md anderson: using oracle exadata in cancer research ... · customer case study: using oracle...
TRANSCRIPT
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Nicholas Collins, Principal ASA Clinical Analytics and Informatics 03 June 2014
Customer Case Study: Using Oracle Exadata in Cancer Research [E4 2014 Tue 3:30PM]
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1. About MD Anderson
2. The Future of Cancer Treatment and Research
3. Oracle Health Sciences at MD Anderson
4. Genomics and NLP Pipelines
5. FIRE Architecture (HDWF Implementation)
6. Oracle HDWF Upgrade to Exadata x4-2
7. Closing/Questions
Topics
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About MD Anderson
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Non-profit Houston-based cancer hospital and research institution, founded in 1941 as part of The University of Texas System
Named after Monroe Dunaway Anderson (a banker and cotton trader, not an MD)
“Making Cancer History” – our mission is to eradicate cancer
Consistently ranked as the #1 hospital for cancer care
About MD Anderson
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About MD Anderson
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About MD Anderson
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Almost 20,000 employees, majority in the Houston area
Occupying over 20 buildings in the Texas Medical Center
The Texas Medical Center has over 50 member institutions, together over 100,000 employees
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The Future of Cancer Treatment and Research
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“The Time is Now. Together we will end cancer.”
Target six forms of cancer
Clear focus on the concept that the answer to curing cancer lies in both clinical and genomic data
MD Anderson Moon Shots Program
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Breast/Ovarian Leukemia (AML/MDS) Leukemia (CLL) Melanoma Lung Prostate
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MD Anderson Moon Shots Program
9 http://www.cancermoonshots.org
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MD Anderson Moon Shots Program
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How do we solve the mysteries of curing cancer?
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It’s in the Data!
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MD Anderson Moon Shots Platforms
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Massive Data Analytics – An infrastructure for complex analytics and clinical decision support using integrated patient information, including clinical and research data
Big Data – An Information Technology infrastructure/environment that enables centralization, integration and secured access of patient and research data and analytical results
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It’s in the Genes!
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MD Anderson Moon Shots Platforms
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Clinical Genomics – Clinical gene sequencing infrastructure, including centralized bio-specimen repository and processing
Omics – Bioinformatics – A high-throughput infrastructure for generation and standardization of large-scale “omic” data, including genomics, proteomics and immune profiling
Adaptive Learning in Genomic Medicine – A framework for bringing clinical medicine and genomic research together to enable rapid learning to improve patient management using Clinical Genomics, Omics-Bioinformatics and Massive Data Analytics platforms within the Big Data environment
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Genomics in the News
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Oracle Health Sciences at MD Anderson
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Oracle Healthcare Data Warehouse Foundation (HDWF)
Oracle Healthcare Analytics Data Integration (OHADI)
Oracle TRC (Translational Research Center) Cohort Explorer
Oracle TRC Omics Data Bank (ODB)
Oracle Health Sciences Products at MD Anderson
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Oracle Database 11gR2
Oracle Exadata (x3 and x4)
Oracle Business Intelligence (OBIEE)
Oracle GoldenGate*
Oracle Technology at MD Anderson
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*Oracle GoldenGate was used to demonstrate replication capabilities in a significant POC, but has not been purchased or put into production. Informatica is commonly used at MD Anderson for data integration; ODI is not currently in use at the institution.
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Oracle Healthcare Data Warehouse Foundation (HDWF)
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HDI HDM
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Oracle Healthcare Analytics Data Integration (OHADI)
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HDI HDM OHADI
Integration code that maps from the HDWF interface tables (HDI) to the HDWF warehouse tables (HDM)
Available as either Informatica or ODI mappings
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Oracle Cohort Explorer
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CDM
Cohort Explorer
CDM (Cohort Data Model) is the Clinical Data Mart used by Oracle Cohort Explorer
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Oracle Cohort Explorer
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Oracle Omics Data Bank (ODB)
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Review of Oracle Health Sciences Products
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HDI HDM OHADI CDM
ODB
Cohort Explorer
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Genomics and NLP Pipelines
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CDM
ODB
Oracle Cohort Explorer
HDM
Genomic Sequencing
Data
Combining Genotypic and Phenotypic Data
Clinical (Phenotypic) Data
Genotypic Data
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Reference Data
What kind of data is loaded to ODB?
