Timeline Service v.2 (Hadoop Summit 2016)
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This document summarizes the new YARN Timeline Service version 2, which was developed to address scalability, reliability, and usability challenges in version 1. Key highlights of version 2 include a distributed collector architecture for scalable and fault-tolerant writing of timeline data, an entity data model with first-class configuration and metrics support, and metrics aggregation capabilities. It stores data in HBase for scalability and provides a richer REST API for querying. Milestone goals include integration with more frameworks and production readiness.
Timeline Service v.2 (Hadoop Summit 2016)
- 1. (Big Data)2 How YARN Timeline Service v.2 Unlocks 360-Degree Pla@orm Insights at Scale Sangjin Lee @sjlee (Twi5er) Li Lu (Hortonworks) Vrushali Channapa5an @vrushalivc (Twi5er)
- 2. Outline • Why v.2? • Highlights • Developing for Timeline Service v.2 • SeIng up Timeline Service v.2 • Milestones • Demo
- 3. Why v.2? • YARN Timeline Service v 1.x • Gained good adopSon: Tez, HIVE, Pig, etc. • Keeps improving with v 1.5 APIs and storage implementaSon • SSll facing some fundamental challenges...
- 4. Why v.2? • Scalability and reliability challenges • Single instance of Timeline Server • Storage (single local LevelDB instance) • Usability • Flow • Metrics and configuraSon as first-class ciSzens • Metrics aggregaSon up the enSty hierarchy
- 5. Highlights v.1 v.2 Single writer/reader Timeline Server Distributed writer/collector architecture Single local LevelDB storage* Scalable storage (HBase) v.1 enSty model New v.2 enSty model No aggregaSon Metrics aggregaSon REST API Richer query REST API
- 6. Architecture • SeparaSon of writers (“collectors”) and readers • Distributed collectors: one collector for each app • Dedicated RM collector for RM-generated data • Collector discovery via RM • Pluggable storage with HBase as default storage
- 7. Distributed collectors & readers !meline reader !meline reader Storage !meline reader AM !meline collector NM !meline reader pool app metrics/events container events/metrics RM !meline collector app/container events user queries (worker node running AM) (worker node running containers) write flow read flow
- 8. Collector discovery RM AM app id => address ! start AM container NM 3meline collector " node heartbeat # allocate response worker node 3meline client
- 9. New enSty model • Flows and flow runs as parents of YARN applicaSon enSSes • First-class configuraSon (key-value pairs) • First-class metrics (single-value or Sme series) • Designed to handle mulS-cluster environment out of the box
- 10. What is a flow? • A flow is a group of YARN applicaSons that are launched as parts of a logical app • Oozie, Scalding, Pig, etc. • name: “frequent_visitor_stat” • run id: 1466097809000 • version: “b9b9068”
- 11. ConfiguraSon and metrics • Now explicit top-level a5ributes of enSSes • Fine-grained updates and queries made possible • “update metric A to value x” • “query enMMes where config A = B” container 1_1 metric: A = 10 metric: B = 100 config: "Foo" = "bar"
- 12. ConfiguraSon and metrics • Now explicit top-level a5ributes of enSSes • Fine-grained updates and queries made possible • “update metric A to value x” • “query enMMes where config A = B” container 1_1 metric: A = 50 metric: B = 100 config: "Foo" = "bar"
- 13. HBase Storage • Scalable backend • Row Key structure • efficient range scans • KeyPrefixRegionSplitPolicy • Filter pushdown • Coprocessors for flow aggregaSon (“readless” aggregaSon) • Cell tags for metadata (applicaSon id, aggregaSon operaSon) • Cell Smestamps generated during put • lei shiied with app id added to avoid overwrites
- 14. Tables in HBase • flow run • application • entity • flow activity • app to flow
- 15. table: flow run Row key: clusterId!userName! flowName! inverted(flowRunId) most recent flow run stored first coprocessor enabled
- 16. table: applicaSon Row key: clusterId!userName! flowName! inverted(flowRunId)! AppId applicaSons within a flow run stored together most recent flow run stored first
