Oracle Database 12c Release 1 (12.1.0.2)ebs-dba.com/dbsig.com/presentations/OOW14_OAUG_12c... · Oracle Database 12c: In-Memory LOB Queries & Updates Speeds up string ops on LOBs

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Oracle Database 12c Release 1 (12.1.0.2)

Andy RivenesProduct ManagerOracle Product DevelopmentSeptember 28, 2014

Copyright © 2014, Oracle and/or its affiliates. All rights reserved. |

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Safe Harbor Statement

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 Overview

Oracle Database In-Memory

Oracle Multitenant

Compression and Information Lifecycle Management (ILM)

Oracle SecureFiles

Feature Factoring

Other Improvements

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2

3

4

5

6

7

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Provide continued

access to data

Oracle Database 12c

Reduce the cost of

storing data

Simplify the

consolidation of

Oracle databases

Ensure secure

access to data


Oracle Database 12c (12.1.0.1)

Oracle Multitenant

• Database consolidation

• Fast Provisioning

• Manage many as one

Oracle Automatic Data Optimization

• Smart Compression

• Automatic Tiering

Data Guard Far Sync

• Zero data loss over large distances

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Oracle Database 12c (12.1.0.1)

Application Continuity

• Replay of failed transaction

Data Redaction

• Masks application data dynamically

• Largely transparent to application

Pattern Matching

• Sophisticated inter row pattern analysis

And over 500 additional improvements…



Exploit memory to

improve

performance

Continue to

improve

consolidation

Simplify access to

Big Data

Improve

application

developers

experience

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Oracle Multitenant


Oracle SecureFiles

Feature Factoring

Other Improvements

1

2

3

4

5

6

7


Accelerate OLTPReal Time Analytics No Changes to

Applications

Exploit latest

generation hardware

CPU

Oracle Database In-Memory Goals

100x 2x

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Until Now Must Choose One Format and Suffer Tradeoffs

Row Format Databases vs. Column Format Databases

Row

� Transactions run faster on row format

– Example: Insert or query a sales order

– Fast processing few rows, many columns

Column

�Analytics run faster on column format

– Example : Report on sales totals by region

– Fast accessing few columns, many rows

SALES

SALES


Breakthrough: Dual Format Database

• BOTH row and column formats for same table

• Simultaneously active and transactionally consistent

• Analytics & reporting use new in-memory Column format

• OLTP uses proven row format

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Memory Memory

SALES SALES

RowFormat

ColumnFormat

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Orders of Magnitude Faster Analytic Data Scans

• Each CPU core scans local in-memory columns

• Scans use super fast SIMD vector instructions

• Originally designed for graphics & science

• Billions of rows/sec scan rate per CPU core

• Row format is millions/sec

14

Ve

cto

r R

eg

iste

rLoadmultipleregion values

VectorCompare all valuesan 1 cycle

CPU

MemoryR

EG

ION

CA

CACA

CA

Example:Find all salesin region of CA

> 100x Faster


Generates Reports Instantly

• Dynamically creates in-memory report outline

• Then report outline filled-in during fast fact scan

• Reports run much faster without predefined cubes

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Example: Report sales of footwear in outlet stores

Sales

Stores

Products

In-Memory Report Outline

Footwear

Ou

tle

ts

$$$

$

$$$

Footwear

SalesOutlets

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00.5

11.5

22.5

33.5

44.5

Row Store in buffer cache Row Store Parallel SQL In-Memory In-Memory Parallel SQL

Elapsed Time (seconds)

In-Memory Scan Performance Example

Scenario – scan 100M row table to find a single row – no indexes

25M Rows/sec

1 Billion Rows/sec


In-Memory Sessions

• Oracle Database In-Memory: The Next Big Thing (CON7247), Monday at 1:30pm, Moscone South – 103

• Top Five Things to Know About Oracle Database In-Memory (CON7248), Tuesday at 12:00pm, Moscone South – 104, Thursday at 9:30am, MosconeSouth - 104

