apache-flink-what-how-why-who-where-by-slim-baltagi
TRANSCRIPT
Apache Flink: What, How, Why, Who, Where?By @SlimBaltagiDirector of Big Data Engineering Capital One
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New York City (NYC) Apache Flink MeetupCivic Hall, NYCFebruary 2nd, 2016
New York City (NYC) Apache Flink MeetupCivic Hall, NYC
February 2nd, 2016
AgendaI. What is Apache Flink stack and how it fits
into the Big Data ecosystem? II. How Apache Flink integrates with Hadoop
and other open source tools? III. Why Apache Flink is an alternative to
Apache Hadoop MapReduce, Apache Storm and Apache Spark?
IV. Who is using Apache Flink? V. Where to learn more about Apache Flink?
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I. What is Apache Flink stack and how it fits into the Big Data ecosystem?
1. What is Apache Flink?2. What is Flink Execution Engine?3. What are Flink APIs?4. What are Flink Domain Specific Libraries?5. What is Flink Architecture?6. What is Flink Programming Model?7. What are Flink tools?
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1. What is Apache Flink?
1.1 Apache project with a cool logo!1.2 Project that evolved the concept of a multi-purpose Big Data analytics framework1.3 Project with a unique vision and philosophy1.4 Only Hybrid ( Real-Time streaming + Batch) engine supporting many use cases1.5 Major contributor to the movement of unification of streaming and batch1.6 The 4G of Big Data Analytics frameworks
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1.1 Apache project with a cool logo! Apache Flink, like Apache Hadoop and
Apache Spark, is a community-driven open source framework for distributed Big Data Analytics.
Apache Flink has its origins in a research project called Stratosphere of which the idea was conceived in late 2008 by professor Volker Markl from the Technische Universität Berlin in Germany.
Flink joined the Apache incubator in April 2014 and graduated as an Apache Top Level Project (TLP) in December 2014.
dataArtisans (data-artisans.com) is a German start-up company based in Berlin and is leading the development of Apache Flink. 5
1.1 Apache project with a cool logo
Squirrel is an animal! This reflects the harmony with other animals in the Hadoop ecosystem (Zoo): elephant, pig, python, camel, …
A squirrel is swift and agile
This reflects the meaning of the word ‘Flink’: German for “nimble, swift, speedy”
Red color of the body This reflects the roots of the project at German universities: In harmony with red squirrels in Germany
Colorful tail This reflects an open source project as the colors match the ones of the feather symbolizing the Apache Software Foundation
1.2 Project that evolved the concept of a multi-purpose Big Data analytics framework
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What is a typical Big Data Analytics Stack: Hadoop, Spark, Flink, …?
1.2 Project that evolved the concept of a multi-purpose Big Data analytics framework
Apache Flink, written in Java and Scala, consists of: 1. Big data processing engine: distributed and
scalable streaming dataflow engine 2. Several APIs in Java/Scala/Python:
• DataSet API – Batch processing• DataStream API – Real-Time streaming analytics
3. Domain-Specific Libraries:• FlinkML: Machine Learning Library for Flink• Gelly: Graph Library for Flink• Table: Relational Queries • FlinkCEP: Complex Event Processing for Flink8
What is Apache Flink stack?
Gel
lyTa
ble
Had
oop
M/R
SAM
OA
DataSet (Java/Scala/Python)Batch Processing
DataStream (Java/Scala)Stream Processing
Flin
kML
LocalSingle JVMEmbedded
Docker
ClusterStandalone
YARN, Mesos (WIP)
CloudGoogle’s GCEAmazon’s EC2
IBM Docker Cloud, …
Goo
gle
Dat
aflo
w
Dat
aflo
w (W
iP)
MR
QL
Tabl
e
Cas
cadi
ng
Runtime - Distributed Streaming Dataflow
Zepp
elin
DEP
LOY
SYST
EMA
PIs
& L
IBR
AR
IES
STO
RA
GE Files
LocalHDFS
S3, Azure StorageTachyon
DatabasesMongoDB
HBaseSQL
…
Streams FlumeKafka
RabbitMQ…
Batch Optimizer Stream Builder
Stor
m
Gel
ly-S
trea
m
• Declarativity• Query optimization• Efficient parallel in-
memory and out-of-core algorithms
• Massive scale-out• User Defined
Functions • Complex data types• Schema on read
• Real-Time Streaming
• Iterations• Memory
Management• Advanced
Dataflows• General APIs
Draws on concepts from
MPP Database Technology
Draws on concepts from
Hadoop MapReduce Technology
Add
1.3 Project with a unique vision and philosophy
Apache Flink’s original vision was getting the best from both worlds: MPP Technology and Hadoop MapReduce Technologies:
1.3 Project with a unique vision and philosophy
All streaming all the time: execute everything as streams including batch!!
Write like a programming language, execute like a database.
Alleviate the user from a lot of the pain of:• manually tuning memory assignment to
intermediate operators• dealing with physical execution concepts (e.g.,
choosing between broadcast and partitioned joins, reusing partitions).
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1.3 Project with a unique vision and philosophy
Little configuration required• Requires no memory thresholds to configure – Flink manages its own memory • Requires no complicated network configurations – Pipelining engine requires much less memory for data exchange • Requires no serializers to be configured – Flink handles its own type extraction and data representation
Little tuning required: Programs can be adjusted to data automatically – Flink’s optimizer can choose execution strategies automatically 12
1.3 Project with a unique vision and philosophy
Support for many file systems:• Flink is File System agnostic. BYOS: Bring Your
Own StorageSupport for many deployment options:
• Flink is agnostic to the underlying cluster infrastructure. BYOC: Bring Your Own Cluster
Be a good citizen of the Hadoop ecosystem• Good integration with YARN
Preserve your investment in your legacy Big Data applications: Run your legacy code on Flink’s powerful engine using Hadoop and Storm compatibility layers and Cascading adapter. 13
1.3 Project with a unique vision and philosophy
Native Support of many use cases on top of the same streaming engine• Batch• Real-Time streaming• Machine learning• Graph processing• Relational queries
Support building complex data pipelines leveraging native libraries without the need to combine and manage external ones.
