, and uses this information to decide whether to switch to using bind-aware cursor sharing for the query. This view summarizes the information that it collects to make this decision. For a sample of executions, it keeps track of the rows processed, buffer gets, and CPU time. The PEEKED column has the value YES if the bind set was used to build the cursor, and NO otherwise.



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Adaptive Cursor Sharing: Example


Consider the data in the slide. There are histogram statistics on the JOB_ID column, showing that there are many thousands times more occurrences of SA_REP than AD_ASST. In this case, if literals were used instead of a bind variable, the query optimizer would see that the AD_ASST value occurs in less than 1% of the rows, whereas the SA_REP value occurs in approximately a third of the rows. If the table has over a million rows in it, the execution plans are different for each of these values’ queries. The AD_ASST query results in an index range scan because there are so few rows with that value. The SA_REP query results in a full table scan because so many of the rows have that value, it is more efficient to read the entire table. But, as it is, using a bind variable causes the same execution plan to be used for both of the values, at first. So, even though there exist different and better plans for each of these values, they use the same plan.

After several executions of this query using a bind variable, the system considers the query bind aware, at which point it changes the plan based on the bound value. This means the best plan is used for the query, based on the bind variable value.


SQL> variable job varchar2(6)SQL> exec :job := 'AD_ASST'SQL> select count(*), max(salary) from emp where job_id=:job;

'AD_ASST'

SelectivityPlan A Plan B

'SA_REP'



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Interacting with Adaptive Cursor Sharing


• Adaptive cursor sharing is independent of the CURSOR_SHARING parameter. The setting of this parameter determines whether literals are replaced by the system-generated bind variables. If they are, adaptive cursor sharing behaves just as it would if the user supplied binds to begin with.

• When using the SPM automatic plan capture, the first plan captured for a SQL statement with bind variables is marked as the corresponding SQL plan baseline. If another plan is found for that same SQL statement (which maybe the case with adaptive cursor sharing), it is added to the SQL statements plan history and marked for verification. It will not be used immediately. So even though adaptive cursor sharing has come up with a new plan based on a new set of bind values, SPM does not let it be used until the plan has been verified. Thus reverting to10g behavior, only the plan generated based on the first set of bind values is used by all subsequent executions of the statement. One possible workaround is to run the system for some time with automatic plan capture set to False, and after the cursor cache has been populated with all the plans a SQL statement with bind has, load the entire plan directly from the cursor cache into the corresponding SQL plan baseline. By doing this, all the plans for a single SQL statement are marked as SQL baseline plans by default. Refer to the lesson titled "SQL Plan Management" for more information.


• CURSOR_SHARING:– If CURSOR_SHARING <> EXACT, statements containing

literals may be rewritten using bind variables.

– If statements are rewritten, adaptive cursor sharing may apply to them.

• SQL Plan Management (SPM):– If OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES is set to

TRUE, only the first generated plan is used.

– As a workaround, set this parameter to FALSE, and run your application until all plans are loaded in the cursor cache.

– Manually load the cursor cache into the corresponding plan baseline.



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Quiz

Answer: a, c, f

Quiz

Which three statements are true about applications that are coded with literals in the SQL statements rather than bind variables?

a. More shared pool space is required for cursors.

b. Less shared pool space is required for cursors.

c. Histograms are used if available.

d. Histograms are not used.

e. No parsing is required for literal values.

f. Every different literal value requires parsing.



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Quiz

Answer: c

Quiz

The CURSOR_SHARING parameter should be set to ________ for systems with large tables and long-running queries such as a data warehouse.

a. Similar

b. Force

c. Exact

d. Literal

e. True

f. False



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Quiz

Answer: b

Quiz

Adaptive Cursor Sharing can be turned off by setting the CURSOR_SHARING parameter to FALSE.

a. True

b. False



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Summary

Summary


• List the benefits of using bind variables

• Use bind peeking

• Use adaptive cursor sharing



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• Using adaptive cursor sharing and bind peeking• Using the CURSOR_SHARING initialization parameter



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SQL Tuning Advisor

Chapter 12 - Page 1

SQL Tuning Advisor

Chapter 12



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SQL Tuning Advisor

Chapter 12 - Page 2

SQL Tuning Advisor

SQL Tuning Advisor



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SQL Tuning Advisor

Chapter 12 - Page 3

Objectives

Objectives


• Describe statement profiling

• Use SQL Tuning Advisor



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SQL Tuning Advisor

Chapter 12 - Page 4

Tuning SQL Statements Automatically


Tuning SQL statements automatically is the capability of the query optimizer to automate the entire SQL tuning process. This automatic process replaces manual SQL tuning, which is a complex, repetitive, and time-consuming function. SQL Tuning Advisor exposes the features of SQL tuning to the user. The enhanced query optimizer has two modes:

• In the normal mode, the optimizer compiles SQL and generates an execution plan. The normal mode of the optimizer generates a reasonable execution plan for the vast majority of SQL statements. In the normal mode, the optimizer operates with very strict time constraints, usually a fraction of a second, during which it must find a good execution plan.

• In the tuning mode, the optimizer performs additional analysis to check whether the execution plan produced under the normal mode can be further improved. The output of the query optimizer in the tuning mode is not an execution plan, but a series of actions, along with their rationale and expected benefit (for producing a significantly superior plan). When called under tuning mode, the optimizer is referred to as Automatic Tuning Optimizer (ATO). The tuning performed by ATO is called system SQL tuning.

Under the tuning mode, the optimizer can take several minutes to tune a single statement. ATO is meant to be used for complex and high-load SQL statements that have a nontrivial impact on the entire system.


• Tuning SQL statements automatically eases the entire SQL tuning process and replaces manual SQL tuning.

• Optimizer modes:– Normal mode

– Tuning mode or Automatic Tuning Optimizer (ATO)

• SQL Tuning Advisor is used to access tuning mode.

• You should use tuning mode only for high-load SQL statements.



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SQL Tuning Advisor

Chapter 12 - Page 5

Application Tuning Challenges


The process of identifying high-load SQL statements and tuning them is very challenging even for an expert. SQL tuning is not only one of the most critical aspects of managing the performance of a database server, but also one of the most difficult tasks to accomplish. Starting with Oracle Database 10g, the task of identifying high-load SQL statements has been automated by Automatic Database Diagnostic Monitor (ADDM). Even though the number of high-load SQL statements that are identified by ADDM may represent a very small percentage of the total SQL workload, the task of tuning them is still highly complex and requires a high level of expertise.

Also, the SQL tuning activity is a continuous task because the SQL workload can change relatively often when new application modules are deployed.

SQL Tuning Advisor, introduced with Oracle Database 10g, is designed to replace the manual tuning of SQL statements. SQL statements that consume high resources (such as CPU, I/O, and temporary space) are good candidates for SQL Tuning Advisor. The advisor receives one or more SQL statements as input and then provides advice on how to optimize the execution plan, a rationale for the advice, estimated performance benefits, and the actual command to implement the advice. You accept the advice, thereby tuning the SQL statements. With the introduction of SQL Tuning Advisor, you can now let the Oracle optimizer tune the SQL code for you.


ADDM

High-loadSQL

SQL workload

How can Itune my

high-loadSQL?

SQL Tuning Advisor

I can doit for you!



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SQL Tuning Advisor

Chapter 12 - Page 6

SQL Tuning Advisor: Overview


SQL Tuning Advisor is primarily the driver of the tuning process. It calls Automatic Tuning Optimizer (ATO) to perform the following four specific types of analysis:

• Statistics Analysis: ATO checks each query object for missing or stale statistics and makes a recommendation to gather relevant statistics. It also collects auxiliary information to supply missing statistics or correct stale statistics in case recommendations are not implemented.

• SQL Profiling: ATO verifies its own estimates and collects auxiliary information to remove estimation errors. It also collects auxiliary information in the form of customized optimizer settings, such as first rows and all rows, based on the past execution history of the SQL statement. It builds a SQL Profile using the auxiliary information and makes a recommendation to create it. When a SQL Profile is created, the profile enables the query optimizer, under normal mode, to generate a well-tuned plan.

• Access Path Analysis: ATO explores whether a new index can be used to significantly improve access to each table in the query, and when appropriate makes recommendations to create such indexes.

• SQL Structure Analysis: Here, ATO tries to identify SQL statements that lend themselves to bad plans, and makes relevant suggestions to restructure them. The suggested restructuring can be syntactic as well as semantic changes to the SQL code.


Add missing index.Run Access Advisor.

Restructure SQL.

Plan tuning(SQL Profile).

Automatic Tuning Optimizer

SQL Analysisoptimization mode

Access Analysis optimization mode

Plan Tuning optimization mode

Statistics Checkoptimizationmode

Detect stale or missing statistics.

Comprehensive SQL tuning

SQL Tuning Advisor



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SQL Tuning Advisor

Chapter 12 - Page 7

Stale or Missing Object Statistics


The query optimizer relies on object statistics to generate execution plans. If these statistics are stale or missing, the optimizer does not have the necessary information it needs and can generate suboptimal execution plans.

ATO checks each query object for missing or stale statistics and produces two types of outputs:

• Auxiliary information in the form of statistics for objects with no statistics, and statistic adjustment factor for objects with stale statistics

• Recommendations to gather relevant statistics for objects with stale or no statistics

For optimal results, you gather statistics when recommended and then rerun Automatic Tuning Optimizer. However, you may be hesitant to accept this recommendation immediately because of the impact it could have on other queries in the system.

EXEC DBMS_STATS.GATHER_TABLE_STATS(ownname=>'SH', tabname=>'CUSTOMERS',estimate_percent=>DBMS_STATS.AUTO_SAMPLE_SIZE);


• Object statistics are key inputs to the optimizer.

• ATO verifies object statistics for each query object.

• ATO uses dynamic sampling and generates:– Auxiliary object statistics to compensate for missing or stale

object statistics

– Recommendations to gather object statistics where appropriate:



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SQL Tuning Advisor

Chapter 12 - Page 8

SQL Statement Profiling


The main verification step during SQL Profiling is the verification of the query optimizer’s own estimates of cost, selectivity, and cardinality for the statement that is tuned.

During SQL Profiling, ATO performs verification steps to validate its own estimates. The validation consists of taking a sample of data and applying appropriate predicates to the sample. The new estimate is compared to the regular estimate, and if the difference is large enough, a correction factor is applied. Another method of estimate validation involves the execution of a fragment of the SQL statement. The partial execution method is more efficient than the sampling method when the respective predicates provide efficient access paths. ATO picks the appropriate estimate validation method.

ATO also uses the past execution history of the SQL statement to determine correct settings. For example, if the execution history indicates that a SQL statement is only partially executed the majority of times, ATO uses the FIRST_ROWS optimization as opposed to ALL_ROWS.

ATO builds a SQL Profile if it has generated auxiliary information either during Statistics Analysis or during SQL Profiling. When a SQL Profile is built, it generates a user recommendation to create a SQL Profile.

In this mode, ATO can recommend the acceptance of the generated SQL Profile to activate it.


• Statement statistics are key inputs to the optimizer.

• ATO verifies statement statistics such as:– Predicate selectivity– Optimizer settings (FIRST_ROWS versus ALL_ROWS)

• Automatic Tuning Optimizer uses:– Dynamic sampling

– Partial execution of the statement

– Past execution history statistics of the statement

• ATO builds a profile if statistics were generated:

exec :profile_name := -dbms_sqltune.accept_sql_profile( -task_name =>'my_sql_tuning_task');



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SQL Tuning Advisor

Chapter 12 - Page 9

Plan Tuning Flow and SQL Profile Creation


A SQL Profile is a collection of auxiliary information that is built during automatic tuning of a SQL statement. Thus, a SQL Profile is to a SQL statement what statistics are to a table or index. After it is created, a SQL Profile is used in conjunction with the existing statistics by the query optimizer, in normal mode, to produce a well-tuned plan for the corresponding SQL statement. A SQL Profile is stored persistently in the data dictionary. However, the SQL profile information is not exposed through regular dictionary views. After creation of a SQL Profile, every time the corresponding SQL statement is compiled in normal mode, the query optimizer uses the SQL Profile to produce a well-tuned plan.

The slide shows the process flow of the creation and use of a SQL Profile. The process consists of two separate phases. They are system SQL tuning phase and regular optimization phase. During the system SQL tuning phase, you select a SQL statement for system tuning and run SQL Tuning Advisor by using either Database Control or the command-line interface. SQL Tuning Advisor invokes ATO to generate tuning recommendations, possibly with a SQL Profile. If a SQL Profile is built, you can accept it. When it is accepted, the SQL Profile is stored in the data dictionary. In the next phase, when an end user issues the same SQL statement, the query optimizer (under normal mode) uses the SQL Profile to build a well-tuned plan. The use of the SQL Profile remains completely transparent to the end user and does not require changes to the application source code.


Optimizer(Tuning mode)

CreateSubmit

Output

SQL Profile

SQL TuningAdvisor

Databaseusers

Well-tunedplan

Optimizer(Normal mode)

Use

No application code change



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SQL Tuning Advisor


SQL Tuning Loop

SQL Tuning Loop

The auxiliary information contained in a SQL Profile is stored in such a way that it stays relevant after database changes, such as addition or removal of indexes, growth in the size of tables, and periodic collection of database statistics. Therefore, when a profile is created, the corresponding plan is not frozen (as when outlines are used).

However, a SQL Profile may not adapt to massive changes in the database or changes that have accumulated over a long period of time. In such cases, a new SQL Profile needs to be built to replace the old one.

For example, when a SQL Profile becomes outdated, the performance of the corresponding SQL statement may become noticeably worse. In such a case, the corresponding SQL statement may start showing up as high-load or top SQL, thus becoming again a target for system SQL Tuning. In such a situation, ADDM again captures the statement as high-load SQL. If that happens, you can decide to re-create a new profile for that statement.

SQL Tuning Loop

SQL Tuning AdvisorHigh load

ADDM

Generateprofiles

Workload



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SQL Tuning Advisor


Access Path Analysis


ATO also provides advice on indexes. Effective indexing is a well-known tuning technique that can significantly improve the performance of SQL statements by reducing the need for full table scans. Any index recommendations generated by ATO are specific to the SQL statement being tuned. Therefore, it provides a quick solution to the performance problem associated with a single SQL statement.

Because ATO does not perform an analysis of how its index recommendations are going to affect the entire SQL workload, it recommends running the Access Advisor on the SQL statement along with a representative SQL workload. The Access Advisor collects advice given on each statement of a SQL workload and consolidates it into global advice for the entire SQL workload.

The Access Path Analysis can make the following recommendations:

• Create new indexes if they provide significantly superior performance.

• Run SQL Access Advisor to perform a comprehensive index analysis based on application workload.


SQL TuningAdvisor

Workload

Indexes

Indexes

SQL AccessAdvisor

Significantperformance

gain

Comprehensiveindex

analysis

CREATE INDEX JFV.IDX$_00002 on JFV.TEST("C");



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SQL Tuning Advisor


SQL Structure Analysis


The goal of the SQL Structure Analysis is to help you identify poorly written SQL statements as well as to advise you on how to restructure them.

There are certain syntax variations that are known to have a negative impact on performance. In this mode, ATO evaluates statements against a set of rules, identifying less efficient coding techniques, and providing recommendations for an alternative statement where possible. The recommendation may be very similar, but not precisely equivalent to the original query. For example, the NOT EXISTS and NOT IN constructors are similar, but not exactly the same. Therefore, you have to decide whether the recommendation is valid. For this reason, ATO does not automatically rewrite the query, but gives advice instead.

