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Apache Flink 101 - the rise of stream processing and beyond
- 1. Apache Flink 101 - The Rise of Stream Processing and Beyond Bowen Li Commiter@Apache Flink, Senior Engineer@Alibaba Nov 20, 2019 @Big Data Bellevue
- 2. ● Flink use cases ● Flink in a nutshell - what makes Flink successful in stream processing ● Beyond stream processing ○ Batch ○ Data warehousing and Notebook ○ AI/Machine Learning ○ Serverless Agenda
- 3. Time = Value Business demands Real Time Computation
- 5. Flink at Alibaba ● Powers real time computations of all business units at Alibaba group ● Powers all search and recommendations, both online and offline ● Provided as cloud service to public on Ali Cloud
- 6. Single’s Day Global Shopping Festival on 11/11
- 7. Single’s Day Stats - Nov 11, 2019 Alibaba ○ GMV ■ $14 million in the first 21s ■ $1.4 billion in the first 96s ■ $38 billion in 24h ○ 982 PB data generated in total ○ 544,000 transactions/sec at peak Flink @ Alibaba ○ 2 billion events/sec, 3 TB/sec - up 111% from 2018
- 8. Flink at Alibaba Use Case 1: online ML
- 9. ○ hundreds of millions events ○ 100+ billions features ○ e2e second latency ○ real-time training, feature and module update Flink at Alibaba Use Case 1: online ML
- 10. Flink at Alibaba Use Case 2: Real Time GMV dashboard
- 11. Flink at Alibaba Use Case 2: Real Time GMV dashboard
- 12. Flink at Scale Real Time AI / ML Real Time AnalyticsReal Time Fraud Detection/Risk Management Real Time Dynamic Pricing
- 13. Flink at Scale Real Time Compute Service on public cloud backed by Flink Kinesis Analytics
- 14. Flink in a Nutshell - key differentiaters from other open source solutions ancient squirrel from Ice Age!
- 15. Flink in a Nutshell Stateful Computations …...
- 16. Stateful Computations …... Why (built-in) state matters? ● computation with context, rather than single record ● no network IO, lower latency ● no external dependency, full control by framework for exactly-once semantics ● ...
- 17. Stateful Computations …... Flink provides built-in state backends that support rich, arbitrary data structure in a fully fault tolerant way ○ In-memory, splliable backend ○ RocksDB backend
- 18. Stateful Computations over Event Streams…... Flink in a Nutshell
- 19. Stateful Computations over Event Streams…... It means a few things… 1. All your data is data streams! ○ batch v.s. streaming - just execution models ○ bounded v.s. unbounded data streams - key difference ○ technically all data processing is stream processing
- 20. Stateful Computations over Event Streams…... 2. Streaming-first, pipelined execution ○ record flowing thru the system -> extremely high throughput, super fast, ultra low latency ○ fondamentally different from batch-first, staged-execution framework ○ can’t achieved by mini-batch workaround
- 21. Stateful Computations over Event Streams…... 3. Event comes with time! ○ event time v.s. processing time ○ windows aggregation, sessionazation, pattern recognition, time-base joins ○ out-of-order and late data Flink support all the most comprehensive time semantics natively from the beginning
- 22. Stateful Computations over Event Streams in an Expressive …... Flink in a Nutshell
- 23. Stateful Computations over Event Streams in an Expressive …... Layered APIs with the most comprehensive semantics
- 24. Flink’s layered APIs Streaming SQL Table API DataStream API ProcessFunction More - declarative - optimizable - understandable - stable - unified for streaming and batch More - advanced - precise control - optimized (if you know what you are doing)
