Efficient Re-computation of Big Data Analytics Processes in the Presence of Changes
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a talk given at University of LaRioja -- "provenance week"
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Efficient Re-computation of Big Data Analytics Processes in the Presence of Changes
- 1. Paolo Missier and Jacek Cala Newcastle University, UK Universidad La Rioja, Spain Oct 29th, 2019 Efficient Re-computation of Big Data Analytics Processes in the Presence of Changes In collaboration with • Institute of Genetic Medicine, Newcastle University
- 2. 2 Context Big Data The Big Analytics Machine Actionable Knowledge Analytics Data Science over time V3 V2 V1 Meta-knowledge Algorithms Tools Libraries Reference datasets t t t
- 3. 3 What changes? • Genomics • Reference databases • Algorithms and libraries • Simulation • Large parameter space • Input conditions • Machine Learning • Evolving ground truth datasets • Model re-training
- 4. 4 Genomics Image credits: Broad Institute https://software.broadinstitute.org/gatk/ https://www.genomicsengland.co.uk/the-100000-genomes-project/ Spark GATK tools on Azure: 45 mins / GB @ 13GB / exome: about 10 hours
- 5. 6 Genomics: WES / WGS, Variant calling Variant interpretation SVI: a simple single-nucleotide Human Variant Interpretation tool for Clinical Use. Missier, P.; Wijaya, E.; Kirby, R.; and Keogh, M. In Procs. 11th International conference on Data Integration in the Life Sciences, Los Angeles, CA, 2015. Springer SVI: Simple Variant Interpretation Variant classification : pathogenic, benign and unknown/uncertain
- 6. 7 Changes that affect variant interpretation What changes: - Improved sequencing / variant calling - ClinVar, OMIM evolve rapidly - New reference data sources Evolution in number of variants that affect patients (a) with a specific phenotype (b) Across all phenotypes
- 7. 10 Blind reaction to change: a game of battleship Sparsity issue: • About 500 executions • 33 patients • total runtime about 60 hours • Only 14 relevant output changes detected 4.2 hours of computation per change Should we care about updates? Evolving knowledge about gene variations
- 8. 11 ReComp http://recomp.org.uk/ Outcome: A framework for selective Re-computation • Generic, Customisable Scope: expensive analysis + frequent changes + not all changes significant Challenge: Make re-computation efficient in response to changes Assumptions: Processes are • Observable • Reproducible Estimates are cheap Insight: replace re-computation with change impact estimation Using history of past executions
- 9. 12 Reproducibility How Selective: - Across a cohort of past executions. which subset of individuals? - Within a single re-execution which process fragments? Change in ClinVar Change in GeneMap Why, when, to what extent
- 10. 13 The rest of the talk • Approach, • Exemplified on the SVI workflow • Architecture
- 11. 14 The ReComp meta-process History DB Detect and quantify changes data diff(d,d’) Record execution history Analytics Process P Log / provenance Partially Re-exec P (D) P(D’) Change Events Changes: • Reference datasets • Inputs For each past instances: Estimate impact of changes Impact(dd’, o) impact estimation functions Scope Select relevant sub-processes Optimisation
- 12. 15 How much do we know about P? Impact estimation Re-execution less more Process structure Execution trace black box I/O provenance I/O only All-or-nothing monolithic process, legacy a complex simulator white box step-by-step provenance workflows, R / python code genomics analyticsTypical process Fine-grained Impact Partial restart trees (*) (*) Cala J, Missier P. Provenance Annotation and Analysis to Support Process Re-Computation. In: Procs. IPAW 2018. London: Springer; 2018.
- 13. 16 Recomp meta-process flow PaoloMissier2019 Identify the subset of executions that are potentially affected by the changes Determine whether changes may have had any impact on outputs Identify and re-execute the minimal fragments of workflow that have been affected
- 14. 17 Change Front t C0 {a1 → a0} CF3 {a3, b1, c2} CF5 {a3, b2, c2, d1} C1 {b1 → b0} C3 {a3 → a2, c2 → c1} C4 {d1 → d0} C5 {b2 → b1} C2 {a2 → a1, c1 → c0} E(…, [a0, b0, e0]) E(…, [a0, b1, d0]) E(…, [a2, b1, c1])
- 15. 18 Re-computation Front We use: wasInformedBy(..., [prov:type=“recomp:re-execution”]) to denote a ReComp-initiated re-execution.