The following categories of problems are detected by the SQL Structure Analysis:

• Use of SQL constructors such as NOT IN instead of NOT EXISTS, or UNION instead of UNION ALL

• Use of predicates involving indexed columns with data-type mismatch that prevents the use of the index

• Design mistakes (such as Cartesian products)


Poorly writtenSQL statement

SQL TuningAdvisor

RestructuredSQL statement

Design mistakes

Type mismatch and indexes

SQL constructs

How can Irewrite it?



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SQL Tuning Advisor


SQL Tuning Advisor: Usage Model


SQL Tuning Advisor takes one or more SQL statements as input. The input can come from different sources:

• High-load SQL statements identified by ADDM

• SQL statements that are currently in cursor cache

• SQL statements from Automatic Workload Repository (AWR): A user can select any set of SQL statements captured by AWR. This can be done using snapshots or baselines.

• Custom workload: A user can create a custom workload consisting of statements of interest to the user. These may be statements that are not in cursor cache and are not high-load to be captured by ADDM or AWR. For such statements, a user can create a custom workload and tune it using the advisor.

SQL statements from cursor cache, AWR, and custom workload can be filtered and ranked before they are input to SQL Tuning Advisor.

For a multistatement input, an object called SQL Tuning Set (STS) is provided. An STS stores multiple SQL statements along with their execution information:

• Execution context: Parsing schema name and bind values

• Execution statistics: Average elapsed time and execution count

Note: Another STS can be a possible source for STS creation.


SQLTuning Advisor

ADDM High-load SQL

Filter

Sources

Manual Selection

Automatic selection

Cursor cache

Custom

AWR

AWR



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SQL Tuning Advisor


Database Control and SQL Tuning Advisor


The easiest way to access the SQL Tuning Advisor from Enterprise Manager is on the Advisor Central page. On the Home page, click the Advisor Central link located in the Related Links section to open the Advisor Central page.

On the Advisor Central page, click the SQL Advisors link. On the SQL Advisors page, click the SQL Tuning Advisor link. This takes you to the Schedule SQL Tuning Advisor page. On this page, you find links to various other pages. You click the Top Activity link to open the Top Activity page.




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SQL Tuning Advisor


Running SQL Tuning Advisor: Example


You can use Database Control to identify the high-load or top SQL statements. There are several locations in Database Control from where SQL Tuning Advisor can be launched with the identified SQL statement or statements, or an STS:

• Tuning ADDM-identified SQL statements: The ADDM Finding Details page shows high-load SQL statements identified by ADDM. Each of these high-load SQL statements is known to consume a significant proportion of one or more system resources. You can use this page to launch SQL Tuning Advisor on a selected high-load SQL statement.

• Tuning top SQL statements: Another SQL source is the list of top SQL statements. This is shown in the slide. You can identify the list of top SQL statements by looking at their cumulative execution statistics based on a selected time window. The user can select one or more top SQL statements identified by their SQL IDs, and then click Schedule SQL Tuning Advisor.

• Tuning a SQL Tuning Set: It is also possible to look at various STSs created by different users. An STS could have been created from a list of top SQL statements, by selecting SQL statements from a range of snapshots created by AWR, or by selecting customized SQL statements.




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SQL Tuning Advisor


Schedule SQL Tuning Advisor


When SQL Tuning Advisor is launched, Enterprise Manager automatically creates a tuning task, provided the user has the appropriate ADVISOR privilege to do so. Enterprise Manager shows the tuning task with automatic defaults on the Schedule SQL Tuning Advisor page. On this page, the user can change the automatic defaults pertaining to a tuning task.

One of the important options is to select the scope of the tuning task. If you select the Limited option, SQL Tuning Advisor produces recommendations based on statistics check, access path analysis, and SQL structure analysis. No SQL Profile recommendation is generated with Limited scope. If you select the Comprehensive option, SQL Tuning Advisor performs all of the Limited scope actions, and invokes the optimizer under SQL Profiling mode to build a SQL Profile if applicable. With the Comprehensive option, you can also specify a time limit for the tuning task, which by default is 30 minutes. Another useful option is to run the tuning task immediately or schedule it to be run at a later time.

When the task is submitted, the Processing page appears. When the task is complete, the Recommendations page appears.




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SQL Tuning Advisor


Implementing Recommendations


On the recommendations page, you can view the various recommendations. For each recommendation, as shown, a SQL Profile has been created; you can implement it if you want, after you view the new plan. Click the eyeglass icon to view the Compare Explain Plan page.




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SQL Tuning Advisor


Compare Explain Plan


The Compare explain Plan page give you the opportunity to view the projected benefits of implementing the recommendation, in this case a SQL profile. You can see the benefits graphically and in a table. Notice the Cost values for the SQL statement in the original and new explain plans. If the difference is not enough or the explain is not acceptable, the recommendation can ignored or deleted.




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SQL Tuning Advisor


Quiz

Answer: a, b, d

SQL Tuning Advisor in comprehensive mode recommends all except deleting indexes. SQL Tuning Advisor focuses on one SQL statement at a time. An entire workload must be considered to determine if deleting an index will help performance.

Quiz

SQL Tuning Advisor will recommend:

a. SQL Profiles

b. Additional Indexes

c. Deleting Indexes

d. Rewriting SQL Statements

e. All of the above



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SQL Tuning Advisor


Quiz

Answer: b

Quiz

The SQL Profile will force the best execution plan even when the data in the table changes.

a. True

b. False



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SQL Tuning Advisor


Summary

Summary

In this lesson, you should have learned the following:

• Statement profiling

• SQL Tuning Advisor



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SQL Tuning Advisor




This practice covers using ADDM and SQL Tuning Advisor to tune your SQL statements



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Using SQL Access Advisor

Chapter 13 - Page 1


Chapter 13



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Chapter 13 - Page 2





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Chapter 13 - Page 3

Objectives

Objectives

After completing this lesson, you should be able to use SQL Access Advisor.



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Chapter 13 - Page 4

SQL Access Advisor: Overview


Defining appropriate access structures to optimize SQL queries has always been a concern for the developer. As a result, there have been many papers and scripts written as well as high-end tools developed to address the matter. In addition, with the development of partitioning and materialized view technology, deciding on access structures has become even more complex.

As part of the manageability improvements in Oracle Database 10g and 11g, SQL Access Advisor has been introduced to address this critical need.

SQL Access Advisor identifies and helps resolve performance problems relating to the execution of SQL statements by recommending which indexes, materialized views, materialized view logs, or partitions to create, drop, or retain. It can be run from Database Control or from the command line by using PL/SQL procedures.

SQL Access Advisor takes an actual workload as input, or the Advisor can derive a hypothetical workload from the schema. It then recommends the access structures for faster execution path. It provides the following advantages:

• Does not require you to have expert knowledge

• Bases decision making on rules that actually reside in the cost-based optimizer (CBO)

• Is synchronized with the optimizer and Oracle database enhancements

• Is a single advisor covering all aspects of SQL access methods


Workload

SQL Access Advisor

Solution

Component of CBO

Provides implementation

script

No expertiserequired

Whatpartitions, indexes, and MVs do I need

to optimizemy entire

workload?



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Chapter 13 - Page 5

• Provides simple, user-friendly GUI wizards

• Generates scripts for implementation of recommendations



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Chapter 13 - Page 6

SQL Access Advisor: Usage Model


SQL Access Advisor takes as input a workload that can be derived from multiple sources:

• SQL cache, to take the current content of V$SQL

• Hypothetical, to generate a likely workload from your dimensional model. This option is interesting when your system is being initially designed.

• SQL Tuning Sets, from the workload repository

SQL Access Advisor also provides powerful workload filters that you can use to target the tuning. For example, a user can specify that the advisor should look at only the 30 most resource-intensive statements in the workload, based on optimizer cost. For the given workload, the advisor then does the following:

• Simultaneously considers index solutions, materialized view solutions, partition solutions, or combinations of all three

• Considers storage for creation and maintenance costs

• Does not generate drop recommendations for partial workloads

• Optimizes materialized views for maximum query rewrite usage and fast refresh

• Recommends materialized view logs for fast refresh

• Recommends partitioning for tables, indexes, and materialized views


Indexes Materializedviews

Materializedviews log

SQL Access Advisor

Hypothetical

SQL cache

Filter Options

STS

Workload

Partitionedobjects



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Chapter 13 - Page 7

• Combines similar indexes into a single index

• Generates recommendations that support multiple workload queries



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Chapter 13 - Page 8

Possible Recommendations


SQL Access Advisor carefully considers the overall impact of recommendations and makes recommendations by using only the known workload and supplied information. Two workload analysis methods are available:

• Comprehensive: With this approach, SQL Access Advisor addresses all aspects of tuning partitions, materialized views, indexes, and materialized view logs. It assumes that the workload contains a complete and representative set of application SQL statements.

• Limited: Unlike the comprehensive workload approach, a limited workload approach assumes that the workload contains only problematic SQL statements. Thus, advice is sought for improving the performance of a portion of an application environment.

When comprehensive workload analysis is chosen, SQL Access Advisor forms a better set of global tuning adjustments, but the effect may be a longer analysis time. As shown in the table, the chosen workload approach determines the type of recommendations made by the advisor.

Note: Partition recommendations can work on only those tables that have at least 10,000 rows, and workloads that have some predicates and joins on columns of the NUMBER or DATE type. Partitioning recommendations can be generated only on these types of columns. In addition, partitioning recommendations can be generated only for single-column interval and


Recommendation Comprehensive Limited

Add new (partitioned) index on table or materialized view. YES YES

Drop an unused index. YES NO

Modify an existing index by changing the index type. YES NO

Modify an existing index by adding columns at the end. YES YES

Add a new (partitioned) materialized view. YES YES

Drop an unused materialized view (log). YES NO

Add a new materialized view log. YES YES

Modify an existing materialized view log to add new columns or clauses.

YES YES

Partition an existing unpartitioned table or index. YES YES



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Chapter 13 - Page 9

hash partitions. Interval partitioning recommendations can be output as range syntax, but interval is the default. Hash partitioning is done to leverage only partitionwise joins.



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SQL Access Advisor Session: Initial Options


The next few slides describe a typical SQL Access Advisor session. You can access the SQL Access Advisor by clicking the Advisor Central link on the Database Home page or through individual alerts or performance pages that may include a link to facilitate solving a performance problem. The SQL Access Advisor consists of several steps during which you supply the SQL statements to tune and the types of access methods you want to use.

On the SQL Access Advisor: Initial Options page, you can select a template or task from which to populate default options before starting the wizard.

Note: The SQL Access Advisor may be interrupted while generating recommendations, thereby allowing the results to be reviewed.

For general information about using SQL Access Advisor, see the "Overview of the SQL Access Advisor" section in the lesson titled "SQL Access Advisor" of the Oracle Data Warehousing Guide.




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SQL Access Advisor Session: Initial Options (continued)

If you select the "Inherit Options from a Task or Template" option on the Initial Options page, you can select an existing task, or an existing template to inherit SQL Access Advisor’s options. By default, the SQLACCESS_EMTASK template is used.

You can view the various options defined by a task or a template by selecting the corresponding object and clicking View Options.




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SQL Access Advisor: Workload Source


You can select your workload source from three different sources:

• Current and Recent SQL Activity: This source corresponds to SQL statements that are still cached in your System Global Area (SGA).

• Use an existing SQL Tuning Set: You also have the possibility of creating and using a SQL Tuning Set that holds your statements.

• Hypothetical Workload: This option provides a schema that allows the advisor to search for dimension tables and produce a workload. This is very useful to initially design your schema.

Using the Filter Options section, you can further filter your workload source. Filter options are:

• Resource Consumption: Number of statements ordered by Optimizer Cost, Buffer Gets, CPU Time, Disk Reads, Elapsed Time, Executions

• Users

• Tables

• SQL Text

• Module IDs

• Actions




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SQL Access Advisor: Recommendation Options


On the Recommendations Options page, you can select whether to limit the SQL Access Advisor to recommendations based on a single access method. You can select the type of structures to be recommended by the advisor. If none of the three possible ones are chosen, the advisor evaluates existing structures instead of trying to recommend new ones.

You can use the Advisor Mode section to run the advisor in one of the two modes. These modes affect the quality of recommendations as well as the length of time required for processing. In Comprehensive Mode, the Advisor searches a large pool of candidates resulting in recommendations of the highest quality. In Limited Mode, the advisor performs quickly, limiting the candidate recommendations by working on the highest-cost statements only.

Note: You can click Advanced Options to show or hide options that allow you to set space restrictions, tuning options, and default storage locations.




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SQL Access Advisor: Schedule and Review


You can then schedule and submit your new analysis by specifying various parameters to the scheduler. The possible options are shown in the screenshots in the slide.




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SQL Access Advisor: Results


From the Advisor Central page, you can retrieve the task details for your analysis. By selecting the task name in the Results section of the Advisor Central page, you can access the Results for Task Summary page, on which you can see an overview of the Access Advisor findings. The page shows you charts and statistics that provide overall workload performance and potential for improving query execution time for the recommendations. You can use the page to show statement counts and recommendation action counts.




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SQL Access Advisor: Results and Implementation


To see other aspects of the results for the Access Advisor task, click one of the three other tabs on the page, Recommendations, SQL Statements, or Details.

On the Recommendations page, you can drill down to each of the recommendations. For each of them, you see important information in the Select Recommendations for Implementation table. You can then select one or more recommendations and schedule their implementation.

If you click the ID for a particular recommendation, you are taken to the Recommendations page that displays all actions for the specified recommendation and, optionally, to modify the tablespace name of the statement. When you complete any changes, click OK to apply the changes. On the Recommendations page, you can view the full text of an action by clicking the link in the Action field for the specified action. You can view the SQL for all actions in the recommendation by clicking Show SQL.

The SQL Statements page (not shown here) gives you a chart and a corresponding table that lists SQL statements initially ordered by the largest cost improvement. The top SQL statement is improved the most by implementing its associated recommendation.

The Details page shows you the workload and task options that were used when the task was created. This page also gives you all journal entries that were logged during the task execution.




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You can also schedule the implementation of the recommendations by clicking the Schedule Implementation button.



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Quiz

Answer: a, d

Quiz

Identify two available workload analysis methods.

a. Comprehensive

b. Complete

c. Partial

d. Limited



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Quiz

Answer: b

Quiz

SQL Access Advisor identifies but cannot help resolve performance problems relating to the execution of SQL statements.

a. True

b. False



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Summary

Summary

In this lesson, you should have learned to use SQL Access Advisor.



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This practice covers the following topics using SQL Access Advisor to change your schema



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Automating SQL Tuning

Chapter 14 - Page 1


Chapter 14



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Chapter 14 - Page 2





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Chapter 14 - Page 3

Objectives

Objectives

After completing this lesson, you should be able to use Automatic SQL Tuning.



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Chapter 14 - Page 4

SQL Tuning Loop

SQL Tuning Loop

Oracle Database 10g introduced SQL Tuning Advisor to help application developers improve the performance of SQL statements. The advisor targets the problem of poorly written SQL, in which SQL statements have not been designed in the most efficient fashion. It also targets the (more common) problem in which a SQL statement performs poorly because the optimizer generated a poor execution plan due to lack of accurate and relevant data statistics. In all cases, the advisor makes specific suggestions for speeding up SQL performance, but it leaves the responsibility of implementing the recommendations to the user.

In addition to SQL Tuning Advisor, Oracle Database 10g has an automated process to identify high-load SQL statements in your system. This is done by ADDM, which automatically identifies high-load SQL statements that are good candidates for tuning.

However, major issues still remain: Although it is true that ADDM identifies some SQL that should be tuned, users must manually look at ADDM reports and run SQL Tuning Advisor on the reports for tuning.