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1.4 The only hybrid (Real-Time Streaming + Batch) open source distributed data processing engine natively supporting many use cases:
Real-Time stream processing Machine Learning at scale
Graph AnalysisBatch Processing
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1.5 Major contributor to the movement of unification of streaming and batch
Dataflow proposal for incubation has been renamed to Apache Beam ( for combination of Batch and Stream) https://wiki.apache.org/incubator/BeamProposal
Apache Beam was accepted to the Apache incubation on February 1st, 2016 http://incubator.apache.org/projects/beam.html
Dataflow/Beam & Spark: A Programming Model Comparison, February 3rd, 2016https://cloud.google.com/dataflow/blog/dataflow-beam-and-spark-comparison
By Tyler Akidau & Frances Perry, Software Engineers, Apache Beam Committers
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1.5 Major contributor to the movement of unification of streaming and batchApache Flink includes DataFlow on Flink
http://data-artisans.com/dataflow-proposed-as-apache-incubator-project/
Keynotes of the Flink Forward 2015 conference: • Keynote on October 12th, 2015 by Kostas Tzoumas and Stephan
Ewen of dataArtisanshttp://www.slideshare.net/FlinkForward/k-tzoumas-s-ewen-flink-forward-keynote/
• Keynote on October 13th, 2015 by William Vambenepe of Googlehttp://www.slideshare.net/FlinkForward/william-vambenepe-google-cloud-dataflow-and-flink-stream-processing-by-default 17
1.6 The 4G of Big Data Analytics frameworks
Apache Flink is not YABDAF (Yet Another Big Data Analytics Framework)!
Flink brings many technical innovations and a unique vision and philosophy that distinguish it from: Other multi-purpose Big Data analytics frameworks
such as Apache Hadoop and Apache Spark Single-purpose Big Data Analytics frameworks
such as Apache Storm Apache Flink is the 4G (4th Generation) of Big Data
Analytics frameworks succeeding to Apache Spark.
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Apache Flink as the 4G of Big Data Analytics
Batch Batch Interactive
Batch Interactive Near-Real
Time Streaming Iterative
processing
Hybrid(Streaming +Batch) Interactive Real-Time
Streaming Native Iterative
processing
MapReduce Direct Acyclic Graphs (DAG)Dataflows
RDD: Resilient Distributed Datasets
Cyclic Dataflows
1st Generation (1G)
2ndGeneration(2G)
3rd Generation (3G)
4th Generation (4G)
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How Big Data Analytics engines evolved?
The evolution of Massive-Scale Data Processing Tyler Akidau, Google. Strata + Hadoop World, Singapore, December 2, 2015. Slides: https://docs.google.com/presentation/d/10vs2PnjynYMtDpwFsqmSePtMnfJirCkXcHZ1SkwDg-s/present?slide=id.g63ca2a7cd_0_527
The world beyond batch: Streaming 101, Tyler Akidau, Google, August 5, 2015
http://radar.oreilly.com/2015/08/the-world-beyond-batch-streaming-101.html
Streaming 102, Tyler Akidau, Google, January 20, 2016 https://www.oreilly.com/ideas/the-world-beyond-batch-streaming-102
It covers topics like event-time vs. processing-time, windowing, watermarks, triggers, and accumulation.
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2. What is Flink Execution Engine?
The core of Flink is a distributed and scalable streaming dataflow engine with some unique features:
1. True streaming capabilities: Execute everything as streams
2. Versatile: Engine allows to run all existing MapReduce, Cascading, Storm, Google DataFlow applications
3. Native iterative execution: Allow some cyclic dataflows4. Handling of mutable state5. Custom memory manager: Operate on managed
memory6. Cost-Based Optimizer: for both batch and stream
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3. Flink APIs
3.1 DataSet API for static data - Java, Scala, and Python3.2 DataStream API for unbounded real-time streams - Java and Scala
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3.1 DataSet API – Batch processing
case class Word (word: String, frequency: Int)
val env = StreamExecutionEnvironment.getExecutionEnvironment()val lines: DataStream[String] = env.fromSocketStream(...)lines.flatMap {line => line.split(" ") .map(word => Word(word,1))} .window(Time.of(5,SECONDS)).every(Time.of(1,SECONDS)) .keyBy("word").sum("frequency") .print()env.execute()
val env = ExecutionEnvironment.getExecutionEnvironment()val lines: DataSet[String] = env.readTextFile(...)lines.flatMap {line => line.split(" ") .map(word => Word(word,1))} .groupBy("word").sum("frequency") .print()env.execute()
DataSet API (batch): WordCount
DataStream API (streaming): Window WordCount
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3.2 DataStream API – Real-Time Streaming Analytics Flink Streaming provides high-throughput, low-latency
stateful stream processing system with rich windowing semantics.
Streaming Fault-Tolerance allows Exactly-once processing delivery guarantees for Flink streaming programs that analyze streaming sources persisted by Apache Kafka.
Flink Streaming provides native support for iterative stream processing.
Data streams can be transformed and modified using high-level functions similar to the ones provided by the batch processing API.
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3.2 DataStream API – Real-Time Streaming Analytics Flink being based on a pipelined (streaming) execution
engine akin to parallel database systems allows to:• implement true streaming & batch• integrate streaming operations with rich windowing
semantics seamlessly• process streaming operations in a pipelined way with
lower latency than micro-batch architectures and without the complexity of lambda architectures.
It has built-in connectors to many data sources like Flume, Kafka, Twitter, RabbitMQ, etc
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3.2 DataStream API – Real-Time Streaming Analytics
Apache Flink: streaming done right. Till Rohrmann. January 31, 2016 https://fosdem.org/2016/schedule/event/hpc_bigdata_flink_streaming/
Web resources about stream processing with Apache Flink at the Flink Knowledge Base http://sparkbigdata.com/component/tags/tag/49-flink-streaming
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4. Flink Domain Specific Libraries
4.1 FlinkML – Machine Learning Library
4.2 Table – Relational queries 4.3 Gelly – Graph Analytics for Flink
4.4 FlinkCEP: Complex Event Processing for Flink
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4.1 FlinkML - Machine Learning Library FlinkML is the Machine Learning (ML) library for Flink.
It is written in Scala and was added in March 2015. FlinkML aims to provide:
• an intuitive API• scalable ML algorithms• tools that help minimize glue code in end-to-end ML
applications FlinkML will allow data scientists to:
• test their models locally using subsets of data• use the same code to run their algorithms at a much
larger scale in a cluster setting.
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4.1 FlinkMLFlinkML unique features are:
1. Exploiting the in-memory data streaming nature of Flink.
2. Natively executing iterative processing algorithms which are common in Machine Learning.
3. Streaming ML designed specifically for data streams.
FlinkML: Large-scale machine learning with Apache Flink, Theodore Vasiloudis. October 21, 2015
Slides: https://sics.app.box.com/s/044omad6200pchyh7ptbyxkwvcvaiowu Video: https://www.youtube.com/watch?v=k29qoCm4c_k&feature=youtu.be
Check more FlinkML web resources at the Apache Flink Knowledge Base: http://sparkbigdata.com/component/tags/tag/51-flinkml
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4.2 Table – Relational Queries
Table API, written in Scala , allows specifying operations using SQL-like expressions instead of manipulating DataSet or DataStream.