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Full Genome Sequence Reference (EMBL) Known Variants (dbSNP/Cosmic) Genes (HUGO) Proteins (SwissProt/UniProt) Pathways (Pathwaycommons) Predictive Phenotyping (Polyphen/SIFT)
Simple Variant (SNP/Indel) Gene Expression Copy Number Variation RNA Sequencing Structural Variants
Result Data
How much data? Three billion bases in human genome
2.8 GB at a byte per base 700 MB at two bits per base
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RNA Codon Chart
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Synonymous Nonsynonymous
Missense Nonsense
Frame Shift (in the case of indels)
Point Mutations
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Load reference data into ODB (initialize it for use, no results)
Lab sequences patient specimens to create result files with variant data (vcf & cnv)
Load result files local to Exadata rack in DBFS
Load from DBFS into ODB via java and PL/SQL loaders (stock ODB functionality, though customer loaders can be written)
Genomic data (in ODB) links to clinical data (in CDM) via a specimen ID for use in Oracle Cohort Explorer and other applications
Genomics Pipeline
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http://www.1000genomes.org/node/101
VCF (Variant Call Format) File Example
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CNV (Copy Number Variation) File Example
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CDM/ODB Implementation - Exadata x3-2 Equipment Purchased
December 2012
Development/Test Environment
Production Environment
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Photo shown courtesy of Mr. Robert Jeffries, Project Manager 35
MD Anderson CAI “War Room” Exadata Implementation Team
January 2013
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Querying in Cohort Explorer often requires many lengthy and complex conditions in the where clause, pulling based on individual column values (i.e. genes, healthcare terminology codes) without benefit of ranges, smart scans improve performance
HCC compression helps in repetition of common values (just take ‘A’, ‘G’, ‘T’, and ‘C’ for instance), also when using non-EAV tables for EAV-style data, common in clinical data
Overall performance requirements of the most advanced Cohort Explorer functionality would be difficult to back with anything other than Exadata
Gains from CDM and ODB on Exadata
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Subject: i got to tell you this about exadata!
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Query
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MD Anderson partnered with IBM to build an Oncology Expert Advisor (OEA) application based on IBM Watson technology
NLP important to make data from clinical notes available to OEA, so the institution began using IBM’s NLP tools, including IBM Content Analytics (ICA)
CAI had a need for NLP to make more clinical data available for Cohort Explorer use - often phenotypic data (i.e. patient diagnosis, comorbidities, family cancer history) is only in the transcribed note
CAI is now collaborating with the IBM Watson team to ensure institutional standards for NLP efforts, and to leverage each others’ work as much as possible
CAI’s NLP team uses Exadata for pre-warehouse staging/processing
Natural Language Processing (NLP)
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Unstructured source documents created by provider (i.e. patient notes, pathology reports), scanned if not originated electronically or transcribed
Documents pulled from various source systems into a single repository (on Exadata)
