PTS DB12c 01 Overview v1


The following is intended to outline our general product

direction. It is intended for information purposes only, and

may not be incorporated into any contract. It is not a

commitment to deliver any material, code, or functionality,

and should not be relied upon in making purchasing

decisions. The development, release, and timing of any

features or functionality described for Oracle's products

remains at the sole discretion of Oracle.


Oracle Database 12c

Technical Training

Platform Technology Solutions

Oracle Server Technologies


Oracle Database 12c

Over 5 years in development

Over 500 new features

Over 2,500 person years of development

Over 3,000 systems used in testing

Over 1 million feature tests run daily

Over 1.2 million hours of stress tests

Development Effort


Major Database Focus Areas

ENGINEERED

SYSTEMS

BIG

DATA

SOCIAL

BLOG

SMART

METER

101100101001

001001101010

101011100101

010100100101

CLOUD

COMPUTING


Gartner: Enterprise Private Cloud Survey

78%

17%

5%

Yes

Maybe

No

Will you be pursuing a private cloud computing strategy by 2014?

Gartner Data Center Conference Poll

December 2011

Source: Gartner Top Five Trends for Private Cloud Computing, February 2012, Thomas J Bittman


Oracle Database 12c c is for the Consolidation and Cloud!

ISVs: Move applications to the cloud transparently and without disruption Manage multi-tenancy (many) as one Ease of upgrade and therefore obvious cost benefits

SIs: Consolidate, standardize and enable cloud deployments with better manageability Reduced OpEx


Private Database Cloud Architectures Using 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 Using Oracle Database 12c

Dedicated Databases

share servers and OS

Virtual Machines

share servers

Pluggable Databases

share servers, OS and database

Increasing Consolidation


Consolidating Databases on Clouds Key Requirements

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


Oracle Database Architecture Requires memory, processes and database files

System Resources


New Multitenant Architecture Memory and processes required at container level only

System Resources



System Resources


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


Consolidating Databases Step1: Upgrade databases in-place


Consolidating Databases Step1: Upgrade databases in-place

Upgrade in Place


Consolidating Databases Step2: Plug-in upgraded databases


Managing Shared Resources Resource Management for consolidated databases

High Priority

Medium Priority

Low Priority


Expand Cluster to Support Flexible Consolidation Model

Services

Single SGA per

CDB Instance

Improved Agility With Changing Workloads

Node1

CDB Instance 1

Node2

CDB Instance 2

Multitenant Container Database (CDB)


Services

Single SGA per

CDB Instance


Node1

CDB Instance 1

Node2

CDB Instance 2

Node3

CDB Instance 3




Pluggable Databases Database Consolidation Using Oracle Database 12c

Managed Globally

by CDBA and

Locally by PDBAs

Container Database

Pluggable DBs

Data Dictionary

Tablespaces

Schemas

Users, Roles &

Services

PDBAs

ERP HR

DB

Seed CRM


Manage Many Databases as One Backup databases as one; recover at pluggable database Level

One Backup

Point-in-time recovery

at pluggable database level


Manage Many Databases as One One standby database covers all pluggable databases


GOLD

SILVER

BRONZE

RAC, Data Guard, Daily Incrementals

Data Guard, Daily Incrementals

Weekly Full Backups

Managing Database Service Level Tiers Change tiers as databases become more mission critical


Fast Provisioning Pluggable databases can be quickly provisioned from seed

0

5

10

15

20

25

Non CDB PDB Clone PDB using Copy-on-Write File

System

Time Taken to Provision New Database


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


Multitenant Architecture for SaaS Multitenancy Implemented by the Database, not the Application


Perfect for ISVs. Packaged apps and reference data are easily distributed


New Multitenant Architecture Ideal for consolidating databases on Clouds

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


Automatic Data Optimization Optimize data storage based on usage

Heat Map

Smart Compression

Automated Tiering

In Database Archiving

Network Compression


Automatic Data Optimization

An in memory heat map tracks access to segments and

blocks

Data is periodically written to disk

Information is accessible by views or stored procedures

Uses can attach policies to tables to compress or tier data

based on access to data

Tables or Partitions can be moved between compression levels

whilst data is still being accessed

Simplifying the life cycle of data

Polic

y 1


Automatic Data Optimization Add compression and tiering policies to tables


Oldest Data Most Recent Data

Polic

y 1

Polic

y 2

POLICY 1:

Compress Partitions with

row compression if they havent been modified in 30 days

POLICY 2:

