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

1,563
-1

Published on

Published in: Technology, Business
0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
1,563
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
79
Comments
0
Likes
1
Embeds 0
No embeds

No notes for slide

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

  1. 1. Exadata - The Facts and Myth Behind A Proof Of Concept Tony Jambu Melbourne, AustraliaTJambu@wizard.cx
  2. 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. 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. 4. Exadata PoC - Summary Transactions 11.6 X faster (avg) •  No code or schema changes •  Up to 90X faster was observed
  5. 5. Exadata PoC - Summary Storage Reduction 84% saving •  Using Oracle’s Hybrid Columnar Compression for Archive mode
  6. 6. Exadata PoC - Summary SQL Loader 10 X faster Consumes less CPU
  7. 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?
  8. 8. Understanding the Figures
  9. 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. 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. 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. 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. 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. 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. 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. 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. 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. 18. Preparatory: Baselining on SUN M9000 Baselining on the SUN M9000
  19. 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. 20. Results – SUN M9000 vs Exadata (No Changes) Lift & Drop test on Exadata – Elapsed time
  21. 21. Results – SUN M9000 vs Exadata (No Changes) Lift & Drop test on Exadata – Performance Gain
  22. 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. 23. Results – SUN M9000 vs Exadata (*16 Degree) Exadata –Parallel Degree x16 - Elapsed time
  24. 24. Results – SUN M9000 vs Exadata (*16 Degree) Exadata –Parallel Degree x16 - Performance Gain
  25. 25. Results – SQL Loader SQL Loader Test •  3.6 M rows •  Rows are ‘transformed’ on load
  26. 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. 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. 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. 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. 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. 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. 32. Speaker : Tony Jambu Paper : Exadata - The Facts and Myth Behind A Proof Of Concept Q&ASelect Star Mailing listhttp://groups.yahoo.com/group/Select_Star/or email Select_Star-subscribe@yahoogroups.comFor feedback & discussion: TJambu@Wizard.CX

×