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Oracle Advanced Compression: Deep Dive into OLTP Table Compression

Gregg Christman -- Senior Product Manager

Vineet Marwah – Principal Member of Technical Staff

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Program Agenda

• Data Growth Challenges

• OLTP Table Compression

• Customer Experience

• Thomson Reuters

• Questions and Answers

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Data Growth Challenges

• Explosion in Data Volumes– Government Regulations (Sarbanes-Oxley, etc)– User Generated Content (Web 2.0)– Application Consolidation

• IT Managers Must Support Larger Volumes of Data with Limited Technology Budgets – Need to optimize storage consumption– Also maintain acceptable application performance

• Intelligent and Efficient Compression Technology can Help Address These Challenges

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Oracle Advanced Compression Option

• Reduces resource requirements and costs – Storage System– Network Bandwidth– Memory Usage

Relational Data Compression

Unstructured Data Compression

Backup Data Compression

Network Data Compression

• OLTP Table Compression

• SecureFiles Deduplication

• SecureFiles Compression

• Data Pump Data Compression

• RMAN Fast Backup Compression

• Data Guard Redo Transport Compression

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Table Compression

• Introduced in Oracle Database 9i Release 2– Compression during bulk load operations (Direct Load, CTAS)– Data modified using conventional DML not compressed

• Optimized compression algorithm for relational data• Improved performance for queries accessing large

amounts of data– Fewer I/Os– Buffer Cache efficiency

• Data is compressed at the database block level• Compression enabled at either the table or partition level• Completely transparent to applications

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OLTP Table Compression

• Oracle Database 11g extends table compression for OLTP data– Support for conventional DML Operations (INSERT, UPDATE)

• New algorithm significantly reduces write overhead– Batched compression minimizes impact for OLTP transactions

• No impact on reads– Reads may actually see improved performance due to fewer

I/Os and enhanced memory efficiency

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OLTP Table Compression Process

Initially Uncompressed

Block

Compressed Block

Partially Compressed

Block

Compressed Block

Empty

Block

Legend

Header Data

Free Space

Uncompressed Data

Compressed Data

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Block-Level Batch Compression

• Patent pending algorithm minimizes performance overhead and maximizes compression

• Individual INSERTs and UPDATEs do not cause recompression• Compression cost is amortized over several DML operations• Block-level (Local) compression keeps up with frequent data

changes in OLTP environments– Competitors use static, fixed size dictionary table thereby

compromising compression benefits

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OLTP Table Compression

SmithJohn3

DoeJane4

DoeJane2

DoeJohn1

LAST_NAMEFIRST_NAMEID

Employee Table Initially Uncompressed Block

INSERT INTO EMPLOYEE

VALUES (5, ‘Jack’, ‘Smith’);

COMMIT;

INSERT INTO EMPLOYEE

VALUES (5, ‘Jack’, ‘Smith’);

COMMIT;

1•John•Doe 2•Jane• Doe 3•John•Smith 4• Jane • Doe

Free Space

Header

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OLTP Table Compression

Block

John=|Doe=|Jane=|Smith=Header

Doe Jane4

Smith John3

Smith Jack5

Doe Jane2

Doe John1

LAST_NAME FIRST_NAMEID

Employee Table

1•John•Doe 2•Jane• Doe 3•John•Smith 4• Jane • Doe

Free Space

1•• 2•• 3•• 4 •

• 5•Jack•

Free Space

Compressed Block

Local Symbol Table

Local Symbol Table

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Table Compression Syntax

OLTP Table Compression Syntax:CREATE TABLE emp (

emp_id NUMBER, first_name VARCHAR2(128), last_name VARCHAR2(128)) COMPRESS FOR OLTP;

Direct Load Compression Syntax (default):CREATE TABLE emp (emp_id NUMBER

, first_name VARCHAR2(128), last_name VARCHAR2(128)) COMPRESS [BASIC];

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Table Compression AdvisorEstimate Potential Storage Savings

• Available in 11g Release 2• Available on OTN *– Supports Oracle Database 9i Release 2 through 11g Release 1– Shows projected compression ratio for uncompressed tables– Reports actual compression ratio for compressed tables (11g Only)

* http://www.oracle.com/technology/products/database/compression/compression-advisor.html

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Monitoring Table Compression

• View: DBA_TABLES, columns:

– COMPRESSION ( ENABLED / DISABLED ) – COMPRESS_FOR ( OLTP / BASIC)

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Table Compression Results

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© 2009 Oracle Corporation - Confidential 16

Oracle IT, Oracle’s Internal E-Business Application DB

• Oracle’s Internal E-Business Suite Production System deployed ACO in 2009– 4-node Sun E25K RAC, 11gR1– Average overall storage savings 3x

• Table compression 4x• Index compression 2x• LOB compression 2.3x

– 231TB of realized storage savings primary, standby and test systems • Additional benefits were also accrued in Dev clones and Backups

• Payroll, Order-2-Cash, AP/AR batch flows, Self-Service flows run without regression, Queries involving full table scans show speedup

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© 2009 Oracle Corporation - Confidential 17

Oracle IT, Oracle’s Internal Beehive Email DB

• Production system on 11gR1 & Exadata for Primary and Standby– Using Exadata Storage Servers for storage– Average Compression Ratio: 2x– Storage savings add up with standby, mirroring, flash recovery area– Compression went production in 2009• Consolidate 90K employees on this email server, more being migrated

– Savings As of April 2010• Beehive Saved 365TB of storage using Advanced Compression– Incrementally saves 2.6TB/day based on db size growth

• Savings higher with Sun user migration

• Compression also helped improve performance by caching only compressed emails in memory and reducing I/O latencies

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© 2009 Oracle Corporation - Confidential 18

SAP R/3, BW, Leading Global Company

• Compression on SAP databases at Leading global company– Tests done using Oracle

database 11gR2– SAP R/3 DB• 4.67TB Uncompressed• 1.93 TB Compressed – 2.4x savings factor

– SAP BW DB• 1.38 TB Uncompressed• .53 TB Compressed– 2.6x savings factor

• Leverage 11g Compression for Tables, Indexes and LOB data

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Metalink

• See Metalink Note #1061366.1– Lists recommended Advanced Compression patches

applicable to all customers

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Customer ExperienceCustomer Experience

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Summary

• Comprehensive data compression capabilities for all types of data• Structured, Unstructured, Backup, Network

• Reduces storage consumption by 2 to 4 times• Improves read performance• Enhances memory, buffer cache utilization• Complete application transparency• Benefits diverse application workloads

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