Copyright © 2013, Oracle and/or its affiliates. All rights ...241B554E51F7B86F18...Oracle Database 11g. Dedicated Databases . ... 6x Less H/W Resource, ... Flexible choice when patching

Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 1


Oracle Database 12c

Patrick Wheeler Senior Director, Product Management Oracle Database

In association with


Major Database Focus Areas

ENGINEERED SYSTEMS

BIG DATA

SOCIAL

BLOG

SMART METER

101100101001001001101010101011100101010100100101

CLOUD COMPUTING


Private Database Cloud Architectures Oracle Database 11g

Dedicated Databases

share servers and OS

Virtual Machines

share servers

Schema Consolidation

share servers, OS and database

Increasing Consolidation


Private Database Cloud Architectures Oracle Database 12c

Dedicated Databases

share servers and OS

Virtual Machines

share servers

Multitenant Database

share servers, OS and database

Increasing Consolidation


Isolation and multitenancy

Fast provisioning and cloning

Secure and highly available

No application changes

Manage many as one

Greater resource utilization

Performant and scalable

Lower IT costs

Consolidating Databases on Clouds Key requirements…


David Storage Manager

Mike IT Manager

James CIO

Laura Dev. Manager

Amy Systems Manager

The Data Center Team

Mark Security Manager

Justin DBA


Consolidating Databases v

Challenge #1


Database Consolidation Challenges

Reduce the number of separate

databases

Reduce IT costs by consolidating more

servers

Keep database applications isolated

and unchanged


Oracle Database Architecture Requires memory, processes and database files

System Resources


New Multitenant Architecture Memory and processes required at multitenant container level only

System Resources


New Multitenant Architecture Memory and processes required at multitenant container level only

System Resources


Consolidating Databases Step 1: Upgrade databases in-place

Upgrade in Place


Consolidating Databases Step 2: Plug-in upgraded databases


OLTP benchmark comparison

Only 3GB of memory vs. 20GB memory used for 50 databases

Pluggable databases scaled to over 250 while separate database instances maxed at 50

Pluggable vs Separate Databases Highly Efficient: 6x Less H/W Resource, 5x more Scalable


Managing Shared Resources Resource management in multitenant environment

High Priority

Medium Priority

Low Priority


Manage Many as One with Multitenant Backup databases as one; recover at pluggable database level

One Backup

Point-in-time recovery At pluggable database level


Manage Many as One with Multitenant One standby database covers all pluggable databases


Multitenant for Simplified Upgrades Apply changes once, all pluggable databases updated

Upgrade in-place


Multitenant for Patching Flexible choice when patching & upgrading databases


GOLD

SILVER

BRONZE

RAC, Data Guard, Daily Incrementals

Data Guard, Daily Incrementals

Weekly Full Backups

PDB migrates through SLAs as it becomes more mission critical Unprecedented Agility with Pluggable Portability


Multitenant for Provisioning

PDBs can be cloned from remote CDBs

PDBs can be cloned from within the same CDB

Fast cloning of PDBs


Multitenant for Fast Provisioning Pluggable databases can be quickly provisioned from seed

0

5

10

15

20

25

Non CDB PDB Clone PDB usingCopy-on-Write File

System

Time Taken to Provision New Database


Advantages of Multitenant Architecture Reduced CapEx & OpEx, Increased Agility, Easy Adoption

Self-contained PDB for each application Applications run unchanged Rapid provisioning (via clones) Portability (via pluggability)

Shared memory and background processes More applications per server

Common operations performed at CDB level Manage many as one (upgrade, HA, backup) Granular control when appropriate


Multitenant for Test and Development Fast, flexible copy and snapshot of pluggable databases


Multitenant. Perfect for SaaS. Multitenancy implemented by the Database, not the Application


Isolation and multitenancy

Fast provisioning and cloning

Secure and highly available

No application changes

Manage many as one

Greater resource utilization

Performant and scalable

Lower IT costs

New Multitenant Architecture Ideal for consolidating databases on Clouds


Lowering Storage Costs v

Challenge #2


Managing Storage Challenges

Compress data, without impacting

performance

Manage more data without incurring additional cost

Tier and compress data based on usage


Automatic Data Optimization Add compression and tiering policies to tables

Copyright © 2012, Oracle and/or its affiliates. All rights reserved. Confidential – Oracle Restricted 36

