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Exadata - The Facts and Myth
             Behind A Proof Of Concept



                     Tony Jambu
                    Melbourne, Australia




TJambu@wizard.cx
Agenda


   •    Myths and Facts of Benchmarks and PoCs

   •    Exadata Proof of Concept

   •    Learnings from Other Exadata sites

 Please note that the views and opinions expressed during this
 presentation are those of the presenters and not the respective
 companies they work for.
Exadata PoC

  Proof of Concept
     •  19 days Proof of Concept carried out in Jan
       2011
     •  ‘Lift and Drop’ approach using one of
       company’s data warehouse
     •  23 hours of testing was carried out on Exadata
       X2-2 at Oracle Data Centre, Sydney
Exadata PoC - Summary

  Transactions


  11.6 X faster (avg)
     •  No code or schema changes
     •  Up to 90X faster was observed
Exadata PoC - Summary

  Storage Reduction


  84% saving
     •  Using Oracle’s Hybrid Columnar Compression for Archive
       mode
Exadata PoC - Summary

  SQL Loader


  10 X faster
  Consumes less CPU
Section 1–Myths & Facts of Benchmarks and PoC

  PoC Figures
     •  What does all these figures mean?
     •  Are they just smoke and mirrors?
     •  What are the details?
  What about the figures quoted by Oracle?
Understanding the Figures
Understanding the Figures

  Comparing Apples to Apples?

     Current System            New System
     Legacy server vs          New server
     Slower storage vs         Latest disk technology
     Previous version of       Latest 11gR2
     Oracle vs
     Full load vs              Partial load
     Individual test results   Average result times
     comparison vs
Oracle Exadata Database Machine
  Not just a database appliance
  An ‘engineered’ solution of
      •  Database servers
      •  Flash Storage
      •  Storage
      •  Interconnect (Infiniband)
      •  Infiniband & Ethernet switches
      •  iDB (modified iSCSI on top of ZDP)
      •  KVM
      The magic sauce – Exadata Storage Server software
Section 2 – Exadata Proof of Concept

  The system chosen was a data warehouse
     •  22 TB single instance database
     •  About 30 main production schema.
     •  Main schema, API5AFS with 8TB was chosen
     •  Work profile
       •  Batch loads
       •  Post load processing &
       •  Reports and End user activities
GDW ADS Data Warehouse

    •  Production sever – SUN M8000
    •  DR, Test, Development server – SUN M9000
    •  Storage – EMC’s latest storage
    •  Application Server – SUN T5240
    •  Database 10gR2
Testing Methodology

  High Level Steps
  1.  A clone of production is created on a SUN M9000 server
  2.  Workload txns are captured on production
  3.  Baseline tests are conducted on this clone
  4.  Export data & Statistics
  5.  Exadata: Import data & statistics
  6.  Exadata: Conduct Baseline tests
  7.  Exadata: Make changes and run tests again.
  8.  Repeat (7) for different conditions
Testing Methodology

  Test Scenarios
    1.  Automated-Using Oracle’s RAT(Real ApplicationTesting)
    2.  Manual –
       (a)  SQLs (16 INSERT/SELECT and 2 SELECT)
       (b)  SQL Loader (key component)
  Preparatory Work
    •  Source: Export using expdp (5 streams)
    •  Source: Export Statistics only
    •  Target: Import using impdp
    •  Target: Import Statistics
Testing Methodology

  RAT Capture
    1.  Stop production
    2.  Snap/clone database to Test server
    3.  Start RAT capture for API5AFS txn only
    4.  Stop RAT capture stopped after 3 hours
  Subset of large production jobs
      •  16 jobs with INSERT/SELECT, 2 jobs with SELECT
      •  SQLs are heavily hinted
      •  All 18 jobs were run executed concurrently to simulate
        production workload
Testing Methodology

  Factors considered
     •  Eliminate network ie not App server to DB Server
        (as test on Exadata were single tier)
     •  Eliminate spool file
        (to /dev/null to eliminate O/S write delays)
     •  Run a baseline test on Exadata with no modifications
        or tweeking
     •  Run jobs concurrently
     •  Ensure no other applications running on your test
        server and Exadata server
Preparatory: Baselining on SUN M9000