• Oracle Multitenant Meets Oracle Database In-Memory (CON7306), Tuesday at 5:00pm, Moscone South – 102

• Oracle Database In-Memory: Under the Hood (CON8367), Wednesday at 2:00pm, Moscone South - 104

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Oracle Multitenant


Oracle SecureFiles

Feature Factoring

Other Improvements

1

2

3

4

5

6

7


New architecture for consolidating databases and simplifying operations

Oracle Multitenant

ERP CRM

DWSelf-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

Complementary to VMs

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Multitenant New Features in 12.1.0.2

• Subset by tablespace

• Metadata-only clone

• Remote clone (including snapshots)

• File system-agnostic cloning via dNFS (clonedb = true)

• New SQL clause to aggregate data across PDBs

select ENAME from containers(scott.EMP)where CON_ID in (45, 49);

• New “standbys” clause (all | none)

• Nologging clause at PDB level

• Flashback data archive

• Temporal SQL Support

• Compatible with DB In-Memory

• Maintains state of PDBs between CDB restarts

Cloning

SQL

Cross PDB Queries

Standby & Logging

PRIMARY STANDBY

AdditionalFeatures





Oracle Multitenant


Oracle SecureFiles

Feature Factoring

Other Improvements

1

2

3

4

5

6

7

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ACO Enhancements for OLTP

• OLTP Background Table Compression (Block level Compression Tiering)

• Currently OLTP Compression slows down Loading Data into the database

• With OLTP Background Compression, data is loaded uncompressed, then compressed in background at a later time

• OLTP Table Compression Feature Completion

– Compression works on tables with more than 254 columns, and with table shrink

– Row and Segment level statistics on last Read and last Update

• Advanced Index Compression

• Index compression missing from ACO option; 30-60% of storage used by indexes

• Up to 3x compression for indexes, similar to OLTP Table Compression

• Row Level Locking for Hybrid Columnar Compression


ILM: Hot/Cold Data ClassificationEnhanced Insight into Data Usage: “heat map”

Recently inserted, actively

updated Infrequently updated,

Frequently Queried

Retained for long term analytics and

compliance with corporate policies and

regulations

ACTIVE

FREQUENT

ACCESS

DORMANT

•Row and Segment level statistics on

last read and last update

•Low overhead

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ILM: Automatic Compression & Storage TieringMoves and Compresses Segments Based on Usage

ALTER TABLE sales

ILM ADD CompressionPolicy

COMPRESS Partitions for Query

AFTER 90 days of no access;

ALTER TABLE sales

ILM ADD TieringPolicy

TIER Partitions TO ‘Archive_TBS’

AFTER 180 days of no

modification;


Compression and ILM Sessions

• 12c: Heat Map and ADO (CON8372), Monday at 11:45am, Moscone South – 305

• Compression Best Practices (CON8376), Tuesday at 10:45am, MosconeSouth – 305

• Wresting Control of Your Oracle Database Data with Heat Map and ILM in Oracle Database 12c (CON2096), Thursday at 10:45am, Moscone South –102

• Deploying Oracle Database 12c with Oracle ZFS Storage Appliance (HOL9715), Monday at 2:45pm, Tuesday at 5:15pm, Hotel Nikko –Mendocino I/II

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Oracle Multitenant


Oracle SecureFiles

Feature Factoring

Other Improvements

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2

3

4

5

6

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Oracle SecureFilesConsolidated Secure Management of Data

• SecureFiles gives file system performance for files in the database

• Introduced with Oracle Database 11g Release 1

• Similar to LOBs but much faster, and with more capabilities

– Transparent encryption (with Advanced Security Option)

– Compression and deduplication (with Advanced Compression Option)

– Extends the security, reliability, and scalability of database to files

– Superset of LOB interfaces allows easy migration from LOBs

• Enables consolidation of file data with associated relational data

– Single security model

– Single view of data

– Single management of data

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SecureFiles Performance & Scalability on Exadata

