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Automating Information Lifecycle Management with Oracle Database 12cOracle Database 12c
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.
Copyright © 2013, Oracle and/or its affiliates. All rights reserved.2
Agenda
� Data Growth
� Heat Map
� Automatic Data Optimization
Copyright © 2013, Oracle and/or its affiliates. All rights reserved.3
� Automatic Data Optimization
� Benefits of Heat Map and ADO
Growth in Data Diversity and Usage1,800 Exabytes of Data in 2011, 20x Growth by 2020
Mobile#1 Internet access device in 2013
Enterprise45% per year growth
in database data
Today’s Drivers Emerging Growth Factors
Copyright © 2013, Oracle and/or its affiliates. All rights reserved.4
Big DataLarge customers top 50PB
Cloud80% of newapplications
and their data
Regulation300 exabytes in archives by 2015
Social Business $30B/year in commerce by 2015
Managing Storage Challenges
Compress data, without impacting
Manage more data without incurring
Tier and compress data
Copyright © 2013, Oracle and/or its affiliates. All rights reserved.5
without impacting performance
without incurring additional cost
compress data based on usage
Data CompressionReduce storage footprint, read compressed data faster
Hot Data
111010101010101001101010101011010001011011000
10101010111010100110101
11000010100010110111010
10100101001001000010001
Warm Data
101010101110101001101011100001010001011011101
10101010111010100110101110000101000101
10111010101001010010010000100010101011
Archive Data
101010101110101001101011100001010001011011101
10101010111010100110101110000101000101101110101
01001010010010000100010101011010010110100111000
Copyright © 2013, Oracle and/or its affiliates. All rights reserved.7 Copyright © 2012, Oracle and/or its affiliates. All rights reserved. Confidential – Oracle Restricted7
010001011011000110100101000001001110001010101101001011010010110001010010011111001001000010001010101101000
10100101001001000010001
01010110100101101001110
00010100100101000010010
00010001010101110011010
010001011011101010100101001001000010001010101101001011010011100001010010010100001001000010001010101101001
01001011010011100001010010010100001001
00001000101010111001101110011000111010
010001011011101010100101001001000010001010101101001011010011100001010010010100001001000010001010101101001
01010010010100001001000010001010101110011011100
3XAdvanced Row Compression
10XColumnar Query Compression
15XColumnar Archive Compression
Oracle Advanced CompressionTransparent, Smaller, Faster
� 100% Application Transparent
� End-to-end Cost/Performance Benefits across CPU, DRAM, Flash, Disk & Network
� Runs Faster: OLTP Apps (Transactional & Analytics) & DW
Copyright © 2013, Oracle and/or its affiliates. All rights reserved.8
� Runs Faster: OLTP Apps (Transactional & Analytics) & DW
� Reduces Database Footprint
– CapEx & OpEx savings
– Increases Cloud ROI through Database Footprint reduction in DRAM Memory
Understanding Data Usage PatternsDatabase ‘heat map’
00 0101 10 10 0101 10
0 010 0111 10 10 0111 100 010 0111 10 00 0111 001 1
10 0101 10 10 0101 001 1
00 0101 10 10 0101 101 110 0100 11 10 0111 100 0
10 0111 10 00 0111 001 1
00 0101 10 10 0101 101 110 0111 10 10 0111 100 010 0101 10 10 0101 001 110 0100 11 10 0111 100 0
10 0111 10 10 0111 100 01 1
1 110 0100 11 10 0111 1010 0111 10 10 0111 1010 0111 10 10 0111 100 0
10 0111 10 00 0111 001 110 0100 11 10 0111 100 0
10 0101 10 10 0101 001 1
00 0101 10 10 0101 101 110 0100 11 10 0111 100 0
00 0101 10 10 0101 101 110 0111 10 10 0111 100 010 0101 10 10 0101 001 110 0100 11 10 0111 100 0
10 0111 10 10 0111 100 010 0111 10 00 0111 001 1
00 0101 10 10 0101 101 110 0111 10 10 0111 100 010 0101 10 10 0101 001 1
00 0101 10 10 0101 101 110 0100 11 10 0111 100 0
10 0111 10 10 0111 100 010 0111 10 00 0111 001 1
00 0101 10 10 0101 101 110 0111 10 10 0111 100 010 0101 10 10 0101 001 1
00 0101 10 10 0101 101 110 0100 11 10 0111 100 0
10 0111 10 00 0111 001 110 0100 11 10 0111 100 0
Copyright © 2013, Oracle and/or its affiliates. All rights reserved.10
10 0111 10 10 0111 101 1
10 0111 10 00 0111 001 1
01 0101 10 00 0101 101 110 0100 11 10 0111 100 0
10 0111 10 10 0111 100 010 0111 00 00 0111 100 110 0100 11 10 0111 100 0
