From data stream management to distributed dataflows and beyond
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Recent efforts by academia and open-source communities have established stream processing as a principal data analysis technology across industry. All major cloud vendors offer streaming dataflow pipelines and online analytics as managed services. Notable use-cases include real-time fault detection in space networks, city traffic management, dynamic pricing for car-sharing, and anomaly detection in financial transactions. At the same time, streaming dataflow systems are increasingly being used for event-driven applications beyond analytics, such as orchestrating microservices and model serving. In the past decades, streaming technology has evolved significantly, however, emerging applications are once more challenging the design decisions of modern streaming systems. In this talk, I will discuss the evolution of stream processing and bring current trends and open problems to the attention of our community.
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Similaire à From data stream management to distributed dataflows and beyond
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From data stream management to distributed dataflows and beyond
- 1. From stream data management To distributed dataflows And beyond... Vasiliki (vasia) Kalavri (vkalavri@bu.edu)
- 2. Stream processing is an established technology in the data analytics stack of the modern business
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- 8. Traffic light adjustment in real time Alibaba City Brain analyzes vehicle locations to: • clear paths for emergency response vehicles • provide scheduling information for public transport • recommend alternative routes Read more: https://edition.cnn.com/2019/01/15/tech/alibaba-city-brain-hangzhou/index.html 6
- 9. Fault-detection for NASA’s Deep Space Network NASA’s DSN Complex Event Processing analyzes real-time network data, predicted antenna pointing parameters, and physical hardware logs to: • ingest, filter, store, and visualize all of the DSN's monitor and control data • ensure the successful DSN tracking, ranging, and communication integrity of dozens of concurrent deep-space missions Read more: https://www.confluent.io/kafka-summit-san-francisco-2019/mission-critical-real-time- fault-detection-for-nasas-deep-space-network-using-apache-kafka/ 7
- 10. • How did we get here? • Are we there yet? • What lies ahead?
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- 12. SIGMOD ’92 9
- 13. SIGMOD ’92 [… A new class of queries, continuous queries, are similar to conventional database queries, except that they are issued once and henceforth run “continually” over the database …] 9
- 16. 10 1992 20132004 Tapestry 20202000 2002 Aurora TelegraphCQ STREAM GigascopeNiagaraCQ Data Stream Management Systems
- 17. Synopsis Maintenance DSMS architecture Synopsis for S1 Synopsis for Sr … Fast approximate answers … S1 S2 Sr 11 InputManager Scheduler QoS Monitor Load Shedder Query Execution Engine QmQ2Q1 Ad-hoc or continuous queries Input streams …
- 18. 12 Data Stream Management Systems 1992 20132004 Tapestry Aurora TelegraphCQ STREAM 20202000 2002 GigascopeNiagaraCQ
- 19. 12 Data Stream Management Systems 1992 20132004 Tapestry Aurora TelegraphCQ STREAM 20202000 2002 GigascopeNiagaraCQ operator semantics event time & progress representations synopses & sketches
- 20. 12 Data Stream Management Systems 1992 20132004 Tapestry Aurora TelegraphCQ STREAM 20202000 2002 GigascopeNiagaraCQ operator semantics event time & progress representations synopses & sketches load management high availability scheduling
- 21. 12 Data Stream Management Systems 1992 20132004 Tapestry Aurora TelegraphCQ STREAM 20202000 2002 Gigascope MapReduce NiagaraCQ operator semantics event time & progress representations synopses & sketches load management high availability scheduling
- 22. 12 Data Stream Management Systems 1992 20132004 Tapestry Aurora TelegraphCQ STREAM 20202000 2002 Gigascope MapReduce Storm S4 NiagaraCQ operator semantics event time & progress representations synopses & sketches load management high availability scheduling
- 23. “Best-effort” low-latency stream processor λ-architecture MapReduce / Batch processing system Fast approximate results 13 InputManager Input data Persistent storage Slow exact results Applications Speed layer Batch layer