Table API can be used in both batch (on structured data sets) and streaming programs (on structured data streams).http://ci.apache.org/projects/flink/flink-docs-master/libs/table.html
Flink Table web resources at the Apache Flink Knowledge Base: http://sparkbigdata.com/component/tags/tag/52-flink-table
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4.2 Table API – Relational Queries
val customers = envreadCsvFile(…).as('id, 'mktSegment) .filter("mktSegment = AUTOMOBILE")
val orders = env.readCsvFile(…) .filter( o => dateFormat.parse(o.orderDate).before(date) ) .as("orderId, custId, orderDate, shipPrio")
val items = orders .join(customers).where("custId = id") .join(lineitems).where("orderId = id") .select("orderId, orderDate, shipPrio, extdPrice * (Literal(1.0f) – discount) as revenue")
val result = items .groupBy("orderId, orderDate, shipPrio") .select("orderId, revenue.sum, orderDate, shipPrio")
Table API (queries)
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4.3 Gelly – Graph Analytics for Flink Gelly is Flink's large-scale graph processing API,
available in Java and Scala, which leverages Flink's efficient delta iterations to map various graph processing models (vertex-centric and gather-sum-apply) to dataflows.
Gelly provides:• A set of methods and utilities to create, transform
and modify graphs • A library of graph algorithms which aims to simplify
the development of graph analysis applications• Iterative graph algorithms are executed leveraging
mutable state32
4.3 Gelly – Graph Analytics for Flink Gelly allows Flink users to perform end-to-end data
analysis, without having to build complex pipelines and combine different systems.
It can be seamlessly combined with Flink's DataSet API, which means that pre-processing, graph creation, graph analysis and post-processing can be done in the same application.
Gelly documentation https://ci.apache.org/projects/flink/flink-docs-master/libs/gelly_guide.html
Introducing Gelly: Graph Processing with Apache Flink http://flink.apache.org/news/2015/08/24/introducing-flink-gelly.html
Check out more Gelly web resources at the Apache Flink Knowledge Base: http://sparkbigdata.com/component/tags/tag/50-gelly33
4.3 Gelly – Graph Analytics for Flink Single-pass Graph Streaming Analytics with Apache
Flink. Vasia Kalavri & Paris Carbone. January 31, 2016 FOSDEM'16. Brussels, BELGIUM.
• Talk description :https://fosdem.org/2016/schedule/event/graph_processing_apache_flink/
• Slides: http://www.slideshare.net/vkalavri/gellystream-singlepass-graph-streaming-analytics-with-apache-flink
Gelly free training! http://www.slideshare.net/FlinkForward/vasia-kalavri-training-gelly-school
http://gellyschool.com/
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4.4 FlinkCEP: Complex Event Processing for FlinkFlinkCEP is the complex event processing library for
Flink. It allows you to easily detect complex event patterns in a stream of endless data.
Complex events can then be constructed from matching sequences. This gives you the opportunity to quickly get hold of what’s really important in your data.
https://ci.apache.org/projects/flink/flink-docs-master/apis/streaming/libs/cep.html
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5. What is Flink Architecture? Flink implements the Kappa Architecture:
run batch programs on a streaming system. References about the Kappa Architecture:
• Questioning the Lambda Architecture - Jay Kreps , July 2nd, 2014 http://radar.oreilly.com/2014/07/questioning-the-lambda-architecture.html
• Turning the database inside out with Apache Samza -Martin Kleppmann, March 4th, 2015o http://www.youtube.com/watch?v=fU9hR3kiOK0 (VIDEO)o http://martin.kleppmann.com/2015/03/04/turning-the-database-inside-out.h
tml(TRANSCRIPT)
o http://blog.confluent.io/2015/03/04/turning-the-database-inside-out-with-apache-samza/ (BLOG)
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5. What is Flink Architecture?5.1 Client5.2 Master (Job Manager)5.3 Worker (Task Manager)
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5.1 Client Type extraction Optimize: in all APIs not just SQL queries as in Spark Construct job Dataflow graph Pass job Dataflow graph to job manager Retrieve job results
Job Manager
Client
case class Path (from: Long, to: Long)val tc = edges.iterate(10) { paths: DataSet[Path] => val next = paths .join(edges) .where("to") .equalTo("from") { (path, edge) => Path(path.from, edge.to) } .union(paths) .distinct() next }
Optimizer
Type extraction
Data Sourceorders.tbl
Filter
Map
DataSourcelineitem.tbl
JoinHybrid Hash
buildHT probe
hash-part [0]hash-part [0]
GroupRed
sort
forward
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5.2 Job Manager (JM) with High Availability Parallelization: Create Execution Graph Scheduling: Assign tasks to task managers State tracking: Supervise the execution
Job Manager
Data Sourceorders.t
bl
FilterMap
DataSourcelineitem.tbl
JoinHybrid HashbuildHT probe
hash-part [0] hash-part [0]
GroupRed
sort
forward
Task Manager
Task Manager
Task Manager
Task Manager
Data Sourceorders.tbl
Filter
Map DataSource
lineitem.tbl
JoinHybrid Hash
buildHT
probe
hash-part [0]
hash-part [0]
GroupRed
sort
forward
Data Sourceorders.tbl
Filter
Map DataSource
lineitem.tbl
JoinHybrid Hash
buildHT
probe
hash-part [0]
hash-part [0]
GroupRed
sort
forward
Data Sourceorders.tbl
FilterMap DataSou
rcelineitem.tbl
JoinHybrid Hash
buildHT
probe
hash-part [0]
hash-part [0]
GroupRed
sort
forward
Data Sourceorders.tbl
Filter
MapDataSourc
elineitem.tbl
JoinHybrid Hash
buildHT
probe
hash-part [0]
hash-part [0]
GroupRedsort
forward
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5.3 Task Manager ( TM) Operations are split up into tasks depending on the
specified parallelism Each parallel instance of an operation runs in a
separate task slot The scheduler may run several tasks from different
operators in one task slot
Task Manager
Slot
Task ManagerTask Manager
Slot
Slot
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6. What is Flink Programming Model? DataSet and DataStream as programming
abstractions are the foundation for user programs and higher layers.
Flink extends the MapReduce model with new operators that represent many common data analysis tasks more naturally and efficiently.
All operators will start working in memory and gracefully go out of core under memory pressure.