Oldest Data Most Recent Data

Po licy 1

Po licy 2

Compress Partitions with Row Compression if they haven’t been modified in 30 days

Compress Partitions with Hybrid Columnar Compression if they haven’t been modified in 180 days


Automatic Data Optimization A heat map tracks the activity of segments and blocks



Po licy 1

Po licy 2


Automatic Data Optimization Policies are automatically applied to tables



Po licy 1

Po licy 2





Po licy 1

Po licy 2





Po licy 1

Po licy 2


Automatic Data Optimization Reduce storage footprint, read compressed data faster



Po licy 1

Po licy 2


Oracle Database Security Solutions

Defense-in-Depth for Maximum Security

Activity Monitoring

Database Firewall

Auditing and Reporting

DETECTIVE

Redaction and Masking

Multi-Factor

Authorization

Encryption

PREVENTIVE ADMINISTRATIVE

Data Discovery

and Classification

Vulnerability Scanning

Database Lifecycle

Management


Redacting Data v

Challenge #3


Redacting Data Challenges

Redact data in applications,

queries and reports

Secure sensitive personal

information

Avoid changing applications,

queries and reports


Soc. Sec. # 115-69-3428

DOB 11/06/71

PIN 5623

Policy enforced redaction of sensitive data

Redacting Sensitive Data Mask Application Data Dynamically

Call Center

Operator

Payroll

Processing


Maximum Availability Architecture

Active Data Guard – Data Protection, DR

– Query Offload

GoldenGate – Active-active

– Heterogeneous

RMAN, Oracle Secure Backup – Backup to tape / cloud

Active Replica

Edition-based Redefinition, Online Redefinition, Data Guard, GoldenGate – Minimal downtime maintenance, upgrades, migrations

RAC – Scalability

– Server HA

Flashback – Human error

correction

Production Site Application Continuity – Application HA

Global Data Services – Service Failover / Load Balancing


Zero Data Loss Challenge

The longer the distance, the larger the performance impact

Trade-off between Zero Data Loss and Performance

Primary Standby

Commit

Commit Acknowledge

Network Send

Network Acknowledge


Data Guard Far Sync No Compromise Between Availability and Performance

Primary Standby

Far Sync Instance

(Active)

Far Sync Instance

(Idle)

Far Sync Instance

(Active)

Far Sync Instance

(Idle)

Primary Standby

sync

async


Data Guard Far Sync Standby between continents with zero data loss

Primary Standby

Far Sync Instance

(Active)

Far Sync Instance

(Idle)

New York Bridgeport

Reading London

sync

async


Application Continuity


Unknown Transaction State

Database and infrastructure outages can cause in-flight work to be lost, leaving users and applications in-doubt

Often leads to User pains Duplicate submissions Rebooting mid-tiers Developer pains

Leads to poor customer experiences



Application Servers

Database Servers

End User

Current Situation

Network Switches

User selects product from application and purchases it from the web checkout

User transaction arrives at application infrastructure. It makes it’s way through the application tiers and results in a database transaction being created



Application Servers

Database Servers

End User

Current Situation

Network Switches

User transaction arrives at application infrastructure and makes it’s way through application tiers and results in a database transaction being created

The transaction commits inside of the database and an acknowledgement begins it return journey

A failure in the infrastructure on its return journey means the application server never receives an acknowledgment



Application Servers

Database Servers

End User

Current Situation

Network Switches

The application is left in an unknown state. It returns an error to the user who may order the product for a second time resulting in them potentially being charged twice.


A Solution to the Problem

• Transaction Guard: a reliable protocol and API that returns the outcome of the last transaction

• Application Continuity: safely attempts to replay in-flight work following outages and planned operations.

Two New Features



Application Servers

Database Servers

End User

A reliable replay of in flight work

Network Switches

User selects product from application and purchases it from the web checkout

User transaction arrives at application infrastructure. It makes it’s way through the application tiers and results in a database transaction being created



Application Servers

Database Servers

End User


Network Switches The infrastructure hosting the database fails just before the transaction is committed to the database.