Crawler pulls new documents for processing by ICA Server, annotators process documents to distill specific attributes and evidence
Attributes loaded to database in structured relational model as intra-document “preliminary” assertions (on Exadata)
IBM WODM Rules Engine takes preliminary assertions and creates cross-document final assertions, also loaded into relational model (on Exadata)
Final assertions loaded into Oracle HDWF as structured clinical data
NLP Pipeline
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FIRE Architecture (HDWF Implementation)
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FIRE - Federated Institutional Reporting Environment
A program level initiative, with many projects and products involved, to provide a unified BI/Reporting solution for all of MD Anderson
Managed by the Clinical Analytics and Informatics (CAI) Department, part of Oracle SDP (Strategic Development Parnter) Program
Implmented Oracle HDWF warehouse as the core of the FIRE Program, beginning in 2012
HDWF for The MD Anderson FIRE Program
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Bring all the data processing together on a single Oracle instance for performance benefits of local movement and transformation
Abstract across all commonalities and patterns to the largest extent possible, avoiding needless one-off solutions, use code generation and automation
Initially implemented on existing AIX hardware, but an ideal candidate for a later “forklift” to Exadata
Architectural Concept
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The FIRE Architecture
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SR SI UI UD HDI/HDM
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Data Movement
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There was a desire from our integration team to use Informatica for ETL because of experience base on the team, not much PL/SQL or ODI knowledge
Architecture proposed use of abstracted code generation via Informatica APIs, jointly used with the push-down optimization option for all non-OHADI internal data movement (i.e. SI to HDI, UI to UD)
Data Movement (Planned)
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Our integration team initially indicated that code generation with Informatica (or other tools) could not be done on account of complexity, and that the push-down optimization option was too expensive
To demonstrate the feasibility, I programmed a PL/SQL-based version of the code generation as proposed in the FIRE Architecture documentation, we used this code in the first release
Data Movement (Actual)
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Data Movement Code Generation
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Procedure iv_tv_ip_gen(name_of_sv_view) for SI layer, generates objects for change detection and movement from SR to HDI
Procedure iv_uv_dv_ip_gen(name_of_sv_view) for UI layer, generates objects for change detection and movement from HDM to UD
All that is needed for generation is the SV view, which conforms to the HDI-based structure, data in certain standard HDI columns determine action
A benefit of the generated views is the ability to see what will happen during the next run, without actually running anything
PL/SQL Procedures for Code Generation
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Had approximately three months to implement, process was difficult, but in the end everything worked and we went to production with the first FIRE release in November 2012
OHADI was somewhat slower than expected but got the job done, Informatica version used, but might be faster with ODI?
Integration team wanted a second chance to get code generation going for Informatica, and wanted more Informatica and less SQL and PL/SQL