10 0110 11 10 0111 100 0
10 0111 10 00 0111 001 1
01 0101 10 00 0101 101 110 0100 11 10 0111 100 0
10 0111 10 10 0111 100 010 0111 00 00 0111 100 1
00 0101 10 10 0101 101 110 0100 11 10 0111 100 0
10 0111 11 10 0111 100 010 0111 10 10 0111 101 110 0110 11 10 0111 100 0
10 0100 11 10 0111 100 0
10 0111 11 10 0111 100 010 0111 10 10 0111 101 110 0110 11 10 0111 100 0
10 0100 11 10 0111 100 0
10 0100 11 10 0111 100 0
10 0100 11 10 0111 100 0
10 0111 10 00 0111 001 1
01 0101 10 00 0101 101 110 0100 11 10 0111 100 0
10 0111 10 10 0111 100 010 0111 00 00 0111 100 110 0100 11 10 0111 100 0
10 0111 10 00 0111 001 1
01 0101 10 00 0101 101 110 0100 11 10 0111 100 0
10 0111 10 10 0111 100 0
10 0111 10 00 0111 001 1
01 0101 10 00 0101 101 110 0100 11 10 0111 100 0
10 0111 10 10 0111 100 0
10 0111 10 00 0111 001 1
01 0101 10 00 0101 101 110 0100 11 10 0111 100 0
10 0111 10 10 0111 100 010 0111 00 00 0111 100 1
00 0101 10 10 0101 101 110 0100 11 10 0111 100 0
10 0111 11 10 0111 100 010 0111 10 10 0111 101 110 0110 11 10 0111 100 0
10 0111 10 10 0111 100 010 0111 10 10 0111 100 0
10 0110 11 10 0111 100 0
10 0100 11 10 0111 100 0
10 0111 11 10 0111 100 010 0111 10 10 0111 101 110 0110 11 10 0111 100 0
Understanding Data Usage PatternsDatabase ‘heat map’
00 0101 10 10 0101 10
0 010 0111 10 10 0111 100 010 0111 10 00 0111 001 1
10 0101 10 10 0101 001 1
00 0101 10 10 0101 101 110 0100 11 10 0111 100 0
10 0111 10 00 0111 001 1
00 0101 10 10 0101 101 110 0111 10 10 0111 100 010 0101 10 10 0101 001 110 0100 11 10 0111 100 0
10 0111 10 10 0111 100 01 1
1 110 0100 11 10 0111 1010 0111 10 10 0111 1010 0111 10 10 0111 100 0
10 0111 10 00 0111 001 110 0100 11 10 0111 100 0
10 0101 10 10 0101 001 1
00 0101 10 10 0101 101 110 0100 11 10 0111 100 0
00 0101 10 10 0101 101 110 0111 10 10 0111 100 010 0101 10 10 0101 001 110 0100 11 10 0111 100 0
10 0111 10 10 0111 100 010 0111 10 00 0111 001 1
00 0101 10 10 0101 101 110 0111 10 10 0111 100 010 0101 10 10 0101 001 1
00 0101 10 10 0101 101 110 0100 11 10 0111 100 0
10 0111 10 10 0111 100 010 0111 10 00 0111 001 1
00 0101 10 10 0101 101 110 0111 10 10 0111 100 010 0101 10 10 0101 001 1
00 0101 10 10 0101 101 110 0100 11 10 0111 100 0
10 0111 10 00 0111 001 110 0100 11 10 0111 100 0
Copyright © 2013, Oracle and/or its affiliates. All rights reserved.11
10 0111 10 10 0111 101 1
10 0111 10 00 0111 001 1
01 0101 10 00 0101 101 110 0100 11 10 0111 100 0
10 0111 10 10 0111 100 010 0111 00 00 0111 100 110 0100 11 10 0111 100 0
10 0110 11 10 0111 100 0
10 0111 10 00 0111 001 1
01 0101 10 00 0101 101 110 0100 11 10 0111 100 0
10 0111 10 10 0111 100 010 0111 00 00 0111 100 1
00 0101 10 10 0101 101 110 0100 11 10 0111 100 0
10 0111 11 10 0111 100 010 0111 10 10 0111 101 110 0110 11 10 0111 100 0
10 0100 11 10 0111 100 0
10 0111 11 10 0111 100 010 0111 10 10 0111 101 110 0110 11 10 0111 100 0
10 0100 11 10 0111 100 0
10 0100 11 10 0111 100 0
10 0100 11 10 0111 100 0
10 0111 10 00 0111 001 1
01 0101 10 00 0101 101 110 0100 11 10 0111 100 0
10 0111 10 10 0111 100 010 0111 00 00 0111 100 110 0100 11 10 0111 100 0
10 0111 10 00 0111 001 1
01 0101 10 00 0101 101 110 0100 11 10 0111 100 0
10 0111 10 10 0111 100 0
10 0111 10 00 0111 001 1
01 0101 10 00 0101 101 110 0100 11 10 0111 100 0
10 0111 10 10 0111 100 0
10 0111 10 00 0111 001 1
01 0101 10 00 0101 101 110 0100 11 10 0111 100 0
10 0111 10 10 0111 100 010 0111 00 00 0111 100 1
00 0101 10 10 0101 101 110 0100 11 10 0111 100 0
10 0111 11 10 0111 100 010 0111 10 10 0111 101 110 0110 11 10 0111 100 0
10 0111 10 10 0111 100 010 0111 10 10 0111 100 0
10 0110 11 10 0111 100 0
10 0100 11 10 0111 100 0
10 0111 11 10 0111 100 010 0111 10 10 0111 101 110 0110 11 10 0111 100 0
Heat MapWhat it tracks � “Heat Map” tracking
– Database level Heat Map shows which tables and partitions are being used
– Block level Heat Map shows last modification at the block level
� Comprehensive
– Segment level shows both reads and writes
Active
Frequent
Access
Actively
updated
Infrequently
updated,
Frequently
queried
Infrequent
HOT
Copyright © 2013, Oracle and/or its affiliates. All rights reserved.12
– Segment level shows both reads and writes
– Distinguishes index lookups from full table scans