- 24. 14 Data Stream Management Systems 1992 20132004 Tapestry Aurora TelegraphCQ STREAM 20202000 2002 Gigascope MapReduce Storm S4 2015 NiagaraCQ operator semantics event time & progress representations synopses & sketches load management high availability scheduling
- 25. 14 Data Stream Management Systems 1992 20132004 Tapestry Aurora TelegraphCQ STREAM 20202000 2002 Gigascope MapReduce Storm S4 2015 Distributed Dataflow Systems NiagaraCQ Spark Streaming Naiad Flink Millwheel Google Dataflow Timely Dataflow Samza operator semantics event time & progress representations synopses & sketches load management high availability scheduling
- 26. Stream processing doesn’t necessarily need to be approximate and lossy
- 27. Worker Task Task state store Task DDS architecture Streaming APIs Distributed File System Coordinator Worker Task Task Task Worker Task Task Task TCP output to application and sinks 16 Event logs Socket TCP (Q, config) client schedule trigger checkpoint status put/get checkpoint
- 28. Spark Streaming 17 Data Stream Management Systems 1992 20132004 Tapestry Aurora TelegraphCQ STREAM 20202000 2002 Gigascope MapReduce Storm S4 Naiad Samza Flink Millwheel 2015 Google Dataflow Distributed Dataflow Systems NiagaraCQ Timely Dataflow operator semantics event time & progress representations synopses & sketches load management high availability scheduling
- 29. Spark Streaming 17 Data Stream Management Systems 1992 20132004 Tapestry Aurora TelegraphCQ STREAM 20202000 2002 Gigascope MapReduce Storm S4 Naiad Samza Flink Millwheel 2015 Google Dataflow Distributed Dataflow Systems NiagaraCQ Timely Dataflow operator semantics event time & progress representations synopses & sketches load management high availability scheduling data parallelism exactly-once fault-tolerance state management general-purpose languages iterations UDFs
- 30. Spark Streaming 17 Data Stream Management Systems 1992 20132004 Tapestry Aurora TelegraphCQ STREAM 20202000 2002 Gigascope MapReduce Storm S4 Naiad Samza Flink Millwheel 2015 Google Dataflow Distributed Dataflow Systems NiagaraCQ Timely Dataflow Are we there yet? operator semantics event time & progress representations synopses & sketches load management high availability scheduling data parallelism exactly-once fault-tolerance state management general-purpose languages iterations UDFs
- 32. 1. Process events online without storing them 18 SIGMOD Record ’05
- 33. 1. Process events online without storing them 18 SIGMOD Record ’05 persistently store events and state
- 34. 1. Process events online without storing them 2. Support a high-level language (SQL-like) 18 SIGMOD Record ’05 persistently store events and state
- 35. 1. Process events online without storing them 2. Support a high-level language (SQL-like) 18 SIGMOD Record ’05 persistently store events and state Java, Scala, Python, and SQL-like
- 36. 1. Process events online without storing them 2. Support a high-level language (SQL-like) 3. Handle missing, out-of-order, delayed data 18 SIGMOD Record ’05 persistently store events and state Java, Scala, Python, and SQL-like
- 37. 1. Process events online without storing them 2. Support a high-level language (SQL-like) 3. Handle missing, out-of-order, delayed data 18 SIGMOD Record ’05 persistently store events and state Java, Scala, Python, and SQL-like with tunable latency trade-offs
- 38. 1. Process events online without storing them 2. Support a high-level language (SQL-like) 3. Handle missing, out-of-order, delayed data 4. Guarantee deterministic (on replay) and correct results (on recovery) 18 SIGMOD Record ’05 persistently store events and state Java, Scala, Python, and SQL-like with tunable latency trade-offs
- 39. 1. Process events online without storing them 2. Support a high-level language (SQL-like) 3. Handle missing, out-of-order, delayed data 4. Guarantee deterministic (on replay) and correct results (on recovery) 18 SIGMOD Record ’05 persistently store events and state Java, Scala, Python, and SQL-like with tunable latency trade-offs and exactly-once
- 40. 1. Process events online without storing them 2. Support a high-level language (SQL-like) 3. Handle missing, out-of-order, delayed data 4. Guarantee deterministic (on replay) and correct results (on recovery) 5. Combine batch and stream processing 18 SIGMOD Record ’05 persistently store events and state Java, Scala, Python, and SQL-like with tunable latency trade-offs and exactly-once
- 41. 1. Process events online without storing them 2. Support a high-level language (SQL-like) 3. Handle missing, out-of-order, delayed data 4. Guarantee deterministic (on replay) and correct results (on recovery) 5. Combine batch and stream processing 18 SIGMOD Record ’05 persistently store events and state Java, Scala, Python, and SQL-like with tunable latency trade-offs and exactly-once batch is a special case of streaming