CAI committee voted to try Informatica alternatives for the next release
Results/Next Steps
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Architectural Changes
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Informatica Code Generation using Java to generate Informatica objects, so not using the PL/SQL code generation with SI and UI views for this release
Integration team wanted an instantiated SI Layer and UI Layer for Informatica-based code generation instead of views in the SI and UI Layer
As a result, hybrid architecture in place with some generated PL/SQL/views and some generated Informatica objects
Architectural Changes
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Informatica Code Generation with Java
55
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HDWF Upgrade – AIX to Exadata x4-2
5
56
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HDWF Hardware Upgrade (from AIX) - Exadata x4-2 Equipment Purchased
December 2013
Development/Test Environment
Production Environment
57
Original AIX Hardware: IBM P550, 8 Physical CPUs /1.65 GHz, 64 GB RAM, AIX 5.3/64Bit
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Migration Methodology
58
Logical migration using data pump via network (impdp)
Develop a test suite of queries for performance tuning, and automate testing runs
Change configuration using single variable controlled experimentation to hone in on best options, also identify which are the most significant performance boosters (SGA/PGA size, index visibility, tables pinned to cell flash, storage index disabled, compression, Auto DOP, etc.)
Lock configuration and attempt further tuning with test incremental data loads (DML rather than query)
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Performance / Times - Indexes
59
Query All Original Indexes Visible All Non-unique Invisible (PKs & UKs visible, FKs invisible) All Indexes Invisble
All Non-unique Invisible except MDACC custom
All Non-unique Invisible except FKs (PKs, UKs, & FKs visible)
Query 1 +00 00:04:15.268206 +00 00:04:18.802606 +00 00:04:17.616623 +00 00:04:15.777681 +00 00:04:15.713001
Query 2 +00 00:00:57.234542 +00 00:00:56.790110 +00 00:00:57.144610 +00 00:00:58.184639 +00 00:00:57.262739
Query 3 +00 00:08:34.878275 +00 00:08:41.121726 never finished +00 00:08:49.401729 +00 00:08:45.867188
Query 4 +00 00:06:41.485783 +00 00:06:34.904931 +00 00:06:29.586784 +00 00:06:42.858168 +00 00:06:42.512104
Query 5 +00 00:24:40.556198 +00 00:24:40.578150 +00 00:26:10.792066 +00 00:24:59.570722 +00 00:24:55.165571
Query 6 +00 00:00:43.029112 +00 00:00:43.410431 +00 00:00:44.056125 +00 00:00:44.123841 +00 00:00:43.635425
Query 7 +00 00:00:01.545078 +00 00:00:01.699091 +00 00:00:01.582558 +00 00:00:01.608624 +00 00:00:01.609783
Query 8 +00 00:00:10.876617 +00 00:00:08.854054 never finished +00 00:00:09.099019 +00 00:00:08.470038
Query 9 +00 00:00:01.710590 +00 00:00:01.946892 +00 00:00:01.823464 +00 00:00:01.734865 +00 00:00:01.886647
Query 10 +00 00:00:03.919420 +00 00:00:04.223222 +00 01:01:53.262450 +00 00:00:04.191241 +00 00:00:04.139644
Query 11 +00 00:00:57.318168 +00 00:00:07.071767 +00 00:00:06.247229 +00 00:00:06.577124 +00 00:00:06.578497
Query 12 +00 00:00:17.095400 +00 00:00:10.208428 +00 00:00:09.547868 +00 00:00:09.634578 +00 00:00:09.882055
Query 13 +00 00:00:21.082866 +00 00:47:44.312674 never finished +00 00:52:08.863991 +00 00:00:27.537543
Query 14 +00 00:51:57.673060 never finished never finished +00 03:26:31.477185 +00 00:49:45.230375
Query 15 +00 00:04:38.656857 +00 00:02:55.667741 never finished +00 00:02:55.759108 +00 00:02:58.397979
