Kostas Tzoumas_Stephan Ewen - Keynote -The maturing data streaming ecosystem and Apache Flink’s accelerated growth
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The document provides details about the Flink Forward 2016 conference, highlighting important logistical information and acknowledgments to sponsors, speakers, and program committee members. It discusses the rapidly growing data streaming ecosystem, Apache Flink's unique capabilities for real-time processing, the importance of innovation in the community, and future directions for Flink, including state-of-the-art streaming features and SQL enhancements. It emphasizes the growing intersection between streaming and batch processing, showcasing Flink's role in enterprise stream processing.
Kostas Tzoumas_Stephan Ewen - Keynote -The maturing data streaming ecosystem and Apache Flink’s accelerated growth
- 2. Some practical information Network name: Flink Forward 2016 Password: #flinkforward16 Twitter handle: @flinkforward Hashtag: #ff16 Group photo today at 3.30 pm All talks will be recorded and can be found on our YouTube channel “Apache Flink Berlin” after the conference FlinkFest today at Palais starting at 6.10 pm Attention: Some last minute changes to the program, please consult online schedule
- 3. 3 The Venue
- 4. 4 A big thanks to our sponsors!
- 5. 5 A big thanks to our program committee! Tyler Akidau Google Stephan Ewen data Artisans Jamie Grier data Artisans Vasia Kalavri KTH Neha Narkhede Confluent
- 6. 6 A big thanks to our speakers!
- 7. 7 A big thanks to our speakers!
- 8. 8 Kostas Tzoumas Stephan Ewen Flink Forward September 12, 2016 The data streaming ecosystem and Apache Flink®: present and future
- 9. 9 Founded by the original creators of Apache Flink®, our goal is to make stream processing accessible to the enterprise Contributing and helping the Flink community grow Providing enterprise support and services
- 10. Streaming is a rapidly growing and maturing market category of its own Streaming is the biggest change in data infrastructure (Flink Forward 2015) 10
- 11. The Flink community has been at the center of this journey. And there is innovation and convergence in all parts of the stack. message transport compute engine programming paradigm 11
- 12. Why? Streaming technology is enabling the obvious: continuous processing on data that is continuously produced Hint: you already have streaming data 12
- 13. Data streaming adoption patterns Real-time products and business monitoring Robust continuous applications Decentralized architecture Unify real-time and historical data 13
- 14. Retail, e-commerce Better product recommendations Process monitoring Inventory management Finance Differentiation via tech Push-based products Fraud detection Telco, IoT, Infrastructure Infrastructure monitoring Anomaly detection Internet & mobile Personalization User behavior monitoring Analytics 14
- 15. 30 Flink applications in production for more than one year. 10 billion events (2TB) processed daily Complex jobs of > 30 operators running 24/7, processing 30 billion events daily, maintaining state of 100s of GB with exactly-once guarantees Largest job has > 20 operators, runs on > 5000 vCores in 1000-node cluster, processes millions of events per second 15
- 16. What is Flink's unique role in the streaming data ecosystem? 16
- 17. Before Flink, users had to make hard choices between: Volume Latency Accuracy 17
- 18. Flink eliminates these tradeoffs 10s of millions events per second for stateful applications Sub-second latency, as low as single-digit milliseconds Accurate computation results 18
- 19. A broader definition of accuracy: the results that I want when I want them 1. Accurate under failures and downtime 2. Accurate under out of order data 3. Results when you need them 4. Accurate modeling of the world 19
- 20. 1. Failures and downtime Checkpoints & savepoints Exactly-once guarantees 2. Out of order and late data Event time support Watermarks 3. Results when you need them Low latency Triggers 4. Accurate modeling True streaming engine Sessions and flexible windows 20
- 21. 5. Batch + streaming One engine Dedicated APIs 6. Reprocessing High throughput, event time support, and savepoints 7. Ecosystem Rich connector ecosystem and 3rd party packages 8. Community support One of the most active projects with over 200 contributors 21 flink -s <savepoint> <job>
- 22. What are the next steps for Flink? 22
- 23. Provide state of the art streaming capabilities (✔) Operate in the largest infrastructures of the world Open up to a wider set of enterprise users Broaden the scope of stream processing 23
- 24. Apache Flink today 24 The Apache Flink community has pushed the boundaries of open source stream processing.