SQL Tuning Loop

SQL Tuning AdvisorHigh load

ADDM

Workload

Run SQL Tuning Advisor.

GenerateSQL profiles.

Automatic

1

2

3

4



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Chapter 14 - Page 5

Automatic SQL Tuning


Oracle Database 11g further automates the SQL Tuning process by identifying problematic SQL statements, running SQL Tuning Advisor on them, and implementing the resulting SQL profile recommendations to tune the statement without requiring user intervention. Automatic SQL Tuning uses the AUTOTASK framework through a task called "Automatic SQL Tuning" that runs every night by default. A brief description of the automated SQL tuning process in Oracle Database 11g is as follows:

• Step 1: Based on the AWR Top SQL identification (SQLs that were top in four different time periods: the past week, any day in the past week, any hour in the past week, or single response time), Automatic SQL Tuning targets for automatic tuning.

• Steps 2 and 3: While the Automatic SQL Tuning task executes during the maintenance window, the previously identified SQL statements are automatically tuned by invoking SQL Tuning Advisor. As a result, SQL profiles are created for them if needed. However, before making any decision, the new profile is carefully tested.

• Step 4: At any point in time, you can request a report about these automatic tuning activities. You then have the option of checking the tuned SQL statements to validate or remove the automatic SQL profiles that were generated.


Auto matic

SQL Tuning

Workload

1

2

3

4

Top SQL

AWR

Reports



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Chapter 14 - Page 6

Automatic Tuning Process


During the tuning process, all the recommendation types are considered and reported, but only SQL profiles can be implemented automatically (when the ACCEPT_SQL_PROFILES task parameter is set to TRUE). Otherwise, only the recommendation to create a SQL profile is reported in the automatic SQL tuning reports.

In Oracle Database 11g, the performance improvement factor has to be at least three before a SQL profile is implemented. As we have already mentioned, the Automatic SQL Tuning process implements only SQL profile recommendations automatically. Other recommendations (to create new indexes, refresh stale statistics, or restructure SQL statements) are generated as part of the SQL tuning process, but are not implemented. These are left for you to review and implement manually, as appropriate.

Here is a short description of the automatic tuning process in general:

Tuning is performed on a per-statement basis. Because only SQL profiles can be implemented, there is no need to consider the effect of such recommendations on the workload as a whole. For each statement (in the order of importance), the tuning process carries out each of the following steps:

1. Tune the statement by using SQL Tuning Advisor. Look for a SQL profile and, if it is found, verify that the base optimizer statistics are current for it.

2. If a SQL profile is recommended, perform the following:


Existingprofile?

Replace profile.

Y

N 3Xbenefit?

3Xbenefit?

Y

Accept profileY

Ignore new profile.N

N

New SQL profile

GATHER_STATS_JOB

Indexes

Not considered forauto implementation

Stalestats

RestructureSQL.

Considered forauto implementation



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Chapter 14 - Page 7

A. Test the new SQL profile by executing the statement with and without it.

B. When a SQL profile is generated and it causes the optimizer to pick a different execution plan for the statement, the advisor must decide whether to implement the SQL profile. It makes its decision according to the flowchart in the slide. Although the benefit thresholds here apply to the sum of CPU and input/output (I/O) time, SQL profiles are not accepted when there is degradation in either statistic. So the requirement is that there is a three-time improvement in the sum of CPU and I/O time, with neither statistic becoming worse. In this way, the statement runs faster than it would without the profile, even with contention in CPU or I/O.

3. If stale or missing statistics are found, make this information available to GATHER_STATS_JOB.

Automatic implementation of tuning recommendations is limited to SQL profiles because they have fewer risks. It is easy for you to reverse the implementation.

Note: All SQL profiles are created in the standard EXACT mode. They are matched and tracked according to the current value of the CURSOR_SHARING parameter. You are responsible for setting CURSOR_SHARING appropriately for your workload.



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Chapter 14 - Page 8

Automatic SQL Tuning Controls


Here is a PL/SQL control example for the Automatic SQL Tuning task: dbms_sqltune.set_tuning_task_parameter('SYS_AUTO_SQL_TUNING_TASK', 'LOCAL_TIME_LIMIT', 1400); dbms_sqltune.set_tuning_task_parameter('SYS_AUTO_SQL_TUNING_TASK', 'ACCEPT_SQL_PROFILES', 'TRUE'); dbms_sqltune.set_tuning_task_parameter('SYS_AUTO_SQL_TUNING_TASK', 'MAX_SQL_PROFILES_PER_EXEC', 50); dbms_sqltune.set_tuning_task_parameter('SYS_AUTO_SQL_TUNING_TASK', 'MAX_AUTO_SQL_PROFILES', 10002); The last three parameters in this example are supported only for the Automatic SQL Tuning task. You can also use parameters such as LOCAL_TIME_LIMIT or TIME_LIMIT, which are valid parameters for the traditional SQL tuning tasks. An important example is to disable test-execute mode (to save time) and to use only execution plan costs to decide about the performance. The TEST_EXECUTE parameter setting determines whether the advisor actually executes the statement and how much time it uses to execute.

In addition, you can control when the Automatic SQL Tuning task runs and the CPU resources that it is allowed to use.


• Autotask configuration:– On/off switch

– Maintenance windows running tuning task

– CPU resource consumption of tuning task

• Task parameters:– SQL profile implementation automatic/manual switch

– Global time limit for tuning task

– Per-SQL time limit for tuning task

– Test-execute mode disabled to save time

– Maximum number of SQL profiles automatically implemented per execution as well as overall

– Task execution expiration period



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Chapter 14 - Page 9

Automatic SQL Tuning Task


As already stated, Automatic SQL Tuning is implemented as an automated maintenance task that is called Automatic SQL Tuning. You can see some high-level information about the last runs of the Automatic SQL Tuning task on the Automated Maintenance Tasks page. To open this page, on your Database Control Home page, click the Server tab. On the Server tabbed page that opens, click the Automated Maintenance Tasks link in the Tasks section.

On the Automated Maintenance Tasks page, you see the predefined tasks. You then access each task by clicking the corresponding link to get more information about the task itself (illustrated in the slide). When you click either the Automatic SQL Tuning link or the latest execution icon (the green area on the timeline), the Automatic SQL Tuning Result Summary page opens.

Note: The execution time shown in this example is very small.




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Configuring Automatic SQL Tuning


You can configure various Automatic SQL Tuning parameters by using the Automatic SQL Tuning Settings page.

To navigate to that page, click the Configure button on the Automated Maintenance Tasks page. You see the Automated Maintenance Tasks Configuration page, on which you see the various maintenance windows that are delivered with Oracle Database 11g.

By default, Automatic SQL Tuning executes on all predefined maintenance windows in MAINTENANCE_WINDOW_GROUP. You can disable it for specific days in the week. On this page, you can also edit each Window to change its characteristics. You can do so by clicking Edit Window Group.

To navigate to the Automatic SQL Tuning Settings page, click the Configure button on the line corresponding to Automatic SQL Tuning in the Task Settings section.

On the Automatic SQL Tuning Settings page, you can specify the parameters shown in the slide. By default, "Automatic Implementation of SQL Profiles" is not selected.

Note: If you set STATISTICS_LEVEL to BASIC, turn off the AWR snapshots by using DBMS_WORKLOAD_REPOSITORY, or if AWR retention is less than seven days, you also stop Automatic SQL Tuning.




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Automatic SQL Tuning: Result Summary


In addition, the Automatic SQL Tuning Result Summary page contains various summary graphs so that you can control the Automatic SQL Tuning task. An example is given in the slide. The first chart in the Overall Task Statistics section shows you the breakdown by finding types for the designated period of time. You can control the period of time for which you want the report to be generated by selecting a value from the Time Period list. In the example, Customized is used; it shows you the latest run. You can select All to cover all executions of the task so far. Users can request it for any time period over the past month, because that is the amount of time for which the advisor persists its tuning history. You then generate the report by clicking View Report.

On the “Breakdown by Finding Type” chart, you can clearly see that only SQL profiles can be implemented. Although many more profiles were recommended, not all of them were automatically implemented for the reasons that have already been explained. Similarly, recommendations for index creation and other types are not implemented. However, the advisor keeps historical information about all the recommendations if you want to implement them later.

In the Profile Effect Statistics section, you can see the Tuned SQL DB Time Benefit chart, which shows you the before-and-after DB Time for implemented profiles and other recommendations.




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Automatic SQL Tuning: Result Details


On the Automatic SQL Tuning Result Details page, you can also see important information for each automatically tuned SQL statement, including its SQL text and SQL ID, the type of recommendation that was done by SQL Tuning Advisor, the verified benefit percentage, whether a particular recommendation was automatically implemented, and the date of the recommendation.

From this page, you can either drill down to the SQL statement itself by clicking its corresponding SQL ID link, or you can select one of the SQL statements and click the View Recommendations button to have more details about the recommendation for that statement.

Note: The benefit percentage shown for each recommendation is calculated using the formula benefit% = (time_old - time_new)/(time_old). With this formula, you can see that a three-time benefit (for example, time_old = 100, time_new = 33) corresponds to 66%. So the system implements any profiles with benefits over 66%. According to this formula, 98% is a 50-times benefit.




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Automatic SQL Tuning Result Details: Drilldown


On the “Recommendations for SQL ID” page, you can see the corresponding recommendations and implement them manually.

By clicking the SQL Test link, you access the SQL Details page, where you see the tuning history as well as the plan control associated with your SQL statement.

In the slide, you see that the statement was tuned by Automatic SQL Tuning and that the associated profile was automatically implemented.




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Automatic SQL Tuning Considerations


Automatic SQL Tuning does not seek to solve every SQL performance issue occurring on a system. It does not consider the following types of SQL:

• Ad hoc or rarely repeated SQL statements: If a SQL statement is not executed multiple times in the same form, the advisor ignores it. SQL statements that do not repeat within a week are also not considered.

• Parallel queries

• Long-running queries (after creating a profile): If a query takes too long to run after being SQL profiled, it is not practical to test-execute and, therefore, it is ignored by the advisor. Note that this does not mean that the advisor ignores all long-running queries. If the advisor can find a SQL profile that causes a query that once took hours to run in minutes, it could be accepted because test-execution is still possible. The advisor would execute the old plan just long enough to determine that it is worse than the new one, and then would terminate test-execution without waiting for the old plan to finish, thereby switching the order of execution.

• Recursive SQL statements

• DMLs, such as INSERT SELECT or CREATE TABLE AS SELECT

With the exception of truly ad hoc SQL, these limitations apply to Automatic SQL Tuning only. Such statements can still be tuned by manually running SQL Tuning Advisor.


• SQL not considered for Automatic SQL Tuning:– Ad hoc or rarely repeated SQL

– Parallel queries

– Long-running queries after profiling

– Recursive SQL statements

– DML and DDL

• These statements can still be manually tuned by using SQL Tuning Advisor.



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Quiz

Answer: a, c

The default settings for Automatic SQL Tuning task is to tune the top SQL statements after prioritizing them based on the AWR Top SQL identification (SQLs that were top in four different time periods: the past week, any day in the past week, any hour in the past week, or single response time).

The time limit is 1200 seconds per statement by default (20 minutes).

Work must stop when the window closes. If any SQL statements remain, they must wait for the next maintenance window.

Any SQL profiles that are generated are not implemented by default, but you can change the configuration so that they are automatically implemented.

Quiz

In the maintenance window, an Automatic SQL Tuning task will run by default. Which two actions will this task perform by default?

a. Prioritizes and tunes the SQL statements with the top resource consumption

b. Works on each statement for a maximum of 30 minutes

c. Stops when all SQL statements are tuned, or when the window closes

d. Automatically implements any recommended SQL profiles



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Summary

Summary

In this lesson, you should have learned to use Automatic SQL Tuning



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This practice covers using Automatic SQL Tuning to tune your statements



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SQL Plan Management

Chapter 15 - Page 1

SQL Plan Management

Chapter 15



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SQL Plan Management

Chapter 15 - Page 2

SQL Plan Management

SQL Plan Management



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SQL Plan Management

Chapter 15 - Page 3

Objectives

Objectives


• Manage SQL performance through changes

• Set up SQL Plan Management

• Set up various SQL Plan Management scenarios



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SQL Plan Management

Chapter 15 - Page 4

Maintaining SQL Performance


Any number of factors that influence the optimizer can change over time. The challenge is to maintain the SQL performance levels in spite of the changes.

Optimizer statistics change for many reasons. Managing the changes to SQL performance despite the changes to statistics is the task of the DBA.

Some SQL statements on any system will stand out as high-resource consumers. It is not always the same statements. The performance of these statements must be tuned, without having to change the code. SQL profiles provide the means to control the performance of these statements.

SQL plan baselines are the key objects that SQL Plan Management uses to prevent unverified change to SQL execution plans. When SQL Plan Management is active, there will not be drastic changes in performance even as the statistics change or as the database version changes. Until a new plan is verified to produce better performance than the current plan, it will not be considered by the optimizer. This in effect freezes the SQL plan.

SQL Outlines have been used in past versions. They are still available for backward compatibility, but Outlines are deprecated in favor of SQL Plan Management.


Maintaining performance may require using SQL plan baselines



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SQL Plan Management

Chapter 15 - Page 5

SQL Plan Management: Overview


Potential performance risk occurs when the SQL execution plan changes for a SQL statement. A SQL plan change can occur due to a variety of reasons such as optimizer version, optimizer statistics, optimizer parameters, schema definitions, system settings, and SQL profile creation.

Various plan control techniques are available in the Oracle Database to address performance regressions due to plan changes. The oldest is the use of hints in the SQL code to force a specific access path. Stored outlines allowed the hints to be stored separate from the code and modified. Both of these techniques focused on making the plan static. SQL profiles created by the SQL Tuning Advisor allow the optimizer to collect and store additional statistics that will guide the choice of a plan; if the plan becomes inefficient, the Tuning Advisor can be invoked to produce a new profile.

SQL Plan Management automatically controls SQL plan evolution by maintaining what is called “SQL plan baselines.” With this feature enabled, a newly generated SQL plan can join a SQL plan baseline only if it has been proven that doing so will not result in performance regression. So, during the execution of a SQL statement, only a plan that is part of the SQL plan baseline can be used. As described later in this lesson, SQL plan baselines can be automatically loaded or can be seeded using SQL tuning sets. Various scenarios are covered later in this lesson.


• SQL Plan Management is automatically controlled SQL plan evolution.

• Optimizer automatically manages SQL plan baselines.– Only known and verified plans are used.

• Plan changes are automatically verified.– Only comparable or better plans are subsequently used.

• The plan baseline can be seeded for critical SQL with SQL tuning set (STS) from SQL Performance Analyzer.



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SQL Plan Management

Chapter 15 - Page 6

The main benefit of the SQL Plan Management feature is performance stability of the system by avoiding plan regressions. In addition, it saves the DBA time that is often spent in identifying and analyzing SQL performance regressions and finding workable solutions.



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SQL Plan Management

Chapter 15 - Page 7

SQL Plan Baseline: Architecture


The SQL Plan Management (SPM) feature introduces necessary infrastructure and services in support of plan maintenance and performance verification of new plans.

For SQL statements that are executed more than once, the optimizer maintains a history of plans for individual SQL statements. The optimizer recognizes a repeatable SQL statement by maintaining a statement log. A SQL statement is recognized as repeatable when it is parsed or executed again after it has been logged. After a SQL statement is recognized as repeatable, various plans generated by the optimizer are maintained as a plan history containing relevant information (such as SQL text, outline, bind variables, and compilation environment) that is used by the optimizer to reproduce an execution plan.

