2. Oracle Database Compression with Oracle Database 12c
Kevin Jernigan
Senior Director Product Management
System Technologies, Oracle

3. The following is intended to outline our general product direction
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.

4. Program Agenda Data Growth Challenges New in Oracle Database 12c
Compression Capabilities
Additional Capabilities
Benefits Summary

5. Data Growth Challenges
IT must support fast-growing amounts of data
Explosion in online access and content
Government data retention regulations
Performance often declines as data grows
Automate the compression and movement of data
Reduce storage costs
Improve performance
Need to manage data growth
Without hurting performance
Without growing costs
With minimal administrative intervention

6. Advanced Compression Option Benefits
Transparent: 100% Application Transparent
Smaller: Reduces Footprint
CapEx: Lowers server & storage costs for primary, standby, backup, test & dev databases …
OpEx: Lowers heating, cooling, floor space costs …
Additional ongoing savings over life of a database as database grows in size
Faster: Transactional, Analytics, DW
Greater speedup from in-memory: 3-10x more data fits in buffer cache & flash cache
Faster queries
Faster backup & restore speeds
End-to-end Cost / Performance Benefits across CPU, DRAM, Flash, Disk & Network

7. New in Oracle Database 12c

8. Heat Map – visualize use of data
Insight helps identify opportunity & drive automation
Table Level Heat Map
Partition Level Heat Map
Row Level Heat Map

9. Heat Map Heat Map of the Database High Performance
Active
Recently inserted, actively updated
Frequent Access
Infrequently updated, frequently queried for reporting
Occasional Access
Infrequent access for query and updates
Dormant
Retained for long term analytics and compliance with corporate policies and regulations
Heat Map of the Database
Database level Heat Map shows when tables and partitions are being used
Block level Heat Map shows access at the row level
Shows both Reads and Writes
Distinguishes Row access from Full Table Scans
Shows Last Access as well as Histogram of access over time
High Performance
“In-Memory” technology
Very low CPU overhead

10. Automatic Data Optimization
As data ages:
Activity declines
Volume grows
Older data primarily
for reporting
Compliance &
Reporting
Reporting
OLTP
10x compressed
15x compressed
alter table … add policy
… compress for query after 3 months of no modification
… compress for archive after 1 year of no modification
This Quarter
This Year
Prior Years
Row Store
for fast OLTP
Compressed
Column Store
for fast analytics
Archive Compressed
Column Store
for max compression
As data cools down, Automatic Data Optimization automatically converts data to columnar compressed Online

11. Automatic Data Optimization (ADO)
Declarative Policy Specification: Condition  Action
ALTER TABLE employee ILM ADD POLICY ROW STORE COMPRESS ADVANCED ROW AFTER 3 DAYS OF NO MODIFICATION;
Conditions are time period after creation, access, modification of data
Actions can be Compression Tiering or Tablespace Tiering
Policies are inherited from the tablespace or table
New tables inherit from tablespace; can also be applied to existing tables
New partitions (including interval partitions) inherit from table
Immediate and background policy execution
Row level policies are executed periodically
Users can configure the frequency of execution
Segment level policies are executed in maintenance windows
Policies can be extended to incorporate Business Rules
Users can add custom conditions to control placement
e.g. 3 months after the ship date of an order

12. Automatic Data Optimization Simple Declarative SQL extension
ALTER TABLE sales ILM add…
Active
Frequent
Access
Occasional
Dormant
OLTP Compressed (2x-4x)
Affects ONLY Candidate Rows
Cached in DRAM & FLASH
compress for Advanced Row
after 2 days of no update
Warehouse Compressed (6x-10x)
High Performance Storage
compress for Query Low
after 1 week of no update
Low Cost Storage
tier to SATA Tablespace
Archive Compressed (10x-15x)
Archival Storage
compress for Archive High
after 6 months no access

13. Advanced Network Compression
Compresses network data to be transmitted at the sending side and then uncompressed at the receiver
Reduces the size of the session data unit (SDU) transmitted over a data connection
Reducing the size of data reduces the time required to transmit the SDU
On narrow bandwidth connections, with faster CPU, it can significantly improve performance
SQL query response becomes faster due to the reduced transmission time

