1. Exadata and Oracle Business Intelligence
Enterprise Edition Retail Case Study
Jon Mead
Managing Director - Rittman Mead Consulting
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Sunday, 26 September 2010
2. Overview
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3. Overview
•Exadata
•OBIEE
•Agile development
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4. Introductions
• Rittman Mead Consulting
‣ Oracle BI Consultancy (UK, US, India)
‣ Platinum Partner
‣ Long(est) running Oracle BI blog
‣ Annual BI Forum
‣ OBIEE Oracle Press book
‣ http://www.rittmanmead.com
‣ @rittmanmead
• Jon Mead
‣ Managing Director/Co-Founder
‣ BI Consultant
‣ @jonmead
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5. Introductions
• On-line retailer
• Didn’t want to be named....
• Direct supplier
• Host market place
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6. Background
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7. Business Problem
It’s a simple case of supply and demand...
A retail organisation needs to be able to make
pricing and purchasing (supply) decisions
based on a near real time view of what is
happening in the its marketplace (demand).
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8. Business Problem
Competitive
Advantage
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9. Business Problem
BUT...
(Its’s not quite that simple)
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10. Business Problem
Accounts
Fraud
Suppliers
Systems
Customers Buyers
Inventory
Competitors
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11. What is needed
• Ability to process large volumes of data
‣ Volume of data
- 1000’s of transactions a minute at peak periods
‣ Complexity of the transformation
- Order lifecycle
• Ability to report on large volumes of data
‣ Year on year reporting
‣ Cross fact analysis
‣ Ad-hoc queries
‣ Deep historic analysis
‣ Near real time analysis during peak periods
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12. What is needed
• Common and consistent business model
‣ Single abstraction of the business
‣ One unified tool to development and deliver
• Reduced development cycles
‣ Business is constantly evolving
‣ Agile development methodology
‣ Quick release cycles
‣ Early involvement from the business
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13. In summary....
• Very fast changing market
• Key Drivers
‣ Optimise product line • Maximise profit
‣ Optimise pricing • Maximise customer satisfaction
‣ Optimise stock levels • Turn data into information quicker
than their competitors
• Coupled with
‣ Fraud analysis
‣ Customer tracking
‣ Order tracking
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14. In summary....
Current Planned
Up to a monthly lag on
Data Near real time
certain data
Could take up to 24
Queries Near instant response
hours
Business Model Fragmented Consistent
Development Long time-frames Month long iterations
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15. Typical Approach
• Multiple tools used to support reporting
‣ Each transactional system supplies its own reporting tool
‣ Multiple business models
• Data warehouse or marts for integrated reporting
• Complex aggregation model
‣ OLAP
• Potentially long development cycles
‣ Not visible to end-users
‣ High level of rework in the ETL development
• Static fact tables
‣ Difficult to re-load the data
• Focus on reduction of I/O during the design process
‣ Lots of performance tuning
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16. New World Order
• New development methodology
• Real-time data access
• Data processing power
• Data querying power
• Single view of the business
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17. New World Order
• New development methodology
• Real-time data access
• Data processing power
• Data querying power
• Single view of the business
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18. New World Order
• New development methodology Agile approach
• Real-time data access Change Data Capture
• Data processing power Exadata
• Data querying power Exadata
• Single view of the business OBIEE
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19. Solution
• Change Data Capture
‣ Asynchronous
• Oracle Warehouse Builder
‣ SCD Framework
• Oracle Reference Data Warehouse Architecture
‣ Staging
‣ ODS
‣ Performance layer
• OBIEE
‣ Dashboards
‣ Answers
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20. What is Change Data Capture
• Asynchronous method of shipping log files
• Creates change tables
• Changed data windows can be extended, read and purged
• Replaced by Golden Gate?
• Ensure Supplemental Logging in enable (if you get the choice)
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21. What is Exadata
• AKA The Oracle Database Machine
‣ Or Sun Oracle Database Machine
‣ Or Oracle Exadata Database Machine
• Combination of hardware and software
• First launched at Oracle Open World 2008
• Original (v1) machine was Oracle and HP...
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22. Oracle and HP
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23. What is Exadata?
• Oracle acquires Sun
• V2 release a year later
‣ OLTP
‣ Flash cache
‣ 11gR2 of the database
‣ Twice as fast for Data Warehousing
• Revised this year
‣ x2-2 - 8 2 core database servers
‣ x2-8 - 2 8 core database servers
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24. Exadata V2 specification - hardware
• Database nodes
‣ Sun Fire x4170
‣ 2 quad-core CPU, 72GB RAM
‣ Infiniband cards
‣ Full rack has 8 database servers
• Infiniband switches
‣ Full rack has 3
‣ 2 for redundancy, 1 to connect to other machines
• Management switch
• Storage cells
‣ Sun Fire x4275
‣ 2 quad-core CPU, 24GB RAM
‣ From 12 x 600GB to 12 x 2 TB drives
‣ Infiniband cards
• Rack
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25. Exadata V2 specification - software
• Database nodes
‣ Oracle Enterprise Linux 5.3
‣ Oracle Database 11g Release 2
- Oracle 11gR2 includes a substantial amount of code to support Exadata’s
features like smart scans and columnar compression, and this code is
simply unused in non-Exadata deployments.
