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Partner DB12c eSeminar: Oracle Database 12c Heat Map and Automatic Data Optimization
 

Partner DB12c eSeminar: Oracle Database 12c Heat Map and Automatic Data Optimization

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https://blogs.oracle.com/imc/entry/partner_eseminars_oracle_database_12c

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    Partner DB12c eSeminar: Oracle Database 12c Heat Map and Automatic Data Optimization Partner DB12c eSeminar: Oracle Database 12c Heat Map and Automatic Data Optimization Presentation Transcript

    • 2 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
    • Oracle Database 12c Heat Map, Automatic Data Optimization & In-Database Archiving Platform Technology Solutions Oracle Database Server Technologies
    • Growth in Data Diversity and Usage 1,800 Exabytes of Data in 2011, 20x Growth by 2020 Emerging Growth Factors Enterprise 45% per year growth in database data Cloud 80% of new applications and their data Regulation 300 exabytes in archives by 2015 4 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. Mobile #1 Internet access device in 2013 Big Data Large customers top 50PB Social Business $30B/year in commerce by 2015
    • Managing Storage Challenges Manage more data without incurring additional cost 5 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. Compress data, without impacting performance Tier and compress data based on usage
    • Information Lifecycle Management Managing Data Over its Lifetime Value at Risk High Value practices, and tools used to align the business value of information with the most appropriate and cost effective IT infrastructure from the time information is conceived through its final Medium Value Low Value Storage Networking Industry Association (SNIA) Data Management Forum $$ $ $$$ Total Cost of Ownership (TCO) 6 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
    • Automatic Data Optimization Optimize data storage based on usage Smart Compression Heat Map Automated Tiering Network Compression In Database Archiving 7 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
    • Data Compression Reduce storage footprint, read compressed data faster Hot Data Archive Data 111010101010101 10101010111010100110101 001101010101011 11000010100010110111010 010001011011000 10100101001001000010001 110100101000001 01010110100101101001110 001110001010101 00010100100101000010010 101001011010010 00010001010101110011010 110001010010011 111001001000010 001010101101000 101010101110101 001101011100001 010001011011101 010100101001001 000010001010101 101001011010011 100001010010010 100001001000010 001010101101001 101010101110101 001101011100001 010001011011101 010100101001001 000010001010101 101001011010011 100001010010010 100001001000010 001010101101001 3X 10X 15X Advanced Row Compression 8 Warm Data Columnar Query Compression Columnar Archive Compression Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 2012, 10101010111010100110101110000101000101 10111010101001010010010000100010101011 01001011010011100001010010010100001001 00001000101010111001101110011000111010 Confidential Oracle Restricted 10101010111010100110101110000101000101101110101 01001010010010000100010101011010010110100111000 01010010010100001001000010001010101110011011100
    • Oracle Advanced Compression Transparent, Smaller, Faster 100% Application Transparent End-to-end Cost/Performance Benefits across CPU, DRAM, Flash, Disk & Network Runs Faster: OLTP Apps (Transactional & Analytics) & DW Reduces Database Footprint CapEx & OpEx savings Increases Cloud ROI through Database Footprint reduction in DRAM Memory 9 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