The DBA may also add plans to the SQL plan baseline by manual seeding a set of SQL statements.

A plan history contains different plans generated by the optimizer for a SQL statement over time. However, only some of the plans in the plan history may be accepted for use. For example, a new plan generated by the optimizer is not normally used until it has been verified not to cause a performance regression. Plan verification is done by default as part of the Automatic SQL Tuning task running as an automated task in a maintenance window.

An Automatic SQL Tuning task targets only high-load SQL statements. For those statements, it automatically implements actions such as making a successfully verified plan an accepted


SQL management base

Statement log

SYSAUX

Plan verification beforeintegration to baseline

RepeatableSQL

statement

SQLprofile

AutomaticSQL tuning

task

Plan history

HJ

GB

HJ

HJ

GB

HJ

HJ

GB

HJ

…

Planbaseline



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SQL Plan Management

Chapter 15 - Page 8

plan. A set of acceptable plans constitutes a SQL plan baseline. The very first plan generated for a SQL statement is obviously acceptable for use; therefore, it forms the original plan baseline. Any new plans subsequently found by the optimizer are part of the plan history but not part of the plan baseline initially.

The statement log, plan history, and plan baselines are stored in the SQL management base (SMB), which also contains SQL profiles. The SMB is part of the database dictionary and is stored in the SYSAUX tablespace. The SMB has automatic space management (for example, periodic purging of unused plans). You can configure the SMB to change the plan retention policy and set space size limits.

Note: With Oracle Database 11g, if the database instance is up but the SYSAUX tablespace is OFFLINE, the optimizer is unable to access SQL management objects. This can affect performance on some of the SQL workload.



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SQL Plan Management

Chapter 15 - Page 9

Loading SQL Plan Baselines


There are two ways to load SQL plan baselines.

• On the fly capture: Uses automatic plan capture by setting the OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES initialization parameter to TRUE. This parameter is set to FALSE by default. Setting it to TRUE turns on automatic recognition of repeatable SQL statements and automatic creation of plan history for such statements. This is illustrated in the graphic on the left in the slide, where the first generated SQL plan is automatically integrated into the original SQL plan baseline when it becomes a repeating SQL statement.

• Bulk loading: Uses the DBMS_SPM package, which enables you to manually manage SQL plan baselines. With procedures from this package, you can load SQL plans into a SQL plan baseline directly from the cursor cache ( A ) using LOAD_PLANS_FROM_CURSOR_CACHE or from an existing SQL tuning set (STS) (B) using LOAD_PLANS_FROM_SQLSET. For a SQL statement to be loaded into a SQL plan baseline from an STS, the plan for the SQL statement needs to be stored in the STS. DBMS_SPM.ALTER_SQL_PLAN_BASELINE enables you to enable and disable a baseline plan and change other plan attributes. To move baselines between databases, use the DBMS_SPM.*_STGTAB_BASELINE procedures to create a staging table, and to


DBA

OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES=TRUE

Plan history

1

2

3

4

Plan historydbms_spm

Stagingtable

HJ

GB

HJ

Cursorcache

Plan history

A

B

HJ

GB

HJ

HJ

GB

HJ

HJ

GB

HJ

HJ

GB

HJ



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SQL Plan Management


export and import baseline plans from a staging table. The staging table can be moved between databases using the Data Pump Export and Import utilities.



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SQL Plan Management


Evolving SQL Plan Baselines


When the optimizer finds a new plan for a SQL statement, the plan is added to the plan history as a nonaccepted plan. The plan will not be accepted into the SQL plan baseline until it is verified for performance relative to the SQL plan baseline performance. Verification means a nonaccepted plan does not cause a performance regression (either manually or automatically). The verification of a nonaccepted plan consists of comparing its performance to the performance of one plan selected from the SQL plan baseline and ensuring that it delivers better performance.

There are two ways to evolve SQL plan baselines:

• By using the DBMS_SPM.EVOLVE_SQL_PLAN_BASELINE function: An example is shown in the slide. The function returns a report that tells you whether some of the existing history plans were moved to the plan baseline. The example specifies a specific plan in the history to be tested. The function also allows verification without accepting the plan.

• By running SQL Tuning Advisor: SQL plan baselines can be evolved by manually or automatically tuning SQL statements using SQL Tuning Advisor. When SQL Tuning Advisor finds a tuned plan and verifies its performance to be better than a plan chosen from the corresponding SQL plan baseline, it makes a recommendation to accept a SQL profile. When the SQL profile is accepted, the tuned plan is added to the corresponding SQL plan baseline.


Plan history

AutomaticSQL Tuning

DBA

SQLTuningAdvisor

>?HJ

GB

HJ

HJ

GB

HJ



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SQL Plan Management


Important Baseline SQL Plan Attributes


When a plan enters the plan history, it is associated with a number of important attributes:

• SIGNATURE, SQL_HANDLE, SQL_TEXT, and PLAN_NAME are important identifiers for search operations.

• ORIGIN allows you to determine whether the plan was automatically captured (AUTO-CAPTURE), manually evolved (MANUAL-LOAD), automatically evolved by SQL Tuning Advisor (MANUAL-SQLTUNE), or automatically evolved by Automatic SQL Tuning (AUTO-SQLTUNE).

• ENABLED and ACCEPTED: Both the ENABLED and ACCEPTED attributes must be set to YES or the plan is not considered by the optimizer. The ENABLED attribute means that the plan is enabled for use by the optimizer. The ACCEPTED attribute means that the plan was validated as a good plan, either automatically by the system or manually when the user changes it to ACCEPTED. When a plan status changes to ACCEPTED, it will continue to be ACCEPTED until DBMS_SPM.ALTER_SQL_PLAN_BASELINE() is used to change its status. An ACCEPTED plan can be temporarily disabled by removing the ENABLED setting.

• FIXED means that the optimizer considers only those plans and not other plans. For example, if you have 10 baseline plans and three of them are marked FIXED, the


select signature, sql_handle, sql_text, plan_name, origin, enabled, accepted, fixed, autopurge

from dba_sql_plan_baselines;

SIGNATURE SQL_HANDLE SQL_TEXT PLAN_NAME ORIGIN ENA ACC FIX AUT

--------- ------------ -------- ---------------- ------------ --- --- --- ---

8.062E+18 SYS_SQL_6fe2 select.. SQL_PLAN_6zsn… AUTO-CAPTURE YES NO NO YES

8.062E+18 SYS_SQL_e23f select.. SQL_PLAN_f4gy… AUTO-CAPTURE YES YES NO YES

…

Plan history

exec :cnt := dbms_spm.alter_sql_plan_baseline(-

sql_handle => 'SYS_SQL_6fe28d438dfc352f', -

plan_name => 'SQL_PLAN_6zsnd8f6zsd9g54bc8843',-

attribute_name => 'ENABLED', attribute_value => 'NO');

Enabled butnot accepted

Enabled andaccepted

HJ

GB

HJHJ

GB

HJ



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SQL Plan Management


optimizer uses only the best plan from these three, ignoring all the others. A SQL plan baseline is said to be FIXED if it contains at least one enabled fixed plan. If new plans are added to a fixed SQL plan baseline, these new plans cannot be used until they are manually declared as FIXED. You can look at each plan’s attributes by using the DBA_SQL_PLAN_BASELINES view, as shown in the slide. You can then use the DBMS_SPM.ALTER_SQL_PLAN_BASELINE function to change some of them. You can also remove plans or a complete plan history by using the DBMS_SPM.DROP_SQL_PLAN_BASELINE function. The example shown in the slide changes the ENABLED attribute of SQL_PLAN_6zsnd8f6zsd9g54bc8843 to NO.

Note: The DBA_SQL_PLAN_BASELINES view contains additional attributes that enable you to determine when each plan was last used and whether a plan should be automatically purged.



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SQL Plan Management


SQL Plan Selection

SQL Plan Selection

If you are using automatic plan capture, the first time that a SQL statement is recognized as repeatable, its best-cost plan is added to the corresponding SQL plan baseline. That plan is then used to execute the statement.

The optimizer uses a comparative plan selection policy when a plan baseline exists for a SQL statement and the OPTIMIZER_USE_SQL_PLAN_BASELINES initialization parameter is set to TRUE (default value). Each time a SQL statement is compiled, the optimizer first uses the traditional cost-based search method to build a best-cost plan. Then it tries to find a matching plan in the SQL plan baseline. If a match is found, it proceeds as usual. If no match is found, it first adds the new plan to the plan history, then calculates the cost of each accepted plan in the SQL plan baseline, and picks the one with the lowest cost. The accepted plans are reproduced using the outline that is stored with each of them. So the effect of having a SQL plan baseline for a SQL statement is that the optimizer always selects one of the accepted plans in that SQL plan baseline.

With SQL Plan Management, the optimizer can produce a plan that could be either a best-cost plan or a baseline plan. This information is dumped in the other_xml column of the plan_table upon explain plan. However, the optimizer can only use an accepted and enabled baseline plan.

SQL Plan Selection

Plan partof history? No

HJ

GB

HJ

HJ

GB

HJ

HJ

GB

HJ

< HJ

GB

HJ

NoHJ

GB

HJ

Yes

Plan history

Planbaseline

HJ

GB

HJ

HJ

GB

HJ

…HJ

GB

HJ

Yes

Plan partof baseline?Yes

optimizer_use_sql_plan_baselines=true?

Yes

HJ

GB

HJ

No

No

Select baseline planwith lowest best cost.



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SQL Plan Management


In addition, you can use the new dbms_xplain.display_sql_plan_baseline function to display one or more execution plans for the specified sql_handle of a plan baseline. If plan_name is also specified, the corresponding execution plan is displayed.

Note: The Stored Outline feature is deprecated. To preserve backward compatibility, if a stored outline for a SQL statement is active for the user session, the statement is compiled using the stored outline. In addition, a plan generated by the optimizer using a stored outline is not stored in the SMB even if automatic plan capture has been enabled for the session.

Stored outlines can be migrated to SQL plan baselines by using the MIGRATE_STORED_OUTLINE procedure from the DBMS_SPM package. When the migration is complete, you should disable or drop the original stored outline using the DROP_MIGRATED_STORED_OUTLINE procedure of the DBMS_SPM package.



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SQL Plan Management


Possible SQL Plan Manageability Scenarios


• Database upgrade: Bulk SQL plan loading is especially useful when the system is being upgraded from an earlier version to Oracle Database 11g. For this, you can capture plans for a SQL workload into a SQL tuning set (STS) before the upgrade, and then load these plans from the STS into the SQL plan baseline immediately after the upgrade. This strategy can minimize plan regressions resulting from the use of the new optimizer version.

• New application deployment: The deployment of a new application module means the introduction of new SQL statements into the system. The software vendor can ship the application software along with the appropriate SQL plan baselines for the new SQL being introduced. Because of the plan baselines, the new SQL statements will initially run with the plans that are known to give good performance under a standard test configuration. However, if the customer system configuration is very different from the test configuration, the plan baselines can be evolved over time to produce better performance.

In both scenarios, you can use the automatic SQL plan capture after manual loading to make sure that only better plans will be used for your applications in the future.

Note: In all scenarios in this lesson, assume that OPTIMIZER_USE_SQL_PLAN_BASELINES is set to TRUE.


DBA

Plan history

HJ

GB

HJ

Database Upgrade

Oracle Database 10g

Oracle Database 11g

HJ

GB

HJ

Well-tunedplan

No planregressions

HJ

GB

HJ

Plan history

HJ

GB

HJ

New Application Deployment

Production database

No planregressions

HJ

GB

HJ

HJ

GB

HJ

Development database

Well-tunedplan

HJ

GB

HJ

Baselineplans

staging table

DBA

Plan history



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SQL Plan Management


SQL Performance Analyzer and SQL Plan Baseline Scenario


A variation of the first method described in the previous slide is through the use of SQL Performance Analyzer. You can capture pre–Oracle Database 11g plans in an STS and import them into Oracle Database 11g. Then set the optimizer_features_enable (O_F_E) initialization parameter to 10.1.0 to make the optimizer behave as if this were a 10g Oracle Database. Next run SQL Performance Analyzer for the STS. When that is complete, set the optimizer_features_enable initialization parameter back to 11.2.0 and rerun SQL Performance Analyzer for the STS.

SQL Performance Analyzer produces a report that lists a SQL statement whose plan has regressed from 10g to 11g. For those SQL statements that are shown by SQL Performance Analyzer to incur performance regression due to the new optimizer version, you can capture their plans using an STS and then load them into the SMB.

This method represents the best form of the plan-seeding process because it helps prevent performance regressions while preserving performance improvements upon database upgrade.


Plan history

HJ

GB

HJ

Oracle Database 10g

Oracle Database 11g

HJ

GB

HJWell-tuned

plans

HJ

GB

HJ

HJ

GB

HJ

O_F_E=10

O_F_E=11

Regressingstatements

Beforechange

Afterchange

HJ

GB

HJ

No planregressions

optimizer_features_enable



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SQL Plan Management


Loading a SQL Plan Baseline Automatically

Loading a SQL Plan Baseline Automatically: Scenario

Another upgrade scenario involves using the automatic SQL plan capture mechanism. In this case, set the initialization parameter optimizer_features_enable (O_F_E) to the pre–Oracle Database 11g version value for an initial period of time such as a quarter, and execute your workload after upgrade by using the automatic SQL plan capture.

During this initial time period, because of the O_F_E parameter setting, the optimizer is able to reproduce pre–Oracle Database 11g plans for a majority of the SQL statements. Because automatic SQL plan capture is also enabled during this period, the pre–Oracle Database 11g plans produced by the optimizer are captured as SQL plan baselines.

When the initial time period ends, you can remove the setting of O_F_E to take advantage of the new optimizer version while incurring minimal or no plan regressions due to the plan baselines. Regressed plans will use the previous optimizer version; nonregressed statements will benefit from the new optimizer version.

Loading a SQL Plan Baseline Automatically

Oracle Database 10g

Well-tunedplans

Oracle Database 11g

optimizer_features_enable=10.2.0.2optimizer_capture_sql_plan_baselines=true

Plan history

Planbaseline

HJ

GB

HJ

HJ

GB

HJ

No planregressions

Oracle Database 11g


Plan history

Planbaseline

HJ

GB

HJ

HJ

GB

HJ

No planregressions

HJ

GB

HJ

New planwaiting

verification

HJ

GB

HJ

HJ

GB

HJ

Oracle Database 11g


Plan history

Betterplans

HJ

GB

HJ

HJ

GB

HJ

HJ

GB

HJ

Plan baseline



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SQL Plan Management


Purging SQL Management Base Policy


The space occupied by the SQL management base (SMB) is checked weekly against a defined limit. A limit based on the percentage size of the SYSAUX tablespace is defined. By default, the space budget limit for the SMB is set to 10 percent of SYSAUX size. However, you can configure SMB and change the space budget to a value between 1 percent and 50 percent by using the DBMS_SPM.CONFIGURE procedure.

If SMB space exceeds the defined percentage limit, warnings are written to the alert log. Warnings are generated weekly until the SMB space limit is increased, the size of SYSAUX is increased, or the size of SMB is decreased by purging some of the SQL management objects (such as SQL plan baselines or SQL profiles).

The space management of SQL plan baselines is done proactively using a weekly purging task. The task runs as an automated task in the maintenance window. Any plan that has not been used for more than 53 weeks is purged. However, you can configure SMB and change the unused plan retention period to a value between 5 weeks and 523 weeks (a little more than 10 years). To do so, use the DBMS_SPM.CONFIGURE procedure.

You can look at the current configuration settings for the SMB by examining the DBA_SQL_MANAGEMENT_CONFIG view. In addition, you can manually purge the SMB by using the DBMS_SPM.DROP_SQL_PLAN_BASELINE function (as shown in the example in the slide).

