Book Image

Learning Apache Flink

By : Tanmay Deshpande
Book Image

Learning Apache Flink

By: Tanmay Deshpande

Overview of this book

<p>With the advent of massive computer systems, organizations in different domains generate large amounts of data on a real-time basis. The latest entrant to big data processing, Apache Flink, is designed to process continuous streams of data at a lightning fast pace.</p> <p>This book will be your definitive guide to batch and stream data processing with Apache Flink. The book begins with introducing the Apache Flink ecosystem, setting it up and using the DataSet and DataStream API for processing batch and streaming datasets. Bringing the power of SQL to Flink, this book will then explore the Table API for querying and manipulating data. In the latter half of the book, readers will get to learn the remaining ecosystem of Apache Flink to achieve complex tasks such as event processing, machine learning, and graph processing. The final part of the book would consist of topics such as scaling Flink solutions, performance optimization and integrating Flink with other tools such as ElasticSearch.</p> <p>Whether you want to dive deeper into Apache Flink, or want to investigate how to get more out of this powerful technology, you’ll find everything you need inside.</p>
Table of Contents (17 chapters)
Learning Apache Flink
About the Author
About the Reviewers
Customer Feedback

Physical partitioning

Flink allows us to perform physical partitioning of the stream data. You have an option to provide custom partitioning. Let us have a look at the different types of partitioning.

Custom partitioning

As mentioned earlier, you can provide custom implementation of a partitioner.

In Java:

inputStream.partitionCustom(partitioner, "someKey"); 
inputStream.partitionCustom(partitioner, 0); 

In Scala:

inputStream.partitionCustom(partitioner, "someKey") 
inputStream.partitionCustom(partitioner, 0) 

While writing a custom partitioner you need make sure you implement an efficient hash function.

Random partitioning

Random partitioning randomly partitions data streams in an evenly manner.

In Java:


In Scala:


Rebalancing partitioning

This type of partitioning helps distribute the data evenly. It uses a round robin method for distribution. This type of partitioning is good when data is skewed.

In Java:


In Scala: