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Extension of caGrid Federated Query for Large Heterogeneous Data Services. Eta S. Berner, EdD Elliot Lefkowitz, PhD John David Osborne, MS Harsh Taneja, MS Niveditha Thota, MS Curtis Hendrickson Don Dempsey, MS Matthew Wyatt, MSHI John-Paul Robinson Poornima Pochana, MS

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extension of cagrid federated query for large heterogeneous data services

Extension of caGrid Federated Query for Large Heterogeneous Data Services

Eta S. Berner, EdD

Elliot Lefkowitz, PhD

John David Osborne, MSHarsh Taneja, MS

Niveditha Thota, MSCurtis HendricksonDon Dempsey, MS

Matthew Wyatt, MSHI

John-Paul Robinson

Poornima Pochana, MS

Shantanu Pavgi, MS

Geoff Gordon, MS

Tim Day, PhD

Greg Fuller

objectives
Objectives
  • Background
  • Customization of caGrid stack
    • Scaling for Large Dataset
    • Optimization of Query
    • Query Chunking in FQP
    • WS-Enumeration in Client(Controller), FQP & Data Services
  • Outstanding Issues
  • Summary
background
Background
  • UAB has developed a Custom “Cohort Discovery” tool
      • Query based upon: Age, race, gender, Labs, Diagnosis, Procedure
      • Aggregate Results (numbers) stratified by: Age, Race, and Gender
  • Two caCORE SDK generated data services
    • Administrative Data (Demographics etc)
      • Patient table with simple demographics (~700 K)
      • Diagnosis, Encounter, Procedures (~12 M)
    • Labs (Lab Results)
      • Patient table (~700K)
      • Lab Result table (~185 M)
  • Federated Query Processor (modified 1.3 Snapshot)
  • Controller generates DCQL for FQP that always targets Admin System’s patient table and (optionally) labs
    • MRN is the identifier to link Admin System’s patient data to lab results
aggregate cohort estimator ace
Aggregate Cohort Estimator (ACE)

Query Constraints could be:

Age, Race, Gender

Labs, Diagnosis, Procedures

ace result screens
ACE Result Screens

Results can be grouped by:

Counts

Gender

Race

Age

Race* Gender

Race * Age

Gender * Age

Race * Gender * Age

architectural overview
Architectural overview

UAB Data Center VLAN (private)

F Q P(internal)Federated Query Processor

Controller (RESTful Web Service)

DCQL Generator

Admin System

~12 M

User Interface

Grid Data Services

Labs~185 M

Shibboleth (AuthN & AuthZ)

Controller DB

problem customization of cagrid stack
Problem – Customization of caGrid Stack
  • Scaling for Large Dataset
  • Optimization of Query
  • Query Chunking in FQP
  • WS-Enumeration in Client(Controller), FQP & Data Services
scaling for large dataset
Scaling for Large Dataset
  • Time out was overridden to 24 hrs in FQP & Data Services
  • Row Count was increased from 1K to 1M in Data Services
  • DCQL was restructured in Controller to avoid table space overflow errors due to the Cartesian joins
    • this occurs only as a result of "AND" statements
    • Occurs only when row count is high
    • This was not required against Admin Systems (12M vs 185 M in labs)
    • And not with “OR” queries against labs, which can run with a join-free SQL statement
  • FQP should be able to analyze DCQL and run it efficiently since similar to how a relational database query analyzer does it
before and after the restructuring
Before and After the Restructuring

Before

Attribute: Lab A

Foreign AssociationGroup: AND

Attribute: Lab B

Attribute: Lab C

After

Association: Lab A

Foreign AssociationGroup: AND

Association: Lab B

  • Foreign AssociationGroup: AND
  • Foreign Association

Association: Lab C

problem customization of cagrid stack1
Problem – Customization of caGrid Stack
  • Scaling for Large Dataset
  • Optimization of Query
  • Query Chunking in FQP
  • WS-Enumeration in Client(Controller), FQP & Data Services
query optimization
Query Optimization

Federated Query Processor

Grid Data Service

Query 1

Response 1 = 250 K

Query 2 + 250 K

Response 2 = 100 K

Query 3 + 100 K

50K

query optimization1
Query Optimization

Step 1: Controller pre-runs count-only CQL queries.

