Apache Flink - A Stream Processing Engine

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Aljoscha Krettek

Quick Introduction to Flink as a distributed stream processing engine.

1
Aljoscha Krettek
@aljoscha
December 8, 2016
Google Singapore
Apache Flink™
A Stream Processing Engine

2
Original creators of Apache
Flink®
Providers of the
dA Platform, a supported
Flink distribution

What is Apache Flink?
3
Distributed Deployment
Stateful Stream Processing
DataStream API, Windows
Table API
Building Block
Core API
Declarative DSL
Basis

Stream Processing
4
Data Stream Data Stream
Your
code

Stateful Stream Processing
5
State
Data Stream Data Stream
Your
code

Stateful Steam Processing
6
Source
Filter /
Transform
State
read/write
Sink

Stateful Steam Processing
7
Scalable embedded state
Access at memory speed &
scales with parallel operators

Stateful Steam Processing
8
Re-load state
Reset positions
in input streams
Rolling back computation
Re-processing

Stateful Steam Processing
9
Restore to different
programs
Bugfixes, Upgrades, A/B testing, etc

Beam/Flink Interactions
10
Dataflow/
MillWheel influenced
runs on

Code Example!
11
Based on http://data-artisans.com/robust-stream-processing-flink-walkthrough/

1
Thank you!
@aljoscha
@ApacheFlink
@dataArtisans
http://flink.apache.org

Appendix
13

We are hiring!
data-artisans.com/careers

15
Flink Forward 2017
San Francisco Berlin
• April 10-11, 2017
• The first Flink Forward event
outside of Berlin
• Register at sf.flink-forward.org/
• September 11-13
• Over 350 attendees at the
last event
• Registration opening soon!

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The Evolution of (Open Source) Data Processing

The Evolution of (Open Source) Data Processing

Aljoscha Krettek

Apache Flink and what it is used for

Apache Flink and what it is used for

Apache Flink and what it is used for

Aljoscha Krettek

Stream processing still evolves and changes at a speed that can make it hard to keep up with the developments. Being at the forefront of stream processing technology, the evolution of Apache Flink has mirrored many of these developments and continues to do so. We will take you on a journey through the major milestones of stream processing technology in past years, diving into the latest additions that Apache Flink and other communities introduced to the stream processing landscape, such as Streamng SQL, Time Versioned Tables, cluster-library-duality, language portability, etc. We will take a sneak peek into our crystal ball and present in what the Flink community is working on next.

The Past, Present, and Future of Apache Flink®

The Past, Present, and Future of Apache Flink®

The Past, Present, and Future of Apache Flink®

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(Past), Present, and Future of Apache Flink

(Past), Present, and Future of Apache Flink

(Past), Present, and Future of Apache Flink

Aljoscha Krettek

Python is popular amongst data scientists and engineers for data processing tasks. The big data ecosystem has traditionally been rather JVM centric. Often Java (or Scala) are the only viable option to implement data processing pipelines. That sometimes poses an adoption barrier for organizations that have already invested in other language ecosystems. The Apache Beam project provides a unified programming model for data processing and its ongoing portability effort aims to enable multiple language SDKs (currently Java, Python and Go) on a common set of runners. The combination of Python streaming on the Apache Flink runner is one example. Let’s take a look how the Flink runner translates the Beam model into the native DataStream (or DataSet) API, how the runner is changing to support portable pipelines, how Python user code execution is coordinated with gRPC based services and how a sample pipeline runs on Flink.

Python Streaming Pipelines with Beam on Flink

Python Streaming Pipelines with Beam on Flink

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Aljoscha Krettek

Stream processing still evolves and changes at a speed that can make it hard to keep up with the developments. Being at the forefront of stream processing technology, the evolution of Apache Flink has mirrored many of these developments and continues to do so. We will take you on a journey through the major milestones of stream processing technology in past years, diving into the latest additions that Apache Flink and other communities introduced to the stream processing landscape, such as Streamng SQL, Time Versioned Tables, cluster-library-duality, language portability, etc. We will take a sneak peek into our crystal ball and present in what the Flink community is working on next.