Application Servers

Database Servers

End User


Network Switches

The jdbc driver detects the failure and checks with an available node in the cluster, using “Transaction Guard”, whether the transaction committed or needs to be replayed

If the transaction needs to be replayed, “Application Continuity” will submit all of the inflight work to a surviving node in the cluster and perform a commit. This all happens transparently to the application



Application Servers

Database Servers

End User


Network Switches

The user receives confirmation that his order has been successfully completed.


Simplified Analysis of Big Data Pattern Matching

Select * from

Ticker MATCH_RECOGNIZE (

…

PATTERN(X+ Y+ W+ Z+)

DEFINE X AS (price < PREV(price)),

Y AS (price > PREV(price)),

W AS (price < PREV(price)),

Z AS (price > PREV(price)

…

)

Scalable discovery of business event sequences

– Clickstream logs: sessionization, search behaviour

– Financial transactions: fraud detection, double bottom (“W”) stock analysis

– Telco: dropped calls – Medical sensors: automated medical

observations and detections

Asc

endi

ng O

rder

“W” event defined as regular expression


package pigstuff;

import java.io.IOException;

import java.util.ArrayList;

import java.util.Iterator;

import org.apache.pig.EvalFunc;

import org.apache.pig.PigException;

import org.apache.pig.backend.executionengine.ExecException;

import org.apache.pig.data.BagFactory;

import org.apache.pig.data.DataBag;

import org.apache.pig.data.DataType;

import org.apache.pig.data.Tuple;

import org.apache.pig.data.TupleFactory;

import org.apache.pig.impl.logicalLayer.FrontendException;

import org.apache.pig.impl.logicalLayer.schema.Schema;

/**

*

* @author nbayliss

*/

public class W_FINDER extends EvalFunc<Tuple> {

private class V0Line {

String state = null;

String[] attributes;

String prev = "";

String next = "";

public V0Line(String[] atts) {

attributes = atts;

}

public String[] getAttributes() {

return attributes;

}

public void setState(String state) {

this.state = state;

}

public String getQuantity() {

return this.attributes[4];

}

public String setState(V0Line linePrev, V0Line lineNext) {

String q = this.getQuantity();

if (linePrev == null) {

prev = "";

} else {

prev = linePrev.getQuantity();

}

if (lineNext == null) {

next = "";

} else {

next = lineNext.getQuantity();

}

if (!q.isEmpty() && (prev.isEmpty() || (eq(q, prev) && gt(q, next)))) {

state = "S";

return state;

}

if (gt(q, prev) && gt(q, next)) {

state = "T";

return state;

}

if (lt(q, prev) && lt(q, next)) {

state = "B";

return state;

}

if (!q.isEmpty() && (next.isEmpty() || (gt(q, prev) && eq(q, next)))) {

state = "E";

return state;

}

if (q.isEmpty() || eq(q, prev)) {

state = "F";

return state;

}

return state;

}

private boolean eq(String a, String b) {

if (a.isEmpty() || b.isEmpty()) {

return false;

}

return a.equals(b);

}

private boolean gt(String a, String b) {


return false;

}

return Double.parseDouble(a) > Double.parseDouble(b);

}

private boolean lt(String a, String b) {


return false;

}

return Double.parseDouble(a) < Double.parseDouble(b);

}

public String getState() {

return this.state;

}

}

BagFactory bagFactory = BagFactory.getInstance();

@Override

public Tuple exec(Tuple input) throws IOException {

long c = 0;

String line = "";

String pbkey = "";

V0Line nextLine;

V0Line thisLine;

V0Line processLine;

V0Line evalLine = null;

V0Line prevLine;

boolean noMoreValues = false;

String matchList = "";

ArrayList<V0Line> lineFifo = new ArrayList<V0Line>();

boolean finished = false;

DataBag output = bagFactory.newDefaultBag();

if (input == null) {

return null;

}

if (input.size() == 0) {

return null;

