Extreme BI with Oracle Exadata

and Business Intelligence Apps

October 4, 2011

Presented by: Martin Paynter - Enkitec Brad Salva – Southwestern Energy

Who am I •  Martin Paynter – BI Practice Manager

•  EBS Developer since 2001 •  16 EBS implementations

•  Business Intelligence Developer since 1999 •  OWB •  ODI •  Informatica •  Hyperion IR •  OBIEE – BI apps •  Discoverer

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Who is Enkitec •  Oracle-centric Consulting Partner with a broad set of DBA & application

development experience

•  Database, application development, Oracle training

•  Database consultants averaging 15+ years Oracle experience

•  Exadata Specialized Partner status (one of a handful globally)

•  Dedicated, In-house Exadata Lab (POV, Patch Validation)

•  60+ Successful Exadata Implementations

•  Enkitec Europe – Partnering with Tanel Poder

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What is Exadata? �  Revolutionary Approach to Oracle Database Processing

�  Database Servers, Storage and Network in a Single Enclosure

�  High Bandwidth Network Connects Database and Storage Servers

�  Intelligent Storage Filters Data and Minimizes Network Traffic

�  Optimized for Both Data Warehouse and OLTP Activity

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What is Exadata?

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Exadata Storage Servers Exadata Database Servers

11gR2 / ASM iDB / RDS

cellsrv

How is Exadata Different?

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The Bottleneck on Many (Most) Large Databases is between the Disk and the DB Server(s)! How to Speed it Up? Make the Pipe Bigger/Faster Reduce Traffic on the Pipe

* The fast way to do anything is not to do it!

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Offloading – The “Secret Sauce” Offloading vs. Smart Scan (what’s the difference) Offloading – generic term meaning doing work at the storage layer instead of at the database layer Smart Scan – query optimizations covered by “cell smart table/index scan” wait events

Traditional Architecture vs Exadata

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Sun e20K 2-Node RAC

48 cores each

Enterprise Class SAN

Batch Process

Execution Time:

24 Hours +

Exadata Quarter

Rack

2-Node RAC 8 cores each

Exadata Storage

Batch Process

Execution Time:

45 Minutes

Traditional Exadata

Execut ion Time Improved by 32x

Business Benefits of Exadata �  Pre-configured - Ready for Production in Weeks, not Months

�  Single Source for Support – Hardware / Storage / Network

�  Extreme Performance*

�  No Need to Modify Oracle-based Applications

�  Highly Scalable

�  Optimized for Mixed Workload Environments

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Who is Using Exadata? �  Healthcare Providers

�  Market Research Companies

�  Retail (Grocer)

�  Logistics

�  Government – State and Federal

�  Mortgage Processing

�  Oil and Gas

�  Document Processing Outsourcing

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Who am I •  Brad Salva - Lead DBA (Southwestern Energy)

•  Team of 6 DBA’s (managing SQL Server and Oracle instances)

•  Database Administration since 1997 •  Oracle and SQL Server •  12 years in the Energy Industry •  Lead DBA at Southwestern Energy since 2006

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Who is SWN •  Southwestern Energy Company (SWN) is a growing

independent energy company primarily engaged in natural gas and crude oil exploration, development and production within North America.

•  We are also focused on creating and capturing additional value through our natural gas gathering and marketing businesses, which we refer to as Midstream Services.

•  www.swn.com

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SWN EBS Project •  Started E-business implementation in 2010 to replace legacy

systems. Project includes Oracle E-business R12, P2ES Tobin Enterprise Land, P2ES Energy Upstream, P2ES Wellcore, OBIEE, Hyperion, Oracle UCM, Single sign-on, and more.

