Chap 5. Disk IO Distribution Chap 6. Index Architecture

Chap 5. Disk IO DistributionChap 5. Disk IO DistributionChap 6. Index ArchitectureChap 6. Index Architecture

Written by Yong-soon Kwon

Summerized By Sungchan Park @ IDS Lab.

2008-07-11

Copyright 2008 by CEBT

OverviewOverview

Chap 5. Disk IO Distribution

Oracle Storage Architecture

Tuning by Disk IO Distribution

Chap 6. Index Architecture

Introduction to Index

– B+-Tree Index

– Bitmap Index

– Reverse Key Index

– Function Base Index

Random Access Tuning

Center for E-Business Technology

Chap 5. Disk IO DistributionChap 5. Disk IO Distribution


Disk IO DistributionDisk IO Distribution

Pros

Improving Performance

Cons

Probability of error increase

– Problem on one disk affects whole table

Hard to manage



Disk IO Distribution RuleDisk IO Distribution Rule

Disk IO Distribution Rule

We should increase the probability that all disks are accessed.

– Data in each table should be distributed to all disks

How to satisfy the rule?

1. Optimizing Disk Organization

2. Segment Extent Striping

3. Applying Table Archtecture

– Partition table, IOT table



Optimizing Disk Organization: Volume GroupOptimizing Disk Organization: Volume Group

Volume Groups(VG)

Groups of Physical Disks

One physical disk cannot belong to multiple VG

Checking VG(HP UX)

“vgdisplay –v vg01”

Important Info

“Cur PV : 4” : This VG consists of four physical disks

“Act PV : 4” : Four physical disks are active in this VG



Optimizing Disk Organization: Logical VolumeOptimizing Disk Organization: Logical Volume

Concept of Logical Volume

Space for file system in VG

Logical Volume Usage

File System

Raw Device



Optimizing Disk Organization: Logical Volume Optimizing Disk Organization: Logical Volume StripingStriping

Logical Volume Striping



Optimizing Disk Organization: Logical Volume Optimizing Disk Organization: Logical Volume StripingStriping

Good Point of Logical Volume Striping

Achieve Disk IO distribution rule for each VG

Checking Logical Volume

“ldisplay –v /dev/vg01/lvol1”

Important Info

“Stripes 4” : This logical volume is striped with 4 disks

“Stripe Size(Kbytes) 64” : The striping size is 64KB



Oracle Storage ArchitectureOracle Storage Architecture



Oracle Storage ArchitectureOracle Storage Architecture

OS Block OS Level IO unit

DB Block n OS Block. DB IO unit

Extent Contiguous DB Blocks. Building unit of Segment

Segment Group of Extents(table, index)

Tablespace Group of Segments

Database Group of Tablespaces

Datafile Stores Segments. n Datafile make one Tablespace



Segment Extent Striping: Create TablespaceSegment Extent Striping: Create Tablespace



Segment Extent Striping: Create Table on Segment Extent Striping: Create Table on TablespaceTablespace



Segment Extent Striping: Create Table on Segment Extent Striping: Create Table on TablespaceTablespace



Segment Extent StripingSegment Extent Striping

Good Points

Storing data evenly over all disks for all tables

Disk IO distribution rule optimization

Checking Exntent Striping



Partition TablePartition Table

Example

Data about 20~30 year old people is frequently accessed

If we just store all data in a table, there can be some disks which do not contain any data about 20~30s

– Data are not properly distributed!

Partition table

Create partition w.r.t. age!



Partition Table: ExamplePartition Table: Example


Result

Data about 20~30s are distributed to all 96 disks!


Wrong IO Distrubution #1Wrong IO Distrubution #1



Wrong IO Distribution #2Wrong IO Distribution #2

Incrementally adding datafile to tablespace


Chap 6. Index ArchitectureChap 6. Index Architecture


Pros. and Cons. ofPros. and Cons. of IndexingIndexing

Pros.

Improving Retrieving Performance

Cons.

Degrading Data Manipulation Performance

– Insert, Delete, …

Storage



ROWIDROWID

ROWID

ID given to all rows in database



Example of ROWIDExample of ROWID



Usage of ROWIDUsage of ROWID

Index Creation

Data Access

SQL Tuning



IndexIndex

B+-Tree Index

Bitmap Index

Reverse Key Index

Function Based Index



B+-Tree IndexB+-Tree Index

B+-Tree Index

Support efficient search, delete, and insert



B+-Tree Index AccessB+-Tree Index Access



B+-Tree Index: Pros. and Cons.B+-Tree Index: Pros. and Cons.

Pros.

Balanced

Range scan using linked list

Good for OLTP small number row access

Cons.

Bad for column with low cardinality

– Index scanning 3%~5%of Table row: Worse than table full scan

Possibility of full scan on OR operation



Bitmap IndexBitmap Index

Bitmap Index

Good for column with low cardinality



Bitmap Index structureBitmap Index structure



Bitmap Index: Pros. and Cons.Bitmap Index: Pros. and Cons.

Pros.

Small space

Good for column with low cardinality

Cons.

Bad for table with frequent DML

– Bitmap is need to be decompressed before DML operation

– Need block level lock

Bad for column with high cardinality



Reversed Key IndexReversed Key Index

Reversed Key Index

B+-Tree is bad for sequentially increasing index key column

– All insert operation need to access left block

– Competetion for left block occur!

Reversed Key Index using reversed key to avoid above situation



Reversed Key Index: Pros. and Cons.Reversed Key Index: Pros. and Cons.

Pros.

Avoid competetion on left block

Cons.

Range scan is impossible



Function Based IndexFunction Based Index

Function Based Index

Index for transformed value

Example

– SELECT employee_id, ceil(salary/12)*(grade-5)

FROM emp

WHERE mod(grade, 10)*1.2 > 10

Creating Function Based Index

– SQL> CREATE INDEX bonus_index on emp(mod(grade, 10)*1.2);



Function Based Index: Pros. and Cons.Function Based Index: Pros. and Cons.

Pros.

Index scan for transformed value is possible

Cons.

DML overhead

Flexibility of index is low

– Only used for where cluases in predefined form



Random AccessRandom Access

Random Access

IO single Block

Should be reduced

Table Full Scan

Multi Block IO

– Not Random Access

Index Scan

Random Access occurs



Reducing Random AccessReducing Random Access

Reducing Random Access

Optimizing Cluster Factor

Using Optimized Index

– Ex) SELECT name, date, item

FROM trans

WHERE id = ‘10’

AND date BETWEEN ‘200401’ AND ‘20040406’



Random Access and Index ScanRandom Access and Index Scan

Random Access and Index Scan

Table Access via Index Scan is Random Access

Index Scan itself is NOT Random Access

Index Scan below 3% of data in table is effective

– Over 3%, Full table Scan is effective


Documents

Chap 5. Disk IO Distribution Chap 6. Index Architecture