State Management in Apache Flink : Consistent Stateful Distributed Stream Processing
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An overview of state management techniques employed in Apache Flink including pipelined consistent snapshots and intuitive usages for reconfiguration, which were presented at vldb 2017.
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State Management in Apache Flink : Consistent Stateful Distributed Stream Processing
- 1. Paris Carbone<parisc@kth.se> - KTH Royal Institute of Technology Stephan Ewen<stephan@data-artisans.com> - data Artisans Gyula Fóra<gyula.fora@king.com> - King Digital Entertainment Ltd Seif Haridi<haridi@kth.se> - KTH Royal Institute of Technology Stefan Richter<s.richter@data-artisans.com> - data Artisans Kostas Tzoumas<kostas@data-artisans.com> - data Artisans 1 State Management in Apache Flink® Consistent Stateful Distributed Stream Processing @vldb17
- 2. Overview • The Apache Flink System Architecture • Pipelined Consistent Snapshots • Operations with Snapshots • Large Scale Deployments and Evaluation 2
- 3. The Apache Flink Framework Cluster Backend Metrics Dataflow Runtime DataStream DataSet SQL Table CEP Graphs ML Libraries Core API Runner Setup 3
- 4. Distributed Architecture Cluster Backend Metrics Dataflow Runtime DataStream DataSet SQL Table CEP Graphs ML Libraries Core API Runner Setup Job Manager Task Manager Task Manager …. Client 4
- 5. Distributed Architecture Cluster Backend Metrics Dataflow Runtime DataStream DataSet SQL Table CEP Graphs ML Libraries Core API Runner Setup Job Manager Task Manager Task Manager …. Client 4
- 6. Distributed Architecture Cluster Backend Metrics Dataflow Runtime DataStream DataSet SQL Table CEP Graphs ML Libraries Core API Runner Setup Job Manager Task Manager Task Manager …. Client optimised logical graph 4
- 7. Distributed Architecture Cluster Backend Metrics Dataflow Runtime DataStream DataSet SQL Table CEP Graphs ML Libraries Core API Runner Setup Job Manager Task Manager Task Manager …. • scheduling • state partitioning • snapshot coordination Client optimised logical graph 4
- 8. Zookeeper • passive failover • snapshot metadata Distributed Architecture Cluster Backend Metrics Dataflow Runtime DataStream DataSet SQL Table CEP Graphs ML Libraries Core API Runner Setup Job Manager Task Manager Task Manager …. • scheduling • state partitioning • snapshot coordination Client optimised logical graph 4
- 9. Zookeeper • passive failover • snapshot metadata Distributed Architecture Cluster Backend Metrics Dataflow Runtime DataStream DataSet SQL Table CEP Graphs ML Libraries Core API Runner Setup Job Manager Task Manager Task Manager …. • scheduling • state partitioning • snapshot coordination Client optimised logical graph • memory management • local snapshot execution • flow control physical long-running tasks 4
- 10. Zookeeper • passive failover • snapshot metadata Distributed Architecture Cluster Backend Metrics Dataflow Runtime DataStream DataSet SQL Table CEP Graphs ML Libraries Core API Runner Setup Job Manager Task Manager Task Manager …. • scheduling • state partitioning • snapshot coordination Client optimised logical graph • memory management • local snapshot execution • flow control physical long-running tasks locally managed state 4
- 11. Zookeeper • passive failover • snapshot metadata Distributed Architecture Cluster Backend Metrics Dataflow Runtime DataStream DataSet SQL Table CEP Graphs ML Libraries Core API Runner Setup Job Manager Task Manager Task Manager …. • scheduling • state partitioning • snapshot coordination Client optimised logical graph • memory management • local snapshot execution • flow control physical long-running tasks locally managed state External Snapshot Store (e.g., hdfs) partial snapshots 4
- 12. 1. End-to-End Guarantees Snapshots 2. Reconfiguration 3. Version Control 4. Isolation Snapshots 5
- 13. 1. End-to-End Guarantees Snapshots 2. Reconfiguration 3. Version Control 4. Isolation Snapshots 6
- 15. Stateful Processing tasktasktask invoke per input record 7
- 16. Stateful Processing tasktasktask readwrite managed state logical operations (collections) invoke per input record 7
