epic clarity running on exadata

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Optimize the Performance of Your Epic Clarity Data Warehouse

Webcast 2/14/2013

| Epic |

© 2013 Oracle Corporation

Anita Salinas Healthcare Bus. Dev. Oracle

Patrick O’Connor Healthcare Sales Consultant Oracle

Bob Bryla Snr. DB Architect & Systems Engineer Epic

Tim Fox Chief Technologist Enkitec

2 © 2013 Oracle Corporation

•  Introductions •  Why optimize? •  Exadata: extreme performance for OLTP and DW •  Customer results: Enkitec

–  Benchmark 1 results –  Benchmark 2 results –  Short demo

•  Epic/Clarity target platforms explained: Epic •  Summary and next steps •  Q&A

Agenda


Exadata Delivers Higher Value To Epic Clarity Users Benefits Realized In Multiple Areas

Epic Clarity on

Oracle Exadata

Business Value •  Improve quality of patient care •  Receive timely critical reports •  Execute reports more frequently as needed •  Strategic partnership IT<->Business

IT Value •  Higher operational excellence, raise IT bar •  Improve service - enhance SLA metrics •  Seamless DW w/OLTP environment •  Higher performance, scalability, throughput •  Standardized complete management tools

IT Cost Advantage •  Reduce core IT costs •  Significant cost benefits •  Lowest industry TCO

What if you could get MORE information

SOONER and USE LESS

hardware to do it?


Business Users Will Realize Significant Benefits Oracle Customers Confirm Benefits

EpicCare Outpatient

Tapestry

OpTime Surgery

ADT Prelude

EpixRx Medication

EpicCare Inpatient

Resolute Hospital Billing

Profess. Billing

Epic Clarity Reports: 5-100x Performance Improvements

Sample Set of Reports • Organ donor list, heart and lung transplant reports • Specific inpatient diagnosis/flowsheet data related to transplants • Currently admitted inpatient data for specific counties • Medication, MAR, dispensed charge data • Orders and treatment plan data • Orders, results, diagnosis for ambulatory visits for specific depts • Outpatient appointment data for a specific county • OR logs excluding specific CPT codes including charge data • OR logs for specific CPT codes including charge data • ED data from the prior day based on trauma diagnosis • ED Order data • ED patient flowsheet data and events


Timely Transplant Reports •  Improved patient care due to timely

information, data confidence •  Enhanced productivity for all

coordinators, supporting personnel •  Improved IT productivity, eliminating

unnecessary running of reports

Customers Confirm Higher Business Value Enhanced Patient Care With Confidence

Other Reports

Finance

Education

Research

IT Ops

Admin

Clinical

~200 Daily Reports, >300 Locations Will Benefit

•  Improved patient care •  Significant productivity boost to clinical,

research, administrative users •  Improved operational effectiveness and

reduced cost to keep the lights on

Finance Provide financial reports to analysts sooner for regular reporting periods

New Research Reports •  Meet new requirements due to

faster report execution


Oracle Exadata Extreme OLTP/DW Performance


Exadata Unified Workload Transformation

Single Machine for… •  OLTP

•  Data Warehousing •  ETL •  Query parallelism

OLTP with Analytics and Parallelism of Warehousing

Warehousing with Interactivity, Availability, and Security of OLTP


Exadata Innovations

•  Intelligent Storage –  Scale-out InfiniBand storage –  Smart Scan query offload

+ + +

•  Hybrid Columnar Compression –  10x compression for warehouses –  15x compression for archives

•  Smart PCI Flash Cache –  Accelerates random I/O up to 30x –  Triples data scan rate

Data remains

compressed for scans

and in Flash

Benefits Cascade to Copies

compress

primary DB

standby test dev backup

uncompressed


•  Significantly reduce query times by orders of magnitude

•  Use fewer indexes to significantly improve daily load times

–  Less space utilization

–  Reduced maintenance of index builds/rebuilds

•  Lower costs by consolidating all workloads on one platform

–  Use Exadata for simultaneous Warehouse and OLTP

•  Accelerate response times by up to 100x (or better)