SYSAUX

20%1% 50% space

time

105

53

SQL ManagementBase

Alert log

10%


SQL> exec dbms_spm.configure('SPACE_BUDGET_PERCENT',20);SQL> exec dbms_spm.configure('PLAN_RETENTION_WEEKS',105);

SQL> exec :cnt := dbms_spm.drop_sql_plan_baseline('SYS_SQL_37e0168b04e73efe');

DBA_SQL_MANAGEMENT_CONFIG



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SQL Plan Management


Enterprise Manager and SQL Plan Baselines


Use the SQL Plan Management page to manage SQL profiles, SQL patches, and SQL plan baselines from one location rather than from separate locations in Enterprise Manager. You can also enable, disable, drop, pack, unpack, load, and evolve selected baselines.

From this page, you can also configure the various SQL plan baseline settings.

To navigate to this page, click the Server tab, and then click the SQL Plan Control entry in the Query Optimizer section.




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SQL Plan Management


Quiz

Answer: b

The optimizer always develops an execution plan. Then it compares the plan with accepted plans in the SQL baseline. If an accepted baseline exists, the baseline is used. If the plan developed by the optimizer is different, it is stored in the plan history, but it is not part of the baseline until it is verified and marked as accepted.

Quiz

When the OPTIMIZER_USE_SQL_PLAN_BASELINES parameter is set to TRUE, the optimizer:

a. Does not develop an execution plan; it uses an accepted plan in the baselines

b. Compares the plan it develops with accepted plans in the baselines

c. Compares the plan it develops with enabled plans in the baselines

d. Does not develop an execution plan; it uses enabled plans in the baselines

e. Develops plans and adds them to the baselines as verified



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SQL Plan Management


Summary

Summary


• Manage SQL performance through changes

• Set up SQL Plan Management

• Set up various SQL Plan Management scenarios



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SQL Plan Management


Practice 15: Overview Using SQL Plan Management

Practice 15: OverviewUsing SQL Plan Management

This practice covers the use of SQL Plan Management.



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SQL Plan Management




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Using Optimizer Hints

Chapter 16 - Page 1


Chapter 16



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Chapter 16 - Page 2





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Chapter 16 - Page 3

Objectives

Objectives

After completing this lesson, you should be able to :

• Use hints when appropriate

• Specify hints for:– Optimizer mode

– Query transformation

– Access path

– Join orders

– Join methods

– Views



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Chapter 16 - Page 4

Optimizer Hints: Overview


Hints enable you to influence decisions made by the optimizer. Hints provide a mechanism to direct the optimizer to select a certain query execution plan based on the specific criteria.

For example, you might know that a certain index is more selective for certain queries. Based on this information, you might be able to select a more efficient execution plan than the plan that the optimizer recommends. In such a case, use hints to force the optimizer to use the optimal execution plan. This is illustrated in the slide example where you force the optimizer to use the EMPFIRSTNAME_IDX index to retrieve the data. As you can see, you can use comments in a SQL statement to pass instructions to the optimizer.

The plus sign (+) causes the system to interpret the comment as a list of hints. The plus sign must follow immediately after the comment delimiter. No space is permitted.

Hints should be used sparingly, and only after you have collected statistics on the relevant tables and evaluated the optimizer plan without hints using the EXPLAIN PLAN statement. Changing database conditions as well as query performance enhancements in subsequent releases can have a significant impact on how hints in your code affect performance.

In addition, the use of hints involves extra code that must be managed, checked, and controlled.


Optimizer hints:

• Influence optimizer decisions

• Example:

• HINTS SHOULD ONLY BE USED AS A LAST RESORT.

• When you use a hint, it is good practice to also add a comment about that hint.

SELECT /*+ INDEX(e empfirstname_idx) skewed col */ *FROM employees eWHERE first_name='David'



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Chapter 16 - Page 5

Types of Hints

Types of Hints

Single-table: Single-table hints are specified on one table or view. INDEX and USE_NL are examples of single-table hints.

Multitable: Multitable hints are like single-table hints, except that the hint can specify one or more tables or views. LEADING is an example of a multitable hint.

Query block: Query block hints operate on single query blocks. STAR_TRANSFORMATION and UNNEST are examples of query block hints.

Statement: Statement hints apply to the entire SQL statement. ALL_ROWS is an example of a statement hint.

Note: USE_NL(table1 table2) is not considered a multitable hint because it is actually a shortcut for USE_NL(table1) and USE_NL(table2).

Types of Hints

Single-table hints Specified on one table or view

Multitable hints Specify more than one table or view

Query block hints Operate on a single query block

Statement hints Apply to the entire SQL statement



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Chapter 16 - Page 6

Specifying Hints

Specifying Hints

Hints apply to the optimization of only the block of the statement in which they appear. A statement block is:

• A simple MERGE, SELECT, INSERT, UPDATE, or DELETE statement

• A parent statement or a subquery of a complex statement

• A part of a compound query using set operators (UNION, MINUS, INTERSECT)

For example, a compound query consisting of two component queries combined by the UNION operator has two blocks, one for each component query. For this reason, hints in the first component query apply only to its optimization, not to the optimization of the second component query.

Optimizer Hint Syntax

Enclose hints within the comments of a SQL statement. You can use either style of comment. The hint delimiter (+) must come immediately after the comment delimiter. If you separate them by a space, the optimizer does not recognize that the comment contains hints.

Specifying Hints

Hints apply to the optimization of only one statement block:• A self-contained DML statement against a table• A top-level DML or a subquery

hint

comment text

*//*+

hint

comment text

--+SELECT

INSERT

DELETE

UPDATE

MERGE

SELECT

INSERT

DELETE

UPDATE

MERGE



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Chapter 16 - Page 7

Rules for Hints

Rules for Hints

• You must place the hint comment immediately after the first keyword (MERGE, SELECT, INSERT, DELETE, or UPDATE) of a SQL statement block.

• A statement block can have only one comment containing hints, but it can contain many hints inside that comment separated by spaces.

• Hints apply to only the statement block in which they appear and override instance- or session-level parameters.

• If a SQL statement uses aliases, hints must reference the aliases rather than the table names.

The Oracle optimizer ignores incorrectly specified hints. However, be aware of the following situations:

• You never get an error message.

• Other (correctly) specified hints in the same comment are considered.

• The Oracle optimizer also ignores combinations of conflicting hints.

Rules for Hints

• Place hints immediately after the first SQL keyword of a statement block.

• Each statement block can have only one hint comment, but it can contain multiple hints.

• Hints apply to only the statement block in which they appear.

• If a statement uses aliases, hints must reference the aliases rather than the table names.

• The optimizer ignores hints specified incorrectly without raising errors.



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Chapter 16 - Page 8

Hint Recommendations


• Use hints as a last remedy when tuning SQL statements.

• Hints may prevent the optimizer from using better execution plans.

• Hints may become less valid (or even invalid) when the database structure or contents change.


• Use hints carefully because they imply a high-maintenance load.

• Be aware of the performance impact of hard-coded hints when they become less valid.



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Chapter 16 - Page 9

Optimizer Hint Syntax: Example


The slide shows an example with a hint that advises the cost-based optimizer (CBO) to use the index. The execution plan is as follows: Execution Plan

----------------------------------------------------------

0 UPDATE STATEMENT Optimizer=ALL_ROWS (Cost=3 …)

1 0 UPDATE OF 'PRODUCTS'

2 1 TABLE ACCESS (BY INDEX ROWID) OF 'PRODUCTS' (TABLE) (Cost…)

3 2 INDEX (RANGE SCAN) OF 'PRODUCTS_PROD_CAT_IX' (INDEX)

(cost…)

4 1 TABLE ACCESS (BY INDEX ROWID) OF 'PRODUCTS' (TABLE) (Cost…) 5 4 INDEX (UNIQUE SCAN) OF 'PRODUCTS_PK' (INDEX (UNIQUE)) (Cost=0 …)

The hint shown in the example works only if an index called PRODUCTS_PROD_CAT_IX exists on the PRODUCTS table in the PROD_CATEGORY column.


UPDATE /*+ INDEX(p PRODUCTS_PROD_CAT_IX)*/ products pSET p.prod_min_price =

(SELECT (pr.prod_list_price*.95)FROM products prWHERE p.prod_id = pr.prod_id)

WHERE p.prod_category = 'Men'AND p.prod_status = 'available, on stock'/



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Hint Categories

Hint Categories

Most of these hints are discussed in the following slides. Many of these hints accept the table and index names as arguments.

Note: Hints for parallel execution is not covered in this course.

Hint Categories

There are hints for:

• Optimization approaches and goals

• Access paths

• Query transformations

• Join orders

• Join operation

• Parallel execution

• Additional hints



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Optimization Goals and Approaches


ALL_ROWS: The ALL_ROWS hint explicitly selects the cost-based approach to optimize a statement block with a goal of best throughput. That is, minimum total resource consumption.

FIRST_ROWS(n): The FIRST_ROWS(n) hint (where n is any positive integer) instructs the Oracle server to optimize an individual SQL statement for fast response. It instructs the server to select the plan that returns the first n rows most efficiently. The FIRST_ROWS hint, which optimizes for the best plan to return the first single row, is retained for backward compatibility and plan stability. The optimizer ignores this hint SELECT statement blocks that include any blocking operations, such as sorts or groupings. Such statements cannot be optimized for best response time because Oracle Database must retrieve all rows accessed by the statement before returning the first row. If you specify this hint in any such statement, the database optimizes for best throughput.

If you specify either the ALL_ROWS or the FIRST_ROWS(n) hint in a SQL statement, and if the data dictionary does not have statistics about tables accessed by the statement, then the optimizer uses default statistical values to estimate the missing statistics and to subsequently select an execution plan.

If you specify hints for access paths or join operations along with either the ALL_ROWS or FIRST_ROWS(n) hint, the optimizer gives precedence to the access paths and join operations specified by the hints.


Note: The ALTER SESSION... SET OPTIMIZER_MODEstatement does not affect SQL that is run from within PL/SQL.

ALL_ROWS Selects a cost-based approach with a goal of best throughput

FIRST_ROWS(n) Instructs the Oracle server to optimize an individual SQL statement for fast response



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Note: The FIRST_ROWS hints are probably the most useful hints.



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Hints for Access Paths


Specifying one of these hints causes the optimizer to choose the specified access path only if the access path is available based on the existence of an index and on the syntactic constructs of the SQL statement. If a hint specifies an unavailable access path, the optimizer ignores it. You must specify the table to be accessed exactly as it appears in the statement. If the statement uses an alias for the table, use the alias rather than the table name in the hint. The table name in the hint should not include the schema name if the schema name is present in the statement.

FULL: The FULL hint explicitly selects a full table scan for the specified table. For example:

SELECT /*+ FULL(e) */ employee_id, last_name

FROM hr.employees e WHERE last_name LIKE 'K%';

The Oracle server performs a full table scan on the employees table to execute this statement, even if there is an index on the last_name column that is made available by the condition in the WHERE clause.

CLUSTER: The CLUSTER hint instructs the optimizer to use a cluster scan to access the specified table. This hint applies only to clustered tables.

HASH: The HASH hint instructs the optimizer to use a hash scan to access the specified table. This hint applies only to tables stored in a table cluster.


FULL Performs a full table scan

CLUSTER Accesses table using a cluster scan

HASH Accesses table using a hash scan

ROWID Accesses a table by ROWID

INDEX Selects an index scan for the specified table

INDEX_ASC Scans an index in ascending order

INDEX_COMBINE Explicitly chooses a bitmap access path



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ROWID: The ROWID hint explicitly chooses a table scan by ROWID for the specified table.

INDEX: The INDEX hint explicitly chooses an index scan for the specified table. You can use the INDEX hint for domain, B*-tree, bitmap, and bitmap join indexes. However, it is better if you use INDEX_COMBINE rather than INDEX for bitmap indexes because it is a more versatile hint. This hint can optionally specify one or more indexes.

If this hint specifies a single available index, the optimizer performs a scan on this index. The optimizer does not consider a full table scan or a scan on another index on the table.

If this hint specifies a list of available indexes, the optimizer considers the cost of a scan on each index in the list and then performs the index scan with the lowest cost. The optimizer can also choose to scan multiple indexes from this list and merge the results, if such an access path has the lowest cost. The optimizer does not consider a full table scan or a scan on an index not listed in the hint.

If this hint specifies no indexes, the optimizer considers the cost of a scan on each available index on the table and then performs the index scan with the lowest cost. The optimizer can also choose to scan multiple indexes and merge the results, if such an access path has the lowest cost. The optimizer does not consider a full table scan.

INDEX_ASC: The INDEX_ASC hint explicitly chooses an index scan for the specified table. If the statement uses an index range scan, the Oracle server scans the index entries in ascending order of their indexed values. Because the server’s default behavior for a range scan is to scan index entries in the ascending order of their indexed values, this hint does not specify anything more than the INDEX hint. However, you might want to use the INDEX_ASC hint to specify ascending range scans explicitly, should the default behavior change.

INDEX_COMBINE: The INDEX_COMBINE hint explicitly chooses a bitmap access path for the table. If no indexes are given as arguments for the INDEX_COMBINE hint, the optimizer uses a Boolean combination of bitmap indexes that has the best cost estimate for the table. If certain indexes are given as arguments, the optimizer tries to use some Boolean combination of those particular bitmap indexes.

For example:

SELECT /*+INDEX_COMBINE(customers cust_gender_bix cust_yob_bix)*/ *

FROM customers WHERE cust_year_of_birth < 70 AND cust_gender = 'M';

Note: For INDEX, INDEX_FFS, and INDEX_SS, there are counter hints, NO_INDEX, NO_INDEX_FFS, and NO_INDEX_SS, respectively to avoid using those paths.



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Hints for Access Paths (continued)

INDEX_JOIN: The INDEX_JOIN hint explicitly instructs the optimizer to use an index join as an access path. For the hint to have a positive effect, a sufficiently small number of indexes must exist that contain all the columns required to resolve the query.

For example, the following query uses an index join to access the employee_id and department_id columns, both of which are indexed in the employees table:

SELECT /*+index_join(employees emp_emp_id_pk emp_department_ix)*/ employee_id, department_id FROM hr.employees WHERE department_id > 50;

INDEX_DESC: The INDEX_DESC hint instructs the optimizer to use a descending index scan for the specified table. If the statement uses an index range scan and the index is ascending, the system scans the index entries in the descending order of their indexed values. In a partitioned index, the results are in the descending order within each partition. For a descending index, this hint effectively cancels out the descending order, resulting in a scan of the index entries in the ascending order. The INDEX_DESC hint explicitly chooses an index scan for the specified table.

For example:


INDEX_JOIN Instructs the optimizer to use an index join as an access path

INDEX_DESC Scans an index in descending order

INDEX_FFS Performs a fast-full index scan

INDEX_SS Performs an index skip scan

NO_INDEX Does not allow using a set of indexes



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SELECT /*+ INDEX_DESC(a ord_order_date_ix) */ a.order_date, a.promotion_id, a.order_id FROM oe.orders a WHERE a.order_date < '01-jan-1985';

INDEX_FFS: The INDEX_FFS hint causes a fast-full index scan to be performed rather than a full table scan.

For example:

SELECT /*+ INDEX_FFS ( o order_pk ) */ COUNT(*) FROM order_items l, orders o WHERE l.order_id > 50 AND l.order_id = o.order_id;

INDEX_SS: The INDEX_SS hint instructs the optimizer to perform an index skip scan for the specified indexes of the specified table. If the statement uses an index range scan, the system scans the index entries in the ascending order of their indexed values. In a partitioned index, the results are in the ascending order within each partition. There are also INDEX_SS_ASC and INDEX_SS_DESC hints.