- 19. 22 Restart Tree Re-computation front handles single executions well. What if the process is more complex than that? pipeline, workflow, complex hierarchical workflow… cf. the NGS pipeline.
- 20. 23 Restart Tree
- 21. 24 Restart Tree
- 22. 25 Restart Tree The provenance trace includes multiple interrelated executions. During re-execution we have to combine all of them within a single context – the top-level execution.
- 23. 26 Execution trace / Provenance User Execution «Association » «Usage» «Generation » «Entity» «Collection» Controller Program Workflow Channel Port wasPartOf «hadMember » «wasDerivedFrom » hasSubProgram «hadPlan » controlledBy controls[*] [*] [*] [*] [*] [*] «wasDerivedFrom » [*][*] [0..1] [0..1] [0..1] [*][1] [*] [*] [0..1] [0..1] hasOutPort [*][0..1] [1] «wasAssociatedWith » «agent » [1] [0..1] [*] [*] [*] [*] [*] [*] [*] [*] [*] [*] [*] [*] [0..1] [0..1] hasInPort [*][0..1] connectsTo [*] [0..1] «wasInformedBy » [*][1] «wasGeneratedBy » «qualifiedGeneration » «qualifiedUsage » «qualifiedAssociation » hadEntity «used » hadOutPorthadInPort [*][1] [1] [1] [1] [1] hadEntity hasDefaultParam
- 24. 27 ProvONE User Execution «Association » «Usage» «Generation » « Controller Program Workflow Channel Port wasPartOf «wasDerivedFrom » hasSubProgram «hadPlan » controlledBy controls[*] [*] [*] [*] [*] [ [0.. [0..1] [*][1] [*] [*] [0..1] [0..1] hasOutPort [*][0..1] [1] «wasAssociatedWith » «agent » [1] [0..1] [*] [*] [*] [*] [*] [*] [*] [*] [*] [*] [*] [*] [0..1] [0..1] hasInPort [*][0..1] connectsTo [*] [0..1] «wasInformedBy » [*][1] «wasGeneratedBy » «qualifiedGeneration » «qualifiedUsage » «qualifiedAssociation » hadEntity «used » hadOutPorthadInPort [*][1] [1] [1] [1] hadEntity hasDefaultP
- 25. 28 Restart Tree To build a restart tree we rely on the provone:wasPartOf statements. CF = {b2, e1}
- 26. 29 Restart Tree Captures the vertical dimension of a single execution the transitive closure of the wasPartOf relation. CF = {b2, e1}
- 27. 30 Recomp meta-process flow PaoloMissier2019 Determine whether changes may have had any impact on outputs
- 28. 31 Changes, data diff, impact 1) Observed change events: (inputs, dependencies, or both) 3) Impact of change C on output y: 2) Type-specific Diff functions: Impact is process- and data-specific:
- 29. 32 Impact Given P (fixed), a change in one of the inputs to P: C={xx’} affects a single output: However a change in one of the dependencies: C= {dd’} affects all outputs yt where version d of D was used
- 30. 33 SVI: data-diff and impact functions - Data-specific - Process-specificomim clinvar Overall impact impact on ‘p1 select genes’ impact on the SVI output
- 31. 34 Diff functions for SVI ClinVar 1/2016 ClinVar 1/2017 diff (unchanged) Relational data simple set difference
- 32. 35 Binary SVI impact function Returns True iff: - Known variants have moved in/out of Red status - New Red variants have appeared - Known Red variants have been retracted
- 33. 36 Impact: “semantic “ example Scope: which cases are affected? - Individual variants have an associated phenotype. - Patient cases also have a phenotype “a change in variant v can only have impact on a case X if V and X share the same phenotype” Importance: “Any variant with status moving from/to Red causes High impact on any X that is affected by the variant”
- 34. 37 Change impact analysis algorithm PaoloMissier2019 Aim: To identify the minimal subset of observed changes that have an actual effect on past outcomes This is done by progressively eliminating changes for which impact has been estimated as null Intuition: - From the workflow, derive an impact graph - This is a new type of dataflow where execution semantics is designed to - Propagate input changes - Compute diff functions - Compute impact functions on diffs - When impact is null, eliminate changes from the inputs - Input: set of changes, eg - Output: set of bindings that indicates which changes are relevant and have non-zero impact on the process
- 35. 38 Role of provenance PaoloMissier2019 Impact facts: - During each execution ReComp records port-data bindings for all the data that flow through annotated input and output ports - Each impact function is able to use some of these bindings as its own inputs - These are the impact facts that the function is evaluated on - To find these bindings, traverse the dependencies of impact to diff functions
- 36. 39 ReComp decision matrix for SVI Impact: yes / no / not assessed delta functions: data diff detected?