The Past, Present, and Future of Apache Flink

The Past, Present, and Future of Apache Flink

The Past, Present, and Future of Apache Flink

Aljoscha Krettek

Apache Beam lets you process unbounded, out-of-order, global-scale data with portable high-level pipelines, but not all use cases are pipelines of simple “map” and “combine” operations. Aljoscha Krettek introduces Beam’s new State API, which brings scalability and consistency to fine-grained stateful processing while interoperating with Beam’s other features such as consistent event-time windowing and windowed side inputs—all while remaining portable to any Beam runner, including Apache Apex, Apache Flink, Apache Spark, and Google Cloud Dataflow. Aljoscha covers the new state and timer features in Beam and shows how to use them to express common real-world use cases in a backend-agnostic manner. Examples of new use cases unlocked by Beam’s new mutable state and timers include: * Microservice-like streaming applications such as new user account verification and digital ordering * Complex aggregations that cannot easily be expressed as an efficient associative combiner * Output based on customized conditions, such as limiting to only “significant” changes in a learned model (resulting in potentially large cost savings in subsequent processing) * Fine control over retrieval and storage of intermediate values during aggregation * Reading from and writing to external systems with detailed management of the nature and size of requests

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Unified stateful big data processing in Apache Beam (incubating)

Unified stateful big data processing in Apache Beam (incubating)

Aljoscha Krettek

Aljoscha Krettek offers an overview of the modern stream processing space, details the challenges posed by stateful and event-time-aware stream processing, and shares core archetypes ("application blueprints”) for stream processing drawn from real-world use cases with Apache Flink. Topics include: * Aggregating IoT event data, in which event-time-aware processing, handling of late data, and state are important * Data enrichment, in which a stream of real-time events is “enriched” with data from a slowly changing database of supplemental data points * Dynamic stream processing, in which a stream of control messages and dynamically updated user logic is used to process a stream of events for use cases such as alerting and fraud detection

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Advanced Flink Training - Design patterns for streaming applications

Advanced Flink Training - Design patterns for streaming applications

Advanced Flink Training - Design patterns for streaming applications

Aljoscha Krettek

If you are in the business of processing a stream of events you sooner or later come upon different notions of time. There is processing time, the current time of the machine your program is running on and event time, the local time at which an event occurred. In this talk we will look at why this distinction is relevant and also how Flink manages to work with these different ideas of time. We will look at how Flink tracks the progress of time and how you can employ windows to perform aggregating operations on an infinite stream of events.

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Adventures in Timespace - How Apache Flink Handles Time and Windows

Adventures in Timespace - How Apache Flink Handles Time and Windows

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Flink 0.10 - Upcoming Features

Flink 0.10 - Upcoming Features

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Apache Flink(tm) - A Next-Generation Stream Processor

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Apache Flink - A Stream Processing Engine

1. 1 Aljoscha Krettek @aljoscha December 8, 2016 Google Singapore Apache Flink™ A Stream Processing Engine

2. 2 Original creators of Apache Flink® Providers of the dA Platform, a supported Flink distribution

3. What is Apache Flink? 3 Distributed Deployment Stateful Stream Processing DataStream API, Windows Table API Building Block Core API Declarative DSL Basis

4. Stream Processing 4 Data Stream Data Stream Your code

5. Stateful Stream Processing 5 State Data Stream Data Stream Your code

6. Stateful Steam Processing 6 Source Filter / Transform State read/write Sink

7. Stateful Steam Processing 7 Scalable embedded state Access at memory speed & scales with parallel operators

8. Stateful Steam Processing 8 Re-load state Reset positions in input streams Rolling back computation Re-processing

9. Stateful Steam Processing 9 Restore to different programs Bugfixes, Upgrades, A/B testing, etc

10. Beam/Flink Interactions 10 Dataflow/ MillWheel influenced runs on

11. Code Example! 11 Based on http://data-artisans.com/robust-stream-processing-flink-walkthrough/

12. 1 Thank you! @aljoscha @ApacheFlink @dataArtisans http://flink.apache.org

13. Appendix 13

14. We are hiring! data-artisans.com/careers

15. 15 Flink Forward 2017 San Francisco Berlin • April 10-11, 2017 • The first Flink Forward event outside of Berlin • Register at sf.flink-forward.org/ • September 11-13 • Over 350 attendees at the last event • Registration opening soon!