– Automatically excludes stats gathering, DDLs, table redefinitions, etc
� High Performance– Object level at no cost– Block level < 5% cost
Access
Occasional
Access
Dormant
Infrequent
access for
query and
updates
Long term
analytics &
compliance
COLD
Heat MapEnterprise Manager
Copyright © 2013, Oracle and/or its affiliates. All rights reserved.13
Archive Data
011100001010001011011
Automatic Data Optimization Usage Based Data Compression
Hot Data Warm Data
1010101011101010011010111000010100
0101101110101010010100100100001000
101010101110101001101011100001010001011011
101010100101001001000010001010101101001011
01110101010010
10000100010101
01011100001010
10101010111010100110101
11000010100010110111010
10100101001001000010001
Copyright © 2013, Oracle and/or its affiliates. All rights reserved.14
0101101110101010010100100100001000
1010101101001011010011100001010010
011100001010001011011
101010100101001001000
010001010101101001011
010101001010010010001
Copyright © 2012, Oracle and/or its affiliates. All rights reserved. Confidential – Oracle Restricted14
3XAdvanced Row Compression
1010101101001011010011100001010010
0101000010010000100010101011010010
10XColumnar Query Compression
1000010100100101001010110111000010
010011100001010010010100001001000010001010
101010101110101001101011100001010001011011
15XColumnar Archive Compression
10100101001001000010001
01010110100101101001110
00010100100101000010010
00010001010101110011010
10100101001001000010001
1110010100100101001010110111011010
101010101110101001101011100001011101011001
Simple Declarative SQL extensionAutomatic Data Optimization
ALTER TABLE sales ILM add
Active
Frequent
Access
� OLTP Compressed (2-4x)� Affects ONLY Candidate Rows� Cached in DRAM & FLASH
row store compress advanced row after 2 days of no update
�Warehouse Compressed (10x)� High Performance Storage
compress for query low after 1 week of no update
Copyright © 2013, Oracle and/or its affiliates. All rights reserved.15
Access
Occasional
Access
Dormant
�Warehouse Compressed (10x)� Low Cost Storage
tier to SATA Tablespace
� Archive Compressed (15-50X)� Archival Storage
compress for archive high after 6 months no access
Automatic Data OptimizationAdd compression and tiering policies to tables
Policy 1
Policy 1
Policy 2
Policy 2
Compress Partitions with columnar compression if they haven’t been modified in 180 days
Copyright © 2013, Oracle and/or its affiliates. All rights reserved.16 Copyright © 2012, Oracle and/or its affiliates. All rights reserved.16
Oldest Data Most Recent Data
Compress Partitions with row compression if they haven’t been modified in 30 days
Automatic Data OptimizationA heat map tracks the activity of segments and blocks
Policy 1
Policy 1
Policy 2
Policy 2
Copyright © 2013, Oracle and/or its affiliates. All rights reserved.17 Copyright © 2012, Oracle and/or its affiliates. All rights reserved.17
Oldest Data Most Recent Data
Automatic Data OptimizationPolicies are automatically applied to tables
Policy 1
Policy 1
Policy 2
Policy 2
Copyright © 2013, Oracle and/or its affiliates. All rights reserved.18 Copyright © 2012, Oracle and/or its affiliates. All rights reserved.18
Oldest Data Most Recent Data
Automatic Data OptimizationPolicies are automatically applied to tables
Policy 1
Policy 1
Policy 2
Policy 2
Copyright © 2013, Oracle and/or its affiliates. All rights reserved.19 Copyright © 2012, Oracle and/or its affiliates. All rights reserved.19
Oldest Data Most Recent Data
Automatic Data OptimizationPolicies are automatically applied to tables
Policy 1
Policy 1
Policy 2
Policy 2
Copyright © 2013, Oracle and/or its affiliates. All rights reserved.20 Copyright © 2012, Oracle and/or its affiliates. All rights reserved.20
Oldest Data Most Recent Data
Automatic Data OptimizationReduce storage footprint, read compressed data faster
Policy 1
Policy 1
Policy 2
Policy 2
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Oldest Data Most Recent Data
Automatic Data OptimizationAutomatically tier data to lower cost storage
Policy 1
Policy 1
Policy 2
Policy 2
Policy 3
Policy 3
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Oldest Data Most Recent Data
If the tablespace is nearly full compress the oldest partition with archive compression and move it to Tier 2 Storage