Compress Partitions with

columnar compression if they

havent been modified in 180 days


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



Polic

y 1

Polic

y 2


Automatic Data Optimization Policies are automatically applied to tables



Polic

y 1

Polic

y 2


Automatic Data Optimization Reduce storage footprint, read compressed data faster

Copyright 2012, Oracle and/or its affiliates. All rights reserved. Confidential Oracle Restricted 41


Polic

y 1

Polic

y 2


Automatic Data Optimization Automatically tier data to lower cost storage



Polic

y 1

Polic

y 2

Polic

y 3

POLICY 3:

If the tablespace is nearly full

compress the oldest partition

with archive compression and

move it to Tier 2 Storage


Automatic Data Optimization Automatically tier data to lower cost storage



Polic

y 1

Polic

y 2

Polic

y 3


Extreme Availability Active Replicas

Data Guard Far Sync

Data Guard Fast Sync

Global Data Services

Active Data Guard Reporting Enhancements

Flex ASM

Rolling Upgrade using Data Guard

Application Continuity

GoldenGate Integrated Capture


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 Ack

Network Send

Network Ack


Data Guard Far Sync No Compromise Between Availability and Performance

Primary Standby

Far Sync

Instance

sync

async




Extends RAC-style service failover, load

balancing (within and across data

centers), and management capabilities to

a set of replicated databases

Takes into account network latency,

replication lag, and service placement

policies

Achieve higher availability, improved

manageability and maximize

performance

Load Balancing and Service Failover for Replicated Databases



Reporting client routed to best database Based on location, response time, data, acceptable

data lag

Reports will automatically run on least loaded server

Reporting client failover

If preferred database not available, will route to

another database in same region or a remote

database

Global service migration

Automatically migrates services based on

failover/switchover - if primary database is down, start

Call Center service on the new primary

Active Data Guard Example

Active Data Guard

Reporting Service

Call Center Service



Call Center Client connections and

requests transparently routed to the

closest / best database

Runtime load balancing metrics give client real-time

information on which database to issue next request

If a database fails, its global services

restarted on another replica

GoldenGate Example

GoldenGate

Call Center Service


Flex ASM (Automatic Storage Management)


ASM Overview Oracle Database 11.2 or earlier

ASM Cluster Pool of Storage

Node2

Disk Group B Disk Group A

Node3 Node5 Node4

Shared Disk

Groups

Wide File Striping

One to One

Mapping of ASM

Instances to

Servers

Node1

ASM Instance

Database Instance

ASM Disk

RAC Cluster


ASM Overview Oracle Database 11.2 or earlier


Node2

Disk Group B Disk Group A

Node3 Node5 Node4

Shared Disk

Groups

Wide File Striping

One to One

Mapping of ASM

Instances to

Servers

Node1

ASM Instance

Database Instance

ASM Disk

RAC Cluster


ASM Overview


Disk Group B Disk Group A Shared Disk

Groups

Wide File Striping

One to One

Mapping of ASM

Instances to

Servers

ASM Instance

Database Instance

ASM Disk

RAC Cluster

Node4 Node3 Node2 Node1 Node5

Oracle Database 12.1

Node1

runs as

ASM

Client to

Node4

Node5

runs as

ASM

Client to

Node4


ASM Overview ASM in12.1, Business as usual



Groups

Wide File Striping

One to One

Mapping of ASM

Instances to

Servers

ASM Instance

Database Instance

ASM Disk

RAC Cluster

Node4 Node3 Node2 Node1 Node5

Node1 runs as ASM Client to Node 2

Node5 runs as ASM Client to Node4


Introducing Flex ASM Oracle 12.1: Removal of One to One Mapping



Groups

Wide File Striping

Databases share

ASM instances ASM Instance

Database Instance

ASM Disk

RAC Cluster

Node5 Node4 Node3 Node2 Node1 Node5

Node2 runs as ASM Client to Node3

Node1 runs as ASM Client to Node 3


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


Solving Application Development Pains

Transaction Guard

A reliable protocol and API

that returns the outcome of

the last transaction

New in Oracle Database 12c


Safely attempts to replay in-

flight work following outages

and planned operations



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 its 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



Application Servers

Database Servers

End User


Network Switches

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.