NO_INDEX: The NO_INDEX hint explicitly disallows a set of indexes for the specified table.

• If this hint specifies a single available index, the optimizer does not consider a scan on this index. Other indexes that are not specified are still considered.

• If this hint specifies a list of available indexes, the optimizer does not consider a scan on any of the specified indexes. Other indexes that are not specified in the list are still considered.

• If this hint specifies no indexes, the optimizer does not consider a scan on any index on the table. This behavior is the same as a NO_INDEX hint that specifies a list of all available indexes for the table.

The NO_INDEX hint applies to function-based, B*-tree, bitmap, or domain indexes. If a NO_INDEX hint and an index hint (INDEX, INDEX_ASC, INDEX_DESC, INDEX_COMBINE, or INDEX_FFS) both specify the same indexes, then both the NO_INDEX hint and the index hint are ignored for the specified indexes and the optimizer considers the specified indexes.

For example:

SELECT /*+NO_INDEX(employees emp_empid)*/ employee_id FROM employees WHERE employee_id > 200; Vijai Sahu (sahuvijay21@gmailฺcom) has a non-transferable license


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The INDEX_COMBINE Hint: Example


The INDEX_COMBINE hint is designed for bitmap index operations. Remember the following:

• If certain indexes are given as arguments for the hint, the optimizer tries to use some combination of those particular bitmap indexes.

• If no indexes are named in the hint, all indexes are considered to be hinted.

• The optimizer always tries to use hinted indexes, whether or not it considers them to be cost effective.

In the example in the slide, suppose that all the three columns that are referenced in the WHERE predicate of the statement in the slide (CUST_MARITAL_STATUS, CUST_GENDER, and CUST_YEAR_OF_BIRTH) have a bitmap index. When you enable AUTOTRACE, the execution plan of the statement might appear as shown in the next slide.


SELECT /*+INDEX_COMBINE(CUSTOMERS)*/cust_last_name

FROM SH.CUSTOMERSWHERE ( CUST_GENDER= 'F' ANDCUST_MARITAL_STATUS = 'single')OR CUST_YEAR_OF_BIRTH BETWEEN '1917' AND '1920';



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The INDEX_COMBINE Hint: Example (continued)

In the example in the slide, the following bitmap row sources are used:

• BITMAP CONVERSION TO ROWIDS: Converts bitmaps into ROWIDs to access a table

• COUNT: Returns the number of entries if the actual values are not needed

• BITMAP OR: Computes the bitwise OR of two bitmaps

• BITMAP AND: Computes the bitwise AND of two bitmaps

• BITMAP INDEX SINGLE VALUE: Looks up the bitmap for a single key

• BITMAP INDEX RANGE SCAN: Retrieves bitmaps for a value range

• BITMAP MERGE: Merges several bitmaps resulting from a range scan into one (using a bitwise AND operator)


Execution Plan---------------------------------------------------| 0 | SELECT STATEMENT || 1 | TABLE ACCESS BY INDEX ROWID | CUSTOMERS| 2 | BITMAP CONVERSION TO ROWIDS || 3 | BITMAP OR || 4 | BITMAP MERGE || 5 | BITMAP INDEX RANGE SCAN | CUST_YOB_BIX| 6 | BITMAP AND || 7 | BITMAP INDEX SINGLE VALUE| CUST_MARITAL_BIX| 8 | BITMAP INDEX SINGLE VALUE| CUST_GENDER_BIX



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Hints for Query Transformation


NO_QUERY_TRANSFORMATION: The NO_QUERY_TRANSFORMATION hint instructs the optimizer to skip all query transformations, including but not limited to OR-expansion, view merging, subquery unnesting, star transformation, and materialized view rewrite.

USE_CONCAT: The USE_CONCAT hint forces combined OR conditions in the WHERE clause of a query to be transformed into a compound query using the UNION ALL set operator. Generally, this transformation occurs only if the cost of the query using the concatenations is cheaper than the cost without them. The USE_CONCAT hint disables IN-list processing.

NO_EXPAND: The NO_EXPAND hint prevents the cost-based optimizer from considering OR-expansion for queries having OR conditions or IN-lists in the WHERE clause. Usually, the optimizer considers using OR expansion and uses this method if it decides that the cost is lower than not using it.

REWRITE: The REWRITE hint instructs the optimizer to rewrite a query in terms of materialized views, when possible, without cost consideration. Use the REWRITE hint with or without a view list. This course does not deal with Materialized Views.

UNNEST: The UNNEST hint instructs the optimizer to unnest and merge the body of the subquery into the body of the query block that contains it, allowing the optimizer to consider them together when evaluating access paths and joins.


NO_QUERY_TRANSFORMATION Skips all query transformation

USE_CONCAT Rewrites OR into UNION ALL and disables INLIST processing

NO_EXPAND Prevents OR expansions

REWRITE Rewrites query in terms of materialized views

NO_REWRITE Turns off query rewrite

UNNEST Merges subquery bodies into surrounding query block

NO_UNNEST Turns off unnesting



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Hints for Query Transformation (continued)

MERGE: The MERGE hint lets you merge a view for each query. If a view’s query contains a GROUP BY clause or a DISTINCT operator in the SELECT list, then the optimizer can merge the view’s query into the accessing statement only if complex view merging is enabled. This is the case by default, but you can disable this mechanism using the NO_MERGE hint. Complex merging can also be used to merge an IN subquery into the accessing statement if the subquery is not correlated.

When the MERGE hint is used without an argument, it should be placed in the view query block. When MERGE is used with the view name as an argument, it should be placed in the surrounding query.

NO_MERGE: The NO_MERGE hint causes the Oracle server not to merge views that can be merged. This hint gives the user more influence over the way in which the view is accessed. When the NO_MERGE hint is used without an argument, it should be placed in the view query block. When NO_MERGE is used with the view name as an argument, it should be placed in the surrounding query.

STAR_TRANSFORMATION: The STAR_TRANSFORMATION hint causes the optimizer to use the best plan in which the transformation has been used. Without the hint, the optimizer could make a cost-based decision to use the best plan that is generated without the transformation, instead of the best plan for the transformed query.


MERGE Merges complex views or subqueries with the surrounding query

NO_MERGE Prevents merging of mergeable views

STAR_TRANSFORMATION Makes the optimizer use the best plan in which the transformation can be used

FACT Indicates that the hinted table should be considered as a fact table

NO_FACT Indicates that the hinted table should not be considered as a fact table



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Even if the hint is given, there is no guarantee that the transformation will take place. The optimizer generates the subqueries only if it seems reasonable to do so. If no subqueries are generated, there is no transformed query, and the best plan for the untransformed query is used regardless of the hint.

FACT: The FACT hint is used in the context of the star transformation to indicate to the transformation that the hinted table should be considered as a fact table.

NO_FACT: The NO_FACT hint is used in the context of the star transformation to indicate to the transformation that the hinted table should not be considered as a fact table.



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Hints for Join Orders


The following hints are used to suggest join orders:

ORDERED: The ORDERED hint causes the Oracle server to join tables in the order in which they appear in the FROM clause. If you omit the ORDERED hint from a SQL statement performing a join, the optimizer selects the order in which to join the tables. You might want to use the ORDERED hint to specify a join order if you know something that the optimizer does not know about the number of rows that are selected from each table. With a nested loops example, the most precise method is to order the tables in the FROM clause in the order of the keys in the index, with the large table at the end. Then use the following hints:

/*+ ORDERED USE_NL(FACTS) INDEX(facts fact_concat) */

Here, facts is the table and fact_concat is the index. A more general method is to use the STAR hint.

LEADING: The LEADING hint instructs the optimizer to use the specified set of tables as the prefix in the execution plan. The LEADING hint is ignored if the tables specified cannot be joined first in the order specified because of dependencies in the join graph. If you specify two or more LEADING hints on different tables, all the hints are ignored. If you specify the ORDERED hint, it overrides all LEADING hints.


ORDERED Causes the Oracle server to join tables in the order in which they appear in the FROM clause

LEADING Uses the specified tables as the first table in the join order



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Hints for Join Operations


Each hint described here suggests a join operation for a table. In the hint, you must specify a table exactly the same way as it appears in the statement. If the statement uses an alias for the table, you must use the alias rather than the table name in the hint. However, the table name in the hint should not include the schema name if the schema name is present in the statement. Use of the USE_NL and USE_MERGE hints is recommended with the ORDERED hint. The Oracle server uses these hints when the referenced table is forced to be the inner table of a join; the hints are ignored if the referenced table is the outer table.

USE_NL: The USE_NL hint causes the Oracle server to join each specified table to another row source with a nested loops join, using the specified table as the inner table. If you want to optimize the statement for best response time or for the minimal elapsed time that is necessary to return the first row selected by the query, rather than for best throughput, then you can force the optimizer to select a nested loop join by using the USE_NL hint.

USE_NL_WITH_INDEX: The USE_NL_WITH_INDEX hint is similar to the USE_NL hint. However, if no index is specified, the optimizer must be able to use some index with at least one join predicate as the index key. If an index is specified, the optimizer must be able to use that index with at least one join predicate as the index key.

NO_USE_NL: The NO_USE_NL hint causes the optimizer to exclude the nested loops join.


USE_NL Joins the specified table using a nested loop join

NO_USE_NL Does not use nested loops to perform the join

USE_NL_WITH_INDEX Similar to USE_NL, but must be able to use an index for the join

USE_MERGE Joins the specified table using a sort-merge join

NO_USE_MERGE Does not perform sort-merge operations for the join

USE_HASH Joins the specified table using a hash join

NO_USE_HASH Does not use hash join

DRIVING_SITE Instructs the optimizer to execute the query at a different site than that selected by the database



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However, in some cases tables can only be joined using nested loops. In such cases, the optimizer ignores the hint for those tables.

In many cases, a nested loop join returns the first row faster than a sort-merge join does. A nested loop join can return the first row after reading the first selected row from one table and the first matching row from the other and combining them. But a sort-merge join cannot return the first row until after reading and sorting all selected rows of both tables and then combining the first rows of each sorted row source.

In the following statement in which a nested loop is forced through a hint, orders is accessed through a full table scan and the l.order_id = h.order_id filter condition is applied to every row. For every row that meets the filter condition, order_items is accessed through the index order_id.

SELECT /*+ USE_NL(l h) */ h.customer_id, l.unit_price * l.quantity FROM oe.orders h ,oe.order_items l WHERE l.order_id = h.order_id;

Adding an INDEX hint to the query could avoid the full table scan on orders, resulting in an execution plan similar to one that is used on larger systems, even though it might not be particularly efficient here.

USE_MERGE: The USE_MERGE hint causes the Oracle server to join each specified table with another row source by using a sort-merge join, as in the following example:

SELECT /*+USE_MERGE(employees departments)*/ * FROM employees, departments WHERE employees.department_id = departments.department_id;

NO_USE_MERGE: The NO_USE_MERGE hint causes the optimizer to exclude the sort-merge join to join each specified table to another row source using the specified table as the inner table.

USE_HASH: The USE_HASH hint causes the Oracle server to join each specified table with another row source using a hash join, as in the following example:

SELECT /*+USE_HASH(l l2) */ l.order_date, l.order_id, l2.product_id, SUM(l2.unit_price*quantity) FROM oe.orders l, oe.order_items l2 WHERE l.order_id = l2.order_id GROUP BY l2.product_id, l.order_date, l.order_id;

Here is another example:

SELECT /*+use_hash(employees departments)*/ * FROM hr.employees, hr.departments WHERE employees.department_id = departments.department_id;

NO_USE_HASH: The NO_USE_HASH hint causes the optimizer to exclude the hash join to join each specified table to another row source using the specified table as the inner table.

DRIVING_SITE: This hint instructs the optimizer to execute the query at a different site than that selected by the database. This hint is useful if you are using distributed query optimization to decide on which site a join should be executed.



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Additional Hints

Additional Hints

APPEND: The APPEND hint lets you enable direct-path INSERT if your database runs in serial mode. Your database is in serial mode if you are not using Enterprise Edition. Conventional INSERT is the default in serial mode, and direct-path INSERT is the default in parallel mode. In direct-path INSERT, data is appended to the end of the table rather than using existing space currently allocated to the table. As a result, direct-path INSERT can be considerably faster than the conventional INSERT. Note: In Enterprise Edition, a session must be placed in parallel mode for direct-path insert to be the default.

NOAPPEND: The NOAPPEND hint disables direct-path INSERT by disabling parallel mode for the duration of the INSERT statement. (Conventional INSERT is the default in serial mode, and direct-path INSERT is the default in parallel mode.)

CURSOR_SHARING_EXACT: The Oracle server can replace literals in SQL statements with bind variables if it is safe to do so. This is controlled with the CURSOR_SHARING startup parameter. The CURSOR_SHARING_EXACT hint causes this behavior to be disabled. In other words, the Oracle server executes the SQL statement without any attempt to replace literals with bind variables.

Additional Hints

APPEND Enables direct-path INSERT

NOAPPEND Enables regular INSERT

CURSOR_SHARING_EXACT Prevents replacing literals with bind variables

CACHE Overrides the default caching specification of the table

PUSH_PRED Pushes join predicate into view

PUSH_SUBQ Evaluates nonmerged subqueries first

DYNAMIC_SAMPLING Controls dynamic sampling to improve server performance



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CACHE: The CACHE hint instructs the optimizer to place the blocks retrieved for the table in the corresponding hot part of the buffer cache when a full table scan is performed. This hint is useful for small lookup tables.

The CACHE and NOCACHE hints affect system statistics table scans (long tables) and table scans (short tables), as shown in the V$SYSSTAT data dictionary view.

PUSH_PRED: The PUSH_PRED hint instructs the optimizer to push a join predicate into the view.

PUSH_SUBQ: The PUSH_SUBQ hint instructs the optimizer to evaluate nonmerged subqueries at the earliest possible step in the execution plan. Generally, subqueries that are not merged are executed as the last step in the execution plan. If the subquery is relatively inexpensive and reduces the number of rows significantly, evaluating the subquery earlier can improve performance. This hint has no effect if the subquery is applied to a remote table or one that is joined using a merge join.

DYNAMIC_SAMPLING: The DYNAMIC_SAMPLING hint lets you control dynamic sampling to improve server performance by determining more accurate selectivity and cardinality estimates. You can set the value of DYNAMIC_SAMPLING to a value from 0 to 10. The higher the level, the more effort the compiler puts into dynamic sampling and the more broadly it is applied. Sampling defaults to the cursor level unless you specify a table.

Consider the following example:

SELECT /*+ dynamic_sampling(1) */ * FROM ...

This example enables dynamic sampling if all the following conditions are true:

• There is more than one table in the query.

• At least one table has not been analyzed and has no indexes.

• The optimizer determines that a relatively expensive table scan is required for the table that has not been analyzed.



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Additional Hints

Additional Hints (continued)

MONITOR: The MONITOR hint forces real-time SQL monitoring for the query, even if the statement is not long running. This hint is valid only when the CONTROL_MANAGEMENT_PACK_ACCESS parameter is set to DIAGNOSTIC+TUNING.

NO_MONITOR: The NO_MONITOR hint disables real-time SQL monitoring for the query.

RESULT_CACHE: The RESULT_CACHE hint instructs the database to cache the results of the current query or query fragment in memory and then to use the cached results in future executions of the query or query fragment.

NO_RESULT_CACHE: The optimizer caches query results in the result cache if the RESULT_CACHE_MODE initialization parameter is set to FORCE. In this case, the NO_RESULT_CACHE hint disables such caching for the current query.

