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Apache Spark 2.x for Java Developers

Apache Spark 2.x for Java Developers

By : Kumar, Gulati
2 (4)
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Apache Spark 2.x for Java Developers

Apache Spark 2.x for Java Developers

2 (4)
By: Kumar, Gulati

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 (12 chapters)
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Spark REPL also known as CLI


In Chapter 1, Introduction to Spark, we learnt that one of the advantages of Apache Spark over the MapReduce framework is interactive processing. Apache Spark achieves the same using Spark REPL.

Spark REPL or Spark shell, also known as Spark CLI, is a very useful tool for exploring the Spark programming. REPL is an acronym for Read-Evaluate-Print Loop. It is an interactive shell used by programmers to interact with a framework. Apache Spark also comes with REPL that beginners can use to understand the Spark programming model.

To launch the Spark REPL, we will execute the command that we executed in the previous section:

$SPARK_HOME/bin/spark-shell

Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel).
16/11/01 16:38:43 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
16/11...
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