Apache Flink. Fast and Reliable Large-Scale Data Processing

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1 Apache Flink Fast and Reliable Large-Scale Data Processing Fabian 1

2 What is Apache Flink? Distributed Data Flow Processing System Focused on large-scale data analytics Real-time stream and batch processing Easy and powerful APIs (Java / Scala) Robust execution backend 2

3 What is Flink good at? It s a general-purpose data analytics system Real-time stream processing with flexible windows Complex and heavy ETL jobs Analyzing huge graphs Machine-learning on large data sets... 3

4 Table API Gelly Library ML Library Apache MRQL Dataflow Apache SAMOA Flink in the Hadoop Ecosystem Libraries Flink Core DataSet API (Java/Scala) Optimizer Runtime DataStream API (Java/Scala) Stream Builder Environments Embedded Local Cluster Yarn Apache Tez Data Sources HDFS HCatalog Hadoop IO JDBC Apache HBase Apache Kafka Apache Flume S3 RabbitMQ... 4

5 Flink in the ASF Flink entered the ASF about one year ago 04/2014: Incubation 12/2014: Graduation Strongly growing community Nov.10 Apr.12 Aug.13 Dec.14 #unique git committers (w/o manual de-dup) 5

6 Where is Flink moving? A "use-case complete" framework to unify batch & stream processing Data Streams Kafka RabbitMQ... Historic data HDFS JDBC... Flink Analytical Workloads ETL Relational processing Graph analysis Machine learning Streaming data analysis Goal: Treat batch as finite stream 6

7 Programming Model & APIs HOW TO USE FLINK? 7

8 Unified Java & Scala APIs Fluent and mirrored APIs in Java and Scala Table API for relational expressions Batch and Streaming APIs almost identical with slightly different semantics in some cases 8

9 DataSets and Transformations Input filter First map Second ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment(); DataSet<String> input = env.readtextfile(input); DataSet<String> first = input.filter (str -> str.contains( Apache Flink )); DataSet<String> second = first.map(str -> str.tolowercase()); second.print(); env.execute(); 9

10 Expressive Transformations Element-wise map, flatmap, filter, project Group-wise groupby, reduce, reducegroup, combinegroup, mappartition, aggregate, distinct Binary join, cogroup, union, cross Iterations iterate, iteratedelta Physical re-organization rebalance, partitionbyhash, sortpartition Streaming Window, windowmap, comap,... 10

11 Rich Type System Use any Java/Scala classes as a data type Tuples, POJOs, and case classes Not restricted to key-value pairs Define (composite) keys directly on data types Expression Tuple position Selector function 11

12 Counting Words in Batch and Stream case class Word (word: String, frequency: Int) DataSet API (batch): val lines: DataSet[String] = env.readtextfile(...) lines.flatmap {line => line.split(" ").map(word => Word(word,1))}.groupBy("word").sum("frequency").print() DataStream API (streaming): val lines: DataStream[String] = env.fromsocketstream(...) lines.flatmap {line => line.split(" ").map(word => Word(word,1))}.window(Count.of(1000)).every(Count.of(100)).groupBy("word").sum("frequency").print() 12

13 Table API Execute SQL-like expressions on table data Tight integration with Java and Scala APIs Available for batch and streaming programs val orders = env.readcsvfile( ).as('oid, 'odate, 'shipprio).filter('shipprio === 5) val items = orders.join(lineitems).where('oid === 'id).select('oid, 'odate, 'shipprio, 'extdprice * (Literal(1.0f) - 'discnt) as 'revenue) val result = items.groupby('oid, 'odate, 'shipprio).select('oid, 'revenue.sum, 'odate, 'shipprio) 13

14 Libraries are emerging As part of the Apache Flink project Gelly: Graph processing and analysis Flink ML: Machine-learning pipelines and algorithms Libraries are built on APIs and can be mixed with them Outside of Apache Flink Apache SAMOA (incubating) Apache MRQL (incubating) Google DataFlow translator 14

15 Processing Engine WHAT IS HAPPENING INSIDE? 15

16 System Architecture Client (pre-flight) Master Flink Program Type extraction stack Cost-based optimizer Recovery metadata Task scheduling Workers Coordination Memory manager Data serialization stack Out-of-core algos Pipelined or Blocking Data Transfer 16