Automatic Data Optimization
10x compressed15x compressed As data cools
down, Advanced
Reporting Compliance & ReportingOLTP
Copyright © 2013, Oracle and/or its affiliates. All rights reserved.23
This Quarter This Year Prior Years
Row Store
for fast OLTP
Compressed Column Storefor fast analytics
down, Advanced
Data Optimization
automatically
converts data to
columnar
compressed
Online
Archive Compressed Column Storefor max compression
Powerful Policy Specification Automatic Data Optimization
� Declarative Policy Specification: Condition ���� Action
– alter table sales ilm add policy row store compress advanced segment after 3 days of no modification;
– Conditions are time period after creation, access, modification of data
– Actions can be Compression Tiering or Tablespace Tiering
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– 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
Up to 15x Smaller Footprint & Faster Queries
� Both Columnar & Archive Compression now complement Advanced Row Compression
� Best Practice:
– Step 1: Use Advanced Row Compression for entire DB and then
Automatic Data Optimization for OLTP
Copyright © 2013, Oracle and/or its affiliates. All rights reserved.25
– Step 2: ADO automatically converts into columnar compressed once the updates cool down, and is used mainly for reporting
=> Query speed of Columnar & 10x smaller footprint
– Step 3: ADO automatically converts into archive compressed once data cools down further and is no longer frequently queried
=> 15-50x smaller footprint
Optimizes Data Based on Heat MapAutomatic Data Optimization for DW
� Data generally comes in via Bulk Loading
� Workload dominated by queries, even during loading
Step 1: Bulk Load directly into Columnar Compressed
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– 10x smaller footprint, Query speed of Columnar
Step 2: ADO automatically converts to Archive Compressed
and moves to Lower Cost Storage once its queried infrequently
– Data remains online, with 15-50x smaller footprint, & lower storage cost
Fast, Flexible Loads & Queries on Columnar
� Fastest Load with uncompressed & Fastest Queries with columnar
– Mixed workloads often use Java app or 3rd party tools to insert and update data that does not use Bulk Loads, so cannot use Columnar
� Step 1: Load into uncompressed, conventional inserts & updates
Automatic Data Optimization – Mixed Use
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– Fast loading, & flexibility of using a regular OLTP app for loading
� Step 2: ADO moves to Row Compressed or Columnar Compressed or Low Cost Storage once updates cool down
– Faster Queries, 3-10x smaller footprint
Scheduled Policy ExecutionAutomatic Data Optimization
� Immediate and background policy execution
– Policies are executed in maintenance windows
– Can be manually executed as needed
Copyright © 2013, Oracle and/or its affiliates. All rights reserved.28
� 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)
Optimized Backups Automated READONLY Data Movement
SQL> ALTER TABLE EMPLOYEE ILM ADD POLICY
TIER TO DATA2 READ ONLY
Copyright © 2013, Oracle and/or its affiliates. All rights reserved.29
TIER TO DATA2 READ ONLY
AFTER 180 DAYS OF NO MODIFICATION
Backup & Recovery Automatic Data Optimization
10x compressed15x compressed As data cools
down, Automatic
Reporting Compliance & ReportingOLTP
Copyright © 2013, Oracle and/or its affiliates. All rights reserved.30
Read / Write
TablespaceRead-mostly data is moved to a READONLY Tablespace
READONLY
TBS
down, Automatic
Data Optimization
automatically
moves it to a
READONLY TBS,
it’s backed up only
once after that
SummaryHeat Map & Automatic Data Optimization
� Heat Map
– Automatically tracks access
– Database-aware: maintenance jobs, backups, etc. don’t affect Heat Map
� Automatic Data Optimization
Copyright © 2013, Oracle and/or its affiliates. All rights reserved.31
� Automatic Data Optimization
– Declarative easy-to-use syntax to define data compression & movement policies
– Extensible with business-specific logic
– Automates and simplifies ILM for Oracle Database data
Copyright © 2013, Oracle and/or its affiliates. All rights reserved.32
