1 / 28

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, MS Harsh Taneja, MS Niveditha Thota, MS Curtis Hendrickson Don Dempsey, MS Matthew Wyatt, MSHI John-Paul Robinson Poornima Pochana, MS

elma
Télécharger la présentation

Extension of caGrid Federated Query for Large Heterogeneous Data Services

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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

  2. 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

  3. 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

  4. Aggregate Cohort Estimator (ACE) Query Constraints could be: Age, Race, Gender Labs, Diagnosis, Procedures

  5. ACE Result Screens Results can be grouped by: Counts Gender Race Age Race* Gender Race * Age Gender * Age Race * Gender * Age

  6. 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

  7. Problem – Customization of caGrid Stack • Scaling for Large Dataset • Optimization of Query • Query Chunking in FQP • WS-Enumeration in Client(Controller), FQP & Data Services

  8. 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

  9. 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

  10. Problem – Customization of caGrid Stack • Scaling for Large Dataset • Optimization of Query • Query Chunking in FQP • WS-Enumeration in Client(Controller), FQP & Data Services

  11. 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

  12. 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.

  13. 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

  14. Problem – Customization of caGrid Stack • Scaling for Large Dataset • Optimization of Query • Query Chunking in FQP • WS-Enumeration in Client(Controller), FQP & Data Services

  15. 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

  16. 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}

  17. Problem – Customization of caGrid Stack • Scaling for Large Dataset • Optimization of Query • Query Chunking in FQP • WS-Enumeration in Client(Controller), FQP & Data Services

  18. 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

  19. 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

  20. 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.

  21. 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.

  22. 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.

  23. 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

  24. 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)

  25. Thank you.Questions?

  26. Lab B DCQL Structure Before Restructuring • Lab C • Lab A

  27. DCQL Structure After Restructuring

  28. DCQL Structure After Restructuring • Lab B • Lab C • Lab A

More Related