Security Defense in Depth

Data Masking at Source

Privilege Analysis

Integrated Audit Framework

Data Redaction

Database Vault Mandatory Realms

Sensitive Data Discovery


Complete Data Security Protecting data at the source

Applications

SQL Monitoring & Blocking

Masking

Privileged user access

Encryption

Data Discovery Compliance Scan Vulnerability Scan

Activity auditing


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

Call Centers

Decision Support Systems

Systems with PII, PHI, PCI data


Privilege Analysis Reduce Attack Surface

Report on actual privileges and roles used in the database

Revoke unnecessary privileges and roles as needed

Help enforce least privilege and reduce risks

Privilege

Analysis

Create Select Update DBA role

APPADMIN role


Data Masking

Mask At-Source

Minimize sensitive data exposure

Securely Provisioning Test Systems

01001011001010100100100100100100100100100100100010

01010100100100100111001001001001001001000010010010

11100100101010010010101010011010100101010010

Subsetted & Masked

Data Pump File

12.1

Prod Test

At-Source Masking

Production data subsetted first

Sensitive data masked separately

0100101100101010010010010010010010010010010010001

0010101001001001001110010010010010010010000100100

1011100100101010010010101010011010100101010010

Prod

Data Subset Clone &Mask

11.2

Test


Automatic Data Optimization

Data Redaction

Data Masking

Sandboxes

Adaptive Query Plans

Temporal

Pattern Matching

Partition Maintenance Enhancements

Data Warehousing & Big Data Enabling Data Scientists


Adaptive Execution Plans Good SQL execution without intervention

HJ

Table scan

T2

Table scan

T1

NL

Index

Scan

T2

Threshold exceeded, plan

switches

Table scan

T1

HJ

Table scan

T2

Plan decision deferred until runtime

Final decision is based on statistics collected during

execution

If statistics prove to be out of range, sub-plans can be

swapped

Bad effects of skew eliminated


Partitioning Enhancements

Enhanced Partition Maintenance operations

Online partition move

Partition maintenance operations on multiple partitions

Asynchronous global index maintenance for DROP and TRUNCATE

Partial global and local indexes

More Efficient Data Management


Temporal Support

Transaction Time Temporal

(Flashback Data Archive)

Tracks transactional changes to a

table over its lifetime

Typically used for compliance and

auditing

Enables the users to see the data

as it was at a point in time in the

past

Valid Time Temporal

Enables user to model & query data

for real world validity

Typically used for insurance policies,

financial markets, trade data & future

changes

Users can model concepts such as

the Life time of an insurance policy


EXEC DBMS_FLASHBACK_ARCHIVE.ENABLE_AT_VALID_TIME('CURRENT');

SELECT custid, start_time, end_time

FROM customers WHERE custid=1;

custid custname custaddr1 custaddr2

custcity custstate custzip start_time end_time

1 Acme Inc 123 Any

Street

Suite 17 Anytown CA 99999 01-JAN-13 31-July-13

1 Acme Inc 456

Another

Street

Anytown CA 99998 01-AUG-13

Valid Time Temporal Example


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

Ascendin

g O

rder

W event defined as regular expression


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 lineFifo = new ArrayList();

boolean finished = false;

DataBag output = bagFactory.newDefaultBag();

if (input == null) {

return null;

}

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

return null;

}

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)


Application Development and Migrations Making Migrations to Oracle easy

Streaming Result Sets

32K Varchar

Identity Data Type

On The Fly Translations

Callout Framework

Fetch First (Top N) Queries

TSQL:

select top 10 * from table_one

PL/SQL:

OPEN v_cursor FOR

SELECT * FROM table_one

FETCH FIRST 10 ROWS ONLY;

DBMS_SQL.RETURN_RESULT(v_cursor);


Oracle Migration : Thirdparty to Oracle

Application

Thirdparty (Non-Oracle)

Database to be migrated

SQL Developer

Step 1: Provision new Oracle Database



Application

SQL Developer

Step 2: Use SQL Developer to extract DDL/Stored Procedures

DDL/Stored Procedures




Table data imported into Oracle


Application

SQL Developer

Step 3: Migrate data using SQL Developer or other method





Application

SQL Developer

Step 4: Run application against migrated Oracle Database




Oracle Migration : Thirdparty to Oracle Step 5: Use SQL Translation Framework to intercept SQL and translate it

SQL Developer

Application


Oracle Migration : Thirdparty to Oracle Step 6: Run migrated application

Application


Plug Into the Cloud

Security & Compliance

Big Data

Consolidation

Data Optimization

Application Development

High Availability

In-Memory

Performance & Scalability

Data Warehousing


The preceding is intended to outline our general product

direction. It is intended for information purposes only, and may

not be incorporated into any contract. It is not a commitment to

deliver any material, code, or functionality, and should not be

relied upon in making purchasing decisions. The development,

release, and timing of any features or functionality described for

Oracle's products remains at the sole discretion of Oracle.

Documents

PTS DB12c 01 Overview v1