OPT_PARAM: The OPT_PARAM hint lets you set an initialization parameter for the duration of the current query only. This hint is valid only for the following parameters: OPTIMIZER_DYNAMIC_SAMPLING, OPTIMIZER_INDEX_CACHING, OPTIMIZER_INDEX_COST_ADJ, OPTIMIZER_SECURE_VIEW_MERGING, and STAR_TRANSFORMATION_ENABLED

Additional Hints

MONITOR Forces real-time query monitoring

NO_MONITOR Disables real-time query monitoring

RESULT_CACHE Caches the result of the query or query fragment

NO_RESULT_CACHE Disables result caching for the query or query fragment

OPT_PARAM Sets initialization parameter for query duration



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Hints and Views

Hints and Views

You should not use hints in or on views because views can be defined in one context and used in another; such hints can result in unexpected plans. In particular, hints in views are handled differently from hints on views depending on whether or not the view is mergeable into the top-level query.

View Optimization

The statement is normally transformed into an equivalent statement that accesses the view’s base tables. The optimizer can use one of the following techniques to transform the statement:

• Merge the view’s query into the referencing query block in the accessing statement.

• Push the predicate of the referencing query block inside the view.

When these transformations are impossible, the view’s query is executed and the result is accessed as if it were a table. This appears as a VIEW step in execution plans.

Mergeable Views

The optimizer can merge a view into a referencing query block if the view definition does not contain the following:

• Set operators (UNION, UNION ALL, INTERSECT, MINUS)

Hints and Views

• Do not use hints in views.

• Use view-optimization techniques:– Statement transformation

– Results accessed like a table

• Hints can be used on mergeable views and nonmergeable views.



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• The CONNECT BY clause

• The ROWNUM pseudocolumn

• Group functions (AVG, COUNT, MAX, MIN, SUM) in the select list

Hints and Mergeable Views

Optimization-approach and goal hints can occur in a top-level query or in views:

• If there is such a hint in the top-level query, that hint is used regardless of any such hints in the views.

• If there is no top-level optimizer-mode hint, mode hints in referenced views are used as long as all mode hints in the views are consistent.

• If two or more mode hints in the referenced views conflict, all mode hints in the views are discarded and the session mode is used, whether default or user specified.

Access-method and join hints on referenced views are ignored unless the view contains a single table (or references another view with a single table). For such single-table views, an access-method hint or a join hint on the view applies to the table in the view.

Access-method and join hints can also appear in a view definition:

• If the view is a subquery (that is, if it appears in the FROM clause of a SELECT statement), all access-method and join hints in the view are preserved when the view is merged with the top-level query.

• For views that are not subqueries, access-method and join hints in the view are preserved only if the top-level query references no other tables or views (that is, if the FROM clause of the SELECT statement contains only the view).

Hints and Nonmergeable Views

With nonmergeable views, optimizer-mode hints in the view are ignored. The top-level query decides the optimization mode.

Because nonmergeable views are optimized separately from the top-level query, access-method and join hints in the view are always preserved. For the same reason, access-method hints on the view in the top-level query are ignored.

However, join hints on the view in the top-level query are preserved because (in this case) a nonmergeable view is similar to a table. Vijai Sahu (sahuvijay21@gmailฺcom) has a non-transferable license


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Global Table Hints

Global Table Hints

Hints that specify a table generally refer to tables in the DELETE, SELECT, or UPDATE query block in which the hint occurs, rather than to tables inside any views that are referenced by the statement. When you want to specify hints for tables that appear inside views, it is recommended that you use global hints instead of embedding the hint in the view.

The table hints can be transformed into global hints by using an extended table specification syntax that includes view names with the table name as shown in the slide. In addition, an optional query block name can precede the table specification.

For example, by using the global hint structure, you can avoid the modification of a view with the specification of an index hint in the body of view.

Note: If a global hint references a table name or alias that is used twice in the same query (for example, in a UNION statement), the hint applies to only the first instance of the table (or alias).

Global Table Hints

• Extended hint syntax enables specifying for tables that appear in views

• References a table name in the hint with a recursive dot notation

CREATE view city_view ASSELECT *FROM customers cWHERE cust_city like 'S%';

SELECT /*+ index(v.c cust_credit_limit_idx) */v.cust_last_name, v.cust_credit_limit

FROM city_view vWHERE cust_credit_limit > 5000;



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Specifying a Query Block in a Hint


You can specify an optional query block name in many hints to specify the query block to which the hint applies. This syntax lets you specify in the outer query a hint that applies to an inline view.

The syntax of the query block argument is of the @queryblock form, where queryblock is an identifier that specifies a query block in the query. The queryblock identifier can either be system-generated or user-specified. When you specify a hint in the query block itself to which the hint applies, you do not have to specify the @queryblock syntax.

The slide gives you an example. You can see that the SELECT statement uses an inline view. The corresponding query block is given the name strange through the use of the QB_NAME hint.

The example assumes that there is an index on the DEPTNO column of the DEPT table so that the optimizer would normally choose that index to access the DEPT table. However, because you specify the FULL hint to apply to the strange query block in the main query block, the optimizer does not use the index in question. You can see that the execution plan exhibits a full table scan on the DEPT table. In addition, the output of the plan clearly shows the system-generated names for each query block in the original query.


explain plan forselect /*+ FULL(@strange dept) */ enamefrom emp e, (select /*+ QB_NAME(strange) */ *

from dept where deptno=10) dwhere e.deptno = d.deptno and d.loc = 'C';

SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY(NULL, NULL, 'ALL'));

Plan hash value: 615168685---------------------------------------------------------------| Id | Operation | Name | Rows | Bytes | Cost(%CPU)|---------------------------------------------------------------| 0 | SELECT STATEMENT | | 1 | 41 | 7 (15)||* 1 | HASH JOIN | | 1 | 41 | 7 (15)||* 2 | TABLE ACCESS FULL| DEPT | 1 | 21 | 3 (0)||* 3 | TABLE ACCESS FULL| EMP | 3 | 60 | 3 (0)|---------------------------------------------------------------Query Block Name / Object Alias (identified by operation id):-------------------------------------------------------------

1 - SEL$DB579D142 - SEL$DB579D14 / DEPT@STRANGE3 - SEL$DB579D14 / E@SEL$1



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Specifying a Full Set of Hints


When using hints, you might sometimes need to specify a full set of hints to ensure the optimal execution plan. For example, if you have a very complex query consisting of many table joins, and if you specify only the INDEX hint for a given table, then the optimizer needs to determine the remaining access paths to be used as well as the corresponding join methods. Therefore, even though you gave the INDEX hint, the optimizer might not necessarily use that hint because the optimizer might have determined that the requested index cannot be used due to the join methods and access paths that were selected by the optimizer.

In the example, the LEADING hint specifies the exact join order to be used. The join methods to be used on the different tables are also specified.


SELECT /*+ LEADING(e2 e1) USE_NL(e1) INDEX(e1 emp_emp_id_pk) USE_MERGE(j) FULL(j) */

e1.first_name, e1.last_name, j.job_id, sum(e2.salary) total_sal

FROM hr.employees e1, hr.employees e2, hr.job_history j

WHERE e1.employee_id = e2.manager_id

AND e1.employee_id = j.employee_id

AND e1.hire_date = j.start_date

GROUP BY e1.first_name, e1.last_name, j.job_id

ORDER BY total_sal;



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Summary

Summary

In this lesson, you should have learned about additional optimizer settings and hints.

By using hints, you can influence the optimizer at the statement level. Use hints as a last remedy when tuning SQL statements. There are several hint categories, one of which includes hints for access-path methods.

To specify a hint, use the hint syntax in the SQL statement.

Summary


• Use hints when appropriate

• Specify hints for:– Optimizer mode

– Query transformation

– Access path

– Join orders

– Join methods

– Views



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Practice Appendix B: Overview

Practice Appendix B: Overview

This practice covers using various hints to influence execution plans.



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Using SQL Developer

Chapter 17 - Page 1

Using SQL Developer

Chapter 17



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Using SQL Developer

Chapter 17 - Page 2

Using SQL Developer

Using SQL Developer



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Using SQL Developer

Chapter 17 - Page 3

Objectives

Objectives

In this appendix, you are introduced to the graphical tool called SQL Developer. You learn how to use SQL Developer for your database development tasks. You learn how to use SQL Worksheet to execute SQL statements and SQL scripts.

Objectives

After completing this appendix, you should be able to do the following:

• List the key features of Oracle SQL Developer

• Identify menu items of Oracle SQL Developer

• Create a database connection

• Manage database objects

• Use SQL Worksheet

• Save and run SQL scripts

• Create and save reports



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Using SQL Developer

Chapter 17 - Page 4



Oracle SQL Developer is a free graphical tool designed to improve your productivity and simplify the development of everyday database tasks. With just a few clicks, you can easily create and debug stored procedures, test SQL statements, and view optimizer plans.

SQL Developer, the visual tool for database development, simplifies the following tasks:

• Browsing and managing database objects

• Executing SQL statements and scripts

• Editing and debugging PL/SQL statements

• Creating reports

You can connect to any target Oracle database schema by using standard Oracle database authentication. When connected, you can perform operations on objects in the database.

The SQL Developer tightly integrates with Developer Migration Workbench that provides users with a single point to browse database objects and data in third-party databases, and to migrate from these databases to an Oracle database. You can also connect to schemas for selected third-party (non-Oracle) databases such as MySQL, Microsoft SQL Server, and Microsoft Access, and you can view metadata and data in these databases.

Additionally, SQL Developer includes support for Oracle Application Express 3.0.1 (Oracle APEX).


• Oracle SQL Developer is a graphical tool that enhances productivity and simplifies database development tasks.

• You can connect to any target Oracle database schema by using standard Oracle database authentication.

SQL Developer



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Using SQL Developer

Chapter 17 - Page 5

Specifications of SQL Developer


Oracle SQL Developer 1.5 is shipped along with Oracle Database 11g Release 2. SQL Developer is developed in Java leveraging the Oracle JDeveloper integrated development environment (IDE). Therefore, it is a cross-platform tool. The tool runs on Windows, Linux, and Mac operating system (OS) X platforms.

Default connectivity to the database is through the JDBC thin driver, and therefore, no Oracle Home is required. SQL Developer does not require an installer and you need to simply unzip the downloaded file. With SQL Developer, users can connect to Oracle Databases 9.2.0.1 and later, and all Oracle database editions including Express Edition.

Note

For Oracle Database versions earlier than Oracle Database 11g Release 2, you will have to download and install SQL Developer. SQL Developer 2.1 is the current version and is freely downloadable from the following link:

http://www.oracle.com/technology/products/database/sql_developer/index.html.

For instructions on how to install SQL Developer 2.1, you can visit the following link:

http://download.oracle.com/docs/cd/E15846_01/index.htm


• Shipped along with Oracle Database 11g Release 2

• Developed in Java

• Supports Windows, Linux, and Mac OS X platforms

• Default connectivity by using the Java Database Connectivity (JDBC) thin driver

• Connects to Oracle Database version 9.2.0.1 and later

• Freely downloadable from the following link:– http://www.oracle.com/technology/products/database/sql_de

veloper/index.html



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Using SQL Developer

Chapter 17 - Page 6

SQL Developer 2.1 Interface


The SQL Developer 2.1 interface contains three main navigation tabs, from left to right:

• Connections tab: By using this tab, you can browse database objects and users to which you have access.

• Files tab: Identified by the Files folder icon, this tab enables you to access files from your local machine without having to use the File > Open menu. This tab does not appear by default, Use the View > Files menu to activate it.

• Reports tab: Identified by the Reports icon, this tab enables you to run predefined reports or create and add your own reports.

General Navigation and Use

SQL Developer uses the left side for navigation to find and select objects, and the right side to display information about selected objects. You can customize many aspects of the appearance and behavior of SQL Developer by setting preferences.

Note: You need to define at least one connection to be able to connect to a database schema and issue SQL queries or run procedures/functions.


You must define a connection to start using

SQL Developer for running SQL queries on a

database schema.

Reports

Files



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Using SQL Developer

Chapter 17 - Page 7

Menus

The following menus contain standard entries, plus entries for features specific to SQL Developer:

• View: Contains options that affect what is displayed in the SQL Developer interface

• Navigate: Contains options for navigating to various panes and for executing subprograms

• Run: Contains the Run File and Execution Profile options that are relevant when a function or procedure is selected, and also debugging options

• Edit: Contains options for use when you edit functions and procedures

• Versioning: Provides integrated support for the following versioning and source control systems: Concurrent Versions System (CVS) and Subversion

• Migration: Contains options related to migrating third-party databases to an Oracle database

• Tools: Invokes SQL Developer tools such as SQL*Plus, Preferences, and SQL Worksheet

Note: The Run menu also contains options that are relevant when a function or procedure is selected for debugging.



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Using SQL Developer

Chapter 17 - Page 8

Creating a Database Connection


A connection is a SQL Developer object that specifies the necessary information for connecting to a specific database as a specific user of that database. To use SQL Developer, you must have at least one database connection, which may be existing, created, or imported.

You can create and test connections for multiple databases and for multiple schemas.

By default, the tnsnames.ora file is located in the $ORACLE_HOME/network/admin directory, but it can also be in the directory specified by the TNS_ADMIN environment variable or registry value. You can use network service names defined in the tnsnames.ora file to specify service names for you connections.

Note: On Windows, if the tnsnames.ora file exists but its connections are not being used by SQL Developer, define TNS_ADMIN as a system environment variable.

Using the Connection dialog menu you can select Create Local connections which will create a connection for every open account on the local database.

You can export connections to an XML file that you can reuse later.

You can create additional connections as different users to the same database or to connect to the different databases.


• You must have at least one database connection to use SQL Developer.

• You can create and test connections for multiple:– Databases

– Schemas

• SQL Developer automatically imports any connections defined in the tnsnames.ora file on your system.

• You can export connections to an Extensible Markup Language (XML) file.

• Each additional database connection created is listed in the Connections Navigator hierarchy.



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Using SQL Developer

Chapter 17 - Page 9


Creating a Database Connection (continued)

To create a database connection, perform the following steps:

1. On the Connections tabbed page, right-click Connections and select New Connection.

2. In the New/Select Database Connection window, enter the connection name. Enter the username and password of the schema that you want to connect to.

a) From the Role drop-down list, you can select either default or SYSDBA. (You choose SYSDBA for the sys user or any user with database administrator privileges.)

b) You can select the connection type as:

- Basic: In this type, enter the host name and SID for the database you want to connect to. Port is already set to 1521. Or you can also choose to enter the Service name directly if you use a remote database connection.

- TNS: You can select any one of the database aliases imported from the tnsnames.ora file.

- LDAP: You can look up database services in Oracle Internet Directory which is a component of Oracle Identity Management.

- Advanced: You can define a custom JDBC URL to connect to the database.

c) Click Test to ensure that the connection has been set correctly.


1 2

3



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Using SQL Developer


d) Click Connect

If you select the Save Password check box, the password is saved to an XML file. So, after you close the SQL Developer connection and open it again, you are not prompted for the password.

3. The connection gets added in the Connections Navigator. You can expand the connection to view the database objects and view object definitions—for example, dependencies, details, statistics, and so on.

Note: From the same New/Select Database Connection window, You can also connect to schemas for selected third-party (non-Oracle) databases, such as MySQL, Microsoft SQL Server, Sybase Adaptive Server, Microsoft Access, and IBM DB2, and view metadata and data. However, these connections are read-only connections that enable you to browse objects and data in that data source.



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Using SQL Developer


Browsing Database Objects


After you create a database connection, you can use the Connections Navigator to browse through many objects in a database schema including Tables, Views, Indexes, Packages, Procedures, Triggers, and Types.

