Extension of caGrid Federated Query for Large Heterogeneous Data Services Eta S. Berner, EdD Elliot...

Extension of caGrid Federated Query for Large Heterogeneous Data Services

Eta S. Berner, EdDElliot Lefkowitz, PhDJohn David Osborne, MSHarsh Taneja, MSNiveditha Thota, MSCurtis HendricksonDon Dempsey, MS Matthew Wyatt, MSHI

John-Paul RobinsonPoornima Pochana, MSShantanu Pavgi, MS

Geoff Gordon, MSTim Day, PhDGreg Fuller

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

• 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)

Query Constraints could be:Age, Race, GenderLabs, Diagnosis, Procedures

Results can be grouped by:

1. Counts2. Gender3. Race4. Age5. Race* Gender6. Race * Age7. Gender * Age8. Race * Gender * Age

ACE Result Screens

Architectural overview

Grid Data Services

Admin System~12 M

Labs~185 M

F Q P (internal)

Federated Query Processor

UAB Data Center VLAN (private)

User Interface

Controller (RESTful

Web Service)

DCQL Generator

Shibboleth (AuthN & AuthZ)

Controller DB

Problem – Customization of caGrid Stack

1. Scaling for Large Dataset2. Optimization of Query3. Query Chunking in FQP4. WS-Enumeration in

Client(Controller), FQP & Data Services

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

Foreign AssociationGroup: AND

Attribute: Lab A

Attribute: Lab C

Attribute: Lab B

Before


Association: Lab A


Association: Lab B

Foreign Association Association: Lab C

After

B

C A

Query Optimization

Grid Data ServiceFederated Query Processor

Query 2 + 250 K

Query 3 + 100 K

Query 1

Response 1 = 250 K

Response 2 = 100 K

50K

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.

B 100K

C50K

A 250K

Query Optimization

Grid Data ServiceFederated Query Processor

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 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

• 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 – 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

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.

WS-Enumeration Enabled Grid Data Service

Federated Query Processor

Controller

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

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

• 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

Working with caGrid Knowledge Center has been very helpful.• Justin D. Permar Senior Consultant, Biomedical Informatics

Director, Center for IT Innovations in Healthcare (CITIH)

• David W. Ervin Biomedical Informatics Consultant

Center for IT Innovations in Healthcare, Team Manager

• William Stephens Senior Biomedical Informatics Consultant

Center for IT Innovations in Healthcare, Team Manager

UAB Team

CCTS (CTSA)Lisa Guay-Woodford, MD (PI)Eta S. Berner, EdD (Director)Elliot Lefkowitz, PhD (Director)Matthew Wyatt, MSHIJohn David Osborne, MSR. Curtis HendricksonHarsh Taneja, MSNiveditha Thota, MSDon 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 MatthewsStephen W DuncanDarlene Green, RN, DSN UAB IT Research ComputingJohn Paul Robinson (Lead)Poornima Pochana MSShantanu Pavgi MS Comprehensive Cancer Center:John Sandefur MBA, CISSP FUNDING:UAB CCTS is funded through a CTSA grant

(5UL1 RR025777)

Thank you.Questions?

DCQL Structure Before Restructuring

Lab A

Lab B

Lab C

DCQL Structure After Restructuring

DCQL Structure After Restructuring

Lab A

Lab B

Lab C

Extension of caGrid Federated Query for Large Heterogeneous Data Services Eta S. Berner, EdD Elliot...

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