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6.1 DataSetDataSet: abstraction for distributed data and the
central notion of the batch programming APIFiles and other data sources are read into DataSets
• DataSet<String> text = env.readTextFile(…)Transformations on DataSets produce DataSets
• DataSet<String> first = text.map(…)DataSets are printed to files or on stdout
• first.writeAsCsv(…)Computation is specified as a sequence of lazily
evaluated transformationsExecution is triggered with env.execute()
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6.1 DataSet
Used for Batch Processing
Data Set Operation Data
SetSource
Example: Map and Reduce operation
Sink
b h
2 1
3 5
7 4
… …
Map Reduce
a
12
…43
6.2 DataStream
Real-time event streams
Data Stream Operation Data
StreamSource Sink
Stock FeedName Price
Microsoft 124
Google 516
Apple 235
… …
Alert if Microsoft
> 120
Write event to database
Sum every 10 seconds
Alert if sum > 10000
Microsoft 124
Google 516Apple 235
Microsoft 124
Google 516
Apple 235
Example: Stream from a live stock feed
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7. What are Apache Flink tools?
7.1 Command-Line Interface (CLI)7.2 Web Submission Client7.3 Job Manager Web Interface7.4 Interactive Scala Shell7.5 Zeppelin Notebook
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7.1 Command-Line Interface (CLI) Flink provides a CLI to run programs that are packaged as
JAR files, and control their execution. bin/flink has 4 major actions
• run #runs a program.• info #displays information about a program.• list #lists scheduled and running jobs• cancel #cancels a running job.
Example: ./bin/flink info ./examples/KMeans.jar
See CLI usage and related examples: https://ci.apache.org/projects/flink/flink-docs-master/apis/cli.html 46
7.2 Web Submission Client
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7.2 Web Submission ClientFlink provides a web interface to:
• Upload programs• Execute programs• Inspect their execution plans• Showcase programs• Debug execution plans• Demonstrate the system as a whole
The web interface runs on port 8080 by default.To specify a custom port set the webclient.port property in the ./conf/flink.yaml configuration file.
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7.3 Job Manager Web Interface
Overall system status
Job execution details
Task Manager resourceutilization
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7.3 Job Manager Web Interface
The JobManager web frontend allows to :• Track the progress of a Flink program
as all status changes are also logged to the JobManager’s log file.
• Figure out why a program failed as it displays the exceptions of failed tasks and allow to figure out which parallel task first failed and caused the other tasks to cancel the execution.
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7.4 Interactive Scala Shellbin/start-scala-shell.sh --host localhost --port 6123
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7.4 Interactive Scala ShellFlink comes with an Interactive Scala Shell - REPL ( Read Evaluate Print Loop ) : ./bin/start-scala-shell.sh Interactive queries Let’s you explore data quickly It can be used in a local setup as well as in a
cluster setup. The Flink Shell comes with command history and
auto completion. Complete Scala API available So far only batch mode is supported. There is
plan to add streaming in the future: https://ci.apache.org/projects/flink/flink-docs-master/scala_shell.html
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7.5 Zeppelin Notebookhttp://localhost:8080/
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7.5 Zeppelin Notebook
Web-based interactive computation environment
Collaborative data analytics and visualization tool
Combines rich text, execution code, plots and rich media
Exploratory data scienceSaving and replaying of written codeStorytelling
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AgendaI. What is Apache Flink stack and how it fits
into the Big Data ecosystem? II. How Apache Flink integrates with Hadoop
and other open source tools? III. Why Apache Flink is an alternative to
Apache Hadoop MapReduce, Apache Storm and Apache Spark?
IV. Who is using Apache Flink? V. Where to learn more about Apache Flink?
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II. How Apache Flink integrates with Hadoop and other open source tools?
Service Open Source ToolStorage/Serving Layer
Data Formats
Data Ingestion Services
Resource Management
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II. How Apache Flink integrates with Hadoop and other open source tools?
Flink integrates well with other open source tools for data input and output as well as deployment.
Flink allows to run legacy Big Data applications: MapReduce, Cascading and Storm applications
Flink integrates with other open source tools
1. Data Input / Output2. Deployment3. Legacy Big Data applications4. Other tools
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1. Data Input / OutputHDFS to read and write. Secure HDFS supportReuse data types (that implement Writables interface)Amazon S3 Microsoft Azure StorageMapR-FS Flink + Tachyon http://tachyon-project.org/
Running Apache Flink on Tachyon http://tachyon-project.org/Running-Flink-on-Tachyon.html
Flink + XtreemFS http://www.xtreemfs.org/
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1. Data Input / Output Crunching Parquet Files with Apache Flinkhttps://medium.com/@istanbul_techie/crunching-parquet-files-with-apache-flink-200bec90d8a7
Here are some examples of how to read/write data from/to HBase:
https://github.com/apache/flink/tree/master/flink-staging/flink-hbase/src/test/java/org/apache/flink/addons/hbase/example
Using MongoDB with Flink: http://flink.apache.org/news/2014/01/28/querying_mongodb.html
https://github.com/m4rcsch/flink-mongodb-example 59
1. Data Input / Output
Apache Kafka, a system that provides durability and pub/sub functionality for data streams.
Kafka + Flink: A practical, how-to guide. Robert Metzger and Kostas Tzoumas, September 2, 2015 http://data-artisans.com/kafka-flink-a-practical-how-to/ https://www.youtube.com/watch?v=7RPQUsy4qOM
Click-Through Example for Flink’s KafkaConsumer Checkpointing. Robert Metzger, September 2nd , 2015. http://www.slideshare.net/robertmetzger1/clickthrough-example-for-flinks-kafkaconsumer-checkpointing
MapR Streams (proprietary alternative to Kafka that is compatible with Apache Kafka 0.9 API) provides out of the box integration with Apache Flinkhttp://sparkbigdata.com/component/tags/tag/61
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1. Data Input / OutputUsing Apache Nifi with Flink:
• Flink and NiFi: Two Stars in the Apache Big Data Constellation. Matthew Ring. January 19th , 2016 http://www.slideshare.net/mring33/flink-and-nifi-two-stars-in-the-apache-big-data-constellation
• Integration of Apache Flink and Apache Nifi. Bryan Bende, February 4th , 2016
http://www.slideshare.net/BryanBende/integrating-nifi-and-flink
Using Elasticsearch with Flink: https://www.elastic.co/
Building real-time dashboard applications with Apache Flink, Elasticsearch, and Kibana. By Fabian Hueske, December 7, 2015.https://www.elastic.co/blog/building-real-time-dashboard-applications-with-apache-flink-elasticsearch-and-kibana
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2. DeploymentDeploy inside of Hadoop via YARN
• YARN Setup http://ci.apache.org/projects/flink/flink-docs-master/setup/yarn_setup.html
• YARN Configuration http://ci.apache.org/projects/flink/flink-docs-master/setup/config.html#yarn
Apache Flink cluster deployment on Docker using Docker-Compose by Simons Laws from IBM.