- 37. 40 Empirical validation PaoloMissier2019 re-executions 495 71 Ideal: 14 But: no false negatives
- 38. 41 SVI implemented using workflow Phenotype to genes Variant selection Variant classification Patient variants GeneMap ClinVar Classified variants Phenotype
- 39. 42 History DB: Workflow Provenance Each invocation of an eSC workflow generates a provenance trace “plan” “plan execution” WF B1 B2 B1exec B2exec Data WFexec partOf partOf usagegeneration association association association db usage ProgramWorkflow Execution Entity (ref data)
- 40. 43 Partial re-execution 1. Change detection: A provenance fact indicates that a new version Dnew of database d is available wasDerivedFrom(“db”,Dnew) :- execution(WFexec), wasPartOf(Xexec,WFexec), used(Xexec, “db”) 2.1 Find the entry point(s) into the workflow, where db was used :- execution(WFexec), execution(B1exec), execution(B2exec), wasPartOf(B1exec, WFexec), wasPartOf(B1exec, WFexec), wasGeneratedBy(Data, B1exec), used(B2exec,Data) 2.2 Discover the rest of the sub-workflow graph (execute recursively) 2. Reacting to the change: Provenance pattern: “plan” “plan execution” Ex. db = “ClinVar v.x” WF B1 B2 B1exec B2exec Data WFexec partOf partOf usagegeneration association association association db usage
- 41. 44 SVI – partial re-execution Overhead: caching intermediate data Time savings Partial re-exec (sec) Complete re-exec Time saving (%) GeneMap 325 455 28.5 ClinVar 287 455 37 Change in ClinVar Change in GeneMap Cala J, Missier P. Provenance Annotation and Analysis to Support Process Re-Computation. In: Procs. IPAW 2018.
- 42. 45 Architecture <eventname> ReComp Core HDB «ProvONE store» Tabular-Diff Service Tabular-Diff Service Difference Function ReExecution Service A ReExecution Service A ReExecution Function Impact Service B Impact Service B Impact Function ReComp Loop User Process Runtime Environment Inputs Outputs Interface Di f f Se r vi c e Interface I mpa c t Se r vi c e Interface Re Exe c Se r vi c e Process and data provenance Prolog facts store/retrieve REST API External services REST API Executes restart trees - React to change events - Construct restart trees
- 43. 46 Customising ReComp in practice <eventname> Enable provenance capture / Map to PROV
- 44. 47 Summary <eventname> Evaluation: case-by-case basis - Cost savings - Ease of customisation Generic framework Black box gray box Fine-grained provenance + control max savings Tested on two cases studies - Genomics - Flood Simulation (not in this talk)
Notas do Editor
- We are going to ignore BDA in this talk And also simulation although it’s a case study
- We are going to use this smaller process as a testbed Changes in the reference databases have an impact on the classification
- returns updates in mappings to genes that have changed between the two versions (including possibly new mappings): $\diffOM(\OM^t, \OM^{t'}) = \{\langle t, genes(\dt) \rangle | genes(\dt) \neq genes'(\dt) \} $\\ where $genes'(\dt)$ is the new mapping for $\dt$ in $\OM^{t'}$. \begin{align*} \diffCV&(\CV^t, \CV^{t'}) = \\ &\{ \langle v, \varst(v) | \varst(v) \neq \varst'(v) \} \\ & \cup \CV^{t'} \setminus \CV^t \cup \CV^t \setminus \CV^{t'} \label{eq:diff-cv} \end{align*} where $\varst'(v)$ is the new class associate to $v$ in $\CV^{t'}$.