• Small Documents: Extremely High Throughput

– Load at 200 Million docs/hr, Read at 780 Million docs/hr

• Large Multimedia: Extremely High Bandwidth

– Load at 4 GB/s and Read at 8 GB/s

0

1000

2000

3000

4000

5000

6000

5-10k 100-500k 1-5m 10-20m 50-100m

MB

/s

SecureFiles Write Performance

0

2000

4000

6000

8000

10000

5-10k 100-500k 1-5m 10-20m 50-100m

MB

/s

SecureFiles Read Performance


Oracle Database 12c: In-Memory LOB Queries & UpdatesSpeeds up string ops on LOBs & updates of Temporary LOBs

• In-Memory optimization to trade PGA memory for speed

• Uses in-memory working area for Temporary LOBs that are small

– Automatically and transparently spills temporary LOB to disk / Flash Cache as LOB grows beyond a threshold

• Speeds up all LOB string operations

– concatenate, append, substr, length, instr, compare, trim, like, replace, pad, nvl using SQL functions or DBMS_LOB package

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Oracle Database 12c Parallel Load & Move LOBs5x to 17x speedup in loading & moving SecureFiles

• Enhanced Parallelism for DML, CTAS & MOVE for SecureFiles

– Intra-partition parallelism

– Parallel move for Non-partitioned table

• Linear Scaling with degree of parallelism

• Helps exploit multi-core & I/O parallelism


SecureFiles Migration

• Enable SecureFiles on new partitions

–Old data stays as BasicFile LOBs

• Migrating existing data requires table rebuild

– Data movement to copy into new SecureFile LOB

– 12c Online Partition Move

• DBMS_REDEFINITION

– No downtime, requires twice the space for each segment

– Existing partitions can be done in parallel

– Consider NOLOGGING the operation

Options to migrate to SecureFiles

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Oracle Multitenant


Oracle SecureFiles

Feature Factoring

Other Improvements

1

2

3

4

5

6

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Feature EE Exadata/Super Cluster Description

Oracle In-Memory Y Y Requires Oracle In-Memory Option. Duplicate

(Fault Tolerance) is only available on Exadata and

Super Cluster. Will be available on ODA in a future

version

Oracle Big Data SQL Y Only available on Exadata and Oracle’s Big Data

Appliance (Available Q3 CY 2014)

JSON Support Y Y Part of base product

Oracle REST Data Services Y Y Part of base product

Oracle Multitenant Y Y Requires the Multitenant option when using more

than one PDB

Advanced Index Compression Y Y Requires Oracle Advanced Compression

Feature Factoring for New Features (Full Details Here)

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Feature EE Exadata/Super Cluster Description

Attribute Clustering Y Y Part of base product

Zone Maps Y Exadata only plus Partitioning Option

Full Database Caching Y Y Part of base product

Automatic Big Table Caching Y Y Part of base product

Approximate Count Distinct Y Y Part of base product

Rapid Home Provisioning Y Y Requires the EM’s “Lifecycle Management Pack”

on each target when used against multiple clusters

Oracle Zero Data Loss

Recovery Appliance

Coming H2 Cy2014 : Pricing to be confirmed

Oracle Key Vault Coming H2 Cy2014 : Pricing to be confirmed

AWR Warehouse Coming H2 Cy2014 : Pricing to be confirmed

Feature Factoring for New Features (Full Details Here)





Oracle Multitenant


Oracle SecureFiles

Feature Factoring

Other Improvements

1

2

3

4

5

6

7

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AWR Warehouse

Source Database 1 Load AWR Snapshotsinto Repository

Central AWR RepositoryDatabase 1 Snapshots

• Central warehouse configured for long term AWR data retention

• Historical and ongoing AWR snapshots collected from databases enabled for AWR warehouse

• Retention period configurable for weeks, months, years or forever (default)

Source Database 1

Source Database 3

Administrators use historical AWR reports in several database diagnosticfeatures

Performance Home

ASH Analytics

AWR Report

Compare Period ADDM

Compare Period Report

Database 2 Snapshots

Database 3 Snapshots

……

Database n Snapshots


AWR Warehouse• Warehouse dashboard tracking

ETL jobs

• All AWR features available on long-term AWR data

• Performance page

• AWR report

• ASH analytics

• Compare Period ADDM

• Compare Period Report

• Integrated seamlessly into EM UI

• Zero runtime overhead on source databases

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Full Database CachingAn ease of use feature when the database fits in memory