•  16 EBS instances

•  7 OBIEE instances

•  12 Hyperion instances

•  2 Standby instances

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Why SWN Chose Exadata

�  Lower TCO �  Exadata v. “Traditional Hardware” v. Hosted

�  Performance

�  Easy Installation/Configuration

�  High Availability/Redundancy

�  Integrated easy to maintain tech stack

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SWN EBS Production �  Apps Tier (4 Servers – 2 internal, 2 DMZ)

�  OS: OEL5

�  Memory: 17GB �  CPU: Intel Xeon X5670  @ 2.93GHz X 4

�  Concurrent Processing Tier ( 2 Servers) �  OS: OEL5

�  Memory: 17GB �  CPU: Intel Xeon X5670  @ 2.93GHz X 4

�  DB Tier (RAC – 4 node) �  OS: OEL5

�  Memory: 96GB per node �  CPU: Intel Xeon X5670 @ 2.93GHz X 24

�  8 GB SGA

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Client X EBS Production �  Apps Tier/ (1 Server)

�  OS: RHEL 5.5

�  Memory: 64GB

�  CPU: Intel Xeon X5670  @ 2.93GHz X 12

�  Concurrent Processing Tier (1 server) �  OS: RHEL 5.5

�  Memory: 64GB

�  CPU: Intel Xeon X5670  @ 2.93GHz X 12

�  DB Tier (RAC – 2 node) �  OS: RHEL 5.5

�  Memory: 64GB per node

�  CPU: Intel Xeon X5670 @ 2.93GHz X 12

�  Version: 11.2.0.2

�  8 GB SGA

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SWN BI Production �  BI Apps Tier (VM)

�  OS: Windows Server 2003 R2 – x64 �  Memory: 16GB �  CPU: Intel Xeon X5670  @ 2.93GHz X 4

�  BI Database Tier (Exadata 4 Node RAC) �  OS: OEL5 �  Memory: 96GB per node �  CPU: Intel Xeon X5670 @ 2.93GHz X 24 �  Version: 11.2.0.2 �  8 GB SGA

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Client X BI Production �  BI Apps Tier (VM)

�  OS: Windows Server 2008 – x64 �  Memory: 16GB �  CPU: Intel Xeon X5670  @ 2.93GHz X 4

�  BI Database Tier (2 Node RAC) �  OS: RHEL 5.5 �  Memory: 64GB per node �  CPU: Intel Xeon X5670 @ 2.93GHz X 12 �  Version: 11.2.0.2 �  8 GB SGA

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Typical BI Considerations �  Source System

�  Availability �  Performance

�  ETL �  Architecture installation/configuration �  Development �  Performance

�  Visualization �  Architecture installation/configuration �  Dashboard/Report development

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SWN/Client X BI Platform �  Source System

�  Oracle EBS R12.1.2 �  11.2.0.2 4 node RAC Database (Client X on 2 Node)

�  ETL �  Informatica 9.0.1 Hotfix 2 �  Oracle BI Applications 7.9.6.3 �  DAC 10.1.3.4 �  11.2.0.2 4 node RAC Database (Client X on 2 Node)

�  Visualization �  OBIEE 11.1.1.5 �  Weblogic 10.3.5.0

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Areas of Focus �  Implementation

�  Exadata configuration �  Installation of Oracle RAC databases �  Create EBS clones and backups �  Install/configure BI Technology stack

�  Performance �  EBS on Exadata �  Informatica/DAC on Exadata �  OBIEE/OBI Apps on Exadata

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�  Configure Exadata and 4 node RAC DB (40 hours) �  Number of DB instances on Exadata ½ rack – 16

�  Production EBS 4 node RAC �  All other installs single node

�  Time to Backup – 1 minute 30 seconds �  RMAN backup, high compression, 16 channels to ASM +RECO disk

group �  Netapp backup takes 30 minutes �  Restore from backup takes 10 minutes

�  Time to Clone – 10 minutes

SWN EBS Implementation Metrics

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SWN BI Implementation Metrics �  OBIEE installed/configured in 8 hours

�  Weblogic �  Enterprise Manager �  BI Server �  Presentation Server �  Admin Tools

�  Informatica and DAC installed/configured in 8 hours �  Powercenter Services and Client �  DAC Server and Client

�  OBI Apps: �  Configured General Ledger subject area and ran initial full load in 24 hours

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Extreme Performance Data Warehousing Integrated Technology Stack

•  Single source of truth

•  Easy to deploy and manage

•  Extreme performance

•  Meets all end user requirements

•  Lower cost of ownership Smart Storage

Database

Data Models ELT Tools

BI Tools

BI Applications

Oracle BI Apps Product Components Example: Financial Analytics

Pre-mapped metadata, including embedded best practice calculations and metrics for financial, executives and other business users