- 42. 1. Process events online without storing them 2. Support a high-level language (SQL-like) 3. Handle missing, out-of-order, delayed data 4. Guarantee deterministic (on replay) and correct results (on recovery) 5. Combine batch and stream processing 6. Ensure availability despite failures 18 SIGMOD Record ’05 persistently store events and state Java, Scala, Python, and SQL-like with tunable latency trade-offs and exactly-once batch is a special case of streaming
- 43. 1. Process events online without storing them 2. Support a high-level language (SQL-like) 3. Handle missing, out-of-order, delayed data 4. Guarantee deterministic (on replay) and correct results (on recovery) 5. Combine batch and stream processing 6. Ensure availability despite failures 18 SIGMOD Record ’05 persistently store events and state Java, Scala, Python, and SQL-like with tunable latency trade-offs and exactly-once batch is a special case of streaming and exactly-once state updates
- 44. 1. Process events online without storing them 2. Support a high-level language (SQL-like) 3. Handle missing, out-of-order, delayed data 4. Guarantee deterministic (on replay) and correct results (on recovery) 5. Combine batch and stream processing 6. Ensure availability despite failures 7. Support distribution and automatic elasticity 18 SIGMOD Record ’05 persistently store events and state Java, Scala, Python, and SQL-like with tunable latency trade-offs and exactly-once batch is a special case of streaming and exactly-once state updates
- 45. 1. Process events online without storing them 2. Support a high-level language (SQL-like) 3. Handle missing, out-of-order, delayed data 4. Guarantee deterministic (on replay) and correct results (on recovery) 5. Combine batch and stream processing 6. Ensure availability despite failures 7. Support distribution and automatic elasticity 18 SIGMOD Record ’05 persistently store events and state Java, Scala, Python, and SQL-like with tunable latency trade-offs and exactly-once batch is a special case of streaming and exactly-once state updates
- 46. 1. Process events online without storing them 2. Support a high-level language (SQL-like) 3. Handle missing, out-of-order, delayed data 4. Guarantee deterministic (on replay) and correct results (on recovery) 5. Combine batch and stream processing 6. Ensure availability despite failures 7. Support distribution and automatic elasticity 8. Offer low-latency 18 SIGMOD Record ’05 persistently store events and state Java, Scala, Python, and SQL-like with tunable latency trade-offs and exactly-once batch is a special case of streaming and exactly-once state updates
- 47. 1. Process events online without storing them 2. Support a high-level language (SQL-like) 3. Handle missing, out-of-order, delayed data 4. Guarantee deterministic (on replay) and correct results (on recovery) 5. Combine batch and stream processing 6. Ensure availability despite failures 7. Support distribution and automatic elasticity 8. Offer low-latency 18 SIGMOD Record ’05 persistently store events and state Java, Scala, Python, and SQL-like with tunable latency trade-offs and exactly-once batch is a special case of streaming and exactly-once state updates high throughput and “acceptable" latency
- 48. 1. Process events online without storing them 2. Support a high-level language (SQL-like) 3. Handle missing, out-of-order, delayed data 4. Guarantee deterministic (on replay) and correct results (on recovery) 5. Combine batch and stream processing 6. Ensure availability despite failures 7. Support distribution and automatic elasticity 8. Offer low-latency 18 SIGMOD Record ’05 persistently store events and state Java, Scala, Python, and SQL-like with tunable latency trade-offs and exactly-once batch is a special case of streaming and exactly-once state updates high throughput and “acceptable" latency
- 49. Some of my recent and ongoing work 19
- 50. Re-configurable Stream Processing Automatic scaling Analyzer invoke re-configure job performance metrics decision Profiler Adaptive scheduling Straggler mitigation Query optimization Instrumented stream processor Some of my recent and ongoing work 19
- 51. Automatic elasticity and reconfiguration 20 heuristic policies if CPU > 80% => scale stop-and-restart migration and reconfiguration
