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<Insert Picture Here> Oracle Advanced Compression: Deep Dive into OLTP Table Compression Gregg Christman -- Senior Product Manager Vineet Marwah – Principal Member of Technical Staff

Program Agenda • Data Growth Challenges <Insert Picture Here> • OLTP Table Compression • Customer Experience • Thomson Reuters • Questions and Answers 3

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 4

Oracle Advanced Compression Option • Secure. Files Deduplication • Secure. Files Compression • OLTP Table Compression Relational Data Compression Unstructured Data Compression • Data Pump Data Compression • RMAN Fast Backup Compression • Data Guard Redo Transport Compression Backup Data Compression Network Data Compression • Reduces resource requirements and costs – Storage System – Network Bandwidth – Memory Usage 5

Table Compression • Introduced in Oracle Database 9 i 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 6

OLTP Table Compression • Oracle Database 11 g 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 7

OLTP Table Compression Process Empty Block Initially Partially Compressed Uncompressed Compressed Block Legend Header Data Uncompressed Data Free Space Compressed Data 8

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 9

OLTP Table Compression Employee Table ID FIRST_NAME LAST_NAME 1 John Doe 2 Jane Doe 3 John Smith 4 Jane Doe Initially Uncompressed Block Header 1 • John • Doe 2 • Jane • Doe 3 • John • Smith 4 • Jane • Doe Free Space INSERT INTO EMPLOYEE VALUES (5, ‘Jack’, ‘Smith’); COMMIT; 10

OLTP Table Compression Employee Table ID FIRST_NAME LAST_NAME 1 John Doe 2 Jane Doe 3 John Smith 4 Jane Doe 5 Jack Smith Compressed Block Header John= |Doe= |Jane= |Smith= 1 • • 2 • Jane • 3 • • 4 • • 1 • John • Doe 5 • Jack • Doe 3 • John • Smith 4 • Jane • Doe Free Space Local Symbol Table 11

Table Compression Syntax OLTP Table Compression Syntax: CREATE TABLE emp ( emp_id NUMBER , first_name VARCHAR 2(128) , last_name VARCHAR 2(128) ) COMPRESS FOR OLTP; Direct Load Compression Syntax (default): CREATE TABLE emp ( emp_id NUMBER , first_name VARCHAR 2(128) , last_name VARCHAR 2(128) ) COMPRESS [BASIC]; 12

Table Compression Advisor Estimate Potential Storage Savings • Available in 11 g Release 2 • Available on OTN * – Supports Oracle Database 9 i Release 2 through 11 g Release 1 – Shows projected compression ratio for uncompressed tables – Reports actual compression ratio for compressed tables (11 g Only) * http: //www. oracle. com/technology/products/database/compression-advisor. html 13

Monitoring Table Compression • View: DBA_TABLES, columns: – COMPRESSION ( ENABLED / DISABLED ) – COMPRESS_FOR ( OLTP / BASIC) 14

<Insert Picture Here> Table Compression Results 15

Oracle IT, Oracle’s Internal E-Business Application DB • Oracle’s Internal E-Business Suite Production System deployed ACO in 2009 – 4 -node Sun E 25 K RAC, 11 g. R 1 – Average overall storage savings 3 x • Table compression 4 x • Index compression 2 x • LOB compression 2. 3 x – 231 TB 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 © 2009 Oracle Corporation - Confidential 16 16

Oracle IT, Oracle’s Internal Beehive Email DB • Production system on 11 g. R 1 & Exadata for Primary and Standby – – Using Exadata Storage Servers for storage Average Compression Ratio: 2 x Storage savings add up with standby, mirroring, flash recovery area Compression went production in 2009 • Consolidate 90 K employees on this email server, more being migrated – Savings As of April 2010 • Beehive Saved 365 TB of storage using Advanced Compression – Incrementally saves 2. 6 TB/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 © 2009 Oracle Corporation - Confidential 17 17

SAP R/3, BW, Leading Global Company • Compression on SAP databases at Leading global company – Tests done using Oracle database 11 g. R 2 – SAP R/3 DB • 4. 67 TB Uncompressed • 1. 93 TB Compressed – 2. 4 x savings factor – SAP BW DB • 1. 38 TB Uncompressed • . 53 TB Compressed – 2. 6 x savings factor • Leverage 11 g Compression for Tables, Indexes and LOB data © 2009 Oracle Corporation - Confidential 18 18

Metalink • See Metalink Note #1061366. 1 – Lists recommended Advanced Compression patches applicable to all customers 19

Customer Experience 20

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 21

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