  Baselining on the SUN M9000
                                                M9K Baseline              M9K Baseline
  JOB NAME       Typical Duration   Operation   (single exec)             (concurrent)
  WF802P01.sql   1.5 hr             SELECT                      00:11:12.0          00:47:38.0
  WG189P03.sql    20-50 mins        INS                         00:06:57.9          00:39:10.2
  WG634P06.sql   1-2 hrs            INS                         00:22:29.9          00:37:51.7
  WG690P03.sql   60 mins            INS                         00:48:18.5          01:38:57.1
  WG703P01.sql   60 mins            INS                         00:19:40.9          00:55:31.0
  WG709P01.sql   45 mins            INS                         00:51:45.2          01:18:18.2
  WG862P01.sql   30-60 mins         INS                         00:02:57.2          00:07:24.0
  WG923P01.sql   30-60 mins         INS                         00:10:21.2          00:43:11.9
  WG923P02.sql   50 mins            INS                         00:10:24.2          00:43:11.1
  WG982P07.sql   2 hrs              INS                         02:15:27.3          03:06:47.9
  WG982P17.sql   10 hrs             INS                         00:01:15.4          00:03:32.0
  WGAVNP01.sql   30-50 mins         INS                         00:24:06.8          01:11:45.9
  WGS41P02.sql   30-40 mins         INS                         00:15:38.2          00:50:26.3
  WGS41P10.sql   40-60 mins         INS                         00:12:52.3          00:39:12.4
  WGS41P14.sql   1 hr 20 mins       INS                         00:20:35.6          01:06:20.2
  WH180P04.sql   40 mins            INS                         00:11:06.8          00:46:22.6
  WH566P01.sql   2-3.5 hrs          SELECT                      01:24:57.0          02:18:34.0
  WHBA3P01.sql   25 mins            INS                         00:27:57.0          01:19:21.7
Preparatory: Baselining on SUN M9000

  Baselining on the SUN M9000
Results – SUN M9000 vs Exadata (No Changes)

  Lift & Drop test on Exadata - Data
                 M9K Baseline   Exadata Test 1 Performance Gain
  JOB NAME                      (baseline)       M9k to Exadata
  WF802P01.sql        00:47:38.0       00:14:26.0                3.3
  WG189P03.sql        00:39:10.2       00:09:37.1                4.1
  WG634P06.sql        00:37:51.7       00:41:17.6               -1.1
  WG690P03.sql        01:38:57.1       01:27:35.2                1.1
  WG703P01.sql        00:55:31.0       00:03:53.9               14.2
  WG709P01.sql        01:18:18.2       00:03:11.9               24.5
  WG862P01.sql        00:07:24.0       00:04:23.7                1.7
  WG923P01.sql        00:43:11.9       00:03:33.5               12.1
  WG923P02.sql        00:43:11.1       00:03:28.8               12.4
  WG982P07.sql        03:06:47.9       01:33:20.9                2.0
  WG982P17.sql        00:03:32.0       00:00:51.6                4.1
  WGAVNP01.sql        01:11:45.9       01:48:17.4               -1.5
  WGS41P02.sql        00:50:26.3       00:03:03.0               16.5
  WGS41P10.sql        00:39:12.4       00:10:11.2                3.8
  WGS41P14.sql        01:06:20.2       00:09:09.9                7.2
  WH180P04.sql        00:46:22.6       00:04:14.8               10.9
  WH566P01.sql        02:18:34.0       00:01:31.0               91.4
  WHBA3P01.sql        01:19:21.7       00:31:07.5                2.5
  Average                                                       11.6
Results – SUN M9000 vs Exadata (No Changes)

  Lift & Drop test on Exadata – Elapsed time
Results – SUN M9000 vs Exadata (No Changes)

  Lift & Drop test on Exadata – Performance Gain
Results – SUN M9000 vs Exadata (*16 Degree)