17 Cool technology inside Flink Batch and Streaming in one system Memory-safe execution Built-in data flow iterations Cost-based data flow optimizer Flexible windows on data streams Type extraction and serialization utilities Static code analysis on user functions and much more... 17

18 Pipelined Data Transfer STREAM AND BATCH IN ONE SYSTEM 18

19 Stream and Batch in one System Most systems are either stream or batch systems In the past, Flink focused on batch processing Flink s runtime has always done stream processing Operators pipeline data forward as soon as it is processed Some operators are blocking (such as sort) Stream API and operators are recent contributions Evolving very quickly under heavy development 19

20 Pipelined Data Transfer Pipelined data transfer has many benefits True stream and batch processing in one stack Avoids materialization of large intermediate results Better performance for many batch workloads Flink supports blocking data transfer as well 20

21 Pipelined Data Transfer Program Large Input map Interm. DataSet Small Input join Result Pipelined Large Input map Pipeline 2 No intermediate materialization! Execution Small Input Pipeline 1 Build HT Probe HT join Result 21

22 Memory Management and Out-of-Core Algorithms MEMORY SAFE EXECUTION 22

23 Memory-safe Execution Challenge of JVM-based data processing systems OutOfMemoryErrors due to data objects on the heap Flink runs complex data flows without memory tuning C++-style memory management Robust out-of-core algorithms 23

24 Managed Memory Active memory management Workers allocate 70% of JVM memory as byte arrays Algorithms serialize data objects into byte arrays In-memory processing as long as data is small enough Otherwise partial destaging to disk Benefits Safe memory bounds (no OutOfMemoryError) Scales to very large JVMs Reduced GC pressure 24

25 Going out-of-core Single-core join of 1KB Java objects beyond memory (4 GB) Blue bars are in-memory, orange bars (partially) out-of-core 25

26 Native Data Flow Iterations GRAPH ANALYSIS 26

27 Native Data Flow Iterations Many graph and ML algorithms require iterations Flink features native data flow iterations Loops are not unrolled But executed as cyclic data flows Two types of iterations Bulk iterations Delta iterations Performance competitive with specialized systems 27

28 Iterative Data Flows Flink runs iterations natively as cyclic data flows Operators are scheduled once Data is fed back through backflow channel Loop-invariant data is cached Operator state is preserved across iterations! Replace initial result interm. result join reduce interm. result result other datasets 28

29 # of elements updated Delta Iterations Delta iteration computes Delta update of solution set Work set for next iteration # of iterations Work set drives computations of next iteration Workload of later iterations significantly reduced Fast convergence Applicable to certain problem domains Graph processing 29

30 Iteration Performance 30 Iterations 61 Iterations (Convergence) PageRank on Twitter Follower Graph 30

31 Roadmap WHAT IS COMING NEXT? 31

32 Flink s Roadmap Mission: Unified stream and batch processing Exactly-once streaming semantics with flexible state checkpointing Extending the ML library Extending graph library Interactive programs Integration with Apache Zeppelin (incubating) SQL on top of expression language And much more 32

33 tl;dr What s worth to remember? Flink is general-purpose analytics system Unifies streaming and batch processing Expressive high-level APIs Robust and fast execution engine 34

34 I Flink, do you? ;-) If you find this exciting, get involved and start a discussion on Flink s ML or stay tuned by subscribing to or on Twitter 35

35 36

36 BACKUP 37

37 Data Flow Optimizer Database-style optimizations for parallel data flows Optimizes all batch programs Optimizations Task chaining Join algorithms Re-use partitioning and sorting for later operations Caching for iterations 38

38 Data Flow Optimizer val orders = val lineitems = val filteredorders = orders.filter(o => dataformat.parse(l.shipdate).after(date)).filter(o => o.shipprio > 2) val lineitemsoforders = filteredorders.join(lineitems).where( orderid ).equalto( orderid ).apply((o,l) => new SelectedItem(o.orderDate, l.extdprice)) val pricesums = lineitemsoforders.groupby( orderdate ).sum( l.extdprice ); 39

39 Data Flow Optimizer Reduce sort[0,1] hash-part [0,1] Combine partial sort[0,1] Join Hybrid Hash Best plan depends on relative sizes of input files Reduce sort[0,1] Join Hybrid Hash buildht probe buildht probe broadcast forward hash-part [0] hash-part [0] Filter DataSource orders.tbl DataSource lineitem.tbl Filter DataSource orders.tbl DataSource lineitem.tbl 40

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