With the advent of massive computer systems, organizations in different domains generate large amounts of data at 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.
This book will be your definitive guide to batch and stream data processing with Apache Flink. The book begins by 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 Hadoop, ElasticSearch, Cassandra, and Kafka.
Whether you want to dive deeper into Apache Flink, or investigate how to get more out of this powerful technology, you’ll find everything inside. This book covers a lot of real-world use cases, which will help you connect the dots.
Chapter 1, Introduction to Apache Flink, introduces you to the history, architecture, features and installation of Apache Flink on single node and multinode clusters.
Chapter 2, Data Processing Using the DataStream API, provides you with the details of Flink’s streaming first concept. You will learn details about data sources, transformation, and data sinks available with DataStream API.
Chapter 3, Data Processing Using the Batch Processing API, enlightens you with the batch processing API, that is, DataSet API. You will learn about data sources, transformations, and sinks. You will also learn about the connectors available with the API.
Chapter 4, Data Processing Using the Table API, helps you understand how to use SQL concepts with Flink data processing frameworks. You will also learn how to apply these concepts to the real-world use case.
Chapter 5, Complex Event Processing, provides insights to you on how to solve complex event processing problems using Flink CEP library. You will learn details about the pattern definition, detection, and alert generation.
Chapter 6, Machine Learning Using Flink ML, covers details on machine learning concepts and how to apply various algorithms to the real-life use cases.
Chapter 7, Flink Graph API - Gelly, introduces you to the graph concepts and what Flink Gelly offers us to solve real-life use cases. It enlightens you on iterative graph processing capabilities provided by Flink.
Chapter 8, Distributed Data Processing Using Flink and Hadoop, covers details on how to use existing Hadoop-YARN clusters to submit Flink jobs. It talks about how Flink works on YARN in detail.
Chapter 9, Deploying Flink on Cloud, provides details on how to deploy Flink on Cloud. It talks in detail about how to use Flink on Google Cloud and AWS.
Chapter 10, Best Practices, covers various best practices developers should follow in order to use Flink in an efficient manner. It also talks about logging, monitoring best practices to control the Flink environment.
You would need a laptop or desktop with any OS such as Windows, Mac, or UNIX. It’s good to have an IDE such as Eclipse or IntelliJ and, of course, you would need a lot of enthusiasm.
This book is for the big data developers who are looking to process batch and real-time data on distributed systems and for data scientists who look for industrializing analytic solutions.
In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.
Code words in text, database table names, folder names, filenames, file extensions, path names, dummy URLs, user input, and Twitter handles are shown as follows: "This will generate the public and private keys in the /flinkuser/.ssh folder."
A block of code is set as follows:
CassandraSink.addSink(input)
.setQuery("INSERT INTO cep.events (id, message) values (?, ?);")
.setClusterBuilder(new ClusterBuilder() {
@Override
public Cluster buildCluster(Cluster.Builder builder) {
return builder.addContactPoint("127.0.0.1").build();
}
})
.build();Any command-line input or output is written as follows:
$sudo tar -xzf flink-1.1.4-bin-hadoop27-scala_2.11.tgz $cd flink-1.1.4 $bin/start-local.sh
New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "Once all our work is done, it is important to shut down the cluster. To do this, we again need to go to AWS console and click on Terminate button".
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