1. <Insert Picture Here>
Oracle Database 11g
Advanced Compression Option

2. Challenges Explosion in data volume managed by Enterprises
Government regulations (Sarbanes-Oxley, HIPPA, etc)
User generated content (Web 2.0)
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
The volume of data being generated and retained by businesses has seen an exponential growth in the last few years. On an average, it is not uncommon to see data growing by 2-3 times every couple of years thanks to a variety of factors such as government regulations that mandate storing historical data for a certain number of years, massive amount of user generated content through web 2.0 applications and increasing adoption of multimedia content – enabled in turn by growing penetration of broadband technologies. Consequently, terabytes sized database – that were a novelty in the past – have becomes so commonplace these days. A key challenge before enterprises today is how to manage this enormous volume of data in a cost effective manner as they still have a limited IT budget. On a more technical level, there is also an associated challenge of ensuring the application performance does not degrade unacceptably while accessing this enormously large volume of data.
This brings compression technology at forefront. We have known and used compression technology in some form of other so it should not need much explanation. The most prominent benefit of compression of course is the reduced storage consumption. However, the compression technology needs to be highly efficient and intelligent before it can be used with enterprise application in view of their demanding performance and scalability requirements.

3. Introducing Advanced Compression Option
Redo logs
Backups
Standby
Oracle Database 11g introduces a comprehensive set of compression capabilities
Structured/Relational data compression
Unstructured data compression
Compression for backup data
Network transport compression
Reduces resource requirements and costs
Storage System
Network Bandwidth
Memory Usage
Oracle database 11g introduces a comprehensive set of technologies to address these challenges. Thanks to a number of innovative technology offering introduced in this release, our customer can compress practically any type of data – be it simple number and text traditionally stored in the database a.k.a structured data, or unstructured data such as documents, images, multimedia, etc or data being backed up. In addition, Oracle database 11g can also compress some of network traffic. Together these compression technology maximize the efficiency of resource utilization and reduces overall resource requirements and costs.

4. Table Compression Introduced in Oracle9i Release 2
Supports 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 IOs
Buffer Cache efficiency
Data is compressed at the database block level
Each block contains own compression metadata – improves IO efficiency
Local symbol table dynamically adapts to data changes
Compression can be specified at either the table or partition levels
Completely transparent to applications
Noticeable impact on write performance
Being self-contained is good b/c in order to read a compressed block we don’t have to access ‘other blocks’ that contain the symbol table or compression dictionary. For example, IBM has a database-wide symbol table. So every time you read a block that is compressed – you also have to read the blocks holding the db-wide symbol table – meaning extra I/O. We don’t have to do that!!

5. OLTP Table Compression
Oracle Database 11g extends compression for OLTP data
Support for conventional DML Operations
(INSERT, UPDATE, DELETE)
New algorithm significantly reduces write overhead
Batched compression ensures no impact for most OLTP transactions
No impact on reads
Reads may actually see improved performance due to fewer IOs and enhanced memory efficiency
Lets first talk about compression of regular relational data. We were the early pioneer in terms of compression technology for databases when we introduced table compression for bulk load operations in Oracle9i. Using this feature, customers can compress data at the time of doing bulk load using operations such as DIRECTLOAD, CTAS, etc. However, until now, compression was not available for regular data manipulation operations such as INSERT, UPDATE and DELETES. In Oracle database 11g, we have extended the compression technology to support these operations as well. Consequently, compression in Oracle Database 11g can be used for all kinds of workload – be it OLTP or DW.
It is important to mention here is that table compression enhancements introduced Oracle database 11g are not just incremental changes. Rather enormous amount of work has gone into making sure that the new compression technology has negligible impact on updates since any noticeable write time penalty due to compression won’t be acceptable in OLTP environment. As a result compression technology in Oracle database 11g is very efficient which could reduce the space consumption up to 2 to 3 times. And while you do that, not only your write performance does not degrade, your read performance or queries actually improves. This is because unlike desktop based compression techniques where you have to wait for data to uncompressed, our technology reads the compressed data directly and does not require any uncompression. This make sure that our query performance does not degrade because of compression. Now, the question is how does the query performance improve. It does so because we need to fetch less data to answer a query due to compression. Another advantage of fetching less data to satisfy a query is that we can now use the memory used to buffer the data read from disk more efficient thereby maximizing the memory utilization efficiency.
Finally, just to make sure that I do not miss any thing obvious, our compression technology is completely application transparent. That mean that you can use this technology with any home grown or packaged application such as SAP, Siebel , EBS, etc.

6. OLTP Table Compression
Overhead
Free Space
Uncompressed
Compressed
Inserts are again uncompressed
Block usage reaches PCTFREE – triggers Compression
Inserts are uncompressed
Block usage reaches PCTFREE – triggers Compression
Adaptable, continuous compression
Compression automatically triggered when block usage reaches PCTFREE
Compression eliminates holes created due to deletions and maximizes contiguous free space in block

7. OLTP Table Compression
Initially Uncompressed Block
Employee Table
Smith
John 3
Doe
Jane 4 2 1
LAST_NAME
FIRST_NAME ID
Header
1•John•Doe 2•Jane• Doe 3•John•Smith 4• Jane • Doe
Free Space
Start with a table ‘Employee’ that has compression enabled and we can see a block containing this data.
In the beginning the block is not compressed because it is NOT full
Then we issue an insert statement which triggers the block to compress…
We create the Symbol Table (or dictionary) in the block header for data that has duplicate values (i.e. ‘John’,’Doe’,and ‘Jane’)
Then we replace the data in the block with the relevant symbol
INSERT INTO EMPLOYEE
VALUES (5, ‘Jack’, ‘Smith’);
COMMIT;