    • Oracle Advanced Compression Oracle Advanced Compression New Features, New Feature Names 10 Oracle Database 11g Oracle Database 12c OLTP Compression Advanced Row Compression Secure Files Compression Advanced LOB Compression Secure Files De-duplication Advanced LOB Deduplication Hybrid Columnar Compression Hybrid Columnar Compression NEW Heat Map (Object and Row Level) NEW Automatic Data Optimization NEW Temporal Copyright © 2013, Oracle and/or its affiliates. All rights reserved. (Advancements)
    • Compression New features in Oracle Database 12c Logminer and GoldenGate support Capture side changes completed in 11.1 logminer Apply side changes in 11.2 Wide tables (>255 columns) for advanced row (OLTP) compression 11 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. Faster queries on advanced row (OLTP) compression Network Compression
    • Automatic Data Optimization Simplifying the life cycle of data An in memory heat map tracks access to segments and blocks Data is periodically written to disk Information is accessible by views or stored procedures y lic Po 1 Uses can attach policies to tables to compress or tier data based on access to data Tables or Partitions can be moved between compression levels whilst data is still being accessed 12 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
    • Heat Map What it tracks HOT Active Frequent Access Occasional Access Dormant 13 Actively updated Infrequently updated, Frequently Queried Database level Heat Map shows which tables and partitions are being used Block level Heat Map shows last modification at the block level Comprehensive Infrequent access for query and updates Segment level shows both reads and writes Long term analytics & compliance Automatically excludes stats gathering, DDLs or table redefinitions COLD Copyright © 2013, Oracle and/or its affiliates. All rights reserved. Distinguishes index lookups from full scans High Performance Object level at no cost Block level < 5% cost
    • Heat Map How to enable Active Enabling Heat Map SQL> alter system set heat_map Frequent Access Occasional Access Disabling Heat Map SQL> alter system set heat_map Dormant 14 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
    • Understanding Data Usage Patterns 0 0101110101001101 1 1 0 0 0 1 0 1 0 11 1 0 1 0 1 0 1 0 011 1010101010100 0 1 10 0 1 0 0 0 1 0 10 10 1 0 1 1 1 1 0 0 0 1 0 11 1 0 1 0 1 0 1 0 1 0 0 1 0 1 0 1 0 1 0 1 0 10 1 0 1 1 1 011 1110100011100 0 1 10 0 1 0 0 0 1 0 10 10 1 0 1 1 1 1 0 0 1 1 0 11 1 0 0 0 1 0 1 0 1 0 0 1 0 1 0 1 0 1 0 1 0 10 1 0 1 1 1 010 1110000011101 1 10 0 1 0 0 0 1 0 10 10 1 0 1 1 1 10 01000101010 101 1 1 0 1 0 1 0 1 0 1 0 1 0 10 1 0 1 1 1 10 01000101010 101 1 1 10 0 1 1 0 0 1 0 10 10 1 0 1 1 1 15 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 1 0 0 1 0 11 1 0 1 0 1 0 1 0 1 0 0 0 1 0 1 0 1 0 1 0 1 0 10 1 0 1 1 1 011 1010101010100 0 1 0 01000101010101 11 1 10 0 1 0 0 0 1 0 10 10 1 0 1 1 1 011 1110101011101 0 1 011 1110100011100 0 1 10 0 1 0 0 0 1 0 10 10 1 0 1 1 1 1 0 0 1 1 0 11 1 0 0 0 1 0 1 0 1 0 0 1 0 1 0 1 0 1 0 1 0 10 1 0 1 1 1 010 1110000011101 1 10 0 1 0 0 0 1 0 10 10 1 0 1 1 1 1 0 0 0 1 0 11 1 0 1 0 1 0 1 0 1 0 1 1 0 1 0 1 0 1 0 1 0 10 1 0 1 1 1 011 1110101011101 0 1 10 0 1 1 0 0 1 0 10 10 1 0 1 1 1 1 0 0 0 1 0 11 1 0 1 0 1 0 1 0 1 0 0 1 0 1 0 1 0 1 0 1 0 10 1 0 1 1 1 011 1010101010100 0 1 0 01000101010101 11 1 0 1 0 0 1 0 11 1 0 1 0 1 0 1 0 1 0 0 1 0 1 0 1 0 1 0 1 0 10 1 0 1 1 1 011 1110100011100 0 1 10 01000101010 101 1 1 1 0 0 1 1 0 11 1 0 0 0 1 0 1 0 1 0 0 1 0 1 0 1 0 1 0 1 0 10 1 0 1 1 1 10 01000101010 101 1 1 10 01000101010 101 1 1 011 1110101011101 0 1 1 1 0 1 0 1 0 1 0 1 0 10 1 0 1 1 1 011 1110101011101 0 1 10 01100101010 101 1 1
