The Flink Big Data Analytics Platform Marton Balassi, Gyula Fora - - PowerPoint PPT Presentation

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The Flink Big Data Analytics Platform Marton Balassi, Gyula Fora - - PowerPoint PPT Presentation

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The Flink Big Data Analytics Platform Marton Balassi, Gyula Fora {mbalassi, gyfora}@apache.org What is Apache Flink? Open Started in 2009 by the Berlin-based database research groups Source In the Apache Incubator since mid 2014 61

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The Flink Big Data Analytics Platform

Marton Balassi, Gyula Fora {mbalassi, gyfora}@apache.org

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What is Apache Flink?

  • Started in 2009 by the Berlin-based database

research groups

  • In the Apache Incubator since mid 2014
  • 61 contributors as of the 0.7.0 release

Open Source

  • Fast, general purpose distributed data processing

system

  • Combining batch and stream processing
  • Up to 100x faster than Hadoop

Fast + Reliable

  • Programming APIs for Java and Scala
  • Tested on large clusters

Ready to use

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What is Apache Flink?

Master Worker Worker

Flink Cluster

Analytical Program Flink Client & Op0mizer

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This Talk

  • Introduction to Flink
  • API overview
  • Distinguishing Flink
  • Flink from a user perspective
  • Performance
  • Flink roadmap and closing

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Open source Big Data Landscape

MapReduce Hive Flink Spark Storm Yarn Mesos HDFS Mahout Cascading Tez Pig Data processing engines App and resource management Applica3ons Storage, streams KaCa HBase Crunch …

11/18/2014 5 Flink - M. Balassi & Gy. Fora

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Flink stack

Common API Storage Streams Hybrid Batch/Streaming Run0me

HDFS

Files S3

Cluster Manager YARN EC2 Na0ve Flink Op0mizer Scala API

(batch)

Graph API

(„Spargel“)

JDBC

Redis Rabbit MQ KaCa Azure

…

Java Collec0ons Streams Builder Apache Tez Python API Java API

(streaming)

Apache MRQL Batch Streaming Java API

(batch)

Local Execu0on

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Flink APIs

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Programming model

DataSet/Stream A A (1) A (1) A (2) A (2) B (1) B (1) B (2) B (2) C (1) C (1) C (2) C (2) X X Y Y

Program Parallel Execution X Y Operator X Operator Y Data abstractions: Data Set, Data Stream

DataSet/Stream B DataSet/Stream C

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Flexible pipelines

Reduce Join Map Reduce Map Iterate Source Sink Source

Map, FlatMap, MapPartition, Filter, Project, Reduce, ReduceGroup, Aggregate, Distinct, Join, CoGoup, Cross, Iterate, Iterate Delta, Iterate-Vertex-Centric, Windowing

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WordCount, Java API

DataSet<String> text = env.readTextFile(input); DataSet<Tuple2<String, Integer>> result = text .flatMap((str, out) -> { for (String token : value.split("\\W")) {

  • ut.collect(new Tuple2<>(token, 1));

}) .groupBy(0) .sum(1);

11/18/2014 10 Flink - M. Balassi & Gy. Fora

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WordCount, Scala API

val input = env.readTextFile(input); val words = input flatMap { line => line.split("\\W+") } val counts = words groupBy { word => word } count()

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WordCount, Streaming API

DataStream<String> text = env.readTextFile(input); DataStream<Tuple2<String, Integer>> result = text .flatMap((str, out) -> { for (String token : value.split("\\W")) {

  • ut.collect(new Tuple2<>(token, 1));

}) .groupBy(0) .sum(1);

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Is there anything beyond WordCount?

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Beyond Key/Value Pairs

DataSet<Page> pages = ...; DataSet<Impression> impressions = ...; DataSet<Impression> aggregated = impressions .groupBy("url") .sum("count"); pages.join(impressions).where("url").equalTo("url") .print() // outputs pairs of matching pages and impressions class Impression { public String url; public long count; } class Page { public String url; public String topic; } // outputs pairs of pages and impressions

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Preview: Logical Types

DataSet<Row> dates = env.readCsv(...).as("order_id", "date"); DataSet<Row> sessions = env.readCsv(...).as("id", "session"); DataSet<Row> joined = dates .join(session).where("order_id").equals("id"); joined.groupBy("date").reduceGroup(new SessionFilter()) class SessionFilter implements GroupReduceFunction<SessionType> { public void reduce(Iterable<SessionType> value, Collector out){ ... } } public class SessionType { public String order_id; public Date date; public String session; }

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Distinguishing Flink

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Hybrid batch/streaming runtime

  • Batch and stream processing in the same system
  • No micro-batches, unified runtime
  • Competitive performance
  • Code reusable from batch processing to streaming,

making development and testing a piece-of-cake

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Flink Streaming

  • Most Data Set operators are also available for

Data Streams

  • Temporal and streaming specific operators

– Window/mini-batch operators – Window join, cross etc.

  • Support for iterative stream processing
  • Connectors for different data sources

– Kafka, Flume, RabbitMQ, Twitter etc.