Query 16 +00 00:00:05.751766 +00 00:00:24.376534 never finished +00 00:00:24.581059 +00 00:00:24.912151
Query 17 +00 00:05:34.114854 +00 00:03:49.423974 never finished +00 00:03:56.030990 +00 00:03:57.470639
Query 18 +00 00:02:40.790018 +00 00:02:39.300987 +00 00:02:39.422138 +00 00:02:40.992558 +00 00:02:41.056058
Query 19 +00 00:00:51.688008 +00 00:00:51.566929 +00 00:00:51.265070 +00 00:00:52.757858 +00 00:00:51.403172
Query 20 +00 00:03:44.814679 +00 00:03:30.898289 +00 00:03:29.446093 +00 00:03:52.102754 +00 00:03:34.554562
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Performance / Times - Compression
60
Query No Compression Compressed Compression Type
Query 1 +00 00:04:18.582828 +00 00:04:50.366265 OLTP
Query 2 +00 00:00:57.554905 +00 00:01:15.014118 OLTP
Query 3 +00 00:06:18.164402 +00 00:06:59.956384 OLTP
Query 4 +00 00:07:11.780093 +00 00:08:14.282119 OLTP
Query 5 +00 00:24:58.411945 +00 00:25:23.206456 OLTP
Query 6 +00 00:00:42.437158 +00 00:00:55.025157 OLTP
Query 7 +00 00:00:01.516546 +00 00:00:01.625790 OLTP
Query 8 +00 00:00:09.569546 +00 00:00:06.663720 OLTP
Query 9 +00 00:00:01.744924 +00 00:00:01.272805 OLTP
Query 10 +00 00:00:04.012592 +00 00:00:05.653153 OLTP
Query 11 +00 00:00:06.040799 +00 00:00:07.085242 OLTP
Query 12 +00 00:00:09.923520 +00 00:00:09.957895 OLTP
Query 13 +00 00:00:30.367873 +00 00:03:41.028555 HCC - QH
Query 14 +00 00:49:22.688745 +00 02:23:08.758924 HCC - QH
Query 15 +00 00:04:29.040059 +00 00:14:09.746344 OLTP
Query 16 +00 00:00:26.161868 +00 00:00:28.511322 OLTP
Query 17 +00 00:04:34.196931 +00 00:06:04.440474 OLTP
Query 18 +00 00:02:40.874603 +00 00:02:57.846432 HCC - QH
Query 19 +00 00:00:51.901180 +00 00:01:01.855314 HCC - QH
Query 20 +00 00:02:54.209488 +00 00:03:44.067049 HCC - QH
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Compression Ratios
61
Table Size in GB Size in GB at HCC - QH
HCC - QH % of Orig Size
Ratio (Orig : HCC - QH)
Size in GB at HCC - QL
HCC - QL % of Orig Size
Ratio (Orig : HCC - QL)
HCC-QH % of HCC-QL
Table 1 51.97851563 2.487487793 5% 21 4.06964111 8% 13 61% Table 2 41.59350586 2.446899414 6% 17 4.51171875 11% 9 54% Table 3 29.55371094 2.084411621 7% 14 3.78485107 13% 8 55% Table 4 25.5625 2.509277344 10% 10 4.18261719 16% 6 60% Table 5 23.5078125 2.664794922 11% 9 4.40570068 19% 5 60% Table 6 16.61914063 1.192260742 7% 14 2.08892822 13% 8 57% Table 7 15.38574219 1.850524902 12% 8 3.13989258 20% 5 59% Table 8 9.083007813 0.700500488 8% 13 1.18804932 13% 8 59% Table 9 6.918334961 0.898925781 13% 8 1.43572998 21% 5 63% Table 10 6.8359375 0.70690918 10% 10 1.28656006 19% 5 55% Table 11 6.801879883 0.730773926 11% 9 1.22528076 18% 6 60% Table 12 5.343933105 0.930297852 17% 6 1.50109863 28% 4 62% Table 13 4.3828125 0.579589844 13% 8 0.96081543 22% 5 60% Table 14 2.6171875 0.157653809 6% 17 0.24041748 9% 11 66% Table 15 2.5 0.128295898 5% 19 0.22894287 9% 11 56% Table 16 2.088867188 0.115844727 6% 18 0.20172119 10% 10 57% Table 17 1.5 0.181213379 12% 8 0.30102539 20% 5 60% Table 18 1.4375 0.122924805 9% 12 0.24169922 17% 6 51% Table 19 1.1875 0.111816406 9% 11 0.20196533 17% 6 55% Table 20 0.853515625 0.10949707 13% 8 0.14990234 18% 6 73%
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Stats – With and Without Histograms
62
Query Baseline (default gather) After stats gathered, forcing histograms (METHOD_OPT => 'FOR ALL COLUMNS SIZE 254‘)
After stats gathered, no histograms (METHOD_OPT => 'FOR ALL COLUMNS SIZE 1‘)