    • Understanding Data Usage Patterns 0 0101110101001101 1 1 0 0 0 1 0 1 0 11 1 0 1 0 1 0 1 0 011 1010101010100 0 1 10 0 1 0 0 0 1 0 10 10 1 0 1 1 1 1 0 0 0 1 0 11 1 0 1 0 1 0 1 0 1 0 0 1 0 1 0 1 0 1 0 1 0 10 1 0 1 1 1 011 1110100011100 0 1 10 0 1 0 0 0 1 0 10 10 1 0 1 1 1 1 0 0 1 1 0 11 1 0 0 0 1 0 1 0 1 0 0 1 0 1 0 1 0 1 0 1 0 10 1 0 1 1 1 010 1110000011101 1 10 0 1 0 0 0 1 0 10 10 1 0 1 1 1 10 01000101010 101 1 1 0 1 0 1 0 1 0 1 0 1 0 10 1 0 1 1 1 10 01000101010 101 1 1 10 0 1 1 0 0 1 0 10 10 1 0 1 1 1 16 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 1 0 0 1 0 11 1 0 1 0 1 0 1 0 1 0 0 0 1 0 1 0 1 0 1 0 1 0 10 1 0 1 1 1 011 1010101010100 0 1 0 01000101010101 11 1 10 0 1 0 0 0 1 0 10 10 1 0 1 1 1 011 1110101011101 0 1 011 1110100011100 0 1 10 0 1 0 0 0 1 0 10 10 1 0 1 1 1 1 0 0 1 1 0 11 1 0 0 0 1 0 1 0 1 0 0 1 0 1 0 1 0 1 0 1 0 10 1 0 1 1 1 010 1110000011101 1 10 0 1 0 0 0 1 0 10 10 1 0 1 1 1 1 0 0 0 1 0 11 1 0 1 0 1 0 1 0 1 0 1 1 0 1 0 1 0 1 0 1 0 10 1 0 1 1 1 011 1110101011101 0 1 10 0 1 1 0 0 1 0 10 10 1 0 1 1 1 1 0 0 0 1 0 11 1 0 1 0 1 0 1 0 1 0 0 1 0 1 0 1 0 1 0 1 0 10 1 0 1 1 1 011 1010101010100 0 1 0 01000101010101 11 1 0 1 0 0 1 0 11 1 0 1 0 1 0 1 0 1 0 0 1 0 1 0 1 0 1 0 1 0 10 1 0 1 1 1 011 1110100011100 0 1 10 01000101010 101 1 1 1 0 0 1 1 0 11 1 0 0 0 1 0 1 0 1 0 0 1 0 1 0 1 0 1 0 1 0 10 1 0 1 1 1 10 01000101010 101 1 1 10 01000101010 101 1 1 011 1110101011101 0 1 1 1 0 1 0 1 0 1 0 1 0 10 1 0 1 1 1 011 1110101011101 0 1 10 01100101010 101 1 1
    • Heat Map for Tables and Partitions O 17 RS DE R Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
    • Heat Map for Blocks O 18 RS DE R Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
    • Viewing Heat Map Data Data Dictionary Views V$HEAT_MAP_SEGMENTS DBA_HEAT_MAP_SEGMENTS OBJECT_NAME SEGMENT_READ_TIME SEGMENT_WRITE_TIME FULL_SCAN LOOKUP_SCAN -------------------- ---------------------- ---------------------- ---------------------- ---------------------- DEPT 20/MAR/2013 10:09:30 19/MAR/2013 10:09:30 21/MAR/2013 04:09:30 EMP 20/MAR/2013 22:00:36 20/MAR/2013 22:09:30 21/MAR/2013 09:49:47 BONUS 21/MAR/2013 10:09:30 21/MAR/2013 10:09:30 SALGRADE 20/MAR/2013 10:09:30 EMPLOYEE 21/MAR/2013 09:49:47 ORDERS 19 18/FEB/2013 09:33:41 21/MAR/2013 15:00:00 19/MAR/2013 10:10:20 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 18/FEB/2013 09:33:41
    • Heat Map Enterprise Manager 20 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
    • Automatic Data Optimization Usage Based Data Compression 01110101010010 10000100010101 01011100001010 Warm Data 101010101110101001101011100001010001011011 0101101110101010010100100100001000 101010100101001001000010001010101101001011 1010101101001011010011100001010010 10101010111010100110101 11000010100010110111010 10100101001001000010001 011100001010001011011 01010110100101101001110 101010100101001001000 00010100100101000010010 010001010101101001011 00010001010101110011010 010101001010010010001 10100101001001000010001 Archive Data 1010101011101010011010111000010100 Hot Data 010011100001010010010100001001000010001010 0101101110101010010100100100001000 0101000010010000100010101011010010 101010101110101001101011100001010001011011 1010101101001011010011100001010010 1000010100100101001010110111000010 101010101110101001101011100001011101011001 1110010100100101001010110111011010 3X 15X Advanced Row Compression 21 10X Columnar Query Compression Columnar Archive Compression Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 2012, Confidential Oracle Restricted
    • Automatic Data Optimization Add compression and tiering policies to tables y lic Po y lic Po 2 1 POLICY 1: Compress Partitions with been modified in 30 days POLICY 2: Compress Partitions with columnar compression if they days Oldest Data 22 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 2012, Most Recent Data
    • Automatic Data Optimization A heat map tracks the activity of segments and blocks y lic Po y lic Po 2 1 Oldest Data 23 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 2012, Most Recent Data
    • Automatic Data Optimization Policies are automatically applied to tables y lic Po y lic Po 2 1 Oldest Data 24 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 2012, Most Recent Data
    • Automatic Data Optimization Policies are automatically applied to tables y lic Po y lic Po 2 1 Oldest Data 25 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 2012, Most Recent Data