8. OLTP Table Compression
Employee Table
Block
1•• 2•• 3•• 4 •  •  5•Jack•
Free Space
Compressed Block
Doe
Jane 4
Smith
John 3
Jack 5 2 1
LAST_NAME
FIRST_NAME ID
Header
John=|Doe=|Jane=|Smith=
1•John•Doe 2•Jane• Doe 3•John•Smith 4• Jane • Doe
Free Space
Start with a table ‘Employee’ that has compression enabled and we can see a block containing this data.
In the beginning the block is not compressed because it is NOT full
Then we issue an insert statement which triggers the block to compress…
We create the Symbol Table (or dictionary) in the block header for data that has duplicate values (i.e. ‘John’,’Doe’,and ‘Jane’)
Then we replace the data in the block with the relevant symbol
Local
Symbol Table

9. OLTP Table Compression
Uncompressed Block
1•• 2•• 3•• 4 •  •  5•Jack•
Free Space
Compressed Block
Header
Header
John=|Doe=|Jane=|Smith=
1•John•Doe 2•Jane• Doe 3•John•Smith 4• Jane • Doe 5•Jack •Smith Free Space
1•John•Doe 2•Jane• Doe 3•John•Smith 4• Jane • Doe
Free Space
Start with a table ‘Employee’ that has compression enabled and we can see a block containing this data.
In the beginning the block is not compressed because it is NOT full
Then we issue an insert statement which triggers the block to compress…
We create the Symbol Table (or dictionary) in the block header for data that has duplicate values (i.e. ‘John’,’Doe’,and ‘Jane’)
Then we replace the data in the block with the relevant symbol
Local
Symbol Table
More Data
Per Block

10. Using OLTP Table Compression
Requires database compatibility level at 11.1 or greater
New Syntax extends the ‘COMPRESS’ keyword
COMPRESS [FOR {ALL | DIRECT_LOAD} OPERATIONS]
DIRECT_LOAD (DEFAULT)
Refers to Bulk load operations from 10g and prior releases
ALL
OLTP + Direct loads
Enable OLTP compression for new table t1
CREATE TABLE t1 COMPRESS FOR ALL OPERATIONS
Enable only direct load compression on existing table
ALTER TABLE t2 COMPRESS
Only new rows are compressed, existing rows are uncompressed

11. SecureFiles - Deduplication
Secure hash
Enables storage of a single physical image for duplicate data
Significantly reduces space consumption
Dramatically improves writes and copy operations
No adverse impact on read operations
May actually improve read performance for cache data
Duplicate detection happens within a table, partition or sub-partition
Specially useful for content management, applications and data archival applications

12. SecureFiles - Compression
Huge storage savings
Industry standard compression algorithms
2-3x compression for typical files (doc, pdf, xml)
Minimal CPU overhead during compression
Automatically detects if SecureFile data is compressible
Skips compression for already compressed data
Auto-turn off compression when space savings are minimal or zero
Two levels of compression provide different compression ratios
Compression Levels: MEDIUM (default), HIGH
Higher the degree of compression, higher the latency and CPU overhead incurred
SecureFiles Compression is independent of table or index compression
Server-side compression
Allows for random reads and writes to SecureFile data
Can be specified at a partition level
Compression algorithm : ZLIB (support for more algorithms upcoming)
Compression Overhead: 3- 5%
Random reads/writes:
utl_compress PL/SQL package which provide for compression/decompression functionality – does NOT provide for random access for read/write.
With SecureFiles, only the required set of data blocks are uncompressed for providing random read/write access. More over, this is completely transparent to user applications. SecureFiles automatically maintains the mapping between uncompressed and compressed offsets. If the compression level is changed from MEDIUM to HIGH, there is no need to change the user application as the mapping is automatically updated to reflect the new algorithm.

13. Data Pump Compression
Metadata compression available since Oracle Database 10g
Oracle Database 11g extends compression to table data during exports
No need to decompress before import
Single step compression of both data and metadata
Compressed data directly hits disk resulting in reduced disk space requirements
75% reduction in dump file size on export of sample OE and SH schemas
Compression factor comparable to GNU gzip utility
Application transparent
Complete Data Pump functionality available on compressed files

14. Backup data and Network transport Compression
Fast RMAN Compression
Compresses the backup set contents before writing them to disk or tape
No extra decompression steps are required during recovery when you use RMAN compression.
High performance, industry standard compression algorithm
40% faster backup compression versus Oracle Database 10g
Suitable for fast, incremental daily backups
Reduces network usage
Data Guard Network Compression
Compression of redo traffic over the network during redo gap resolution
Improves redo transport performance
Gap resolution is up to 2x faster

15. Summary
Advanced Compression Option contains comprehensive data compression capabilities for all types of data
Structured, Unstructured, Backup, Network Transport
Reduces storage consumption by 2 to 3 times
Improves read performance
Enhances memory, buffer cache utilization
Complete application transparency
Benefits diverse application workloads
So, to sum it up, as we have seen Advanced Compression Option in Oracle Database 11g contains a compelling set of compression technologies to help customers deal with data volume explosion in a cost effective manner without compromising application performance. Our compression technologies not only reduce disk space requirements, they improve application performance, enhance memory and network efficiency and can be used with any application out of the box. We are very excited about this technology and truly hope that this offering will help customers manage their large volume of data in the most cost effective manner.