• Historically the Oracle Database may not have cached blocks from full tables scans

– This was done to prevent “Thrashing”

• To take advantage of all the available memory and potentially improve performance the Oracle Database will cache the data from scans if the entire database fits in the buffer cache

• It is also possible to force the Oracle Database to cache everything using the command

– ALTER DATABASE FORCE FULL DATABASE CACHING

PRODUCTS

CUSTOMERS

ORDERS


Automatic Big Table Caching

• Supports situations where the entire database won’t fit in memory but some large objects will

• Avoids thrashing by replacing objects rather than blocks.

• To enable the feature– db_big_table_cache_percent_target = <Percentage of Buffer Cache>

• For a simple table scan workload, the latency was reduced by up to 4x

• For a TPCH workload, it increased the throughput by 14% and cut down I/O by 3x

PRODUCTS

CUSTOMERS

ORDERS

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Temporal Database• Application Need

– Apps in many industries need to track time period for “real world validity”

– Customers currently develop and maintain complex logic to keep track of validity and write complex queries to work with valid time

• Use Cases

– Financial Market and Trade Data is typically annotated with transaction & valid time

• Transaction Time helps view the state of the market at the time the trade was executed

• Corrections to data must be tracked correctly, occur almost continuously

• Ratings Changes are often effective at a future date while External Trades are backdated

–Most Packaged Apps and many DWs also need to track Change of address, Customer or Employee Status Changes, Pricing Changes over time

• Native Valid Time SQL support replaces complex application logic

– Integrated with ILM to give automatic compression & storage tiering


Temporal Database, 12c Release 1

• New “Valid Time” tracking feature; complements Total Recall feature

– Two timestamps are used to denote user defined start and end times for the row

• Temporal SQL for “point in time” and “all versions” queries– select … EMP AS OF … ‘01-Jan-98’ (returns “R2: Smith, San Jose …”)

– select … EMP VERSIONS … BETWEEN ‘01-Jan-90’ and MAXVALUE (returns both current valid time rows “R2: Smith San Jose …” & “R3: Smith Redwood City …”)

Name AddressValid time

start

Valid time

end

Transaction

time start

Transaction

time end

R1:

SmithSan Jose

09-JAN-

9702-DEC-96 01-JAN-99

R2:

SmithSan Jose

09-JAN-

97

06-AUG-

9901-JAN-99

R3:

Smith

Redwood

City

06-AUG-

9901-JAN-99

Historical Row moved to

Flashback Data Archive

Row updated on 01-Jan-99

with Smith’s new address

effective 06-Aug-99 onwards

De

lete

d

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Oracle Database 12c In-Database Archive

• Applications typically work with recent data

– But often need to retain data for 5 to 10 years

• In-DB Archiving provides the ability to archive infrequently used data within the database

– Archived Data is invisible by default

• Speed up upgrades & Reports by up to 5x

– Works with Partition Pruning and Exadata Storage Indexes to eliminate I/O for archived data

• Archived data remains online for SQL Query & DMLs & is upgraded with the App

5x speedup for upgrades and reports Easily enabled for a table:

alter table

… row archival

Application can marks rows as archived:

update SALES_ORDERS …

set ORA_ARCHIVE_STATE = 1

Sessions can set default visibility to see all

data or active data only (default)

alter session set

row archival visibility = [all | active]