14. Flashback Data Archive History Tables
Flashback Data Archive - FDA
Transparently tracks changes when they are made
Tracked in separate history tables
History tables are secure, tamper-proof
FDA History Table Optimization
Advanced Row Compression
Advanced LOB Compression and Deduplication
Compression Tiering
Use Cases
Governance / regulatory / compliance (GRC)
Substitute for app development
Error correction
Replacement for CDC and DW

15. Additional ACO Capabilities
Storage Snapshot Optimization
Storage snapshots can be recovered in one step
No need to put database in online backup mode
Supports full recovery or point-in-time
Online Move Partition (any compression level)
ALTER TABLE ... MOVE PARTITION ONLINE enables non-blocking online DDL
DML operations continue to run uninterrupted on the partition that is being moved
Supports all compression formats - BASIC, ACO or HCC
Oracle Database Enterprise Edition
Data Partitioning
Option
Advanced
Compression Option
Hybrid Columnar Compression
2x to 4x Compression Typical
Active
Less
Active
Historical

16. Compression Capabilities

17. Compression Techniques
COMPRESSION TYPE:
SUITABLE FOR:
Basic Compression
Read only tables and partitions in Data Warehouse environments or “inactive” data partitions in OLTP environments
Advanced Row Compression
Active tables and partitions in OLTP and Data Warehouse environments
Advanced LOB Compression and Deduplication
Non-relational data in OLTP and Data Warehouse environments
Advanced Network Compression and Data Guard Redo Transport Compression
All environments
RMAN/Data Pump Backup Compression
Index Key Compression
Indexes on tables for OLTP and Data Warehouse
Hybrid Columnar Compression – Warehouse Level
Read only tables and partitions in Data Warehouse environments
Hybrid Columnar Compression – Archive Level
“Inactive” data partitions in OLTP and Data Warehousing environments
Basic Compression
Basic compression, first made available with Oracle9i Database Enterprise Edition, is used to compress read-only data that may be found in a data warehouse, or in the non-active partitions of a large partitioned table underneath OLTP applications. The data is compressed as it is loaded into the table (or moved into the partition), with typically around a 2 to 4 times compression ratio achieved.
SecureFiles Compression
The Advanced Compression option of Oracle Database 11g also supports compression of data stored as large objects (LOBs) in the database. This is especially important for environments that mix structured data with unstructured data underneath rich applications – for example, XML, spatial, audio, image or video information stored in Oracle Database 11g can also be compressed. Compression ratios are dependent on the type of media being compressed, however three levels of compression are provided to allow the desired level of compression to be achieved based on available CPU resources
Index Compression
Oracle Database 11g not only compresses row data, but also compresses the indexes associated with these rows. Indexes can be compressed independent of whether the underlying table data is compressed or not. Index compression can significantly reduce the storage associated with large indexes in large database environments.
Backup Compression
Oracle Database 11g can also compress backups of data stored in the rows and indexes in databases, independent of whether the data in these rows or indexes have in turn been compressed.

18. ACO Compression Capabilities
Advanced Row Compression
Compress data partitions/tables
Advanced LOB Compression Advanced LOB Deduplication
Compress and deduplicate unstructured data
RMAN/DataPump Compression
Compress backups
Data Guard Redo Transport Compression
Advanced Network Compression
Compress network traffic
Flashback Data Archive History Table
Oracle Database Enterprise Edition
Data Partitioning
Option
Advanced
Compression Option
Flashback Data Archive
History table
2x to 4x Compression Typical
Active
Less
Active
Historical 18

19. Advanced Row Compression
Partition/table/tablespace data compression
Support for conventional DML Operations (INSERT, UPDATE)
2x to 4x compression ratio’s typical
Significantly eliminates/reduces write overhead of DML’s
Batched compression minimizes impact on transaction performance
“Database-Aware” compression
Does not require data to be uncompressed – keeps data compressed in memory
Reads often see improved performance due to fewer I/Os and enhanced memory efficiency

20. Real World Compression Results - ERP Database 10 Largest Tables
Storage Utilization MB
3x Smaller
Table Scan Performance
(seconds)
Time
2.5x Faster
DML Performance
Less than
3% Overhead

21. Why Hybrid Columnar Compression
Traditionally, data has been organized in a row format
Alternative approach: store data in a column format
Storing column data together dramatically increases compression
Exadata storage optimized to maximize query performance on HCC-compressed tables
Takes advantage of the processing power, memory and Infiniband network that are integral parts of the Exadata storage server
Hybrid Columnar Compression extended to Pillar Axiom and Sun ZFS Storage Appliance (ZFSSA) storage