• Storage cells
‣ This is where the magic happens....
‣ Set of software programs (CELLSRV, RS, MS etc) handling high-level I/O
processing
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26. Exadata key features (V1 focused)
• Smart scan
‣ Reduce data transfer between database and storage servers
• Storage Indexes
‣ Partition pruning like effect
‣ Is Partitioning required?
• Hybrid Columnar Compression
‣ V2 feature (11gR2 feature, could upgrade V1)
• Flash Cache (V2)
• But also use Data Warehousing features
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27. Smart Scan
• Data processing for Full Table Scan
Data Data Data Data Data Data
Memory Storage
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28. Smart Scan
• Smart Scan for Full Table Scan
Data Data Data
Memory Storage
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29. Compression
• Compressed data uses less I/O, but more CPU cycles
Data Data Data Data Data Data
Memory Storage
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30. What is OBIEE?
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31. Oracle Business Intelligence Enterprise Edition Plus
• Dashboards, Ad-hoc Reporting, Alerts, Microsoft Office Integration
‣ High quality graphical, role/user based views
‣ Multiple views of same data
• Point and click ease of use
• Heterogeneous database access - true Enterprise deployment
• Unified semantic/logical view of data from multiple sources
• Alerts, scheduling and distribution
• Powerful/visual metadata design tool
‣ Common Enterprise Information Model
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32. Architecture
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33. Next-Generation Reference DW Architecture
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34. Staging Layer
Exadata
OWB
Foundation Layer
Performance Layer
Reporting Solution
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35. Staging Layer
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36. • Change Tables for CDC
‣ Temporary holding area for incremental data
‣ Asynchronous Autolog Online Change Data
Capture
• Reject Tables
‣ Foundation layer
‣ Performance layer
• Staging tables for Event Syslogs
• Elimination of “noise” data
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37. Foundation Layer
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38. Foundation Logical Model
• Process-neutral
• Devoid of business requirements
• Tables will be represented as they are in the
source systems
‣ Normalized structure maintained
‣ Additional elements added by CDC
‣ Primary key plus Transaction Date
‣ Transaction Source
• Event table will be modeled away from key value
pairs to represent true event data
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39. Foundation Physical Model
• Partition tables on Transaction Date
‣ Segmentation of data
‣ Improved query performance
‣ Local indexes
• Aggregation in the foundation layer
‣ Materialized views for Query Rewrite
‣ Partition Change Tracking (PCT)
‣ Materialized views on Prebuilt Tables
‣ “Sessionizing” aggregation if desired
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40. Performance Layer
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41. Logical Model
• Data Bus Architecture
‣ Conformed dimensions
‣ Fact tables at the lowest level of granularity
• Hierarchies maintained in a single table whenever
possible
‣ Lowest-common denominator reporting tool
support
‣ Removal of complexity for self service model
‣ Better performance
• No aggregation in the logical model
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42. Physical Model
• Partitioning
‣ Hash-partitioned dimension tables
‣ Range-partitioned fact tables
• Star transformation
• Aggregation
‣ Materialized views for Query Rewrite
‣ Partition Change Tracking (PCT)
‣ Materialized views on Prebuilt Tables
• New 11gR2 Parallelism
• Exadata Smart Scan
• Database Machine Compression
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43. Approach
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44. Key approaches
• SQL Modelling
• Early visibility of data to
• Re-aligning OBIEE end users
physical layer
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45. High level project approach
Month 1 Month 2 Month 3
Discovery Analysis
Review 1
Iteration 1 Development
Review 2
Cycle 1 rework
Iteration 2 +
Development
Review 3
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46. What did we not do?