Oracle Exadata: Extreme Performance and Scale Advantages


Compression Ratio of Real-World Data

0 10 20 30

Telecom HTelecom TTelecom A

Financial HFinancial UFinancial BFinancial P

Healthcare BHealthcare C

Query Compression Ratio(Average= 13x) •  Compression ratio varies by

customer and table

•  Trials were run on largest table at 10 ultra large companies •  Average revenue > $60 BB

•  13x – Avg query compression ratio

•  On top of Oracle’s already highly efficient format


Secure Database Machine

•  Moves decryption from software to hardware •  Over 5x faster

•  Near zero overhead for fully encrypted database

•  Queries decrypt data at hundreds of Gigabytes/second


Epic Clarity on Exadata Benchmark 1 Details


•  Data model has many very wide tables but rarely are all columns in a single report

•  Data model loaded on daily / usually requires significant DB server resources

•  Thousands of reports are run against Clarity on a daily basis

•  Up to 120 reports may execute concurrently

•  Clarity customers look for database configurations which improve throughput. Often, the result is non-default Oracle configurations

•  Customer-written report queries are often more complex than Epic-released reports, and are challenging to tune with traditional methods

Observations - Epic Clarity on Exadata


•  1.5T Clarity database imported to Exadata X2-2 Quarter Rack (excluding audit tables) •  One BizObj server (VM) used to generate reporting load for 40 concurrent report jobs

•  Evaluated automated reporting batches for execution time, load characteristics •  Customer supplied specific, long-running queries tested individually on Exadata •  Where applicable, Exadata features induced to explore performance •  Exadata’s Hybrid Columnar Compression (HCC) not used to compress tables during

the POC, but compression tests were run on large tables •  Tests on CLARITY_TDL_TRAN table show the following results

•  Query High HCC Compression ratio – 8x to 10x •  Can reduce a 30GB table to 3GB

•  Query Performance of HCC Compressed data often execute faster

Epic Clarity on Exadata POC - Approach


•  Customer supplied queries were executed under the following conditions: •  Database configured per Customer (matches current production)

•  Reduced buffer cache to 2GB / multi-block read count = 128 / all non-PK indexes made invisible

•  Configuration changes were made to show that Exadata performs better, for most DW workloads, with a smaller memory footprint

•  The following page displays the results of the individual query testing done for a Clarity customer on Enkitec’s Exadata X2-2 quarter rack

Query Execution


Results – Query Execution Average Performance Improvement – 91x


Epic Clarity on Exadata Benchmark 2 Details


•  Customer provided 2T production Clarity database export, 20 specific queries •  Supplied queries were run unmodified under three configurations:

*8 GB SGA (equivalent to current production) *15 GB SGA *40 GB SGA

•  PARALLEL_MAX_SERVERS =24 •  Used standard formula maximum parallel Servers = 2 * Core Count

•  Each query executed 2x to ensure at least some relevant data in buffer cache •  Hybrid Columnar Compression (HCC) was not used •  No tables were pinned in Exadata Smart Flash Cache •  The entire POC was run on a single node Exadata Quarter Rack •  Parallel slaves were confined to one node of the RAC •  All serial processes were run on a single node of the RAC

Epic Clarity on Exadata POC – Approach


Results – Query Execution

Currnent System 8G SGA

Exadata 8G SGA

Exadata 15G SGA

Exadata 40G SGA

Parallel Degree Exadata Improvement Factor (based on 8G SGA)