22. About Hybrid Columnar Compression
Hybrid Columnar Compressed Tables
Compressed tables can be modified using conventional DML operations
Useful for data that is bulk loaded and queried
How it Works
Tables are organized into Compression Units (CUs)
CUs are multiple database blocks
Within Compression Unit, data is organized by column instead of by row
Column organization brings similar values close together, enhancing compression
Compression Unit
Column 1
Column 2
Column 3
Column 4
Column 5
10x to 15x Reduction

23. Logical Compression Unit
Compression Units
Compression Unit
Logical structure spanning multiple database blocks
Data organized by column during data load
Each column compressed separately
All column data for a set of rows stored in compression unit
CU HEADER
BLOCK HEADER C3 C4 C1 C2 C7 C5 C6 C8
Logical Compression Unit

24. Query and Archive Compression
Query Compression
10x average storage savings
10x reduction in Scan IO
Archive Compression
15x average storage savings
Up to 70x on some data
For cold or historical data
Optimized for Speed on Exadata
Optimized for Space
Smaller Warehouse
Faster Performance
Reclaim 93% of Disks
Keep Data Online

25. Query Compression Levels
Query Compression provides two levels of compression
HIGH typically provides a 10x reduction in storage
LOW typically provides a 6x reduction
Optimized to increase scan query performance
Default is HIGH
Maximizes storage savings and query performance benefits

26. Archive Compression
Archive Compression provides two levels of compression
HIGH typically provides a 15x reduction in storage
LOW typically provides a 10x reduction
Best approach for Information Lifecycle Management (ILM) and data archival
Minimum storage footprint
No need to move data to tape or less expensive disks
Data is always online and always accessible
Intended for tables or partitions that store data that is rarely accessed
Run queries against historical data (without recovering from tape)
Update historical data
Supports schema evolution (add/drop columns)

27. Customer Compression Examples
Data Warehouse Customers (Warehouse Compression)
Top Financial Services 1: 11x
Top Financial Services 2: 24x
Top Financial Services 3: 18x
Top Telco 1: 8x
Top Telco 2: x
Top Telco 3: 6x
Scientific Data Customer (Archive Compression)
Top R&D customer (with PBs of data): 28x
OLTP Archive Customer (Archive Compression)
SAP R/3 Application, Top Global Retailer: 28x
Oracle E-Business Suite, Oracle Corp.: 23x
Custom Call Center Application, Top Telco: 15x

28. Other Compression Capabilities

29. Advanced LOB Compression/Deduplication
LOBS typically experience a reduction of 2x to 3x times in size
Automatically avoids compressing data that would not benefit from compression
Useful for content management, applications and data archival applications
No adverse impact on read operations
Often improves read performance for cache data
Enables storage of a single physical image for duplicate data
Significantly reduces space consumption
Dramatically improves writes and copy operations

30. DataPump Compression Metadata compression since Oracle Database 10g
Compression for 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
Internal tests reduced dump file size up to 75%
Application transparent
Complete DataPump functionality available on compressed files

31. RMAN Backup Compression
Dramatically reduce the storage requirements for backup data
Backup data is compressed before it is written to disk or tape and doesn’t need to be uncompressed before recovery
Three levels of RMAN Compression: LOW, MEDIUM, and HIGH
Compression Level LOW
Best suited when backup is constrained by CPU
Compression Level MEDIUM
Balance between CPU usage and compression ratio
Compression LEVEL HIGH
Best compression ratio and highest CPU utilization
Best suited when backup is constrained by network or I/O

32. Data Guard Redo Transport Compression
Compress network traffic between primary and standby databases
Lower bandwidth networks (<100Mbps)
15-35% less time required to transmit 1 GB of data
Bandwidth consumption reduced up to 35%
High bandwidth networks (>100 Mbps)
Compression will not reduce transmission time
But will reduce bandwidth consumption up to 35%

33. Oracle Database Compression Benefits
Transparent: 100% Application Transparent
Smaller: Reduces Footprint
Faster: Transactional, Analytics, DW
End-to-end Cost / Performance Benefits across CPU, DRAM, Flash, Disk & Network
New in Oracle Database 12c
Automation: Heat Map and ADO
Network compression
Additional storage optimizations

34. Questions?