Talk at the Flink Forward in Berlin on October 12, 2015. Slides:
http://www.slideshare.net/FlinkForward/simon-laws-apache-flink-cluster-deployment-on-docker-and-dockercompose
Video recording (40’:49): https://www.youtube.com/watch?v=CaObaAv9tLE62
3. Legacy Big Data applications
Flink’s MapReduce compatibility layer allows to: • run legacy Hadoop MapReduce jobs • reuse Hadoop input and output formats • reuse functions like Map and Reduce.
References: • Documentation: https://ci.apache.org/projects/flink/flink-docs-release-0.7/
hadoop_compatibility.html
• Hadoop Compatibility in Flink by Fabian Hüeske - November 18, 2014 http://flink.apache.org/news/2014/11/18/hadoop-compatibility.html
• Apache Flink - Hadoop MapReduce Compatibility. Fabian Hüeske, January 29, 2015 http://www.slideshare.net/fhueske/flink-hadoopcompat20150128
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3. Legacy Big Data applications Cascading on Flink allows to port existing Cascading-MapReduce
applications to Apache Flink with virtually no code changes. http://www.cascading.org/cascading-flink/
Expected advantages are performance boost and less resources consumption.
References: • Cascading on Apache Flink, Fabian Hueske, data Artisans. Flink
Forward 2015. October 12, 2015• http://www.slideshare.net/FlinkForward/fabian-hueske-training-cascading-on-
flink• https://www.youtube.com/watch?v=G7JlpARrFkU
• Cascading connector for Apache Flink. Code on Github https://github.com/dataArtisans/cascading-flink
• Running Scalding jobs on Apache Flink, Ian Hummel, December 20, 201http://themodernlife.github.io/scala/hadoop/hdfs/sclading/flink/streaming/realtime/2015/12/20/running-scalding-jobs-on-apache-flink/
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3. Legacy Big Data applications
Flink is compatible with Apache Storm interfaces and therefore allows reusing code that was implemented for Storm: • Execute existing Storm topologies using Flink as the underlying
engine. • Reuse legacy application code (bolts and spouts) inside Flink
programs. https://ci.apache.org/projects/flink/flink-docs-master/apis/streaming/storm_compatibility.html
A Tale of Squirrels and Storms. Mathias J. Sax, October 13, 2015. Flink Forward 2015
http://www.slideshare.net/FlinkForward/matthias-j-sax-a-tale-of-squirrels-and-stormshttps://www.youtube.com/watch?v=aGQQkO83Ong
Storm Compatibility in Apache Flink: How to run existing Storm topologies on Flink. Mathias J. Sax, December 11, 2015
http://flink.apache.org/news/2015/12/11/storm-compatibility.html 65
Ambari service for Apache Flink: install, configure, manage Apache Flink on HDP, November 17, 2015
https://community.hortonworks.com/repos/4122/ambari-service-for-apache-flink.html
Exploring Apache Flink with HDPhttps://community.hortonworks.com/articles/2659/exploring-apache-flink-with-hdp.html
Apache Flink + Apache SAMOA for Machine Learning on streams http://samoa.incubator.apache.org/
Flink Integrates with Zeppelin http://zeppelin.incubator.apache.org/http://www.slideshare.net/FlinkForward/moon-soo-lee-data-science-lifecycle-with-apache-flink-and-apache-zeppelin
Flink + Apache MRQL http://mrql.incubator.apache.org
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4. Other tools
Google Cloud Dataflow (GA on August 12, 2015) is a fully-managed cloud service and a unified programming model for batch and streaming big data processing. https://cloud.google.com/dataflow/ (Try it FREE)
Flink-Dataflow is a Google Cloud Dataflow SDK Runner for Apache Flink. It enables you to run Dataflow programs with Flink as an execution engine.
References: Google Cloud Dataflow on top of Apache Flink,
Maximilian Michels, data Artisans. Flink Forward conference, October 12, 2015 http://www.slideshare.net/FlinkForward/maximilian-michels-google-cloud-
dataflow-on-top-of-apache-flink Slides
https://www.youtube.com/watch?v=K3ugWmHb7CE Video recording
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4. Other tools
AgendaI. What is Apache Flink stack and how it fits
into the Big Data ecosystem? II. How Apache Flink integrates with Hadoop
and other open source tools for data input and output as well as deployment?
III. Why Apache Flink is an alternative to Apache Hadoop MapReduce, Apache Storm and Apache Spark?
IV. Who is using Apache Flink? V. Where to learn more about Apache Flink?
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III. Why Apache Flink is an alternative to Apache Hadoop MapReduce, Apache Storm and Apache Spark?
1. Why Flink is an alternative to Hadoop MapReduce?
2. Why Flink is an alternative to Apache Storm?3. Why Flink is an alternative to Apache Spark?4. What are the benchmarking results against
Flink?
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2. Why Flink is an alternative to Hadoop MapReduce?
1. Flink offers cyclic dataflows compared to the two-stage, disk-based MapReduce paradigm.
2. The application programming interface (API) for Flink is easier to use than programming for Hadoop’s MapReduce.
3. Flink is easier to test compared to MapReduce.4. Flink can leverage in-memory processing, data
streaming and iteration operators for faster data processing speed.
5. Flink can work on file systems other than Hadoop. 70
2. Why Flink is an alternative to Hadoop MapReduce?
6. Flink lets users work in a unified framework allowing to build a single data workflow that leverages, streaming, batch, sql and machine learning for example.
7. Flink can analyze real-time streaming data.8. Flink can process graphs using its own Gelly library.9. Flink can use Machine Learning algorithms from its
own FlinkML library.10. Flink supports interactive queries and iterative
algorithms, not well served by Hadoop MapReduce.
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2. Why Flink is an alternative to Hadoop MapReduce?
11. Flink extends MapReduce model with new operators: join, cross, union, iterate, iterate delta, cogroup, …
Input Map Reduce Output
DataSet DataSetDataSet
Red Join
DataSet Map DataSet
OutputS
Input
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3. Why Flink is an alternative to Storm?