Query 1 +00 00:04:18.069513 +00 00:04:14.479659 +00 00:04:12.718458 Query 2 +00 00:00:56.012300 +00 00:00:55.937563 +00 00:00:56.399873 Query 3 +00 00:12:03.440581 +00 00:06:32.384087 +00 00:08:15.304644 Query 4 +00 00:06:40.072502 +00 00:07:23.185157 +00 00:06:32.371709 Query 5 +00 00:24:59.584111 +00 00:24:52.754278 +00 00:24:43.723337 Query 6 +00 00:00:43.457988 +00 00:00:43.332688 +00 00:00:43.154288 Query 7 +00 00:00:01.825817 +00 00:00:01.735274 +00 00:00:01.684231 Query 8 +00 00:00:08.743574 +00 00:00:09.016219 +00 00:00:06.635865 Query 9 +00 00:00:01.911499 +00 00:00:01.268445 +00 00:00:01.816548 Query 10 +00 00:00:04.326782 +00 00:00:03.879728 +00 00:00:03.953567 Query 11 +00 00:00:07.088747 +00 00:00:09.095229 +00 00:00:07.355436 Query 12 +00 00:00:10.576206 +00 00:00:09.860471 +00 00:00:09.813917 Query 13 +00 00:00:30.217194 +00 00:00:29.858610 +00 00:00:24.543118 Query 14 +00 00:49:28.997130 +00 01:42:28.740533 +00 00:28:13.714565 Query 15 +00 00:03:26.125896 +00 00:03:30.789577 +00 00:03:07.800214 Query 16 +00 00:00:23.819626 +00 00:00:23.579613 +00 00:00:23.572567 Query 17 +00 00:04:23.784166 +00 00:03:49.886384 +00 00:03:49.866113 Query 18 +00 00:02:43.559441 +00 00:03:44.672787 +00 00:02:39.627688 Query 19 +00 00:00:52.545641 +00 00:00:52.185251 +00 00:00:51.524320 Query 20 +00 00:03:23.480463 +00 00:04:35.762286 +00 00:03:21.034797
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Performance / Times – Auto DOP
63
Query Baseline Auto, DL 4, Force Local Auto, DL 8, Force Local Auto, DL 16, Force Local
Query 1 +00 00:04:12.718458 +00 00:04:21.658909 +00 00:04:16.253238 +00 00:04:15.601514
Query 2 +00 00:00:56.399873 +00 00:00:56.412281 +00 00:00:55.648165 +00 00:00:56.170821
Query 3 +00 00:08:15.304644 +00 00:08:17.698308 +00 00:08:12.276071 +00 00:08:17.515982
Query 4 +00 00:06:32.371709 +00 00:06:45.082731 +00 00:06:39.860725 +00 00:06:44.585730
Query 5 +00 00:24:43.723337 +00 00:24:57.991367 +00 00:25:09.900648 +00 00:24:50.583881
Query 6 +00 00:00:43.154288 +00 00:00:43.439962 +00 00:00:42.944854 +00 00:00:43.224442
Query 7 +00 00:00:01.684231 +00 00:00:01.691277 +00 00:00:01.610445 +00 00:00:01.715049
Query 8 +00 00:00:06.635865 +00 00:00:06.469042 +00 00:00:06.241034 +00 00:00:06.570801
Query 9 +00 00:00:01.816548 +00 00:00:01.840649 +00 00:00:01.748562 +00 00:00:01.765074
Query 10 +00 00:00:03.953567 +00 00:00:03.933501 +00 00:00:03.754602 +00 00:00:03.810805
Query 11 +00 00:00:07.355436 +00 00:00:09.900409 +00 00:00:09.615272 +00 00:00:09.421509
Query 12 +00 00:00:09.813917 +00 00:00:09.482519 +00 00:00:09.710079 +00 00:00:09.892875
Query 13 +00 00:00:24.543118 +00 00:00:15.723340 +00 00:00:18.721824 +00 00:00:20.151063
Query 14 +00 00:28:13.714565 +00 00:33:37.034347 +00 00:33:25.797660 +00 00:33:33.464341
Query 15 +00 00:03:07.800214 +00 00:03:28.656200 +00 00:03:43.276557 +00 00:03:27.005350
Query 16 +00 00:00:23.572567 +00 00:00:15.264801 +00 00:00:16.036919 +00 00:00:15.330395
Query 17 +00 00:03:49.866113 +00 00:04:04.479679 +00 00:04:11.732845 +00 00:04:08.134070
Query 18 +00 00:02:39.627688 +00 00:02:45.298123 +00 00:02:45.883673 +00 00:02:45.424077
Query 19 +00 00:00:51.524320 +00 00:00:15.818625 +00 00:00:13.755202 +00 00:00:13.746067
Query 20 +00 00:03:21.034797 +00 00:02:57.888175 +00 00:02:57.619376 +00 00:02:57.591926
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Performance / Times – DOP Hint
64
Query Initial Run all parallel 24 all parallel 16 all parallel 8 all parallel 6 all parallel 4
Query 1 +00 00:04:16.672367 +00 00:00:25.938898 +00 00:00:26.449299 +00 00:00:34.633897 +00 00:00:43.261151 +00 00:01:01.939329
Query 2 +00 00:00:55.772976 +00 00:00:14.462675 +00 00:00:14.936694 +00 00:00:14.448895 +00 00:00:13.395207 +00 00:00:15.068520
Query 3 +00 00:08:11.134460 +00 00:04:02.388953 +00 00:03:55.116240 +00 00:03:41.027493 +00 00:01:57.306787 +00 00:03:29.615242
Query 4 +00 00:06:41.349366 +00 00:00:39.683390 +00 00:00:44.865423 +00 00:01:26.220494 +00 00:01:37.733822 +00 00:02:08.823786