- 17. table: enSty Row key: userName!clusterId!flowName! inverted(flowRunId)!AppId!entityType! entityId enSSes within an applicaSon within a flow run stored together per type • for example, all containers within a yarn applicaSon will be stored together pre-split table stores information per entity run like info, relatesTo, relatedTo, events, metrics, config
- 18. table: flow acSvity Row key: clusterId! inverted(TopOfTheDay)! userName!flowName shows the flows that ran on that day stores informaSon per flow like number of runs, the run ids, versions
- 19. table: appToFlow Row key: clusterId!appId - stores mapping of appId to flowName and flowRunId
- 20. Metrics aggregaSon • ApplicaSon level • Rolls up sub-applicaSon metrics • Performed in real Sme in the collectors in memory • Flow run level • Rolls up app level metrics • Performed in HBase region servers via coprocessors • Offline aggregaSon (TBD) • Rolls up on user, queue, and flow offline periodically • Phoenix tables Container 1_1 “bytes” : 23 Container 1_2 “bytes” : 135 Container 2_1 “bytes” : 50 Container 3_1 “bytes” : 64 App1 “bytes”: 158 App2 “bytes”: 50 App3 “bytes”: 64 flow1 “bytes”: 208 flow2 “bytes”: 64 user1 “bytes”: 272 queue1 “bytes”: 272 App aggregation In collector flow aggregation In hbase offline aggregation
- 23. Reader REST API: paths • URLs under /ws/v2/Smeline • Canonical REST style URLs: /ws/v2/Smeline/clusters/cluster_name/ users/user_name/flows/flow_name/runs/run_id • Path elements may be omi5ed if they can be inferred • flow context can be inferred by app id • default cluster is assumed if cluster is omi5ed
- 24. Reader REST API: query params • limit, createdTimeStart, createdTimeEnd: constrain the enSSes • fields (ALL | EVENTS | INFO | CONFIGS | METRICS | RELATES_TO | IS_RELATED_TO): limit the contents to return • metricsToRetrieve, confsToRetrieve: further limit the contents to return • metricsLimit: limits the number of values in a Sme series
- 25. Reader REST API: query params • relatesTo, isRelatedTo: filters by associaSon • *Filters: filters by info, config, metric, event, … • Supports complex filters including operators • metricFilter=(((metric1 eq 50) AND (metric2 gt 40)) OR (metric1 lt 20))
- 26. Developing: TimelineClient In your application master: // create TimelineClient v.2 style TimelineClient client = TimelineClient.createTimelineClient(appId); client.init(conf); client.start(); // bind it to AM/RM client to receive the collector address amRMClient.registerTimelineClient(client); // create and write timeline entities TimelineEntity entity = new TimelineEntity(); client.putEntities(entity); // when the app is complete, stop the timeline client client.stop();
- 27. Developing: Flow context In your app submitter: ApplicationSubmissionContext appContext = app.getApplicationSubmissionContext(); // set the flow context as YARN application tags Set<String> tags = new HashSet<>(); tags.add(TimelineUtils.generateFlowNameTag("distributed grep")); tags.add(TimelineUtils.generateFlowVersionTag( "3df8b0d6100530080d2e0decf9e528e57c42a90a")); tags.add(TimelineUtils.generateFlowRunIdTag(System.currentTimeMillis())); appContext.setApplicationTags(tags);
- 28. SeIng up Timeline Service v.2 • Set up the HBase cluster (1.1.x) • Add the Smeline service jar to HBase • Install the flow run coprocessor • Create tables via TimelineSchemaCreator uSlity • Configure the YARN cluster • Enable Timeline Service v.2 • Add hbase-site.xml for the Smeline collector and readers • Start the Smeline reader daemon
- 29. Milestone 1 ("Alpha 1") • Merge discussion (YARN-2928) in progress as we speak! ✓ Complete end-to-end read/write flow ✓ Real Sme applicaSon and flow aggregaSon ✓ New enSty model ✓ HBase Storage ✓ Rich REST API ✓ IntegraSon with Distributed Shell and MapReduce ✓ YARN generic events and system metrics
- 30. Milestones - Future • Milestone 2 (“Alpha 2”) • IntegraSon with new YARN UI • IntegraSon with more frameworks • Beta • Freeze API and storage schema • Security • Collectors as containers • Storage fault tolerance • ProducSon-ready • MigraSon-ready
- 31. Demo
- 32. Contributors • Li Lu, Junping Du, Vinod Kumar Vavilapalli (Hortonworks) • Varun Saxena, Naganarasimha G. R. (Huawei) • Sangjin Lee, Vrushali Channapa5an, Joep RoInghuis (Twi5er) • Zhijie Shen (now at Facebook) • The HBase and Phoenix community!
- 33. Thank you!