Book Image

Apache Spark 2.x for Java Developers

By : Sourav Gulati, Sumit Kumar
Book Image

Apache Spark 2.x for Java Developers

By: Sourav Gulati, Sumit Kumar

Overview of this book

Apache Spark is the buzzword in the big data industry right now, especially with the increasing need for real-time streaming and data processing. While Spark is built on Scala, the Spark Java API exposes all the Spark features available in the Scala version for Java developers. This book will show you how you can implement various functionalities of the Apache Spark framework in Java, without stepping out of your comfort zone. The book starts with an introduction to the Apache Spark 2.x ecosystem, followed by explaining how to install and configure Spark, and refreshes the Java concepts that will be useful to you when consuming Apache Spark's APIs. You will explore RDD and its associated common Action and Transformation Java APIs, set up a production-like clustered environment, and work with Spark SQL. Moving on, you will perform near-real-time processing with Spark streaming, Machine Learning analytics with Spark MLlib, and graph processing with GraphX, all using various Java packages. By the end of the book, you will have a solid foundation in implementing components in the Spark framework in Java to build fast, real-time applications.
Table of Contents (19 chapters)
Title Page
Credits
Foreword
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Streaming sources


Streaming sources are segregated into two categories in Spark Streaming, that is, basic source and advance source. All those sources that are directly available through StreamingContext, such as filesystem and socket streams are called basic sources while sources that require dependency linkages, as in the case of Kafka, Flume, and so on are called advanced sources. Streaming sources can also be defined on the basis of reliability; if an acknowledgement is sent to the source system after receiving and replicating the messages then such receivers are called reliable receivers, such as the Kafka API. Similarly if the system does not send an acknowledgement to the source system then they are termed as unreliable.

Some common streaming sources apart from socket streaming, which were discussed in previous examples, are explained in the next section.

fileStream

Data files from any directory can be read from a directory using the fileStream() API of StreamingContext. The fileStream...