}

Object o = input.get(0);

if (o == null) {

return null;

}

//Object o = input.get(0);

if (!(o instanceof DataBag)) {

int errCode = 2114;

String msg = "Expected input to be DataBag, but"

+ " got " + o.getClass().getName();

throw new ExecException(msg, errCode, PigException.BUG);

}

DataBag bag = (DataBag) o;

Iterator<Tuple> valueIt = bag.iterator();

while (!finished) {

if (valueIt.hasNext()) {

Tuple nextTuple = valueIt.next();

line = nextTuple.get(0).toString();

String ordk = nextTuple.get(1).toString();

String part = nextTuple.get(2).toString();

String supp = nextTuple.get(3).toString();

String q = nextTuple.get(4).toString();

String ship = nextTuple.get(5).toString();

pbkey = nextTuple.get(6).toString();

thisLine = new V0Line(new String[]{line, ordk, part, supp, q, ship, pbkey});

if (lineFifo.isEmpty()) {

thisLine.setState("S");

} /*else {

if (!lineFifo.get(0).getGroup().equals(thisLine.getGroup())) {

thisLine.setState("S");

}

} */

lineFifo.add(0, thisLine);

if (!valueIt.hasNext()) {

noMoreValues = true;

thisLine.setState("E");

}

} else {

thisLine = null;

}

String evalState = null;

if (lineFifo.size() == 3) {

processLine = lineFifo.get(1);

if (processLine.getState() == null) {

prevLine = lineFifo.get(0);

nextLine = lineFifo.get(2);

processLine.setState(prevLine, nextLine);

}

evalLine = lineFifo.remove(2);

evalState = evalLine.getState();

} else {

if (noMoreValues) {

evalLine = lineFifo.remove(lineFifo.size() - 1);

evalState = evalLine.getState();

}

}

if (evalState != null) {

matchList += evalState;

if (matchList.length() > 5) {

matchList = matchList.substring(1);

}

if (matchList.equals("SBTBE")

|| matchList.equals("TBTBE")

|| matchList.equals("SBTBT")

|| matchList.equals("TBTBT")) {

//String[] atts = evalLine.getAttributes();

c++;

}

}

if (lineFifo.isEmpty()) {

finished = true;

}

}

Tuple outputTuple = TupleFactory.getInstance().newTuple(3);

outputTuple.set(0, line);

outputTuple.set(1, pbkey);

outputTuple.set(2, new Long(c));

return outputTuple;

}

@Override

public Schema outputSchema(Schema input) {

Schema.FieldSchema linenumber = new Schema.FieldSchema("linenumber", DataType.CHARARRAY);

Schema.FieldSchema pbykey = new Schema.FieldSchema("pbykey", DataType.CHARARRAY);

Schema.FieldSchema count = new Schema.FieldSchema("count", DataType.LONG);

Schema tupleSchema = new Schema();

tupleSchema.add(linenumber);

tupleSchema.add(pbykey);

tupleSchema.add(count);

return new Schema(tupleSchema);

}

}

Pattern Matching Finding Double Bottom (W)

SELECT first_x, last_z

FROM ticker MATCH_RECOGNIZE (

PARTITION BY name ORDER BY time

MEASURES FIRST(x.time) AS first_x,

LAST(z.time) AS last_z

ONE ROW PER MATCH

PATTERN (X+ Y+ W+ Z+)

DEFINE X AS (price < PREV(price)),

Y AS (price > PREV(price)),

W AS (price < PREV(price)),

Z AS (price > PREV(price) AND

z.time - FIRST(x.time) <= 7 ))

250+ Lines of Java and PIG 12 Lines of SQL

20x less code, 5x faster


Security & Compliance

Big Data

Consolidation

Data Optimization

Application Development

High Availability

In-Memory

Performance & Scalability

Data Warehousing

Plug into the Cloud


The Customer’s View v

Oracle Database 12c


Foundation of Private and Public Clouds



Documents

Copyright © 2013, Oracle and/or its affiliates. All rights ...241B554E51F7B86F18...Oracle Database 11g. Dedicated Databases . ... 6x Less H/W Resource, ... Flexible choice when patching