1. Higher Level and easier to use API2. Lower latency
• Thanks to pipelined engine
3. Exactly-once processing guarantees• Variation of Chandy-Lamport
4. Higher throughput• Controllable checkpointing overhead
5. Flink Separates application logic from recovery
• Checkpointing interval is just a configuration parameter 73
3. Why Flink is an alternative to Storm?
6. More light-weight fault tolerance strategy7. Stateful operators8. Native support for iterative stream processing. 9. Flink does also support batch processing10. Flink offers Storm compatibility
• Flink is compatible with Apache Storm interfaces and therefore allows reusing code that was implemented for Storm.
https://ci.apache.org/projects/flink/flink-docs-master/apis/storm_compatibility.html
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3. Why Flink is an alternative to Storm?
Extending the Yahoo! Streaming Benchmark, by Jamie Grier. February 2nd, 2016
http://data-artisans.com/extending-the-yahoo-streaming-benchmark/
Code at Github: https://github.com/dataArtisans/yahoo-streaming-benchmark
Results show that Flink has a much better throughput compared to storm and better fault-tolerance guarantees: exactly-once.High-throughput, low-latency, and exactly-once
stream processing with Apache Flink. The evolution of fault-tolerant streaming architectures and their performance – Kostas Tzoumas, August 5th 2015
http://data-artisans.com/high-throughput-low-latency-and-exactly-once-stream-processing-with-apache-flink/ 75
4. Why Flink is an alternative to Spark?
4.1 True Low latency streaming engine • Spark’s micro-batches aren’t good enough!• Unified batch and real-time streaming in a single
engine• The streaming model of Flink is based on the
Dataflow model similar to Google Dataflow4.2 Unique windowing features not available in Spark
• support for event time• out of order streams• a mechanism to define custom windows based on
window assigners and triggers. 76
4. Why Flink is an alternative to Spark?
4.3 Native closed-loop iteration operators • make graph and machine learning applications run
much faster 4.4 Custom memory manager
• no more frequent Out Of Memory errors!• Flink’s own type extraction component• Flink’s own serialization component
4.5 Automatic Cost Based Optimizer • little re-configuration and little maintenance when
the cluster characteristics change and the data evolves over time
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4. Why Flink is an alternative to Apache Spark?
4.6 Little configuration required 4.7 Little tuning required 4.8 Flink has better performance
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4.1 True low latency streaming engine Some claim that 95% of streaming use cases can be
handled with micro-batches!? Really!!! Spark’s micro-batching isn’t good enough for many
time-critical applications that need to process large streams of live data and provide results in real-time.
Below are Several use cases, taken from real industrial situations where batch or micro batch processing is not appropriate.
References: • MapR Streams FAQ https://www.mapr.com/mapr-streams-faq#question12
• Apache Spark vs. Apache Flink, January 13, 2015. Whiteboard walkthrough by Balaji Narasimhalu from MapRhttps://www.youtube.com/watch?v=Dzx-iE6RN4w 79
4.1 True low latency streaming engine Financial Services
– Real-time fraud detection.– Real-time mobile notifications.
Healthcare– Smart hospitals - collect data and readings from hospital
devices (vitals, IVs, MRI, etc.) and analyze and alert in real time.– Biometrics - collect and analyze data from patient devices that
collect vitals while outside of care facilities.Ad Tech
– Real-time user targeting based on segment and preferences.Oil & Gas
– Real-time monitoring of pumps/rigs.
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4.1 True low latency streaming engine
Retail– Build an intelligent supply chain by placing sensors or RFID
tags on items to alert if items aren’t in the right place, or proactively order more if supply is low.
– Smart logistics with real-time end-to-end tracking of delivery trucks.
Telecommunications– Real-time antenna optimization based on user location data.– Real-time charging and billing based on customer usage, ability
to populate up-to-date usage dashboards for users.– Mobile offers.– Optimized advertising for video/audio content based on what
users are consuming.81
4.1 True low latency streaming engine “I would consider stream data analysis to be a major
unique selling proposition for Flink. Due to its pipelined architecture Flink is a perfect match for big data stream processing in the Apache stack.” – Volker Markl
Ref.: On Apache Flink. Interview with Volker Markl, June 24th 2015 http://www.odbms.org/blog/2015/06/on-apache-flink-interview-with-volker-markl/
Apache Flink uses streams for all workloads: streaming, SQL, micro-batch and batch.
Batch is just treated as a finite set of streamed data. This makes Flink the most sophisticated distributed open source Big Data processing engine.
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4.2 Unique windowing features not available in Spark StreamingBesides arrival time, support for event time or a mixture
of both for out of order streamsCustom windows based on window assigners and
triggers. How Apache Flink enables new streaming applications.
Part I: The power of event time and out of order stream processing. December 9, 2015 by Stephan Ewen and Kostas Tzoumas http://data-artisans.com/how-apache-flink-enables-new-streaming-applications-part-1/
How Apache Flink enables new streaming applications. Part II: State and versioning. February 3, 2016 by Ufuk Celebi and Kostas Tzoumas
http://data-artisans.com/how-apache-flink-enables-new-streaming-applications/83
4.2 Unique windowing features not available in Spark Streaming
Flink 0.10: A significant step forward in open source stream processing. November 17, 2015. By Fabian Hueske and Kostas Tzoumashttp://data-artisans.com/flink-0-10-a-significant-step-forward-in-open-source-stream-processing/
Dataflow/Beam & Spark: A Programming Model Comparison. February 3, 2016. By Tyler Akidau & Frances Perry, Software Engineers, Apache Beam Committershttps://cloud.google.com/dataflow/blog/dataflow-beam-and-spark-comparison
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4.3 Iteration OperatorsWhy Iterations? Many Machine Learning and Graph processing algorithms need iterations! For example:
Machine Learning Algorithms • Clustering (K-Means, Canopy, …) • Gradient descent (Logistic Regression, Matrix
Factorization) Graph Processing Algorithms
• Page-Rank, Line-Rank • Path algorithms on graphs (shortest paths,
centralities, …) • Graph communities / dense sub-components • Inference (Belief propagation) 85
4.2 Iteration Operators Flink's API offers two dedicated iteration operations:
Iterate and Delta Iterate. Flink executes programs with iterations as cyclic
data flows: a data flow program (and all its operators) is scheduled just once.
In each iteration, the step function consumes the entire input (the result of the previous iteration, or the initial data set), and computes the next version of the partial solution
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4.3 Iteration Operators Delta iterations run only on parts of the data that is
changing and can significantly speed up many machine learning and graph algorithms because the work in each iteration decreases as the number of iterations goes on.