    • Automatic Data Optimization Policies are automatically applied to tables y lic Po y lic Po 2 1 Oldest Data 26 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 2012, Most Recent Data
    • Automatic Data Optimization Reduce storage footprint, read compressed data faster y lic Po y lic Po 2 1 Oldest Data 27 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 2012, Most Recent Data Confidential Oracle Restricted
    • Automatic Data Optimization Automatically tier data to lower cost storage y lic Po y lic Po y lic Po 3 2 1 Oldest Data 28 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. 2012, POLICY 3: If the tablespace is nearly full compress the oldest partition with archive compression and Most Recent Data move it to Tier 2 Storage
    • Automatic Data Optimization Reporting OLTP Compliance & Reporting 10x compressed 15x compressed This Quarter Row Store for fast OLTP 29 This Year Compressed Column Store for fast analytics Copyright © 2013, Oracle and/or its affiliates. All rights reserved. Prior Years Archive Compressed Column Store for max compression As data cools down, Advanced Data Optimization automatically converts data to columnar compressed Online
    • Up to 15x Smaller Footprint & Faster Queries Automatic Data Optimization for OLTP Both Columnar & Archive Compression now complement Advanced Row Compression Best Practice: Step 1: Use Advanced Row Compression for entire DB and then 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 30 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
    • Optimizes Data Based on Heat Map Automatic 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 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 31 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
    • Fast, Flexible Loads & Queries on Columnar Automatic Data Optimization Mixed Use 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 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 32 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
    • Powerful Policy Specification Automatic Data Optimization Declarative Policy Specification: Condition Action alter table orders ilm add policy row store compress advanced row after 3 days of no modification; Conditions are time period after creation, no access or no 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 33 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
    • Simple Declarative SQL extension Automatic Data Optimization Active Frequent Access Occasional Access Dormant 34 OLTP Compressed (2-4x) Affects ONLY Candidate Rows Cached in DRAM & FLASH Warehouse Compressed (10x) High Performance Storage Warehouse Compressed (10x) Low Cost Storage Archive Compressed (15-50X) Archival Storage Copyright © 2013, Oracle and/or its affiliates. All rights reserved. ALTER TABLE orders row store compressadd ILM advanced row after 2 days of no update compress for query low after 1 week of no update tier to SATA Tablespace compress for archive high after 6 months no access
    • Scheduled Policy Execution Automatic Data Optimization 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) 35 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
    • Automatic Data Optimization Optimized Back up Reporting OLTP Compliance & Reporting 10x compressed 15x compressed Read / Write Tablespace 36 Read-mostly data is moved to a READONLY Tablespace Copyright © 2013, Oracle and/or its affiliates. All rights reserved. READONLY TBS As data cools down, Automatic Data Optimization automatically moves it to a READONLY only once after that
    • Automatic Data Optimization Optimized Backups with Automated READONLY data movement 37 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. As tables grow in size ILM policies compress data Tablespace containing partitions reaches ILM tiering threshold 3. S ER 1. 2. D OR Partitions are moved to new read only tablespace on lower spec disk group
    • Automatic Data Optimization Optimized Backups with Automated READONLY data movement SQL> ALTER TABLE ORDERS ILM ADD POLICY TIER TO DATA2 READ ONLY AFTER 180 DAYS OF NO MODIFICATION 38 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