- 17. Local State Backend physical operations In-Memory(Heap) Embedded Off-heap+Disk Key-Value Store (RocksDB) Stateful Processing tasktasktask readwrite managed state logical operations (collections) invoke per input record 7
- 18. Local State Backend physical operations In-Memory(Heap) Embedded Off-heap+Disk Key-Value Store (RocksDB) Stateful Processing tasktasktask readwrite managed state logical operations (collections) invoke per input record state = f(input) 7
- 19. 8
- 23. local statesinput streams capture all local states after completing an epoch divide computation into epochs stream processor 8
- 24. local statesinput streams capture all local states after completing an epoch divide computation into epochs stream processor can rollback input and state to captured point in the past 8
- 25. Snapshot Store copy states A Synchronous Approach master 9
- 26. drain epoch 1 Snapshot Store copy states A Synchronous Approach master 9
- 27. drain epoch 1 Snapshot Store copy states A Synchronous Approach master 9
- 28. drain epoch 1 Snapshot Store copy states A Synchronous Approach master 9
- 29. drain epoch 2 Snapshot Store copy states A Synchronous Approach master 9
- 30. drain epoch 2 Snapshot Store copy states A Synchronous Approach master 9
- 31. drain epoch 2 Snapshot Store copy states A Synchronous Approach master 9
- 32. • In use: Storm Trident and Spark Streaming • A conservative approach, equivalent to batching • Can cause unnecessary latency (master coordination) • Processing is no longer continuous • Forces many tasks to be idle • Instead, in Apache Flink snapshots are pipelined Synchronous Snapshots 10
- 33. Pipelined Snapshots Snapshot Store async state copy 11
- 34. Pipelined Snapshots Snapshot Store async state copy insert markers 11
- 35. Pipelined Snapshots Snapshot Store async state copy insert markers A B C D E 11
- 36. Pipelined Snapshots Snapshot Store async state copy A B C D E 11
- 37. Pipelined Snapshots Snapshot Store async state copy A B C D E B 11
- 38. Pipelined Snapshots Snapshot Store async state copy epoch alignment A B C D E B 11
- 39. Pipelined Snapshots Snapshot Store async state copy epoch alignment A B C D E B A 11
- 40. Pipelined Snapshots Snapshot Store async state copy A B C D E B A C 11
- 41. Pipelined Snapshots Snapshot Store async state copy A B C D E B A C D E 11
- 42. Pipelined Snapshots Snapshot Store async state copy snapshot completes A B C D E B A C D E 11
- 44. Pipelined Snapshots (cycles) Problem: we cannot wait indefinitely for records in cycles 12
- 45. Pipelined Snapshots (cycles) Problem: we cannot wait indefinitely for records in cycles Solution: log in snapshot inflight records within a cycle Replay upon recovery. 12
- 46. • Offers exactly-once processing guarantees • Issued periodically/externally by the user • Naturally respects flow control mechanisms • Channel state logging limited to cycles only • Multiple epoch snapshots can be pipelined • Can offer weaker at-least-once processing guarantees by simply dropping aligning vs no alignment cost Technique Highlights 13
- 47. 1. End-to-End Guarantees Snapshots 2. Reconfiguration 3. Version Control 4. Isolation Snapshots Usages 14
- 48. Exactly-Once: Input and Processing Important Assumptions • Input streams are persisted with offset indexes (e.g., Kafka, Kinesis) • Data Channels are FIFO and reliable (no loss) Each epoch either completes or repeats 15
- 49. • Idempontency ~ repeated operations can be tolerated after recovery/rollback (works for mutable stores). • Transactional Processing ~ Requires a two-phase coordination. A snapshot completion eventually leads to external commit (e.g., Flink’s HDFS RollingSink*) in-progress committedpendingpending epoch n-1 epoch n-2 epoch n-3epoch n Exactly-Once Output 16
- 50. Snapshots 2. Reconfiguration 3. Version Control 4. Isolation Snapshots Usages 1. End-to-End Guarantees 17
- 55. Dataflow Reconfiguration stop snap-1 snap-2 snap-3 … change parallelism Problem: How is state repartitioned from a snapshot? 18
- 57. Reconfiguration: The Issue 0x100: bob … … … … 0x449: alice reconfigure case I full scan Scan Remote Storage for Responsible Keys 19