  Exadata – Increase Parallel Degree x16 - Data
                                               Performance Gain
                 M9K Baseline Exadata Test 2 M9k to Exadata       Performance Gain
  JOB NAME                     (*16 Degree)    (unchanged)        M9k to Exadata(*16DEG)
  WF802P01.sql       00:47:38.0      00:19:43.0                3.3                     2.4
  WG189P03.sql       00:39:10.2      00:08:41.2                4.1                     4.5
  WG634P06.sql       00:37:51.7      01:15:08.4               -1.1                    -2.0
  WG690P03.sql       01:38:57.1      01:45:55.5                1.1                    -1.1
  WG703P01.sql       00:55:31.0      00:03:54.0               14.2                    14.2
  WG709P01.sql       01:18:18.2      00:02:21.0               24.5                    33.3
  WG862P01.sql        Avg Gain with
                     00:07:24.0      00:11:54.0                1.7                    -1.6
  WG923P01.sql        increase in
                     00:43:11.9      00:01:58.4               12.1                    21.9
  WG923P02.sql       00:43:11.1      00:01:54.5               12.4                    22.6
  WG982P07.sql        Parallel degree
                     03:06:47.9      01:28:31.4                2.0                     2.1
  WG982P17.sql       00:03:32.0      00:00:43.3                4.1                     4.9
  WGAVNP01.sql       01:11:45.9      01:45:50.9               -1.5                    -1.5
  WGS41P02.sql       00:50:26.3      00:05:12.2               16.5                     9.7
  WGS41P10.sql    Avg Gain with no
                     00:39:12.4      00:02:32.1                3.8                    15.5
  WGS41P14.sql       01:06:20.2      00:04:29.4                7.2                    14.8
  WH180P04.sql    changes
                     00:46:22.6      00:12:50.0               10.9                     3.6
  WH566P01.sql       02:18:34.0      00:01:06.0               91.4                   126.0
  WHBA3P01.sql       01:19:21.7      00:26:52.3                2.5                     3.0
  Average                                                     11.6                    15.1
Results – SUN M9000 vs Exadata (*16 Degree)

  Exadata –Parallel Degree x16 - Elapsed time
Results – SUN M9000 vs Exadata (*16 Degree)

  Exadata –Parallel Degree x16 - Performance Gain
Results – SQL Loader

  SQL Loader Test
     •  3.6 M rows
     •  Rows are ‘transformed’ on load
Results – SQL Loader

  SQL Loader Result
   •  6X faster
   •  94 % less CPU
   •  CPU to Elapsed time 27%


                          Elapsed time CPU time consumed    CPU to Elapsed %
         M9000 server:     01:39:00.00        01:21:00.00               82%
        Exadata server:    00:17:30.92        00:04:42.18               27%
     Exadata vs M9000:            18%                 6%
     Performance gain:             6X                17 X
Results – Exadata Hybrid Columnar Compression

  Compression Test
     •  Single Table with
       •  1+ billion rows
       •  1 TB
       •  430 Partitions
     •  Due to time constraint, only 254 partitions were
      compressed
Results – Exadata Hybrid Columnar Compression

  Compression Result




 Size before HCC: 555 GB
   Size with HCC: 89 GB
    Space savings: 466 GB
        % savings:    84%
Compression Ratio: 1:6.25
PoC – Summary

  Apples to Apples comparison (M9000 test
   server to Exadata)
  What worked
    •  Simple Lift and Drop approach
    •  Minor changes can give significant performance
      advantage
  What did not work/complete
    •  Real Application Testing
    •  Removing embedded SQL hints
Section 3 - Learnings from Other Exadata sites

     •  Are indexes still required?
     •  What skills are required to manage the
       machine?
     •  The DMA – Database Machine Administrator
     •  High Capacity or High Performance SAS
       drives?
     •  Do not under estimate data migration effort
     •  Last but not least – Managing expectation
Summary
   Ran a Proof-of-Concept of Oracle’s Exadata Database
      machine using a real data warehouse and these
      are the results