Query 1 46:13.00 00:00.02 00:00.02 00:00.02 24 138,650 Query 2 58:55.00 00:00.05 00:01.66 00:01.94 24 70,700 Query 3 32:24.00 11:47.44 10:29.40 08:20.10 24 3 Query 4 06:57.00 00:15.81 00:15.45 00:15.80 24 26 Query 5 8:45:12.00 13:17.68 10:36.32 11:05.40 24 40 Query 6 14:04.00 00:25.14 00:11.60 00:11.83 24 34 Query 7 04:47.00 00:16.46 00:16.80 00:18.97 24 17 Query 8 08:33.00 00:36.71 00:35.31 00:35.22 12 14 Query 9 6:38:10.00 02:50.14 02:49.07 02:48.65 Serial 140 Query 10 19:59.00 10:43.30 06:48.19 03:33.01 12 2

Improvement factors are based on the current system compared to Exadata with an 8G SGA

hr:min:sec:10th sec


Results – Query Execution Continued

Currnent System 8G SGA

Exadata 8G SGA

Exadata 15G SGA

Exadata 40G SGA

Parallel Degree Exadata Improvement Factor (based on 8G SGA)

Query 11 28:07 00:13.18 00:13.66 00:14.24 24 128 Query 12 40:07 01:58.41 01:52.75 01:55.74 24 20 Query 13 36:08 00:12.15 00:13.82 00:11.96 24 178 Query 14 1:10:27 09:29.83 03:25.33 00:13.52 Serial 7 Query 15 04:45 00:13.68 00:13.80 00:13.37 24 21 Query 16 02:57 01:33.33 00:00.46 00:02.14 24 2 Query 17 1:27:26 08:05.57 00:13.49 00:13.32 Serial 11 Query 18 42:32 02:24.66 01:21.20 00:58.96 24 18 Query 19 18:23 00:05.03 00:14.04 00:13.76 24 219 Query 20 3:13:31 00:18.39 00:15.75 00:16.67 24 631


To test Hybrid Columnar Compression on Clarity data, the Compression Advisor (DBMS_COMPRESSION) was used to simulate compression of the CLARITY_TDL_TRAN table

Results – HCC Compression Test

HCC Compression Level Compression Ratio

Query Low 3 to 1

Query High 6 to 1

Archive Low 8 to 1

Archive High 10 to 1


Demo and Conclusions


•  Epic Clarity workload hits the sweet spot for Exadata –  Large data volume, long running queries

•  It is impossible to match Exadata’s IO capability for large table scans with any other Oracle-capable platform

•  Additional benefits are available –  Hybrid Columnar Compression, Exadata Flash, and Parallelism

•  With minimal effort, Customer can identify the business benefit of extreme performance gains shown during this POC

•  Exadata supports improved performance with smaller memory –  More databases can be run on same hardware vs. custom built systems

Conclusions

1

2

3

4

5


•  Target platform definition

•  Supported platforms

•  Customer demand

•  Industry trends

•  Exadata in-house at Epic

Epic Clarity Target Platforms Epic


Summary and Next Steps


Summary What can YOU do generating MORE reports FASTER on LESS hardware?

•  Extreme Epic Clarity performance on Exadata –  Up to 100x faster

•  Do more (reports) with less (hardware) in less (time) –  512 reports in 12 hours vs. 1604 reports in 4 hours –  3x # of reports completed in ¼ the time

–  Lower costs, consolidate workloads on same hardware

•  Improve care quality –  More timely = better intelligence

–  Actionable data at your fingertips sooner and/or more often


Next Steps

Join us at HIMSS13! •  Oracle and Enkitec Breakfast Briefing

Wed, March 6, 2013 7:30-9:00am Register here

•  Continue the Conversation Reception Wed, March 6, 2013 4.30-7.30pm Invite forthcoming

Investigate further –  Exadata website –  Schedule a private consultation

Consultation •  Assess performance of Epic Clarity DW

•  Review reports and queries to identify opportunities that improve reporting

•  Compare system to benchmark results

•  Written performance recommendations Contact [email protected]


Q & A


For More Information

•  Visit: Oracle Healthcare Website •  Read: Oracle Healthcare Solutions •  Join: Oracle Healthcare on Facebook •  Follow: Oracle Healthcare on Twitter •  Call: Oracle Healthcare Representative


