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

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. 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.
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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!)
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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. 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. 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. 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)
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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
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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
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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
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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
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12. Query testings (1/6)
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• 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. 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
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RANKING

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
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SIMPLE ONE
HIGHER
PROJECTION

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
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RATIO

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
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RANKING

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
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SIMPLE ONE
FULL OLAP
PROJECTION

18. Screenshots
18
MicroStrategy
web results

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. Results
• Storage comparison
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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. 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)
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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.
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23. Conclusion
We need no Iron Man to be our company heroes…..
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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
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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
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26. Alexandre Marini
Senior Informix DBA
Orizon Brazil: www.orizon.com.br
alexandre@briug.org
Questions?
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