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
About the Authors
About the Reviewer
Customer Feedback

RDD persistence and cache

Spark jobs usually contains multiple intermediate RDDs on which multiple actions can be called to compute different problems. However, each time an action is called the complete DAG for that action gets called, this not only increases the computing time, but is also wasteful as per CPU and other resources are concerned. To overcome the limitation of re-computing the entire iterative job, Spark provides two different options for persisting the intermediate RDD, that is, cache() and persist(). The cache() method persists the data unserialized in the memory by default .This possibly is the fastest way to retrieve the persisted data, However, use of cache() comes with some trade off. Each node computing a partition of the RDD persist the resultant on that node itself and hence in case of node failure the data of the RDD partition gets lost. It is then recomputed again, but certain computation time gets lost in the process. Similarly, the persisted data is also unserialized...