You can see the definition of the objects broken into tabs of information that is pulled out of the data dictionary. For example, if you select a table in the Navigator, the details about columns, constraints, grants, statistics, triggers, and so on are displayed on an easy-to-read tabbed page.

If you want to see the definition of the EMPLOYEES table as shown in the slide, perform the following steps:

1. Expand the Connections node in the Connections Navigator.

2. Expand Tables.

3. Click EMPLOYEES. By default, the Columns tab is selected. It shows the column description of the table. Using the Data tab, you can view the table data and also enter new rows, update data, and commit these changes to the database.


Use the Connections Navigator to:

• Browse through many objects in a database schema

• Review the definitions of objects at a glance



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Using SQL Developer


Displaying the Table Structure


In SQL Developer, you can also display the structure of a table using the DESCRIBE command. The result of the command is a display of column names and data types as well as an indication if a column must contain data.


Use the DESCRIBE command to display the structure of a table:



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Using SQL Developer


Browsing Files


You can use the File Navigator to browse and open system files.

• To view the files navigator, click the Files tab, or select View > Files.

• To view the contents of a file, double-click a file name to display its contents in the SQL worksheet area.

Browsing Files

Use the File Navigator to explore the file system and open system files.



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Using SQL Developer


Creating a Schema Object


SQL Developer supports the creation of any schema object by executing a SQL statement in SQL Worksheet. Alternatively, you can create objects using the context menus. When created, you can edit the objects using an edit dialog box or one of the many context-sensitive menus.

As new objects are created or existing objects are edited, the DDL for those adjustments is available for review. An Export DDL option is available if you want to create the full DDL for one or more objects in the schema.

The slide shows how to create a table using the context menu. To open a dialog box for creating a new table, right-click Tables and select New Table. The dialog boxes to create and edit database objects have multiple tabs, each reflecting a logical grouping of properties for that type of object.


• SQL Developer supports the creation of any schema object by:– Executing a SQL statement in SQL Worksheet

– Using the context menu

• Edit the objects by using an edit dialog box or one of the many context-sensitive menus.

• View the data definition language (DDL) for adjustments such as creating a new object or editing an existing schema object.



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Using SQL Developer


Creating a New Table: Example


In the Create Table dialog box, if you do not select the Advanced check box, you can create a table quickly by specifying columns and some frequently used features.

If you select the Advanced check box, the Create Table dialog box changes to one with multiple options, in which you can specify an extended set of features while you create the table.

The example in the slide shows how to create the DEPENDENTS table by selecting the Advanced check box.

To create a new table, perform the following steps:

1. In the Connections Navigator, right-click Tables.

2. Select New TABLE.

3. In the Create Table dialog box, select Advanced.

4. Specify column information.

5. Click OK.

Although it is not required, you should also specify a primary key by using the Primary Key tab in the dialog box. Sometimes, you may want to edit the table that you have created; to do so, right-click the table in the Connections Navigator and select Edit.




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Using SQL Developer


Using the SQL Worksheet


When you connect to a database, a SQL Worksheet window for that connection automatically opens. You can use the SQL Worksheet to enter and execute SQL, PL/SQL, and SQL*Plus statements. The SQL Worksheet supports SQL*Plus statements to a certain extent. SQL*Plus statements that are not supported by the SQL Worksheet are ignored and not passed to the database.

You can specify actions that can be processed by the database connection associated with the worksheet, such as:

• Creating a table

• Inserting data

• Creating and editing a trigger

• Selecting data from a table

• Saving the selected data to a file

You can display a SQL Worksheet by using one of the following:

• Select Tools > SQL Worksheet.

• Click the Open SQL Worksheet icon.


• Use the SQL Worksheet to enter and execute SQL, PL/SQL, and SQL *Plus statements.

• Specify any actions that can be processed by the database connection associated with the worksheet.

Click the Open SQL Worksheet icon.

Select SQL Worksheet from the Tools menu, or



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Using SQL Developer



Using the SQL Worksheet (continued)

You may want to use the shortcut keys or icons to perform certain tasks such as executing a SQL statement, running a script, and viewing the history of SQL statements that you have executed. You can use the SQL Worksheet toolbar that contains icons to perform the following tasks:

1. Execute Statement: Executes the statement where the cursor is located in the Enter SQL Statement box. You can use bind variables in the SQL statements, but not substitution variables.

2. Run Script: Executes all statements in the Enter SQL Statement box by using the Script Runner. You can use substitution variables in the SQL statements, but not bind variables.

3. Autotrace: Generates trace information for the statement

4. Execute Explain Plan: Generates the execution plan, which you can see by clicking the Explain tab

5. Commit: Writes any changes to the database and ends the transaction

6. Rollback: Discards any changes to the database, without writing them to the database, and ends the transaction

7. Unshared SQL Worksheet: Create a unshared SQL Worksheet


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Using SQL Developer


8. Change Case: Step through: To Uppercase, and Lower Case, and Initial Capitalization

9. Clear: Erases the statement or statements in the Enter SQL Statement box

10. SQL History: Displays a dialog box with information about SQL statements that you have executed



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Using SQL Developer



Using the SQL Worksheet (continued)

When you connect to a database, a SQL Worksheet window for that connection automatically opens. You can use the SQL Worksheet to enter and execute SQL, PL/SQL, and SQL*Plus statements. All SQL and PL/SQL commands are supported as they are passed directly from the SQL Worksheet to the Oracle database. SQL*Plus commands used in the SQL Developer have to be interpreted by the SQL Worksheet before being passed to the database.

The SQL Worksheet currently supports a number of SQL*Plus commands. Commands not supported by the SQL Worksheet are ignored and are not sent to the Oracle database. Through the SQL Worksheet, you can execute SQL statements and some of the SQL*Plus commands.

You can display a SQL Worksheet by using any of the following two options:

• Select Tools > SQL Worksheet.

• Click the Open SQL Worksheet icon.


• Use the SQL Worksheet to enter and execute SQL, PL/SQL, and SQL*Plus statements.

• Specify any actions that can be processed by the database connection associated with the worksheet.

Enter SQL statements.

Results are shown here.



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Using SQL Developer


Executing SQL Statements


The example in the slide shows the difference in output for the same query when the Run Statement command (Ctrl+Enter key) is used versus the output when Run Script (F5 key) is used.


Use the Enter SQL Statement box to enter single or multiple SQL statements.

Ctrl+Enter F5



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Using SQL Developer


Saving SQL Scripts

Saving SQL Scripts

You can save your SQL statements from the SQL Worksheet into a text file. To save the contents of the Enter SQL Statement box, follow these steps:

1. Click the Save icon or use the File > Save menu item.

2. In the Windows Save dialog box, enter a file name and the location where you want the file saved.

3. Click Save.

After you save the contents to a file, the Enter SQL Statement window displays a tabbed page of your file contents. You can have multiple files open at the same time. Each file displays as a tabbed page.

Script Pathing

You can select a default path to look for scripts and to save scripts. Under Tools > Preferences > Database > Worksheet Parameters, enter a value in the “Select default path to look for scripts” field.

Saving SQL Scripts

Click the Save icon to save your SQL statement to a file.

The contents of the saved file are visible and editable in your SQL Worksheet window.

Identify a location, enter a file name, and click Save.

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Using SQL Developer


Executing Saved Script Files: Method 1


To open a script file and display the code in the SQL Worksheet area, perform the following:

1. In the files navigator, select (or navigate to) the script file that you want to open.

2. Double-click to open. The code of the script file is displayed in the SQL Worksheet area.

3. Select a connection from the connection drop-down list.

4. To run the code, click the Run Script (F5) icon on the SQL Worksheet toolbar. If you have not selected a connection from the connection drop-down list, a connection dialog box will appear. Select the connection you want to use for the script execution.

Alternatively, you can also:

1. Select File > Open. The Open dialog box is displayed.

2. In the Open dialog box, select (or navigate to) the script file that you want to open.

3. Click Open. The code of the script file is displayed in the SQL Worksheet area.

4. Select a connection from the connection drop-down list.

5. To run the code, click the Run Script (F5) icon on the SQL Worksheet toolbar. If you have not selected a connection from the connection drop-down list, a connection dialog box will appear. Select the connection you want to use for the script execution.

1. Use the Files tab to locate the script file that you want to open.

2. Double-click the script to display the code in the SQL Worksheet.


To run the code, click either:

• Run Script (F5), or

• Run Statement (Ctrl +Enter)

1

3

Select a connection from the drop-down list.

2



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Using SQL Developer




To run a saved SQL script, perform the following:

1. Use the @ command, followed by the location, and name of the file you want to run, in the Enter SQL Statement window.

2. Click the Run Script icon.

The results from running the file are displayed on the Script Output tabbed page. You can also save the script output by clicking the Save icon on the Script Output tabbed page. The Windows Save dialog box appears and you can identify a name and location for your file.


Use the @ command followed by the location and name of the file you want to execute, and click the Run Script icon.

The output from the script is displayed on the Script Output tabbed page.



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Using SQL Developer


Formatting the SQL Code


You may want to beautify the indentation, spacing, capitalization, and line separation of the SQL code. SQL Developer has a feature for formatting SQL code.

To format the SQL code, right-click in the statement area and select Format SQL.

In the example in the slide, before formatting, the SQL code has the keywords not capitalized and the statement not properly indented. After formatting, the SQL code is beautified with the keywords capitalized and the statement properly indented.


Before formatting

After formatting



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Using SQL Developer


Using Snippets

Using Snippets

You may want to use certain code fragments when you use the SQL Worksheet or create or edit a PL/SQL function or procedure. SQL Developer has the feature called Snippets. Snippets are code fragments such as SQL functions, Optimizer hints, and miscellaneous PL/SQL programming techniques. You can drag snippets into the Editor window.

To display Snippets, select View > Snippets.

The Snippets window is displayed at the right side. You can use the drop-down list to select a group.

Using Snippets

Snippets are code fragments that may be just syntax or examples.

From the drop-down list, you can select

the functions category that you

want.



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Using SQL Developer


Using Snippets: Example


To insert a Snippet into your code in a SQL Worksheet or in a PL/SQL function or procedure, drag the snippet from the Snippets window into the desired place in your code. Then you can edit the syntax so that the SQL function is valid in the current context. To see a brief description of a SQL function in a tool tip, place the cursor over the function name.

The example in the slide shows that CONCAT(char1, char2)is dragged from the Character Functions group in the Snippets window. Then the CONCAT function syntax is edited and the rest of the statement is added as in the following:

SELECT CONCAT(first_name, last_name)

FROM employees;


Inserting asnippet

Editing the snippet



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Using SQL Developer


Debugging Procedures and Functions


In SQL Developer, you can debug PL/SQL procedures and functions. Using the Debug menu options, you can perform the following debugging tasks:

• Find Execution Point goes to the next execution point.

• Resume continues execution.

• Step Over bypasses the next method and goes to the next statement after the method.

• Step Into goes to the first statement in the next method.

• Step Out leaves the current method and goes to the next statement.

• Step to End of Method goes to the last statement of the current method.

• Pause halts execution but does not exit, thus allowing you to resume execution.

• Terminate halts and exits the execution. You cannot resume execution from this point; instead, to start running or debugging from the beginning of the function or procedure, click the Run or Debug icon on the Source tab toolbar.

• Garbage Collection removes invalid objects from the cache in favor of more frequently accessed and more valid objects.

These options are also available as icons on the debugging toolbar.


• Use SQL Developer to debug PL/SQL functions and procedures.

• Use the “Compile for Debug” option to perform a PL/SQL compilation so that the procedure can be debugged.

• Use the Debug menu options to set breakpoints, and to perform the step into and step over tasks.



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Using SQL Developer


Database Reporting

Database Reporting

SQL Developer provides many reports about the database and its objects. These reports can be grouped into the following categories:

• About Your Database reports

• Database Administration reports

• Table reports

• PL/SQL reports

• Security reports

• XML reports

• Jobs reports

• Streams reports

• All Objects reports

• Data Dictionary reports

• User-Defined reports

To display reports, click the Reports tab at the left side of the window. Individual reports are displayed in tabbed panes at the right side of the window; and for each report, you can select (using a drop-down list) the database connection for which to display the report. For reports

Database Reporting

SQL Developer provides a number of predefined reports about the database and its objects.



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Using SQL Developer


about objects, the objects shown are only those visible to the database user associated with the selected database connection, and the rows are usually ordered by Owner. You can also create your own user-defined reports.



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Using SQL Developer


Creating a User-Defined Report


User-defined reports are reports created by SQL Developer users. To create a user-defined report, perform the following steps:

1. Right-click the User Defined Reports node under Reports, and select Add Report.

2. In the Create Report dialog box, specify the report name and the SQL query to retrieve information for the report. Then click Apply.

In the example in the slide, the report name is specified as emp_sal. An optional description is provided indicating that the report contains details of employees with salary >= 10000. The complete SQL statement for retrieving the information to be displayed in the user-defined report is specified in the SQL box. You can also include an optional tool tip to be displayed when the cursor stays briefly over the report name in the Reports navigator display.

You can organize user-defined reports in folders, and you can create a hierarchy of folders and subfolders. To create a folder for user-defined reports, right-click the User Defined Reports node or any folder name under that node and select Add Folder. Information about user-defined reports, including any folders for these reports, is stored in a file named UserReports.xml under the directory for user-specific information.


Create and save user-defined reports for repeated use.

Organize reports in folders.



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Using SQL Developer


External Tools

Search Engines and External Tools

To enhance productivity of the SQL developers, SQL Developer allows you to add shortcut icons to some of the frequently used tools such as Notepad, Microsoft Word, and Dreamweaver, available to you.

You can add external tools to the existing list or even delete shortcuts to tools that you do not use frequently. To do so, perform the following:

1. From the Tools menu, select External Tools.

2. In the External Tools dialog box, perform the following:

A. Click New to invoke the wizard to add new tools.

B. Click Delete to remove any tool from the list.

C. Click Edit to invoke the wizard to modify the availability and parameters of the selected tool.

External Tools

Shortcuts to frequently used tools

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Using SQL Developer


Setting Preferences

Setting Preferences

You can customize many aspects of the SQL Developer interface and environment by modifying SQL Developer preferences according to your preferences and needs. To modify SQL Developer preferences, select Tools, then Preferences.

The preferences are grouped into the following categories:

• Environment

• Accelerators (keyboard shortcuts)

• Code Editors

• Database

• Debugger

• Documentation

• Extensions

• File Types

• Migration

• PL/SQL Compilers

• PL/SQL Debugger, and so on

Setting Preferences

• Customize the SQL Developer interface and environment.

• In the Tools menu, select Preferences.



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Using SQL Developer


Resetting the SQL Developer Layout


While working with SQL Developer, if the Connections Navigator disappears or if you cannot dock the Log window in its original place, perform the following steps to fix the problem:

1. Exit from SQL Developer.

2. Open a terminal window and use the locate command to find the location of windowinglayout.xml.

3. Go to the directory which has windowinglayout.xml and delete it.

4. Restart SQL Developer.




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Using SQL Developer


Summary

Summary

SQL Developer is a free graphical tool to simplify database development tasks. Using SQL Developer, you can browse, create, and edit database objects. You can use SQL Worksheet to run SQL statements and scripts. SQL Developer enables you to create and save your own special set of reports for repeated use.

Summary

In this appendix, you should have learned how to use SQL Developer to do the following:

• Browse, create, and edit database objects

• Execute SQL statements and scripts in SQL Worksheet

• Create and save custom reports



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Documents

Oracle Database 11g SQL Tuning Workshop.pdf