   •    A ‘Lift & Drop’ approach is feasible and found

   •    Transactions were 11.6 X faster

   •    84% space savings on uncompressed data

   •    SQL Loader 6X faster and consume 94 % less
        CPU
Speaker : Tony Jambu
    Paper : Exadata - The Facts and Myth
            Behind A Proof Of Concept




                                Q&A
Select Star Mailing list
http://groups.yahoo.com/group/Select_Star/
or email Select_Star-subscribe@yahoogroups.com

For feedback & discussion: TJambu@Wizard.CX

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Oracle Systems _ Tony Jambu _ Exadata The Facts and Myths behing a proof of concept.pdf

  • 1. Exadata - The Facts and Myth Behind A Proof Of Concept Tony Jambu Melbourne, Australia TJambu@wizard.cx
  • 2. Agenda •  Myths and Facts of Benchmarks and PoCs •  Exadata Proof of Concept •  Learnings from Other Exadata sites Please note that the views and opinions expressed during this presentation are those of the presenters and not the respective companies they work for.
  • 3. Exadata PoC Proof of Concept •  19 days Proof of Concept carried out in Jan 2011 •  ‘Lift and Drop’ approach using one of company’s data warehouse •  23 hours of testing was carried out on Exadata X2-2 at Oracle Data Centre, Sydney
  • 4. Exadata PoC - Summary Transactions 11.6 X faster (avg) •  No code or schema changes •  Up to 90X faster was observed
  • 5. Exadata PoC - Summary Storage Reduction 84% saving •  Using Oracle’s Hybrid Columnar Compression for Archive mode
  • 6. Exadata PoC - Summary SQL Loader 10 X faster Consumes less CPU
  • 7. Section 1–Myths & Facts of Benchmarks and PoC PoC Figures •  What does all these figures mean? •  Are they just smoke and mirrors? •  What are the details? What about the figures quoted by Oracle?
  • 9. Understanding the Figures Comparing Apples to Apples? Current System New System Legacy server vs New server Slower storage vs Latest disk technology Previous version of Latest 11gR2 Oracle vs Full load vs Partial load Individual test results Average result times comparison vs
  • 10. Oracle Exadata Database Machine Not just a database appliance An ‘engineered’ solution of •  Database servers •  Flash Storage •  Storage •  Interconnect (Infiniband) •  Infiniband & Ethernet switches •  iDB (modified iSCSI on top of ZDP) •  KVM The magic sauce – Exadata Storage Server software
  • 11. Section 2 – Exadata Proof of Concept The system chosen was a data warehouse •  22 TB single instance database •  About 30 main production schema. •  Main schema, API5AFS with 8TB was chosen •  Work profile •  Batch loads •  Post load processing & •  Reports and End user activities
  • 12. GDW ADS Data Warehouse •  Production sever – SUN M8000 •  DR, Test, Development server – SUN M9000 •  Storage – EMC’s latest storage •  Application Server – SUN T5240 •  Database 10gR2
  • 13. Testing Methodology High Level Steps 1.  A clone of production is created on a SUN M9000 server 2.  Workload txns are captured on production 3.  Baseline tests are conducted on this clone 4.  Export data & Statistics 5.  Exadata: Import data & statistics 6.  Exadata: Conduct Baseline tests 7.  Exadata: Make changes and run tests again. 8.  Repeat (7) for different conditions