For example:

Count(A) = 250K,

Count(B) = 100K &

Count(C) = 50K

Step 2: Reorder DCQL query so that the most restrictive statements are executed first.

query optimization2
Query Optimization

Federated Query Processor

Grid Data Service

Query 1

Response 1 50 K

Query 2 with 50K

Response 2 50K

Query 3 with 50K

Response 3 50K

Smallest-Data-Set-First reduces size of all sub queries

problem customization of cagrid stack2
Problem – Customization of caGrid Stack
  • Scaling for Large Dataset
  • Optimization of Query
  • Query Chunking in FQP
  • WS-Enumeration in Client(Controller), FQP & Data Services
problem with large sub queries
Problem with Large Sub Queries
  • Problem: Too many identifiers (>300k MRNs from Labs in our case)
    • FQP
      • Passes huge OR clause down to data service
    • Data Services
      • Uses hibernate which parses OR clause recursively, thus blowing the stack for large results with typical JVM settings
      • Solution – fix both hibernate and JVM stack size setting
    • Database
      • Chokes on large queries consisting of
        • Where In (MRN1, MRN2, …. MRNn) or
        • Where Attribute1 = value1 or Attribute2 = value2 or … AttributeN = valueN
      • No success with either Oracle or MySQL even after adjusting settings like max packet size, etc
solutions query chunking in fqp
Solutions - Query Chunking in FQP
  • Introduced Query Chunking in FQP --limits number of MRNs in where clause of native queries at database
  • Controlled by a new “chunk size” parameter in FQP
  • If any sub-CQLQuery returns more rows than the “chunk size”, the dependent query will be run N times, once per chunk

e.g. say Chunk Size (d)= 1000 & Result Size (c) = 10096

This resulted in successful completion of Complex Query in finite amount of time.

Number of CQL Queries (n) = Result Size (c)/ Chunk Size (d)

No. of CQL Queries (n) = 10096 / 1000 = 11 CQL Queries {Smallest with 96 parameters}

problem customization of cagrid stack3
Problem – Customization of caGrid Stack
  • Scaling for Large Dataset
  • Optimization of Query
  • Query Chunking in FQP
  • WS-Enumeration in Client(Controller), FQP & Data Services
problem xml serialization and de serialization is expensive
Problem – XML Serialization and De-serialization is Expensive
  • XML is used to deliver results of CQL queries
    • A single XML result file is generated
    • WS-Enumeration can break a result down into smaller file pieces but
      • Was not used by FQP to query the grid data services
      • Data service, grid and FQP all serve WS-Enumeration requests by de-serializing entire object in memory
      • The entire object is then written to disk as a resource to serve the client
solution ws enumeration in client controller fqp data services
Solution: WS-Enumeration in Client(Controller), FQP & Data Services

To utilize WS-Enumeration

  • Grid Data Services were generated with caGrid WS-Enumeration enabled.
  • FQP: implemented new code to support WS-Enumeration
  • Used Federated Query Results Client’s Enumerate method in Controller.

Using WS Enumeration end-to-end allowed transfer of larger data sets over SOAP from Data Service to ACE Controller.

Controller

WS-Enumeration Enabled Grid Data Service

Federated Query Processor

non standard configuration settings
Non Standard Configuration Settings
  • WS-Enumeration services returned ALL associations associated on the target object and generated lazy load exceptions
  • David Erwin’s patch permitted lazy loading and prevented unwanted associations on the target object from being returned. This vastly reduced the size of returned results and subsequent network overhead.
  • Changed default JVM sizes for data services and FQP (currently 15G and 6G respectively)
  • Turned off ECache as unsuitable for our application, Caches consume memory, and disk space.
outstanding issues
Outstanding Issues

We did not resolve the issue with translation of CQL to efficient SQL with Associations in them, and we worked around this by Joining using Foreign Associations, whereas fixing the CQL to SQL would (theoretically) have been more appropriate.

summary
Summary
  • After several bug fixes, FQP is able to handle extremely large data sets.
  • With Customizations in caGrid Stack we are able to utilize the benefits of the technology that enables us to share information and analytical resources efficiently.
  • With ACE application built on the caGrid Stack we are able to facilitate the inter-departmental data sharing within UAB.
acknowledgements
Acknowledgements

Working with caGrid Knowledge Center has been very helpful.

  • Justin D. Permar

Senior Consultant, Biomedical InformaticsDirector, Center for IT Innovations in Healthcare (CITIH)

  • David W. Ervin

Biomedical Informatics ConsultantCenter for IT Innovations in Healthcare, Team Manager

  • William Stephens

Senior Biomedical Informatics ConsultantCenter for IT Innovations in Healthcare, Team Manager

uab team
UAB Team

CCTS (CTSA)

Lisa Guay-Woodford, MD (PI)

Eta S. Berner, EdD (Director)

Elliot Lefkowitz, PhD (Director)

Matthew Wyatt, MSHI

John David Osborne, MS

R. Curtis Hendrickson

Harsh Taneja, MS

Niveditha Thota, MS

Don Dempsey, MS

Health Systems Information Systems (HSIS)

Geoff Gordon, MS (Web Development Director)

Steve Osburne (IT)

Terrell W Herzig (Data Security Officer)

Tim Day, PhD

Greg Fuller (GUI)

Suresh Nair (DBA)

UAB Health System Data Resources Group

Andy Matthews

Stephen W Duncan

Darlene Green, RN, DSN

UAB IT Research Computing

John Paul Robinson (Lead)

Poornima Pochana MS

Shantanu Pavgi MS

Comprehensive Cancer Center:

John Sandefur MBA, CISSP

FUNDING:

UAB CCTS is funded through a CTSA grant (5UL1 RR025777)

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