- 25. Flink's unique combination of features 25 Low latency High Throughput Well-behaved flow control (back pressure) Consistency Works on real-time and historic data Performance Event Time APIs Libraries Stateful Streaming Savepoints (replays, A/B testing, upgrades, versioning) Exactly-once semantics for fault tolerance Windows & user-defined state Flexible windows (time, count, session, roll-your own) Complex Event Processing Fluent API Out-of-order events Fast and large out-of-core state
- 26. Flink v1.1 26 Connectors Metric System (Stream) SQL Session Windows Library enhancements
- 27. Flink v1.1 + current threads 27 Connectors Session Windows (Stream) SQL Library enhancements Metric System Metrics & Visualization Dynamic Scaling Savepoint compatibility Checkpoints to savepoints More connectors Stream SQL Windows Large state Maintenance Fine grained recovery Side in-/outputs Window DSL Security Mesos & others Dynamic Resource Management Authentication Queryable State
- 28. Flink v1.1 + current threads 28 Connectors Session Windows (Stream) SQL Library enhancements Metric System Operations Ecosystem Application Features Metrics & Visualization Dynamic Scaling Savepoint compatibility Checkpoints to savepoints More connectors Stream SQL Windows Large state Maintenance Fine grained recovery Side in-/outputs Window DSL Broader Audience Security Mesos & others Dynamic Resource Management Authentication Queryable State
- 29. Flink v1.1 + current threads 29 Connectors Session Windows (Stream) SQL Library enhancements Metric System Operations Ecosystem Application Features Metrics & Visualization Dynamic Scaling Savepoint compatibility Checkpoints to savepoints More connectors Stream SQL Windows Large state Maintenance Fine grained recovery Side in-/outputs Window DSL Broader Audience Security Mesos & others Dynamic Resource Management Authentication Queryable State
- 30. Queryable State Flink v1.1 + current threads 30 Connectors Session Windows (Stream) SQL Library enhancements Metric System Operations Ecosystem Application Features Metrics & Visualization Dynamic Scaling Savepoint compatibility Checkpoints to savepoints More connectors Stream SQL Windows Large state Maintenance Fine grained recovery Side in-/outputs Window DSL Broader Audience Security Mesos & others Dynamic Resource Management Authentication More details in the Talk "The Future of Apache Flink" (Monday, 11:00)
- 31. Security / Authentication 31 No unauthorized data access Secured clusters with Kerberos-based authentication • Kafka, ZooKeeper, HDFS, YARN, HBase, … No unencrypted traffic between Flink Processes • RPC, Data Exchange, Web UI Largely contributed by Prevent malicious users to hook into Flink jobs See talk "Flink Security Enhancements" (Tuesday, 11.45)
- 32. Checkpoints / Savepoints 32 Recover a running job into a new job Recover a running job onto a new cluster Application state backwards compatibility • Flink 1.0 made the APIs backwards compatible • Now making the savepoints backwards compatible • Applications can be moved to newer versions of Flink even when state backends or internals change v1.x v2.0v1.y
- 33. Dynamic scaling 33 Changing load bears changing resource requirements • Need to adjust parallelism of running streaming jobs Re-scaling stateless operators is trivial Re-scaling stateful operators is hard (windows, user state) • Efficiently re-shard state time Workload Resources Re-scaling Flink jobs preserves exactly-once guarantees See talk "Dynamic scaling: How Apache Flink adapts to changing workloads" (Tuesday, 14.45)