Appendix – Query Execution

Current Customer System

Exadata per Customer

16GB Buffer

Exadata per Enkitec

4GB Buffer

Exadata per Enkitec

2GB Buffer

Exadata No Indexes

2GB Buffer

8488_sec_aun8fmpug9jk4 03:46:17.33 00:55:41.50 00:50:10.97 01:08:15.80 00:22:08.46 8207_sec_1vauja2xan534 06:14:16.34 01:23:19.67 01:06:36.11 01:19:04.93 00:52:50.74 6881_sec_232b9Czqbnn9 06:06:15.45 01:22:36.91 01:05:02.66 01:15:24.70 00:52:53.65 6833_sec_18mgrhn25hvk8 02:53:32.03 00:49:11.32 00:30:56.22 00:35:38.72 00:18:15.83 6827_sec_facj6p8f68drf 01:40:23.40 00:28:55.56 00:25:10.01 00:26:51.30 00:11:50.95 6820_sec_azgu4cxwvub3n 00:27:34.90 00:10:30.08 00:01:52.60 00:02:05.00 00:00:38.90 5890_sec_57rgm8v0jzpc1 00:31:11.20 00:04:25.41 00:13:44.35 00:12:29.06 00:03:00.75 5695_sec_5a02q7wg0k05x 00:50:19.90 00:25:42.47 00:23:57.29 00:23:35.28 00:00:46.47 5546_sec_at3uwh0bmvygv 00:49:20.10 00:06:56.32 00:07:38.94 00:07:03.25 00:07:13.97

All queries improved in performance on Exadata with no tuning. No parallelism was used. All queries were run on one node of the two node RAC.


Appendix – Query Execution

Current Customer System

Exadata per Customer

16GB Buffer

Exadata per Enkitec

4GB Buffer

Exadata per Enkitec

2GB Buffer

Exadata No Indexes

5282_sec_13w3x29huvpzs 00:38:40.30 00:04:55.45 00:05:05.03 00:05:05.76 00:12:46.55 4742_sec_g6hmtqdhggcs7 00:31:12.80 00:04:55.45 00:00:01.28 00:00:00.95 00:00:02.20 4736_sec_1jkjps3basyz7 00:16:34.40 Killed Killed Killed 00:17:52.99 4728_sec_9fy866srqj1hz 00:28:07.20 00:33:24.63 00:23:31.59 00:24:51.67 00:09:54.17 4716_sec_1wuj2pzmdf0wk 00:47:45.80 00:11:31.62 00:06:15.23 00:04:59.86 00:10:29.17 4120_sec_3vu8b5sfmr8r6 14:35:44.65 TEMP TEMP TEMP TEMP 3534_sec_fv2hr8d15q4tr 00:09:22.60 00:11:08.64 00:09:12.01 00:09:36.35 00:00:33.87 3383_sec_dvztmf02uqcya 00:12:54.30 00:00:09 00:00:11.52 00:00:04.15 00:00:04.57 3184_sec_gg5jrs56h19t2 00:36:13.60 00:00:00.52 00:00:03.63 00:00:03.52 00:00:07.08 3182_sec_gazv5xbhh0w5s 00:08:08.50 00:00:52.88 00:02:38.70 00:02:18.52 00:01:33.66

All queries improved in performance on Exadata with no tuning with the exception of two queries, both of which experienced plan digression due to database version change.


Appendix – Additional Tuning

•  Query 4736 ran for 16 minutes at Customer. Due to execution plan changes from 10g to 11g, the query never finished on Exadata.

•  After removing all non-PK indexes, Query 4736 finished in 17 minutes on Exadata (1 minute longer than on Customer production).

•  The largest table in the query was still using a PK index. After removing this index (via hint) the query ran in 3 minutes 42 seconds on Exadata (5x faster).

epic clarity running on exadata

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