- 58. Reconfiguration: The Issue 0x100: bob … … … … 0x449: alice reconfigure case I full scan Scan Remote Storage for Responsible Keys too slow 19
- 59. Reconfiguration: The Issue case II 0x100: bob … … … … 0x449: alice reconfigure Include Key Locations in Snapshot Metadata bob: 0x100 carol: 0x344 … alice: 0x449 chuck: 0x630 … 0x100: bob … … … … 0x449: alice reconfigure case I full scan Scan Remote Storage for Responsible Keys too slow 19
- 60. Reconfiguration: The Issue case II 0x100: bob … … … … 0x449: alice reconfigure Include Key Locations in Snapshot Metadata bob: 0x100 carol: 0x344 … alice: 0x449 chuck: 0x630 … 0x100: bob … … … … 0x449: alice reconfigure case I full scan Scan Remote Storage for Responsible Keys too slow too much 19
- 61. Reconfiguration: Key Groups Pre-partition state in hash(K) space, into key-groups bob… … … … … … alice 20
- 62. Reconfiguration: Key Groups Pre-partition state in hash(K) space, into key-groups bob… … … … … … • Snapshot Metadata: Contains a reference per stored Key-Group (less metadata) • Reconfiguration: Contiguous key-group allocation to available tasks (less IO) alice 20
- 63. Reconfiguration: Key Groups Pre-partition state in hash(K) space, into key-groups bob… … … … … … • Snapshot Metadata: Contains a reference per stored Key-Group (less metadata) • Reconfiguration: Contiguous key-group allocation to available tasks (less IO) alice Note: number of key groups controls trade-off between metadata to keep and reconfiguration speed 20
- 64. Snapshots 2. Reconfiguration 3. Version Control 4. Isolation Snapshots Usages 1. End-to-End Guarantees 21
- 67. Version Control fork and update Pipeline v.1 Pipeline v.2 22
- 68. Version Control fork and update Pipeline v.1 Pipeline v.2 22
- 69. Version Control fork and update Pipeline v.1 Pipeline v.3 Pipeline v.2 22
- 70. Version Control fork and update Pipeline v.1 Pipeline v.3 Pipeline v.2 22
- 71. Snapshots 2. Reconfiguration 3. Version Control 4. Isolation Snapshots Usages 1. End-to-End Guarantees 23
- 73. Isolation Levels select from facebook.userID, clients.name … inner join clients on … read-committed (snapshot) read-uncommitted (dirty read on latest state) external query 24
- 74. Large Scale Deployment at King 25
- 75. Large Scale Deployment at King100 200 300 400 500 Global State Size (GB) 0 50 100 150 200 250 TotalSnapshottingTime(sec) total time / snapshot (alignment + async copies) 25
- 76. Large Scale Deployment at King100 200 300 400 500 Global State Size (GB) 0 50 100 150 200 250 TotalSnapshottingTime(sec) total time / snapshot (alignment + async copies) ~runtime overhead 25
- 77. Large Scale Deployment at King 30 50 70 Parallelism 0 200 400 600 800 1000 1200 1400 TotalAlignmentTime(msec) PROC WIN OUT alignment cost 100 200 300 400 500 Global State Size (GB) 0 50 100 150 200 250 TotalSnapshottingTime(sec) total time / snapshot (alignment + async copies) ~runtime overhead 25
- 78. Large Scale Deployment at King 30 50 70 Parallelism 0 200 400 600 800 1000 1200 1400 TotalAlignmentTime(msec) PROC WIN OUT alignment cost 100 200 300 400 500 Global State Size (GB) 0 50 100 150 200 250 TotalSnapshottingTime(sec) total time / snapshot (alignment + async copies) ~runtime overhead 25
- 79. Large Scale Deployment at King 30 50 70 Parallelism 0 200 400 600 800 1000 1200 1400 TotalAlignmentTime(msec) PROC WIN OUT alignment cost 100 200 300 400 500 Global State Size (GB) 0 50 100 150 200 250 TotalSnapshottingTime(sec) total time / snapshot (alignment + async copies) ~runtime overhead • #shuffles (keyby) • parallelism 25
- 80. Teaser: More paper highlights • We can use the same technique to coordinate externally managed state with snapshots. • Epoch markers can act as on-the-fly reconfiguration points. • Internals of asynchronous and incremental snapshots. 26
- 81. Paris Carbone<parisc@kth.se> - KTH Royal Institute of Technology Stephan Ewen<stephan@data-artisans.com> - data Artisans Gyula Fóra<gyula.fora@king.com> - King Digital Entertainment Ltd Seif Haridi<haridi@kth.se> - KTH Royal Institute of Technology Stefan Richter<s.richter@data-artisans.com> - data Artisans Kostas Tzoumas<kostas@data-artisans.com> - data Artisans 27 State Management in Apache Flink® Consistent Stateful Distributed Stream Processing @vldb17