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)

The Sparksession


In computer science a session is a semi-permanent interactive information interchange between two communicating devices or between a computer and a user. SparkSession is something similar, which provides a time bounded interaction between a user and the Spark framework and allows you to program with DataFrames and Datasets. We have used SparkContext in the previous chapters while working with RDDs, but Spark Session should be your go-to starting point when starting to work with Data Frames or Datasets.

Creating a SparkSession

In Scala, Java, and Python you will use the Builder pattern to create a SparkSession. It is important to understand that when you are using spark-shell or pyspark, Spark session will already be available as a spark object:

Figure 4.6: Spark session in Scala shell

The following image shows SparkSession in an Python shell:

Figure 4.7: SparkSession in Python shell

Example 4.1: Scala - Programmatically creating a Spark Session:

import org.apache.spark.sql...