Relational

Oracle Support for Any Data Management System

Hadoop

Change the Business

• Scale-out, low cost store

• Collect any data

• Map-reduce, SQL

• Analytic applications

NoSQL

Scale the Business

• Scale-out, low cost store

• Collect key-value data

• Find data by key

• Web applications

Run the Business

• Scale-out and scale-up

• Collect any data

• SQL

• Transactional and analytic

applications for the enterprise

• Secure and highly available

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Big Data Management System

Advanced Query & Analysis Full Power of SQL and Advanced Analytics

Leverages All Your DataRelational, Hadoop and NoSQL

Secure Unified Governance on All Data

Fastest Performance Utilize SQL Processing Across the Platform

Transparent to ApplicationsNo Changes to Application Code


Oracle Database 12c for the Developer

SQL API Document Store API

Java�

H2 2014

.NET�

Use REST API

Node.js�

H2 2014

REST (ORDS)�

DB 12.1.0.2

Ruby�

Use REST API

Python�

Use REST API

PHP�

Use REST API

R�

Use REST API

Perl�

Use REST API

Use new document-store API’s to build applications without writing SQL

Supporting all major development environments and API’s

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Zone Maps and Attribute Clustering

Combined Benefits:

• Improved query performance and concurrency

– Reduced physical data access

– Significant IO reduction for highly selective operations

• Optimized space utilization

– Less need for indexes

– Improved compression ratios through data clustering

• Full application transparency

– Any application will benefit

Attribute Clustering

Orders data so that columns values are stored together on disk

X

Zone maps

Stores min/max of specified columns per zone

Used to filter un-needed data during query execution


Approximate Count Distinct

• Not every query requires a completely accurate result

– “How many distinct individuals visited our website last week?”

• New SQL function for approximate results for COUNT DISTINCT aggregates

– APPROX_COUNT_DISTINCT()

• Approximate results can be significantly faster and use less resources than exact calculations

– 5x to 50x ++ times faster (depending upon number of distinct values and complexity of SQL)

– Accuracy > 97% (with 95% confidence)

1,2,3...

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Other Application Development Enhancements

• Application Express (APEX)

– Enhancements for the opportunistic and departmental developer

• Plug-in architecture (community-based extensions)

• Dynamic actions (declarative AJAX)

• RESTful Services

• HTML5 and Mobile Web Application Development

• Support for 12c data types and 32K varchars

– Pre-built Productivity Applications

• 30+ one-click deployable applications

• Bug, project, customer, use case tracking etc.

– Pluggable Database Support

• Supporting both container and root installation

• Schema based “workspace” model (11g)

• Pluggable Database based “workspace” model (12c)

• Other Migration

– SQL CROSS APPLY

– OUTER APPLY

– LATERAL

– Oracle Database Driver for MySQL Applications

• .Net Support

– Entity Framework and LINQ


Adaptive Execution PlansGood 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

• Dramatic improvements seen

with EBS reports

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Oracle Rapid Home ProvisioningInstall Once Use Many

• Patching is complex and time consuming

– Even when automated

• New way to deploy upgrades

– Create reference homes on Centralized Home Server

• Apply patches once (Enterprise) on Home Server

• Distribute homes on-demand or policy

– Fast and Efficient

• Rapid distribution (network efficient)

• Space efficient (snapshots)

• Local caches


Oracle Rapid Home Provisioning

Oracle Enterprise Manager

Database Cloud

Cluster

ClusterCluster

Cluster

Cluster

Cluster

Differential Copy

NFS Mount

Local Gold Image

S/W Distribution

ServiceCatalog

Grid HomeServer

Provisioning

Monitoring and Configuration

Capacity and Resource

Patching

Performance and Tuning

Service Level

Application

Database

Grid

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01001011001010100100

10010010010010010010

01001000100101010010

Introducing Oracle Data Masking and SubsettingReduces Risk in Sharing by Obfuscating or Removing Sensitive Data

NAME SALARY

AGUILAR 50135.56

BENSON 35789.89

CHANDRA 60765.23

DONNER 103456.82

NAME SALARY

AGUILAR 35676.24

CHANDRA 76546.89

Discover Sensitive DataDiscover Sensitive Data

Mask Data Using Format LibraryMask Data Using Format Library

Subset Based on Conditions/GoalSubset Based on Conditions/Goal

Mask/subset in Export or on StagingMask/subset in Export or on Staging

Retain Application IntegrityRetain Application Integrity


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