Book Image

Apache Spark Quick Start Guide

By : Shrey Mehrotra, Akash Grade
Book Image

Apache Spark Quick Start Guide

By: Shrey Mehrotra, Akash Grade

Overview of this book

Apache Spark is a ?exible framework that allows processing of batch and real-time data. Its unified engine has made it quite popular for big data use cases. This book will help you to get started with Apache Spark 2.0 and write big data applications for a variety of use cases. It will also introduce you to Apache Spark – one of the most popular Big Data processing frameworks. Although this book is intended to help you get started with Apache Spark, but it also focuses on explaining the core concepts. This practical guide provides a quick start to the Spark 2.0 architecture and its components. It teaches you how to set up Spark on your local machine. As we move ahead, you will be introduced to resilient distributed datasets (RDDs) and DataFrame APIs, and their corresponding transformations and actions. Then, we move on to the life cycle of a Spark application and learn about the techniques used to debug slow-running applications. You will also go through Spark’s built-in modules for SQL, streaming, machine learning, and graph analysis. Finally, the book will lay out the best practices and optimization techniques that are key for writing efficient Spark applications. By the end of this book, you will have a sound fundamental understanding of the Apache Spark framework and you will be able to write and optimize Spark applications.
Table of Contents (10 chapters)

Spark Optimizations

In the previous chapters, we learned how to use Spark to implement a variety of use cases using features such as RDDs, DataFrames, Spark SQL, MLlib, GraphX/Graphframes, and Spark Streaming. We also discussed how to monitor your applications to better understand their behavior in production. However, sometimes, you would want your jobs to run efficiently. We measure the efficiency of any job on two parameters: runtime and storage space. In the Spark application, you might also be interested in the statistic of the data shuffles between the nodes. We discussed some of the optimizations in the earlier chapters, but, in this chapter, we'll discuss more optimizations that can help you achieve some performance benefits.

Most developers focus only on writing their applications on Spark and do not focus on optimizing their job for a variety of reasons. This chapter...