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

Structured Streaming

Structured is a brand new edition in Apache Spark's streaming processing vertical. It is a stream processing engine built on top of the Spark SQL engine. With the introduction of structured streaming, a unification bond of batch processing and stream processing as it allows us to develop a stream processing is enabled application similar to the batch processing application. At the same time, it is scalable and fault tolerant as well.

As per Apache Spark's documentation,

Structured Streaming provides fast, scalable, fault-tolerant, end-to-end exactly-once stream processing without the user having to reason about streaming


Instead of using DStream in structured streaming, the dataset API can be used and it is the responsibility of the Spark SQL engine to keep the dataset updated as new streaming data arrives. As the dataset API is used, all the Spark SQL operations are available. Therefore, users can use SQL queries on the stream data using the optimized Spark SQL engine...