Documentation on iterations with Apache Flinkhttp://ci.apache.org/projects/flink/flink-docs-master/apis/iterations.html
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4.3 Iteration Operators
StepStep
Step Step Step
Client
for (int i = 0; i < maxIterations; i++) {
// Execute MapReduce job}
Non-native iterations in Hadoop and Spark are implemented as regular for-loops outside the system.
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4.3 Iteration Operators Although Spark caches data across iterations, it still
needs to schedule and execute a new set of tasks for each iteration.
In Spark, it is driver-based looping: • Loop outside the system, in driver program• Iterative program looks like many independent jobs
In Flink, it is Built-in iterations:• Dataflow with Feedback edges• System is iteration-aware, can optimize the job
Spinning Fast Iterative Data Flows - Ewen et al. 2012 : http://vldb.org/pvldb/vol5/p1268_stephanewen_vldb2012.pdf The Apache Flink model for incremental iterative dataflow processing.
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4.4 Custom Memory Manager Features:
C++ style memory management inside the JVM User data stored in serialized byte arrays in JVM Memory is allocated, de-allocated, and used strictly
using an internal buffer pool implementation. Advantages:
1. Flink will not throw an OOM exception on you.2. Reduction of Garbage Collection (GC)3. Very efficient disk spilling and network transfers4. No Need for runtime tuning5. More reliable and stable performance
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4.4 Custom Memory Manager
public class WC {public String
word; public int count;}
emptypage
Pool of Memory Pages
Sorting, hashing, caching
Shuffles/ broadcasts
User code objects
Man
aged
Unm
anag
edFlink contains its own memory management stack. To do that, Flink contains its own type extraction and serialization components.JVM Heap
91Net
wor
k B
uffe
rs
4.4 Custom Memory ManagerFlink provides an Off-Heap option for its memory
management componentReferences:
• Peeking into Apache Flink's Engine Room - by Fabian Hüske, March 13, 2015 http://flink.apache.org/news/2015/03/13/peeking-into-Apache-Flinks-Engine-Room.html
• Juggling with Bits and Bytes - by Fabian Hüske, May 11,2015
https://flink.apache.org/news/2015/05/11/Juggling-with-Bits-and-Bytes.html
• Memory Management (Batch API) by Stephan Ewen- May 16, 2015
https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=53741525
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4.4 Custom Memory Manager
Compared to Flink, Spark is catching up with its project Tungsten for Memory Management and Binary Processing: manage memory explicitly and eliminate the overhead of JVM object model and garbage collection. April 28, 2014https://databricks.com/blog/2015/04/28/project-tungsten-bringing-spark-closer-to-bare-metal.html
It seems that Spark is adopting something similar to Flink and the initial Tungsten announcement read almost like Flink documentation!!
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4.5 Built-in Cost-Based Optimizer Apache Flink comes with an optimizer that is
independent of the actual programming interface. It chooses a fitting execution strategy depending
on the inputs and operations. Example: the "Join" operator will choose between
partitioning and broadcasting the data, as well as between running a sort-merge-join or a hybrid hash join algorithm.
This helps you focus on your application logic rather than parallel execution.
Quick introduction to the Optimizer: section 6 of the paper: ‘The Stratosphere platform for big data analytics’http://stratosphere.eu/assets/papers/2014-VLDBJ_Stratosphere_Overview.pdf
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4.5 Built-in Cost-Based Optimizer
Run locally on a data sample
on the laptopRun a month later
after the data evolved
Hash vs. SortPartition vs. Broadcast
CachingReusing partition/sortExecution
Plan A
ExecutionPlan B
Run on large fileson the cluster
ExecutionPlan C
What is Automatic Optimization? The system's built-in optimizer takes care of finding the best way to execute the program in any environment.
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4.5 Built-in Cost-Based Optimizer In contrast to Flink’s built-in automatic optimization,
Spark jobs have to be manually optimized and adapted to specific datasets because you need to manually control partitioning and caching if you want to get it right.
Spark SQL uses the Catalyst optimizer that supports both rule-based and cost-based optimization. References:
• Spark SQL: Relational Data Processing in Sparkhttp://people.csail.mit.edu/matei/papers/2015/sigmod_spark_sql.pdf
• Deep Dive into Spark SQL’s Catalyst Optimizer https://databricks.com/blog/2015/04/13/deep-dive-into-spark-sqls-catalyst-optimizer.html
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4.6 Little configuration required Flink requires no memory thresholds to
configure• Flink manages its own memory
Flink requires no complicated network configurations• Pipelining engine requires much less
memory for data exchange Flink requires no serializers to be configured
• Flink handles its own type extraction and data representation
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4.7 Little tuning requiredFlink programs can be adjusted to data automatically
• Flink’s optimizer can choose execution strategies automatically
According to Mike Olsen, Chief Strategy Officer of Cloudera Inc. “Spark is too knobby — it has too many tuning parameters, and they need constant adjustment as workloads, data volumes, user counts change. Reference: http://vision.cloudera.com/one-platform/
Tuning Spark Streaming for Throughput By Gerard Maas from Virdata. December 22, 2014 http://www.virdata.com/tuning-spark/
Spark Tuning: http://spark.apache.org/docs/latest/tuning.html
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4.8 Flink has better performanceWhy Flink provides a better performance?
• Custom memory manager• Native closed-loop iteration operators make graph
and machine learning applications run much faster .• Role of the built-in automatic optimizer. For example,
more efficient join processing• Pipelining data to the next operator in Flink is more
efficient than in Spark. Reference:
• A comparative performance evaluation of Flink, Dongwon Kim, Postech. October 12, 2015http://www.slideshare.net/FlinkForward/dongwon-kim-a-comparative-performance-evaluation-of-flink
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5. What are the benchmarking results against Flink?I am maintaining a list of resources related to
benchmarks against Flink: http://sparkbigdata.com/102-spark-blog-slim-baltagi/14-results-of-a-benchmark-between-apache-flink-and-apache-spark
A couple resources worth mentioning:• A comparative performance evaluation of Flink, Dongwon
Kim, POSTECH, Flink Forward October 13, 2015 http://www.slideshare.net/FlinkForward/dongwon-kim-a-comparative-performance-evaluation-of-flink
• Benchmarking Streaming Computation Engines at Yahoo December 16, 2015 Code at github: http://yahooeng.tumblr.com/post/135321837876/benchmarking-streaming-computation-engines-at
https://github.com/yahoo/streaming-benchmarks
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AgendaI. What is Apache Flink stack and how it fits
into the Big Data ecosystem? II. How Apache Flink integrates with Hadoop
and other open source tools for data input and output as well as deployment?
