Book Image

Learning Apache Spark 2

Book Image

Learning Apache Spark 2

Overview of this book

Apache Spark has seen an unprecedented growth in terms of its adoption over the last few years, mainly because of its speed, diversity and real-time data processing capabilities. It has quickly become the preferred choice of tool for many Big Data professionals looking to find quick insights from large chunks of data. This book introduces you to the Apache Spark framework, and familiarizes you with all the latest features and capabilities introduced in Spark 2. Starting with a detailed introduction to Spark’s architecture and the installation procedure, this book covers everything you need to know about the Spark framework in the most practical manner. You will learn how to perform the basic ETL activities using Spark, and work with different components of Spark such as Spark SQL, as well as the Dataset and DataFrame APIs for manipulating your data. Then, you will perform machine learning using Spark MLlib, as well as perform streaming analytics and graph processing using the Spark Streaming and GraphX modules respectively. The book also gives special emphasis on deploying your Spark models, and how they can be operated in a clustered mode. During the course of the book, you will come across implementations of different real-world use-cases and examples, giving you the hands-on knowledge you need to use Apache Spark in the best possible manner.
Table of Contents (12 chapters)

Commonly Supported File Formats


We've already seen the ease with which you can manipulate text files using Spark with the textFile() method on SparkContext. However, you'll be pleased to know that Apache Spark supports a large number of other formats, which are increasing with every release of Spark. With Apache Spark release 2.0, the following file formats are supported out of the box:

  • TextFiles (already covered)
  • JSON files
  • CSV Files
  • Sequence Files
  • Object Files

Text Files

We've already seen various examples in Chapter 1, Architecture and Installation and Chapter 2Transformations and Actions with Spark RDDs on how to read text files using the textFile() function. Each line in the text file is assumed to be a new record. We've also seen examples of wholeTextFiles(), which return a PairRDD, with the key being the identifier of the file. This is very useful in ETL jobs, where you might want to process data differently based on the key, or even pass that on to downstream processing.

An...