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Putting Lipstick on Pig:

Putting Lipstick on Pig:. Enabling Database-styleWorkflow Provenance. YaelAmsterdamer,Susan B.Davidson,Daniel Deutch Tova Milo,Julia Stoyanovich,ValTannen. Using slides by Guozhang Wang. Provenance in context of Workflows .

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Putting Lipstick on Pig:

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  1. PuttingLipstickonPig: EnablingDatabase-styleWorkflow Provenance YaelAmsterdamer,SusanB.Davidson,DanielDeutch TovaMilo,JuliaStoyanovich,ValTannen Using slides by Guozhang Wang

  2. Provenance in context of Workflows • Data-Intensive complex computational processes often generate many final and intermediate data products • Scientists and engineers need to expend substantial effort managing data and recording provenance information so that basic questions can be answered.

  3. Provenance in context of Workflows • Who created this data product and when? • When was it modified and by whom? • What was the process used to create the • data product? • Were two data products derived from the same raw data?

  4. Provenance in context of Workflows • an essential component to allow for • result reproducibility • verifiability • sharing and knowledge re-use in the scientific community

  5. WorkflowProvenance  MotivatedbyScientificWorkflows ◦Community:IPAW ◦Interests:process documentation,data derivationand annotation,etc ◦Model:OPM

  6. OPMModel Annotateddirectedacyclicgraph ◦Artifact:immutablepieceofstate ◦Process:actionsperformedonartifacts,result innewartifacts ◦Agents:executeandcontrolprocesses Aimstocapturecausaldependencies betweenagents/processes Eachprocessistreatedasa“black-box”   

  7. Example:CarDealership

  8. DataProvenance (forRelationalDBandXML)  MotivatedbyProb.DB,datawarehousing.. ◦Community: SIGMOD/PODS ◦Interests:data auditing,datasharing, etc ◦Model:Semiring(etc)

  9. Semiring K-relations ◦Eachtupleisuniquelylabeledwitha provenance“token” Operations: ◦•:join ◦+:projection ◦0and1:selectionpredicates  

  10. Workflow Provenance Researchers DataProvenance Researchers

  11. SemiringComestoMeetOPM

  12. OPM’sDrawbacksinSemiring People’sEyes Theblack-boxassumption:eachoutputof themoduledependssolelyonallits inputs ◦Cannotleveragethecommonfactthatsome outputonlydependsonsmallsubsetofinputs ◦Doesnotcaptureinternalstateofamodule So:replaceitwithSemirings!  

  13. TheIdea Generalworkflowmodulesare complicated,andthushardtocaptureits internallogicbyannotations However,moduleswritteninPigLatinis verysimilartoNestedRelationalCalculus (NRC),thusaremuchmorefeasible  

  14. PigLatin   Data:unordered(nested)bagoftuples Operators: ◦FOREACHtGENERATEf1,f2,…OP(f0) ◦FILTERBYcondition ◦GROUP/COGROUP ◦UNION,JOIN,FLATTEN,DISTINCT…

  15. Example:CarDealership

  16. BidRequestHandlinginPigLatin

  17. ProvenanceAnnotation

  18. ProvenanceAnnotation1.1 Provenancenodeandvaluenodes ◦Workflowinputnodes ◦Moduleinvocationnodes ◦Moduleinput/outputnodes 

  19. ProvenanceAnnotationI.2 Statenodes ◦P-nodeforthetuple ◦P-nodeforthestate 

  20. ProvenanceAnnotation2.1 FOREACH(projection,noOP) ◦P-nodewith“+” 

  21. ProvenanceAnnotation2.2 JOIN ◦P-nodewith“*” 

  22. ProvenanceAnnotation2.3 GROUP ◦P-nodewith“∂” 

  23. ProvenanceAnnotation2.4 FOREACH(aggregation,OP) ◦V-nodewiththeOPname 

  24. ProvenanceAnnotation2.5 COGROUP ◦P-nodewith“∂” 

  25. ProvenanceAnnotation2.6 FOREACH(UDFBlackBox) ◦P-node/V-nodewiththeUDFname 

  26. QueryProvenanceGraph Zoom-Inv.s.Zoom-Out  Coarse-grained Fine-grained

  27. QueryProvenanceGraph DeletionPropagation ◦DeletethetupleP-nodeanditsout-edges ◦RepeateddeleteP-nodesif Allitsin-edgesaredeleted Ithaslabel•andoneofitsin-edgesisdeleted 

  28. ImplementationandExperiments Lipstickprototype ◦ProvenanceannotationcodedinPigLatin, withthegraphwrittentofiles ◦QueryprocessingcodedinJavaandrunsin memory. Benchmarkdata ◦Cardealership:fixedworkflowand#dealers ◦ArcticStation:Variedworkflowstructureand size  

  29. AnnotationOverhead Overheadincreaseswithexecutiontime 

  30. AnnotationOverhead Parallelismhelpswithupto#modules 

  31. LoadingGraphOverhead Increasewithgraphsize (comp.time<8sec) 

  32. LoadingGraphOverhead Feasiblewithvarioussizes (comp.time<3sec) 

  33. SubgraphQueryTime Queryefficientlywithsub-secondtime 

  34. Conclusions ThankYou! Studied fine-grained provenance for workflows Individual modules implemented in Pig Latin Provenance model for Pig Latin queries DataprovenanceideassuchasSemirings canbebroughttoworkflowprovenance

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