    • Automatic Data Optimization Data Classification WHAT Scope Tablespace level Group level Segment level: Table/Partition/ Subpartition Clustered table Row level: Table 39 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. Automatic Detection IF and WHEN If conditions met Which operation to track? Creation Low access No data modification Validity expired When? After 3 days After 1 year Tablespace full Automatic Action Then DO Then Actions Compression Types: OLTP Move to other storage Both compress + move
    • Automatic Data Optimization Why Oracle? Oracle Storage Vendors Disk Saving Extensive Partial Performance Speeds Queries Adds Overhead Deep integration Integration with Database excludes maintenance tasks includes memory access Integrated with RMAN and Active Data Guard 40 Copyright © 2013, Oracle and/or its affiliates. All rights reserved. Zero integration maintenance tasks considered real access
    • Automatic Data Optimization Summary Automatic Data Optimization Active Recently inserted, actively updated OLTP compressed (2-4x) Cached in DRAM & Flash Frequent Access Infrequent updates, frequent reports High compression (10x) High performance storage Occasional Access Infrequent access High compression (10x) Low cost storage Dormant 41 Retained for long-term analytics and compliance with corporate policies Archive compressed (15-50x) Archival storage (database or tape) Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
    • In-Database Archiving Speed up upgrade and reports Applications typically work with recent data But often need to retain data for 5 to 10 years In-Database Archiving provides the ability to archive infrequently used data within the database Archived data is invisible by default Works with partition pruning and Exadata storage indexes to eliminate I/O for archived data Archived data remains online for SQL Query & DMLs 42 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
    • In-Database Archiving How to enable Easily enabled for a table: alter table Application can marks rows as archived: set ORA_ARCHIVE_STATE = 1 Sessions can set default visibility to see all data or active data only (default) alter session set row archival visibility = [all | active] 43 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
    • In-Database Archiving Why Oracle? Oracle Others Required Supply Knowledge Packs for various Oracle Apps. Supports custom rules. Consultant cost usually required. No cost for functionality (included in EE) Typical deal $$$ Schema Changes Minimal Required (typically shadow table) Operational Impact Minimal Access to archive data requires special support, app dev effort Application Knowledge Cost 44 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
    • In-Database Archiving Vs Flashback Archive Flashback Data Archive provides the ability to track and store transactional changes to a table over its lifetime. A Flashback Data Archive is useful for compliance with record stage policies and audit reports. It archives previous states of rows, the current state of a record is always visible in the table. In-Database Archiving only keeps the current state of a record but allows the application to hide infrequently used data within the database. 45 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
    • Heat Map, Automatic Data Optimization and In-Database Archiving Summary Heat Map Automatically tracks access Database- Automatic Data Optimization Declarative easy-to-use syntax to define data compression & movement policies Extensible with business-specific logic In-Database Archiving Keep archive data accessible, minimize impact on storage and performance 46 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
    • DEMO 47 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.
    • 48 Copyright © 2013, Oracle and/or its affiliates. All rights reserved.