Query 5 +00 00:24:28.216851 +00 00:01:30.732530 +00 00:02:04.158495 +00 00:02:28.018871 +00 00:04:15.199074 +00 00:05:43.970349
Query 6 +00 00:00:43.078030 +00 00:00:07.565337 +00 00:00:07.655455 +00 00:00:09.543481 +00 00:00:10.952646 +00 00:00:14.283483
Query 7 +00 00:00:01.686269 +00 00:00:00.747075 +00 00:00:00.558309 +00 00:00:00.679023 +00 00:00:00.272988 +00 00:00:00.818093
Query 8 +00 00:00:06.936982 +00 00:00:02.711387 +00 00:00:02.898431 +00 00:00:03.273516 +00 00:00:02.385028 +00 00:00:04.060303
Query 9 +00 00:00:01.827659 +00 00:00:00.686610 +00 00:00:00.460302 +00 00:00:00.474744 +00 00:00:00.377457 +00 00:00:00.681739
Query 10 +00 00:00:03.920712 +00 00:00:02.807461 +00 00:00:02.467666 +00 00:00:02.347095 +00 00:00:01.599735 +00 00:00:02.430706
Query 11 +00 00:00:07.290813 +00 00:00:09.330830 +00 00:00:10.142888 +00 00:00:09.091451 +00 00:00:04.770838 +00 00:00:06.636206
Query 12 +00 00:00:09.591401 +00 00:00:09.964417 +00 00:00:09.468584 +00 00:00:08.968741 +00 00:00:10.821744 +00 00:00:09.175446
Query 13 +00 00:00:24.930160 +00 00:00:23.852529 +00 00:00:17.268112 +00 00:00:17.450359 +00 00:00:15.524086 +00 00:00:15.191239
Query 14 +00 00:27:57.732468 +00 00:09:06.884239 +00 00:09:37.026665 +00 00:12:55.954573 +00 00:20:28.144051 +00 00:20:36.553182
Query 15 +00 00:02:27.918861 +00 00:01:42.524856 +00 00:01:42.352751 +00 00:01:48.366352 +00 00:01:11.173652 +00 00:02:05.916376
Query 16 +00 00:00:14.481427 +00 00:00:10.335290 +00 00:00:10.449666 +00 00:00:12.522837 +00 00:00:08.010176 +00 00:00:11.036114
Query 17 +00 00:03:39.617132 +00 00:01:09.896561 +00 00:01:12.347061 +00 00:01:15.938717 +00 00:01:03.228098 +00 00:01:34.219806
Query 18 +00 00:02:38.285368 +00 00:02:05.803792 +00 00:02:12.261332 +00 00:02:44.436150 +00 00:03:18.629559 +00 00:05:26.549267
Query 19 +00 00:00:52.881317 +00 00:00:13.131290 +00 00:00:13.126071 +00 00:00:13.192107 +00 00:00:14.278196 +00 00:00:15.489964
Query 20 +00 00:03:23.152004 +00 00:02:30.883228 +00 00:02:35.797302 +00 00:03:28.339995 +00 00:04:08.225930 +00 00:05:56.041736
1 hr 27 min 24 min 26 min 32 min 40 min 50 min
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alter system set parallel_adaptive_multi_user=FALSE scope=SPFILE; alter system set parallel_degree_limit=16 scope=SPFILE; alter system set parallel_degree_policy=LIMITED scope=SPFILE; alter system set parallel_force_local=TRUE scope=SPFILE; alter system set parallel_min_time_threshold=30 scope=SPFILE; alter system set "_OPTIMIZER_USE_FEEDBACK"=FALSE scope=SPFILE;
truncate table resource_io_calibrate$;
insert into resource_io_calibrate$ values (CURRENT_TIMESTAMP,CURRENT_TIMESTAMP, 0,0,300,0,0);
Parameters at Go-live
65
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Most stock indexes were necessary, even FKs were needed (contrary to the HS GBU's recommendation), but a lot of our custom indexes (including bitmaps) unecessary and even impairing performance in some cases
Compression got some fantastic disk space savings, but performance lagged uncompressed in many cases, and was only better in one test case
Gathering stats with histograms caused some performance degradation in our test queries, went live without using histograms
Auto DOP had very little impact on queries (not picking up), but forcing parallelism with hints brought significant results, so the power of parallelism is there, perhaps Auto DOP may be better with this in 12c?
Overall daily incremental load was reduced from 5.5+ hours to 3.5 hours, further improvements down to almost 3 hours and still tuning, have seen problems in Informatica that may be holding us up from better performance
Results
66