  • 14. Testing Methodology Test Scenarios 1.  Automated-Using Oracle’s RAT(Real ApplicationTesting) 2.  Manual – (a)  SQLs (16 INSERT/SELECT and 2 SELECT) (b)  SQL Loader (key component) Preparatory Work •  Source: Export using expdp (5 streams) •  Source: Export Statistics only •  Target: Import using impdp •  Target: Import Statistics
  • 15. Testing Methodology RAT Capture 1.  Stop production 2.  Snap/clone database to Test server 3.  Start RAT capture for API5AFS txn only 4.  Stop RAT capture stopped after 3 hours Subset of large production jobs •  16 jobs with INSERT/SELECT, 2 jobs with SELECT •  SQLs are heavily hinted •  All 18 jobs were run executed concurrently to simulate production workload
  • 16. Testing Methodology Factors considered •  Eliminate network ie not App server to DB Server (as test on Exadata were single tier) •  Eliminate spool file (to /dev/null to eliminate O/S write delays) •  Run a baseline test on Exadata with no modifications or tweeking •  Run jobs concurrently •  Ensure no other applications running on your test server and Exadata server
  • 17. Preparatory: Baselining on SUN M9000 Baselining on the SUN M9000 M9K Baseline M9K Baseline JOB NAME Typical Duration Operation (single exec) (concurrent) WF802P01.sql 1.5 hr SELECT 00:11:12.0 00:47:38.0 WG189P03.sql 20-50 mins INS 00:06:57.9 00:39:10.2 WG634P06.sql 1-2 hrs INS 00:22:29.9 00:37:51.7 WG690P03.sql 60 mins INS 00:48:18.5 01:38:57.1 WG703P01.sql 60 mins INS 00:19:40.9 00:55:31.0 WG709P01.sql 45 mins INS 00:51:45.2 01:18:18.2 WG862P01.sql 30-60 mins INS 00:02:57.2 00:07:24.0 WG923P01.sql 30-60 mins INS 00:10:21.2 00:43:11.9 WG923P02.sql 50 mins INS 00:10:24.2 00:43:11.1 WG982P07.sql 2 hrs INS 02:15:27.3 03:06:47.9 WG982P17.sql 10 hrs INS 00:01:15.4 00:03:32.0 WGAVNP01.sql 30-50 mins INS 00:24:06.8 01:11:45.9 WGS41P02.sql 30-40 mins INS 00:15:38.2 00:50:26.3 WGS41P10.sql 40-60 mins INS 00:12:52.3 00:39:12.4 WGS41P14.sql 1 hr 20 mins INS 00:20:35.6 01:06:20.2 WH180P04.sql 40 mins INS 00:11:06.8 00:46:22.6 WH566P01.sql 2-3.5 hrs SELECT 01:24:57.0 02:18:34.0 WHBA3P01.sql 25 mins INS 00:27:57.0 01:19:21.7
  • 18. Preparatory: Baselining on SUN M9000 Baselining on the SUN M9000
  • 19. Results – SUN M9000 vs Exadata (No Changes) Lift & Drop test on Exadata - Data M9K Baseline Exadata Test 1 Performance Gain JOB NAME (baseline) M9k to Exadata WF802P01.sql 00:47:38.0 00:14:26.0 3.3 WG189P03.sql 00:39:10.2 00:09:37.1 4.1 WG634P06.sql 00:37:51.7 00:41:17.6 -1.1 WG690P03.sql 01:38:57.1 01:27:35.2 1.1 WG703P01.sql 00:55:31.0 00:03:53.9 14.2 WG709P01.sql 01:18:18.2 00:03:11.9 24.5 WG862P01.sql 00:07:24.0 00:04:23.7 1.7 WG923P01.sql 00:43:11.9 00:03:33.5 12.1 WG923P02.sql 00:43:11.1 00:03:28.8 12.4 WG982P07.sql 03:06:47.9 01:33:20.9 2.0 WG982P17.sql 00:03:32.0 00:00:51.6 4.1 WGAVNP01.sql 01:11:45.9 01:48:17.4 -1.5 WGS41P02.sql 00:50:26.3 00:03:03.0 16.5 WGS41P10.sql 00:39:12.4 00:10:11.2 3.8 WGS41P14.sql 01:06:20.2 00:09:09.9 7.2 WH180P04.sql 00:46:22.6 00:04:14.8 10.9 WH566P01.sql 02:18:34.0 00:01:31.0 91.4 WHBA3P01.sql 01:19:21.7 00:31:07.5 2.5 Average 11.6
  • 20. Results – SUN M9000 vs Exadata (No Changes) Lift & Drop test on Exadata – Elapsed time
  • 21. Results – SUN M9000 vs Exadata (No Changes) Lift & Drop test on Exadata – Performance Gain
