PoC of IBM Informix Warehouse Accelerator and Storage Optimization Feature

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Proof of concept of IBM Informix IWA+SOF in brazilian private health sector company

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PoC of IBM Informix Warehouse Accelerator and Storage Optimization Feature

  1. 1. Alexandre Marini Senior Informix DBA – Orizon Brazil alexandre@briug.org PoC of IWA with Informix storage optimization, and its great value to Health Insurance systems 1
  2. 2. Abstract This presentation will cover a PoC of Informix Warehouse Accelerator, together with implementation of storage optimization features in the Informix OLAP engine, based on health insurance systems, made to demonstrate the product capabilities to reduce enterprise costs, ease administration, and lower the report generation periods, compared to our market competitors. The idea of this PoC was to provide our company (Orizon Brazil) a better product, with lower costs and higher speed, to increase it´s portfolio of products, with unmatched IT and information values to offer our clients. 2
  3. 3. Alexandre Marini - Personal Profile – Started working with IBM Informix 1998 - Brazilian state government (4gl / DBA) – First IBM Informix On Campus in Brazil in 2011 – Worked with MC Software in 2011 – Worked in Cleartech in 2011/2012 – Started working in Orizon in October, 2012 – My First IIUG presentation (please be patient!) 3
  4. 4. Agenda • About Orizon • A little about IWA • A little about SOF • Business and company needs • Implementation of this PoC • Results • Conclusion • References 4
  5. 5. About Orizon 5 MORE THAN 10 YEARS of history in the health care market LEADER SHAREHOLDERS in their segments Bradesco Seguros Group Cielo CASSI 1 OUT OF 3 LIFES in private health care are touched by our systems OVER 140 MILLION of health transactions per year, each one completed in less than 0.5 second GREATEST MARKET companies are our clients More than a system, we offer a SERVICES PLATFORM fully attached to our customers needs 18 MILLION lives 130 THOUSAND of connected providers 8.5 THOUSAND Drugstores
  6. 6. About Orizon 6 Providers 100% electronic medical bills Electronic Receipt validation ClientsPlatform Electronic Authorization From low to high complexity “Autorize” Platform AUTORIZE is an electronic platform for capture and validation of requests, electronic receipts and processing of medical/ hospital care, with application of SMART ELIGIBILITY rules
  7. 7. A little about IWA • Designed with in-memory acceleration for Informix DW databases, mixed or not with OLTP data • Introduced in 11.70.xC2, March 2011 – one node only • Last release 12.10.xC2, October 2013 – works on multiple nodes, loading from single or multiple clusters, TimeSeries acceleration, external tables acceleration • Hardware prerequisites: Linux 64 bits Intel box with SSE3 (recommendation: separate box from Informix engine) 7
  8. 8. A little about SOF • Dictionary based for Informix databases • Introduced in 11.50.xC4, May 2009 – basic data types only, table data only • Last release 12.10.xC2, October 2013 – compression of B-tree indexes, simple large objects, automatic data compression (xC1 features) • License as a separated pack, available for Informix Enterprise Edition • Average of storage savings: around 70% • Rather different from other engine vendors 8
  9. 9. Business and company needs • Integrate all Orizon DWs, from different vendors • Improve stability and speed, bringing economy and new capabilities for company reports generation • Informix and its best: stability, confidence, and low TCO costs (cheaper at least 31% than SQL Server – published September 2010) • IWA proposed, migration of all DWs to Informix 12.10, plus storage optimization to reduce storage usage and costs, so a PoC was needed to prove Orizon needs • Purpose of this PoC is not the best performance: lab for demonstration purposes, for comparison (IWA) and storage (SOF) • Informix 12.10: index compression, Smart object compression, NoSQL features, if needed 9
  10. 10. Implementation specs • Hardware is HP Intel Blade Xeon (2 sockets), product installed into a VM with 4 cores and 16GB of memory • Informix 12.10.FC2TL on a two node VM cluster (prim + SDS), running on a RHES 6.4 with GFS clustering • IWA on primary Informix node, NUM_NODES=4, WORKER_SHM=9GB and COORDINATOR_SHM=1GB • Raw devices in a HP P4500 storage, with RAID level 0 10
  11. 11. Implementation – numbers • Historical health care OLAP database was created, with one fact table and 8 dimensional tables, more than 6 months of data are loaded • Fact table with 14.9 million rows, populated from production OLTP data for real results output, demanding 496.83MB of storage • OLAP database size is 1.01GB • Load data mart timing (stop + load process): 1m5.815s 11
