Book Image

Apache Spark 2.x Cookbook

By : Rishi Yadav
Book Image

Apache Spark 2.x Cookbook

By: Rishi Yadav

Overview of this book

While Apache Spark 1.x gained a lot of traction and adoption in the early years, Spark 2.x delivers notable improvements in the areas of API, schema awareness, Performance, Structured Streaming, and simplifying building blocks to build better, faster, smarter, and more accessible big data applications. This book uncovers all these features in the form of structured recipes to analyze and mature large and complex sets of data. Starting with installing and configuring Apache Spark with various cluster managers, you will learn to set up development environments. Further on, you will be introduced to working with RDDs, DataFrames and Datasets to operate on schema aware data, and real-time streaming with various sources such as Twitter Stream and Apache Kafka. You will also work through recipes on machine learning, including supervised learning, unsupervised learning & recommendation engines in Spark. Last but not least, the final few chapters delve deeper into the concepts of graph processing using GraphX, securing your implementations, cluster optimization, and troubleshooting.
Table of Contents (19 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Optimizing memory


Spark is a complex distributed computing framework and has many moving parts. Various cluster resources, such as memory, CPU, and network bandwidth, can become bottlenecks at various points. As Spark is an in-memory compute framework, the impact of the memory is the biggest.

Another issue is that it is common for Spark applications to use a huge amount of memory, sometimes more than 100 GB. This amount of memory usage is not common in traditional Java applications.

In Spark, there are two places where memory optimization is needed: one at the driver level and the other at the executor level. The following diagram shows the two levels (driver level and executor level) of operations in Spark:

How to do it...

  1. Set the driver memory using the spark-shell command:
        $ spark-shell --drive-memory 8g
  1. Set the driver memory using the spark-submit command:
$ spark-submit --drive-memory 8g
  1. Set the executor memory using the spark-shell command:
$ spark-shell --executor-memory 8g
  1. Set the...