  • 22. Results – SUN M9000 vs Exadata (*16 Degree) Exadata – Increase Parallel Degree x16 - Data Performance Gain M9K Baseline Exadata Test 2 M9k to Exadata Performance Gain JOB NAME (*16 Degree) (unchanged) M9k to Exadata(*16DEG) WF802P01.sql 00:47:38.0 00:19:43.0 3.3 2.4 WG189P03.sql 00:39:10.2 00:08:41.2 4.1 4.5 WG634P06.sql 00:37:51.7 01:15:08.4 -1.1 -2.0 WG690P03.sql 01:38:57.1 01:45:55.5 1.1 -1.1 WG703P01.sql 00:55:31.0 00:03:54.0 14.2 14.2 WG709P01.sql 01:18:18.2 00:02:21.0 24.5 33.3 WG862P01.sql Avg Gain with 00:07:24.0 00:11:54.0 1.7 -1.6 WG923P01.sql increase in 00:43:11.9 00:01:58.4 12.1 21.9 WG923P02.sql 00:43:11.1 00:01:54.5 12.4 22.6 WG982P07.sql Parallel degree 03:06:47.9 01:28:31.4 2.0 2.1 WG982P17.sql 00:03:32.0 00:00:43.3 4.1 4.9 WGAVNP01.sql 01:11:45.9 01:45:50.9 -1.5 -1.5 WGS41P02.sql 00:50:26.3 00:05:12.2 16.5 9.7 WGS41P10.sql Avg Gain with no 00:39:12.4 00:02:32.1 3.8 15.5 WGS41P14.sql 01:06:20.2 00:04:29.4 7.2 14.8 WH180P04.sql changes 00:46:22.6 00:12:50.0 10.9 3.6 WH566P01.sql 02:18:34.0 00:01:06.0 91.4 126.0 WHBA3P01.sql 01:19:21.7 00:26:52.3 2.5 3.0 Average 11.6 15.1
  • 23. Results – SUN M9000 vs Exadata (*16 Degree) Exadata –Parallel Degree x16 - Elapsed time
  • 24. Results – SUN M9000 vs Exadata (*16 Degree) Exadata –Parallel Degree x16 - Performance Gain
  • 25. Results – SQL Loader SQL Loader Test •  3.6 M rows •  Rows are ‘transformed’ on load
  • 26. Results – SQL Loader SQL Loader Result •  6X faster •  94 % less CPU •  CPU to Elapsed time 27% Elapsed time CPU time consumed CPU to Elapsed % M9000 server: 01:39:00.00 01:21:00.00 82% Exadata server: 00:17:30.92 00:04:42.18 27% Exadata vs M9000: 18% 6% Performance gain: 6X 17 X
  • 27. Results – Exadata Hybrid Columnar Compression Compression Test •  Single Table with •  1+ billion rows •  1 TB •  430 Partitions •  Due to time constraint, only 254 partitions were compressed
  • 28. Results – Exadata Hybrid Columnar Compression Compression Result Size before HCC: 555 GB Size with HCC: 89 GB Space savings: 466 GB % savings: 84% Compression Ratio: 1:6.25
  • 29. PoC – Summary Apples to Apples comparison (M9000 test server to Exadata) What worked •  Simple Lift and Drop approach •  Minor changes can give significant performance advantage What did not work/complete •  Real Application Testing •  Removing embedded SQL hints
  • 30. Section 3 - Learnings from Other Exadata sites •  Are indexes still required? •  What skills are required to manage the machine? •  The DMA – Database Machine Administrator •  High Capacity or High Performance SAS drives? •  Do not under estimate data migration effort •  Last but not least – Managing expectation
  • 31. Summary Ran a Proof-of-Concept of Oracle’s Exadata Database machine using a real data warehouse and these are the results •  A ‘Lift & Drop’ approach is feasible and found •  Transactions were 11.6 X faster •  84% space savings on uncompressed data •  SQL Loader 6X faster and consume 94 % less CPU
  • 32. Speaker : Tony Jambu Paper : Exadata - The Facts and Myth Behind A Proof Of Concept Q&A Select Star Mailing list http://groups.yahoo.com/group/Select_Star/ or email Select_Star-subscribe@yahoogroups.com For feedback & discussion: TJambu@Wizard.CX