  12. 12. Query testings (1/6) 12 • Queries tested: simple ones, with aggregation, ranking select a13.year year, a13.month month, a12.razao_social razao_social, sum(a11.total_proce), sum(a11.total_trans) from fato_transacao a11, dim_prestador a12, dim_data a13 where a11.id_prestador = a12.id_prestador and a11.id_data = a13.id_data and (a13.year in (2011,2012,2013) and a13.month in (3,5,6,9, 10,12)) group by a13.year, a13.month, a12.razao_social • Rows retrieved: 78752 SIMPLE ONE, LONG RESULT SET
  13. 13. Query testings (2/6) select id_ems, a13.year year, a13.month month, sum(a11.total_proce), sum(a11.total_trans), RANK() over (order by a13.year, a13.month) as rank from fato_transacao a11, dim_prestador a12, dim_data a13 where a11.id_prestador = a12.id_prestador and a11.id_data = a13.id_data group by id_ems, year, month 13 RANKING
  14. 14. Query testings (3/6) select a13.year year, a13.month month, a12.desc_situacao desc_situacao, sum(a11.total_proce) from fato_transacao a11, dim_situacao a12, dim_data a13 where a11.id_situacao = a12.id_situacao and a11.id_data a13.id_data and (a13.year in (2013) and a13.month in (6, 7, 8, 9, 10, 11,12)) group by a13.year, a13.month, a12.desc_situacao 14 SIMPLE ONE HIGHER PROJECTION
  15. 15. Query testings (4/6) select a11.id_ems ems, a13.year year, a13.month month, sum(a11.total_proce) SUM_TOTAL_PROCE, sum(a11.total_trans) SUM_TOTAL_TRANS, RATIO_TO_REPORT(a11.total_proce) OVER() *100 AS RATIO_TOTAL_PROCE, RATIO_TO_REPORT(a11.total_trans) OVER() *100 AS RATIO_TOTAL_TRANS from fato_transacao a11, dim_data a13 where a11.id_data = a13.id_data and (a13.year in (2011,2012,2013) and a13.month in (1,2,3,4,5,6)) group by a11.id_ems, a13.year, a13.month, a11.total_proce, a11.total_trans order by 1,2 15 RATIO
  16. 16. Query testings (5/6) select id_ems, a13.year year, a13.month month, sum(a11.total_proce), sum(a11.total_trans), PERCENT_RANK() over (order by id_ems) as perc_rank from fato_transacao a11, dim_prestador a12, dim_data a13 where a11.id_prestador = a12.id_prestador and a11.id_data = a13.id_data group by id_ems, year, month 16 RANKING
  17. 17. Query testings (6/6) select a13.year year, a13.month month, a12.razao_social razao_social, sum(a11.total_proce) , sum(a11.total_trans) from fato_transacao a11, dim_prestador a12, dim_data a13 where a11.id_prestador = a12.id_prestador and a11.id_data = a13.id_data and (a13.year in (2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032, 2033, 2034, 2035) and a13.month in (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) group by a13.year, a13.month, a12.razao_social • Rows retrieved: 141792 17 SIMPLE ONE FULL OLAP PROJECTION
  18. 18. Screenshots 18 MicroStrategy web results
  19. 19. Results • Timing comparison 19 Informix Informix + IWA Time reduction Times faster query 1 00:04:37.86 00:00:03.42 00:04:34.44 81.25 query 2 00:03:11.63 00:00:01.47 00:03:10.16 130.36 query 3 00:07:49.37 00:00:01.16 00:07:48.00 404.63 query 4 00:01:26.11 00:00:00.93 00:01:25.18 92.59 query 5 00:03:11.28 00:00:01.48 00:03:09.80 129.24 query 6 00:04:00.41 00:00:04.01 00:03:56.40 59.95 Average enhancements: 149.67 Obs: Time format HH:MM:SS.00
  20. 20. Results • Storage comparison 20 Informix 12 Engine1 Engine2 Row size 44 47 52 Data Size (MB) 465.43 680.74 753.16 Storage costs - +31.63% +38.20% Informix 12 storage (SOF) compared to other market databases
  21. 21. Conclusion • IWA - higher value to our information services – quicker report generations - increase our product portfolio to our clients, a new perspective. – Reports will run in seconds instead of hours – Ease administration, on indexes/table reorgs, installation was very simple • Informix approx. 2 years savings in storage space (OLAP size 3TB, in data, HP P4500 storage) : – US$ 117K+ (compared to a market leader product Engine2) – US$ 97K+ (compared to another Engine1) 21
  22. 22. Conclusion • In memory technology considerations – Source data ammount does not impact result timings • IWA licensing features – Two brands of distribution packages • Advanced Workgroup Edition: only PVU, 16 cores and 48GB of memory, neither include SOF nor HA/ER • Advanced Enterprise Edition: full features • Combination of IWA + SOF is absolutely a “state of the art” for health insurance systems. 22
  23. 23. Conclusion We need no Iron Man to be our company heroes….. 23
  24. 24. References • Query acceleration for Business using Informix Warehouse Accelerator (IBM RedBook): http://www.redbooks.ibm.com/Redbooks.nsf/RedbookAbstracts/SG248150.html • Informix Warehouse Accelerator (youtube): http://youtu.be/C-dvl_EptLY • IBM Informix 12 Compression: Helps Optimize Storage (youtube): http://buff.ly/1gSN5di 24
  25. 25. References • Informix Warehouse Accelerator (IBM DeveloperWorks blog): https://www.ibm.com/developerworks/community/blogs/2fa81a5c-cb30-4873-b775- 1370151e3614/entry/introducing_informix_warehouse_accelerator9?lang=en • Keshava Murthy blog (DeveloperWorks): https://www.ibm.com/developerworks/community/blogs/Keshav/?maxresults=15&lang=en _us • Fred Ho blog (DeveloperWorks): https://www.ibm.com/developerworks/community/blogs/fredho66/?maxresults=15&lang= en_us 25
  26. 26. Alexandre Marini Senior Informix DBA Orizon Brazil: www.orizon.com.br alexandre@briug.org Questions? 26

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