A “best practice” library of over 360 pre-built metrics, 30 intelligent dashboards, 200+ reports plus alerts for CFO, Finance Controller, Financial Analyst, AR/AP Managers and Executives

Pre-built ETL to extract data from over 3,000 operational tables and load it into the DW, sourced from PSFT, Oracle EBS and other sources

Pre-built warehouse with 16 star-schemas designed for analysis and reporting on financial analytics

•  Presentation layer •  Logical business

model •  Physical sources

1 3

2 4

Performance �  EBS on Exadata

�  GL Journal Load

�  Informatica/DAC on Exadata �  Extract from EBS �  General Ledger subject area load into OBI DW

�  OBIEE/OBI Apps on Exadata �  General Ledger Dashboard/report processing against OBI DW

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Tuning Options �  OBIEE

�  Aggregate navigation

�  Presentation cache

�  BI Server cache

�  Informatica/DAC on Exadata

�  Commit frequency

�  DTM Buffer size

�  Indexes

�  Integration Services

�  OBI DW

�  Partitioning

�  Parallelism

�  Compression

�  OLAP

�  Materialized views

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EBS Journal Load

SWN and Client X use the gl_interface table to load in ~3,000,000 journal lines per day

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Task # Rows SWN Time Client X Gain

Load to gl_interface from external table

~3,000,000 3 min 40 min 13X

Import Journals ~3,000,000 15 min 7 hrs 28X

Post Journals 10 min 2 hrs 12X

Total Time Spent 28 min ~10 hrs 21X

OBI General Ledger Subject Area

�  268 Tasks

�  Over 600 bitmap indexes

�  Two main fact tables: �  W_GL_OTHER_F – GL Transactions �  W_GL_BALANCE_F – GL Balances

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OBI DW Load �  Full load for General Ledger subject area with creation of

indexes ~ 40,000,000 in 4 hours

�  Incremental loads with indexes ~ 3,000,000 in 2 hours

�  Low effort tuning: �  Identify long running tasks and change DTM Buffer and commit

interval in PowerCenter Workflow Manager �  Increase number of Integration Services �  Confirm Bulk loading is occurring (/*+ SYS_DL_CURSOR*/) �  Drop bitmap indexes in DAC

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Informatica Performance

�  DTM Buffer Size �  Changed default of 32MB to Auto

�  Target Commit Frequency �  For high volume loads changed commit frequency from

10,000 to 1,000,000

�  Make sure direct loading is occurring: �  /*+ SYS_DL_CURSOR*/ in v$sql

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ETL – Extraction from EBS

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EBS on Exadata provides an edge during the extraction process.

�  SDE_ORA_GLJournals (2,995,320)

�  SWN – 1 minute (All time spent on writing)

�  Client X – 10 minutes (50/50 read and write)

SQL_ID EXECS AVG_ETIME OFFLOADABLE OFFLOADED OFFLOAD_ELIGIBLE ------------------- ---------- ----------------- ----------------- -------------------- ----------------------------- fjh00b5mzq64c 1 11.61 Yes 5,917,486,584 10,090,102,784

DAC Performance

�  Increase number of Integration Services �  Evaluated the DAC execution plan and determined an

increase from 10 to 30 was optimal

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DAC Performance

�  Drop bitmap index creation �  Saves 46 minutes for SWN, but allows for partitioning

without additional configuration as well as facilitates smart scan query processing

Before Dropping indexes: After Dropping indexes:

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DAC Performance

�  Client X spends 82 minutes creating indexes during incremental load. It is not an option for them to drop indexes because queries would never return.