III. Why Apache Flink is an alternative to Apache Hadoop MapReduce, Apache Storm and Apache Spark.
IV. Who is using Apache Flink? V. Where to learn more about Apache Flink?
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IV. Who is using Apache Flink? You might like what you saw so far about
Apache Flink and still reluctant to give it a try!You might wonder: Is there anybody using
Flink in pre-production or production environment?
I asked this question to our friend ‘Google’ and I came with a short list in the next slide!
I also heard more about who is using Flink in production at the Flink Forward conference on October 12-13, 2015 in Berlin, Germany! http://flink-forward.org/
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IV. Who is using Apache Flink? How companies are using Flink as presented at Flink
Forward 2015. Kostas Tzoumas and Stephan Ewen. http://www.slideshare.net/stephanewen1/flink-use-cases-bay-area-meetup-october-2015
Powered by Flink page: https://cwiki.apache.org/confluence/display/FLINK/Powered+by+Flink103
IV. Who is using Apache Flink? 6 Apache Flink Case Studies from the 2015 Flink
Forward conference http://sparkbigdata.com/102-spark-blog-slim-baltagi/21-6-apache-flink-case-studies-from-the-2015-flinkforward-conference
Mine the Apache Flink User mailing list to discover more!
Gradoop: Scalable Graph Analytics with Apache Flink
• Gradoop project page http://dbs.uni-leipzig.de/en/research/projects/gradoop
• Gradoop: Scalable Graph Analytics with Apache Flink @ FOSDEM 2016. January 31, 2016http://www.slideshare.net/s1ck/gradoop-scalable-graph-analytics-with-apache-flink-fosdem-2016 104
PROTEUS http://www.proteus-bigdata.com/
a European Union funded research project to improve Apache Flink and mainly to develop two libraries (visualization and online machine learning) on top of Flink core.PROTEUS: Scalable Online Machine Learning by
Rubén Casado at Big Data Spain 2015 • Video: https://www.youtube.com/watch?v=EIH7HLyqhfE
• Slides: http://www.slideshare.net/Datadopter/proteus-h2020-big-data
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IV. Who is using Apache Flink?
IV. Who is using Apache Flink? has its hack week and the winner was a Flink based streaming project! December 18, 2015
• Extending the Yahoo! Streaming Benchmark and Winning Twitter Hack-Week with Apache Flink. Posted on February 2, 2016 by Jamie Grier http://data-artisans.com/extending-the-yahoo-streaming-benchmark/
did some benchmarks to compare performance of their use case implemented on Apache Storm against Spark Streaming and Flink. Results posted on December 18, 2015http://yahooeng.tumblr.com/post/135321837876/benchmarking-streaming-computation-engines-at 106
AgendaI. What is Apache Flink stack and how it fits
into the Big Data ecosystem? II. How Apache Flink integrates with Hadoop
and other open source tools for data input and output as well as deployment?
III. Why Apache Flink is an alternative to Apache Hadoop MapReduce, Apache Storm and Apache Spark?
IV. Who is using Apache Flink? V. Where to learn more about Apache Flink?
107
V. Where to learn more about Apache Flink?
1. What is Flink 2016 roadmap? 2. How to get started quickly with Apache
Flink?3. Where to find more resources about
Apache Flink?4. How to contribute to Apache Flink?5. What are some Key Takeaways?
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1 What is Flink 2016 roadmap? SQL/StreamSQL and Table APICEP Library: Complex Event Processing library for the
analysis of complex patterns such as correlations and sequence detection from multiple sources https://github.com/apache/flink/pull/1557 January 28, 2015
Dynamic Scaling: Runtime scaling for DataStream programs
Managed memory for streaming operatorsSupport for Apache Mesos
https://issues.apache.org/jira/browse/FLINK-1984
Security: Over-the-wire encryption of RPC (Akka) and data transfers (Netty)
Additional streaming connectors: Cassandra, Kinesis109
1 What is Flink roadmap? Expose more runtime metrics: Throughput / Latencies,
Backpressure monitoring, Spilling / Out of CoreMaking YARN resource dynamicDataStream API enhancementsDataSet API EnhancementsReferences:
• Apache Flink Roadmap Draft, December 2015https://docs.google.com/document/d/1ExmtVpeVVT3TIhO1JoBpC5JKXm-778DAD7eqw5GANwE/edit
• What’s next? Roadmap 2016. Robert Metzger, January 26, 2016. Berlin Apache Flink Meetup. http://www.slideshare.net/robertmetzger1/january-2016-flink-community-update-roadmap-2016/9
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2. How to get started quickly with Apache Flink?
Step-By-Step Introduction to Apache Flinkhttp://www.slideshare.net/sbaltagi/stepbystep-introduction-to-apache-flink
Implementing BigPetStore with Apache Flink http://www.slideshare.net/MrtonBalassi/implementing-bigpetstore-with-apache-flink
Apache Flink Crash Coursehttp://www.slideshare.net/sbaltagi/apache-flinkcrashcoursebyslimbaltagiandsrinipalthepu
Free training from Data Artisans http://dataartisans.github.io/flink-training/
All talks at the Flink Forward 2015http://sparkbigdata.com/102-spark-blog-slim-baltagi/22-all-talks-of-the-2015-flink-forward-conference
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3. Where to find more resources about Flink? Flink at the Apache Software Foundation: flink.apache.org/
data-artisans.com
@ApacheFlink, #ApacheFlink, #Flink
apache-flink.meetup.com
github.com/apache/flink
[email protected] [email protected]
Flink Knowledge Base http://sparkbigdata.com/component/tags/tag/27-flink
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4. How to contribute to Apache Flink?
Contributions to the Flink project can be in the form of:• Code• Tests• Documentation• Community participation: discussions, questions,
meetups, … How to contribute guide ( also contains a list of
simple “starter issues”)http://flink.apache.org/how-to-contribute.html
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5. What are some key takeaways?1. Although most of the current buzz is about Spark,
Flink offers the only hybrid (Real-Time Streaming + Batch) open source distributed data processing engine natively supporting many use cases.
2. With the upcoming release of Apache Flink 1.0, I foresee more adoption especially in use cases with Real-Time stream processing and also fast iterative machine learning or graph processing.
3. I foresee Flink embedded in major Hadoop distributions and supported!
4. Apache Spark and Apache Flink will both have their sweet spots despite their “Me Too Syndrome”!
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Thanks!
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• To all of you for attending!• To Bloomberg for sponsoring this event. • To data Artisans for allowing me to use some of
their materials for my slide deck.• To Capital One for giving me time to prepare and
give this talk. • Yes, we are hiring for our New York City offices
and our other locations! http://jobs.capitalone.com
• Drop me a note at [email protected] if you’re interested.