- 52. Automatic elasticity and reconfiguration 21 Accuracy: no over/under-provisioning Stability:no oscillations Performance: fast convergence Safe migration: correct results Three steps is all you need: fast, accurate, automatic scaling decisions for distributed streaming dataflows (OSDI ’18). Megaphone: Latency-conscious state migration for distributed streaming dataflows (VLDB’19). github.com/strymon-system/ds2 github.com/strymon-system/megaphone o1 cannot keep up waiting for output waiting for input src o1 o2
- 53. Re-configurable Stream Processing Automatic scaling Analyzer invoke re-configure job performance metrics decision Profiler Adaptive scheduling Straggler mitigation Query optimization Instrumented stream processor 22
- 54. Performance analysis of streaming dataflows is itself a challenging streaming computation with strict latency requirements Re-configurable Stream Processing Automatic scaling Analyzer invoke re-configure job performance metrics decision Profiler Adaptive scheduling Straggler mitigation Query optimization Instrumented stream processor 22
- 55. Performance analysis of streaming dataflows is itself a challenging streaming computation with strict latency requirements Re-configurable Stream Processing Automatic scaling Analyzer invoke re-configure job performance metrics decision Profiler Adaptive scheduling Straggler mitigation Query optimization Instrumented stream processor 22 Snailtrail: Generalizing critical paths for online analysis of distributed dataflows (NSDI’18). github.com/li1/snailtrail
- 56. 1. Process events online without storing them 2. Support a high-level language (SQL-like) 3. Handle missing, out-of-order, delayed data 4. Guarantee deterministic (on replay) and correct results (on recovery) 5. Combine batch and stream processing 6. Ensure availability despite failures 7. Support distribution and automatic elasticity 8. Offer low-latency 23 SIGMOD Record ’05 persistently store events and state Java, Scala, Python, and SQL-like with tunable latency trade-offs and exactly-once batch is a special case of streaming and exactly-once state updates high throughput and “acceptable" latency accurate, stable, latency-aware
- 57. reliability, production readiness and community can be more important than raw performance In open-source software 24
- 58. reliability, production readiness and community can be more important than raw performance In open-source software 24 Apache Flink, Nexmark Q4 latency (ms) CDF 1.0 0.8 0.6 0.4 0.2 0.0 In-memory state RocksDB state 1000080006000400020000 serde at every access
- 59. 25 write-heavy, large state RMW a single value globally configured store
- 60. 25 write-heavy, large state RMW a single value globally configured store Type-aware, flexible state management provides up to an order of magnitude latency improvement We need configurable streaming backends New streaming state benchmarks
- 61. Beyond…
- 62. Model serving 27 Stream Processor Model Server RPC input stream predictions Stream Processor op input stream predictions Model management and versioning 1. Model stored externally 2. Model stored in managed state Exactly-once guarantees? Latency trade-offs unclear What kind of state store to use?
- 63. Stateful serverless (FaaS) 28 Automatic scaling Function orchestration Support for transactions External requests Events and function triggers f λ f f f output Apache Flink Stateful Functions: https://statefun.io Stateful Functions as a Service in Action (VLDB’19)
- 64. Graph streaming & online trainingdatarate analytics complexity low high low high Machine Learning Data Mining Streaming CEP Relational analytics Graph processing Complex streaming data analytics Streaming Graph Partitioning: An Experimental Study (VLDB’18). Practice of Streaming and Dynamic Graphs: Concepts, Models, Systems, and Parallelism (arxiv.org/abs/1912.12740). 29 Graph state management Data-parallel graph synopses Languages & operator semantics Adaptive graph partitioning
- 65. Spark Streaming 30 Data Stream Management Systems 1992 20132004 Tapestry Aurora TelegraphCQ STREAM 20202000 2002 Gigascope MapReduce Storm S4 Naiad Samza Flink Millwheel 2015 Google Dataflow Distributed Dataflow Systems NiagaraCQ Timely Dataflow ML operator semantics event time & progress representations synopses & sketches load management high availability scheduling data parallelism exactly-once fault-tolerance state management general-purpose languages iterations UDFs Graphs FaaS Edge Modern hardware
- 66. From stream data management To distributed dataflows And beyond... Vasiliki (vasia) Kalavri (vkalavri@bu.edu)