- 25. Streaming SQL Flink SQL> CREATE TABLE test(user BIGINT, msg VARCHAR, ts VARCHAR) WITH ( 'connector.type' = 'kafka', 'connector.topic' = 'topic_name', 'format.type' = 'avro', 'connector.startup-mode' = 'earliest-offset' ) Flink SQL> SELECT * FROM mytest; Flink SQL> INSERT INTO unique_user SELECT DISTINCT user FROM test;
- 26. Table API // Java/Scala/Python tableEnvironment .connect( new Kafka().version("1.0") .topic("topic_name") .startFromEarliest()) .schema(...) .isAppendMode() .registerTableSource("test"); Table test = tableEnv.scan("test"); test.select("user").disintct().insertInto("unique_user");
- 27. DataStream API // Java/Scala FlinkKafkaConsumer<...> consumer = new FlinkKafkaConsumer<>("test", ...); consumer.setStartFromEarlist(); DataStream<...> stream = env.addSource(consumer); stream .keyBy("user") .flatMap(new DataStreamDistinctReduce() { … });
- 28. Does SQL Make Sense in Streaming? Stream-Table Duality Word Count Hello 1 World 1 Hello 2 World 2 Hello 3 Hello 4 Word Count Hello 4 World 2 Stream Table materialized changelog popular as CDC in database replication
- 29. Does SQL Make Sense in Streaming? Stream Data Dynamic Table Dynamic Table Stream Data Continuous Query Static Table ← snapshot → Static Table Flink Static QueryBatch Data Batch Data
- 30. Stateful Computations over Event Streams in an Expressive, Scalable …... Flink in a Nutshell
- 31. Stateful Computations over Event Streams in an Expressive, Scalable …... ● Horizontally scalable ● Battle tested ○ trillions of records per day ○ terabytes of state ○ run on thousands of cores
- 32. Stateful Computations over Event Streams in an Expressive, Scalable, Operational-focused …... Flink in a Nutshell
- 33. Stateful Computations over Event Streams in an Expressive, Scalable, Operational-focused …... ● Deploy anywhere ○ kubernetes, yarn, mesos, standalone ● Deploy flexibly ○ per-job mode, session mode ● Highly available ○ with HA setup
- 34. Stateful Computations over Event Streams in an Expressive, Scalable, Operational-focused, Fault Tolerant way Flink in a Nutshell
- 35. Stateful Computations over Event Streams in an Expressive, Scalable, Operational-focused, Fault Tolerant way ● Checkpoint/Savepoint ○ on-the-fly, don’t scrafice performance ○ support incremental ● Exactly-once ○ State consistency ○ End-to-end with transactional connectors
- 36. Apache Flink: Stateful Computations over Event Streams in an Expressive, Scalable, Operational-focused, Fault Tolerant way The only open source framework that provide all the above capabilities
- 37. Going Beyond Stream Processing ● Batch -> Unified Data Processing ● Data Warehousing and Notebook ● Machine Learning / AI / DL ● Serverless
- 38. Recap the lambda architecture ...... ○ infra: high operation cost ○ dev: costly maintenance, and hard to learn 2+ stack ○ business: hard to sync to guarantee logic consistency Why Unified Streaming and Batch Data Processing? MQ / Pub-Sub HDFS / S3 Stream Processing (online) Batch Processing (offline) Combine Results (serving)
- 39. Why Flink? Flink’s philosophy: streaming first, with batch is a special case of streaming
- 40. State-of-the-Art Batch Processing on a Stream Processor <= Flink 1.8 from Flink 1.9
- 41. Performance of Blink versus Spark in the TPC-DS benchmark, aggregate time for all queries together. Presentation by Xiaowei Jiang at Flink Forward Beijing, 2018.
