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
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface

Transformations


Data transformations transform the dataset from one form into another. The input could be one or more datasets and the output could also be zero, or one or more data streams. Now let's try to understand each transformation one by one.

Map

This is one of the simplest transformations where the input is one dataset and output is also one dataset.

In Java:

inputSet.map(new MapFunction<Integer, Integer>() { 
  @Override 
  public Integer map(Integer value) throws Exception { 
        return 5 * value; 
      } 
    }); 

In Scala:

inputSet.map { x => x * 5 } 

In Python:

inputSet.map { lambda x : x * 5 } 

Flat map

The flat map takes one record and outputs zero, or one or more than one records.

In Java:

inputSet.flatMap(new FlatMapFunction<String, String>() { 
    @Override 
    public void flatMap(String value, Collector<String> out) 
        throws Exception { 
        for(String word: value.split(" ")){ 
            out.collect(word); 
        } 
    } 
}); 

In Scala...