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OBI DW Load Times - Total

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0 2 4 6 8 10 12

Client X

SWN OOB

SWN Tuned

Time - Hours

Time

OBI DW Load Times – Top Tasks

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TIME (minutes)

Task Client X SWN OOB SWN Tuned Gain

SDE_ORA_GLJournals 18 3.5 1 18X

SDE_ORA_GLBalanceFact 15 2 2 8X

SDE_ORA_Stage_GLJournals_Derive 20 2 1 20X

SDE_ORA_STAGE_GLOtherFact_Derive 28 8 1.5 19X

SIL_GLOtherFact 35 8 6.5 5X

SIL_GLBalanceFact 38 8 6.5 6X

PLP_GLBalanceAggrByAcctSegCodes 62 21 11 6X

OBI DW Load – Incremental Tuned

�  Top Task Metrics – Incremental load Tuned (32 minutes)

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Task # Rows Time (min) Throughput/min SDE_ORA_GLJournals 2,995,320 1 2,995,320

SDE_ORA_GLBalanceFact 3,174,070 2 1,587,035

SDE_ORA_Stage_GLJournals_Derive 2,995,320 1 2,995,320

SDE_ORA_STAGE_GLOtherFact_Derive 2,995,320 1.5 1,330,213

SIL_GLOtherFact 990,404 6.5 152,369

SIL_GLBalanceFact 3,174,070 6.5 488,319

PLP_GLBalanceAggrByAcctSegCodes 18,666,814 11 1,116,676

OBI DW Query Performance

How will my queries perform with… •  No Bitmaps •  Parallelism

•  Partitioning •  Compression

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Look ma, no bit maps

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�  Every query is different, however there is no negative impact to performance overall (Client X ~ 10 min)

SQL_ID EXECS AVG_ETIME OFFLOADABLE OFFLOADED OFFLOAD_ELIGIBLE IO Saved ------------------- ---------- ----------------- ----------------- -------------------- ----------------------------- ------------ 5ycwp44p7crmg 1 13.61 Yes 1,173,569,000 27,537,399,808 95.74%

But is it faster…

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�  Query execution with bit maps in place:

SQL_ID EXECS AVG_ETIME OFFLOADABLE OFFLOADED OFFLOAD_ELIGIBLE IO Saved ------------------- ---------- ----------------- ----------------- -------------------- ----------------------------- ------------ 5m8q3bkuyrguk 1 439.25 NO 0 0 0%

But is it faster…

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�  Query execution with no bitmaps:

SQL_ID EXECS AVG_ETIME OFFLOADABLE OFFLOADED OFFLOAD_ELIGIBLE IO Saved ------------------- ---------- ----------------- ----------------- -------------------- ----------------------------- ------------ Drdwhzcsm4cyf 1 65.98 Yes 1,264,079,768 28,597,270,528 95.58%

Parallelism

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Query dropped from 3 min to 8 seconds (Client X – 20 min)

Parallelism

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�  Where can we add parallelism: �  OBIEE Analysis report �  OBIEE RPD physical table (previous slide) �  Informatica source definition �  OBI DW tables

Partitioning �  Our desire is to get direct path reads in order to take advantage

of Smartscan functionality �  Risk size of partition being too small

�  We now have the option of partitioning without having to spend time adding in DAC Actions

�  Partitioned W_GL_OTHER_F and W_GL_BALANCE_F by period �  Load time stayed at 32 minutes

�  Mixed query performance results depending on size of partitions

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Compression

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�  Now that we have the tables partitioned, we can compress historical data that is no longer being actively updated: �  W_GL_OTHER_FACT SEP11 partition HCC Query High:

�  Before HCC ~ 23 GB, query execution 11.61 seconds �  After HCC ~ 699 MB, query execution 8.24 seconds

Compress ion of 33x

Exadata Capabilities – Wrap-up

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•  Speed to Market •  Easy Transition from Legacy Systems to Exadata

•  Simple and Accurate Application Migration

•  Consolidation Platform

•  Extreme Performance

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The Kübler-Ross grief cycle

Exposure to Exadata

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Expert Oracle Exadata, co-authored by Kerry Osborne, Randy Johnson & Tanel Poder to be published July 18, 2011

Visit Enkitec at Oracle OpenWorld at Moscone Center in San Francisco from October 2 – 6 at Booth 1721

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Questions?

Fastest Growing Companies in Dallas

Contact Information : Martin Paynter

martin.paynter@enkitec.com kerryosborne.oracle-guy.com

www.enkitec.com