- In four cases change in the caller version changes the classification
- Threats: Will any of the changes invalidate prior findings? Opportunities: Can the findings be improved over time? Can we do better in a generic way? We need to control re-computation on two dimensions Across a population Within a single process
- Success criteria: performance, but this is on a case-by-case basis Ease of customization. The focus of this paper
- The framework is a meta-process… Changes can also occur to OS, libraries and other dependencies but these are out of scope
- The black box case is illustrated here and is less interesting. The more interesting SVI case is in the next slide
- Simple change – wait until CF Composite change Only the changed artefacts Change front CF3 Only the newest references – transitive closure of wDF on the set of all derivations of artefact D.v. The open system – users can introduce execs with non-recent versions. More changes Change front CF5 Includes all change artefacts – keeps the system open. Prioritisation within impact analysis may cause that not all past execs are recomputed. Windowing, windowing policy Explain that P may depend on many other components but C and CF include only the changed ones. Also, mention that CF includes the references to only the newest version of the dependencies and includes all of them –> it is important because various executions may depend on earlier versions, not necessarily the immediate predecessor. Also, the user can introduce executions which depend on versions well before the last CF. Mention that the windowing policy may vary widely, e.g. fixed window size or adaptive window based on some measure of change event significance.
- Shows Essential ProvONE fragment used by ReComp
- Grey arrows indicate the data flow: solid lines – the usage of data, dotted lines – the communication / generation-usage pattern Black dashed arrows reflect the structure of the process.
- Mention that for the sake of simplicity we are not showing the port-data usage, which is also needed.
- \diff{X}(X^t, X^{t'}), \quad \diff{Y}(Y^t, Y^{t'}), \quad \diff{D}(D^t,D^{t'}) C = \{\update{D^{t'}}{D^t}, \quad \update{X^{t'}}{X^t} \} y^{t} &= \mathit{exec}(P, x^{t}, d^{t}) \\ y^{t'} &= \mathit{exec}(P, x^{t'}, d^{t'}) \\ \mathit{imp}_{P}(C,y) &= f_{Y}( \mathit{diff}_Y(y^t, y^{t'})) \mathit{imp}_{SVI} (C,y) = f_{Y}( \mathit{diff}_{Y}(y^t, y^{t'})) \in \{ \texttt{None}, \texttt{Low}, \texttt{High} \}
- \impact_{P}( \{\update{x^{t'}}{x^t} \},y) &= f_{Y}( \diff{Y}(y^t, \exec(P, x^{t'}, d^{t}) )) \impact_{P}( \{\update{d^{t'}}{d^t} \},y) &= f_{Y}( \diff{Y}(y^t, \exec(P, x^t, d^{t'}) ))
- Impact functions are currently only binary
- \delta_4(\text{CV}^t, \text{CV}^{t'}) = & \langle \delta_4^+, \delta_4^-, \delta_4^{\pm} \rangle
- \phi_5( \delta_4^+, \delta_4^-, \delta_4^{\pm}, \mathit{val}(o_5)) \in \{ \text{True}, \text{False}\} \delta_1(\text{GM}^t, \text{GM}^{t'}) = & \langle \delta_1^+, \delta_1^-, \delta_1^{\pm} \rangle \\ \delta_4(\text{CV}^t, \text{CV}^{t'}) = & \langle \delta_4^+ \delta_4^-, \delta_4^{\pm} \rangle \phi_1( \delta_1^+, \delta_1^-, \delta_1^{\pm}) \phi_5( \delta_4^+, \delta_4^-, \delta_4^{\pm}, \mathit{val}(o_5)) \phi_5( \delta_4^+, \delta_4^-, \delta_4^{\pm}, \mathit{val}(o_5)) \\ &\text{returns True iff: } \\ - \quad&\delta_4^- ~\text{or}~ \delta_4^+~\text{includes a Red variant} \\ - \quad &\text{pathogenic status changed for any variant in}~ \delta_4^{\pm}
- \text{let } v \in \diff{Y}(Y^t, Y^{t'}): \\ \text{for any $X$: } \impact_{P}(C,X) = \texttt{High} \text{ if }\\ v.\texttt{status:} \begin{cases} * \rightarrow \texttt{red} \\ \texttt{red} \rightarrow * \end{cases}
- \delta_1, \delta_4 \phi_1, \phi_5
- This shows the good case of “Gerry box” workflow and box-level provenance SVI workflow with automated provenance recording Cohort of about 100 exomes (neurological disorders) Changes in ClinVar and OMIM GeneMap
- How these two restart trees are discovered is explained in the two papers IPAW BDC
- uses difference and impact services to analyse the impact of the changes on past executions and submits a subset of affected executions to rerun. HDB will have been discussed earlier Facts stored and queried using Prolog store/retrieve REST API. Canned queries or ad hoc queries (advanced interface) Impact functions realized as external services reachable through a REST API reExec function takes restart tress and executes them – this may not always be possible in fact it’s a major limitation for current systems ReComp loop produces recomp/no-recop decisions at the level of each restart tree Data diff is an additional external service