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

Yet Another Resource Negotiator (YARN)

Hadoop YARN is one of the most popular resource managers in the big data world. Apache Spark provides seamless integration with YARN. Apache Spark applications can be deployed to YARN using the same spark-submit command.

Apache Spark requires HADOOP_CONF_DIR or YARN_CONF_DIR environment variables to be set and pointing to the Hadoop configuration directory, which contains core-site.xml, yarn-site.xml, and so on. These configurations are required to connect to the YARN cluster.

To run Spark applications on YARN, the YARN cluster should be started first. Refer to the following official Hadoop documentation that describes how to start the YARN cluster:

YARN in general consists of a resource manager (RM) and multiple node managers (NM) where resource manager is the master node and node managers are slave nodes. NMs send detailed report to RM at every defined interval that tell RM how many resources (such as CPU slots and RAM...