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Flink Streaming

//Build new model on every second of new data DataStream<Double[]> model= env .addSource(new TrainingDataSource()) .window(1000) .reduceGroup(new ModelBuilder()); //Predict new data using the most up-to-date model DataStream<Integer> prediction = env .addSource(new NewDataSource()) .connect(model) .map(new Predictor());

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Lambda architecture

Source: https://www.mapr.com/developercentral/lambda-architecture

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Lambda architecture in Flink

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Dependability

JVM Heap

Flink Managed Heap

Network Buffers

Unmanaged Heap

(next version unifies network buffers and managed heap)

User Code Hashing/Sor0ng/Caching

  • Flink manages its own memory
  • Caching and data processing happens in a dedicated

memory fraction

  • System never breaks the


JVM heap, gracefully spills Shuffles/Broadcasts

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  • serializes data every time



  Highly robust, never gives up on you

  • works on objects, RDDs may be stored serialized



 Serialization considered slow, only when needed

  • makes serialization really cheap:



  partial deserialization, operates on serialized form
  Efficient and robust!

Operating on Serialized Data

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Operating on Serialized Data

Microbenchmark

  • Sorting 1GB worth of (long, double) tuples
  • 67,108,864 elements
  • Simple quicksort

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Memory Management

public class WC { public String word; public int count; }

empty page

Pool of Memory Pages

  • Works on pages of bytes, maps objects transparently
  • Full control over memory, out-of-core enabled
  • Algorithms work on binary representation
  • Address individual fields (not deserialize whole object)
  • Move memory between operations

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Flink from a user perspective

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Flink programs run everywhere

Cluster (Batch) Cluster (Streaming) Local Debugging

Fink Run3me or Apache Tez As Java Collec0on Programs

Embedded (e.g., Web Container)

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Migrate Easily

Flink out-of-the-box supports

  • Hadoop data types (writables)
  • Hadoop Input/Output Formats
  • Hadoop functions and object model

Input Map Reduce Output

DataSet DataSet DataSet

Red Join

DataSet

Map

DataSet Output

S

Input

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  • Requires no memory thresholds to configure

– Flink manages its own memory

  • Requires no complicated network configs

– Pipelining engine requires much less memory for data exchange

  • Requires no serializers to be configured

– Flink handles its own type extraction and data representation

  • Programs can be adjusted to data automatically

– Flink’s optimizer can choose execution strategies automatically

Little tuning or configuration required

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Understanding Programs

Visualizes the operations and the data movement of programs

Analyze after execution

Screenshot from Flink’s plan visualizer

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Understanding Programs

Analyze after execution (times, stragglers, …)

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Iterations in other systems

Step Step Step Step Step

Client

Loop outside the system

Step Step Step Step Step

Client

Loop outside the system

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Iterations in Flink

Streaming dataflow
 with feedback

map join red. join

System is iteration-aware, performs automatic optimization

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Automatic Optimization for Iterative Programs

Caching Loop-invariant Data Pushing work
 „out of the loop“ Maintain state as index

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Performance

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Distributed Grep

Filter Term 1

HDFS

Filter Term 2 Filter Term 3

Matches Matches Matches

  • 1 TB of data (log files)
  • 24 machines with
  • 32 GB Memory
  • Regular HDDs
  • HDFS 2.4.0
  • Flink 0.7-incubating-

SNAPSHOT

  • Spark 1.2.0-SNAPSHOT

Flink up to 2.5x faster

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Distributed Grep: Flink Execution

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Distributed Grep: Spark Execution

Filter Term 1

HDFS

Matches

Spark executes the job in 3 stages:

Stage 1

Filter Term 2

HDFS

Matches

Stage 2

Filter Term 3

HDFS

Matches

Stage 3

11/18/2014 38 Flink - M. Balassi & Gy. Fora

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Spark in-memory pinning

Filter Term 1

HDFS

Filter Term 2 Filter Term 3

Matches Matches Matches

In- Memory RDD Cache in-memory to avoid reading the data for each filter

JavaSparkContext sc = new JavaSparkContext(conf); JavaRDD<String> file = sc.textFile(inFile).persist(StorageLevel.MEMORY_AND_DISK()); for(int p = 0; p < patterns.length; p++) { final String pattern = patterns[p]; JavaRDD<String> res = file.filter(new Function<String, Boolean>() { … }); }

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Spark in-memory pinning

37 minutes 9 minutes

RDD is 100% in- memory Spark starts spilling RDD to disk

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PageRank

  • Dataset:

– Twitter Follower Graph – 41,652,230 vertices (users) – 1,468,365,182 edges (followings) – 12 GB input data

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PageRank results

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Why is there a difference?

  • Lets have a look at the iteration times:

Flink average: 48 sec., Spark average: 99 sec.

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PageRank on Flink with Delta Iterations

  • The algorithm runs 60 iterations until convergence (runtime

includes convergence check)

100 minutes 8.5 minutes

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Again, explaining the difference

On average, a (delta) interation runs for 6.7 seconds

Flink (Bulk): 48 sec. Spark (Bulk): 99 sec.

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Streaming performance

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Flink Roadmap

  • Flink has a major release every 3 months,

with >=1 big-fixing releases in-between

  • Finer-grained fault tolerance
  • Logical (SQL-like) field addressing
  • Python API
  • Flink Streaming, Lambda architecture support
  • Flink on Tez
  • ML on Flink (e.g., Mahout DSL)
  • Graph DSL on Flink
  • … and more

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Marton Balassi, Gyula Fora {mbalassi, gyfora}@apache.org

http://flink.incubator.apache.org github.com/apache/incubator-flink @ApacheFlink

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