- 34. Cluster management 34 Series of improvements to seamlessly interoperate with various cluster managers • YARN, Mesos, Docker, Standalone, … • Proper isolation of jobs, clean support for multi-job sessions Dynamic acquire/release of resources Using mixed container sizes Driven by Mesos integration contributed by and
- 35. Cluster management 35 Series of improvements to seamlessly interoperate with various cluster managers • YARN, Mesos, Docker, Standalone, … • Proper isolation of jobs, clean support for multi-job sessions Dynamic acquire/release of resources Using mixed container sizes Driven by Mesos integration contributed by and See talk "Introducing Flink on Mesos" (Tuesday, 11.30) See talk "Running Flink Everywhere" (Monday, 16.45)
- 36. Stream SQL 36 SQL is the standard high-level query language A natural way to open up streaming to more people Problem: There is no Streaming SQL standard • At least beyond the basic operations • Challenging: Incorporate windows and time semantics Flink community working with Apache Calcite to draft a new model
- 37. Stream SQL 37 SQL is the standard high-level query language A natural way to open up streaming to more people Flink community working with users and with Apache Calcite to draft a new model Problem: There is no Streaming SQL standard • At least beyond the basic operations • Challenging: Incorporate windows and time semantics See talk "Streaming SQL" (Monday, 11:00) See talk "Taking a look under the hood of Apache Flink’s relational APIs" (Monday, 16.45)
- 39. Streaming and batch 39 The separation of batch and streaming … … is quite artificial … has been largely technology driven (not by use cases) In fact – several talks here are about batch processing… People are approaching Flink for batch processing as well
- 40. Streaming and batch 40 2016-3-1 12:00 am 2016-3-1 1:00 am 2016-3-1 2:00 am 2016-3-11 11:00pm 2016-3-12 12:00am 2016-3-12 1:00am 2016-3-11 10:00pm 2016-3-12 2:00am 2016-3-12 3:00am… partition partition
- 41. Streaming and batch 41 2016-3-1 12:00 am 2016-3-1 1:00 am 2016-3-1 2:00 am 2016-3-11 11:00pm 2016-3-12 12:00am 2016-3-12 1:00am 2016-3-11 10:00pm 2016-3-12 2:00am 2016-3-12 3:00am… partition partition Stream (low latency) Stream (high latency)
- 42. Streaming and batch 42 2016-3-1 12:00 am 2016-3-1 1:00 am 2016-3-1 2:00 am 2016-3-11 11:00pm 2016-3-12 12:00am 2016-3-12 1:00am 2016-3-11 10:00pm 2016-3-12 2:00am 2016-3-12 3:00am… partition partition Stream (low latency) Batch (bounded stream) Stream (high latency)
- 43. Why use batch at all now? 43 … or Flink's DataSet API … dedicated batch processors Cost of fault tolerance and accuracy Resource elasticity / efficiency Missing primitives (example: BSP iterations) Possible to add to DataStream API Deeper integration between batch and streaming techniques
- 44. Some batch proof points… 44 TeraSort Relational Join Classic Batch Jobs Graph Processing Linear Algebra
- 45. State in stream processing 45 Stateless Streaming (Apache Storm) Stateful Streaming (Apache Samza) Accurate Stateful Streaming (Apache Flink) State sizes in Flink today (my assessment): 10s gigabytes per operator How to scale this to many terabytes? • Queryable State • Data driven triggers over large state
- 46. Large-state streaming 46 How to scale the stream processor state? … and maintain fast checkpoint intervals? … and have very fast recovery on machine failures? More and more database techniques coming into Flink
- 47. …in conclusion 1. Flink is running in some of the largest streaming setups 2. Community is working on adding many state-of-the-art operational features 3. Available to broader audiences, via Stream SQL 4. Streaming has even more potential to subsume batch and will hold more and more application state 47
Editor's Notes
- #14: Uber, Netflix, Alibaba, Zalando, King