- 42. Data Warehousing Initial integration in Flink 1.9 for Hive 2.3.4 and 1.2.1 Full integration (read, write, udf) in Flink 1.10 for all Hive 1.x, 2.x, and 3.x
- 43. Notebook
- 44. Machine Learning & AI & DL
- 45. Machine Learning & AI Recap the “lambda” architecture, again, for ML MQ / Pub-Sub HDFS / S3 Online Training Offline Training Model Validation Preprocessing Dynamic Model Static Model Static Model Preprocessing Inference
- 46. Flink is popular for online ML now MQ / Pub-Sub HDFS / S3 Online Training Offline Training Model Validation Preprocessing Dynamic Model Static Model Static Model Preprocessing Inference
- 47. Streaming-Batch Unified ML Use Flink everywhere to reduce maintenance and operation cost of code and infra MQ / Pub-Sub HDFS / S3 Online Training Offline Training Model Validation Preprocessing Dynamic Model Static Model Static Model Preprocessing Inference
- 48. Machine Learning & AI ML Stage ML Flow Efforts & Requirements MQ / Pub-Sub HDFS / S3 Online Training Offline Training Model Validation Preprocessing Dynamic Model Static Model Static Model Preprocessing Inference Data Acquisition Preprocessing Model Training Model Validation & Serving Inference Rich Connectors Dataset Management Stream-Batch unification Strong API & SQL Support Enhanced Iteration Flink ML lib DL on Flink (TF, PyTorch) Model Serving Model Registry & Management Rollout/Rollback Online Evaluation Flink ML Pipeline Python API support
- 49. Machine Learning & AI ML Stage ML Flow Efforts & Requirements MQ / Pub-Sub HDFS / S3 Online Training Offline Training Model Validation Preprocessing Dynamic Model Static Model Static Model Preprocessing Inference Data Acquisition Preprocessing Model Training Model Validation & Serving Inference Rich Connectors Dataset Management Stream-Batch unification Strong API & SQL Support Enhaced Iteration Flink ML lib DL on Flink (TF, PyTorch) Model Serving Model Management Rollout/Rollback Online Evaluation Flink ML Pipeline Python API support
- 50. Flink ML Libs ● Completely rewrote ● Based on ML pipeline, powered by Table API ● Battle tested algorithms ○ K-means ○ Naive Bayes ○ Linear regression ○ GBDT ○ Decision tree ○ PCA ○ Random forest ○ Correlation ○ ….
- 51. Flink ML Pipeline training inference input table 1 ModelTransformerEstimatorTransformer input table 2 result tableModelTransformer Two type of operators ● Transformer: data -> data ● Estimator: data -> model Estimator Pipeline Model Pipeline Model Pipeline pipeline.fit(input1) pipeline.transform(input2)
- 52. Deep Learning Pipeline Data Acquisition Data Process & Tranformation Model Training Model Validation Model Serving Parameter Tuning
- 53. Deep Learning Pipeline source 1 source 2 join udtf Flink Cluster External MQ / FS Tensorflow Cluster worker workerworker PS PS
- 54. Flink Deep Learning Pipeline Data Acquisition Data Process & Tranformation Model Training Model Validation Model Serving Parameter Tuning
- 55. Flink + Tensorflow Integration source 1 source 2 join udtf Flink Cluster External MQ / FS Tensorflow Cluster worker workerworker PS PS source 1 source 2 join udtf a single Flink Cluster worker workerworker PS PS
- 56. DL on Flink ML Pipeline source 1 source 2 join udtf worker workerworker PS PS Transformer Estimator check out flink-ai-extended https://github.com/alibaba/flink-ai-extended
- 57. Serverless
- 58. Serverless Event Driven Function as a Service Benefits: ● elastic ● lightweight Challenges: ○ state ■ consistency ■ IO ■ capacity ○ hard to build complex applications
- 59. Event Driven State Management Composable isn’t that….
- 60. Event Driven State Management Composable isn’t that…. Stream Processing!
- 61. Check out project State Function, announced in Oct 2019! https://statefun.io/ It officially became part of Apache Flink last week.
- 62. Thanks! Twitter: @Bowen__Li ML: dev / user@flink.apache.org Meetup: Seattle Flink Meetup https://www.meetup.com/seattle-apache-flink/
- 63. Q & A