• Typical steps to ‘create’ an Exadata environment
‣ Operating System installation on all four nodes
‣ Setting it up network-wise (including the IB switches)
‣ Setting it up Oracle-wise
‣ Disks config on each of the 7 Exadata Storage Servers
‣ Grid Infrastructure installation on all 4 nodes
‣ RDBMS Installation on all 4 nodes
‣ ASM config upon the grid disks
‣ Smart Flash Cache config
‣ RAC enabled database creation on the ASM diskgroups
‣ Testing and Documenting
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47. High level project approach - analysis
• Requirements analysis
‣ User interviews
‣ Email forms
‣ Reviewing existing reports
• In parallel data analysis
‣ SQL modelling
‣ Defining test cases
‣ Defining acceptance criteria
• Architecture
‣ System architecture
‣ Environments
‣ Best practices
• Models
‣ Logical
‣ Physical
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48. Analysis
Month 1
Requirements Analysis
Data Analysis
Discovery Architecture
High level design
Planning
Review 1
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49. Approach - iterations
• Selection and prioritisation
• Start to build ETL
‣ Use existing or new SQL models if required
• Design logical layer and implement in OBIEE
• Implement physical layer to support logical layer
‣ Good enough approach
• Give end users visibility
‣ Test acceptance criteria
• Re-aligning OBIEE physical layer
• Fact tables may be reloaded
• Work more advanced calculation back into logical, physical and potentially
performance layer
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50. Re-aligning OBIEE physical layer
• Swapping out physical tables
Presentation Layer
Common Enterprise Information Layer
Prototype Prototype Prototype
Customer Customer Product
Dimension Dimension Dimension
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51. Re-aligning OBIEE physical layer
• Swapping out physical tables
Presentation Layer
Common Enterprise Information Layer
Prototype
Actual Prototype Prototype
Customer Customer Product
Dimension Dimension Dimension
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52. Re-aligning OBIEE physical layer
• Swapping out physical tables
Presentation Layer
Common Enterprise Information Layer
Prototype
Actual Prototype
Actual Prototype
Actual
Customer Customer Product
Dimension Dimension Dimension
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53. Iteration 1
Month 2
Selection
Detailed Design
Iteration 1 ETL Development
OBIEE Development
End User Testing
Review 2
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54. Design
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55. Design considerations
• Do we change the design for Exadata?
‣ Best practices
‣ Performance
‣ Where is processing done
• Preconceptions
‣ Don’t use indexes
‣ Use HASH partitioning
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56. Design considerations
• Do we need a Data Warehouse?
‣ Data quality
‣ Tracking dimensional change
‣ Combining sources in OBIEE with high volumes of data can lead to
performance problems WHATEVER the data source
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57. Design considerations - principals
• Use an atomic dimensional data model
• Minimise the number of downstream facts
• Don’t have an aggregation strategy
• Use the logical separation in OBIEE
• Use Data Warehousing features to compliment Exadata features
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58. Combining Exadata features and Data Warehousing features
• Full Table Scan (Smart Scan) with Partitioning
Data Data Data
Memory Storage
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59. Combining Exadata features and Data Warehousing features
• Full Table Scan (Smart Scan) with Partitioning
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60. Combining Exadata features and Data Warehousing features
• Full Table Scan (Smart Scan) with Partitioning
Data Data Data
Memory Storage
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61. Observations
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62. Observations - data processing
• I/O intensive operations are lightning fast.
‣ A pivot operation on 80M rows completed in a < 10s
• It seems as the memory-intensive processes such as
SQL analytics perform roughly the same
‣ However Exadata does have a lot of RAM so PGA
should be large
• V2 note: on a subsequent project we saw Bloom
Filtering with SQL Analytics statements
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63. Observations - data querying
• Parallelism and hence performance works best with partition-wise operations
‣ HASH partition on surrogate keys for dimensions (may work for business key)
‣ RANGE partitioning reduces I/O on date related queries
‣ Would have been good to HASH sub-partition fact on product dimension
• Indexes
‣ Still useful when a specific rows required, e.g. tree-walking with CONNECT BY
PRIOR
‣ Not required for bulk operations
‣ Bitmap indexes on fact are still good for STAR transformation
• V2 note: we have seen STAR transformation out perform Smart Scan in some
queries
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64. Observations - environment
• There was only one Exadata box
• Develop in production?
‣ Green field project
• Resulted in testing being very difficult if not on Exadata
‣ No surprise: it didn’t perform
• Became much harder for future iterations
‣ However particularly for the ETL the historic data was already in the
Foundation layer
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65. Observations
• Advanced compression
‣ Big gain was disk space
• Sequences
‣ Must increase size of Sequence pool
‣ Common to all RAC instances
• Patching
‣ There is still some complexity in the patching process
• Not a magic wand
‣ It is still possible to produce badly designed star schemas
‣ It is still possible to produce badly designed OBIEE repositories
‣ It is still possible to run a query requesting every cell of data in the database
‣ It can still run slow
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66. Wins - the numbers...
• ETL
‣ 10 facts; 15 dimensions; 300 tables; 157 OWB mappings
- Daily load: 1 hour 30 minutes
- Daily load : approximately 130 million rows pushed
through staging area and into the ODS and the Data
Warehouse
‣ One of the facts is refreshed with approximately 100
million rows and only takes 10 minutes
• Reporting
‣ Reports could be run over multiple fact over multiple
years
‣ Report that took 24 hours to run now completes in 15
minutes
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67. Summary
• Benefits
‣ Organisation now has near real time supply and demand data
‣ Ad-hoc reporting capability
‣ Reduced turnaround of generating reports from 2 to 3 days of spreadsheet
crunching to instant available dashboard
‣ One source of truth
• Made possible by
‣ Extreme performance in the ETL layer
‣ Extreme performance in the Reporting layer
‣ Agile development approach
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68. Summary
• Exadata and OBIEE provided means to successfully deliver project
‣ Fast data processing
‣ Support for agile approach
• Combination of software, hardware and approach (methodology)
‣ Exadata
‣ OBIEE
‣ Agile approach
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69. Questions
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