Book Image

Hands-On Big Data Analytics with PySpark

By : Rudy Lai, Bartłomiej Potaczek
Book Image

Hands-On Big Data Analytics with PySpark

By: Rudy Lai, Bartłomiej Potaczek

Overview of this book

Apache Spark is an open source parallel-processing framework that has been around for quite some time now. One of the many uses of Apache Spark is for data analytics applications across clustered computers. In this book, you will not only learn how to use Spark and the Python API to create high-performance analytics with big data, but also discover techniques for testing, immunizing, and parallelizing Spark jobs. You will learn how to source data from all popular data hosting platforms, including HDFS, Hive, JSON, and S3, and deal with large datasets with PySpark to gain practical big data experience. This book will help you work on prototypes on local machines and subsequently go on to handle messy data in production and at scale. This book covers installing and setting up PySpark, RDD operations, big data cleaning and wrangling, and aggregating and summarizing data into useful reports. You will also learn how to implement some practical and proven techniques to improve certain aspects of programming and administration in Apache Spark. By the end of the book, you will be able to build big data analytical solutions using the various PySpark offerings and also optimize them effectively.
Table of Contents (15 chapters)

Reusing the same rdd for different actions

In this section, we will reuse the same rdd for different actions. First, we will minimize the execution time by reusing the rdd. We will then look at caching and a performance test for our code.

The following example is the test from the preceding section but a bit modified, as here we take start by currentTimeMillis() and the result. So, we are just measuring the result of all actions that are executed:

//then every call to action means that we are going up to the RDD chain
//if we are loading data from external file-system (I.E.: HDFS), every action means
//that we need to load it from FS.
val start = System.currentTimeMillis()
println(rdd.collect().toList)
println(rdd.count())
println(rdd.first())
rdd.foreach(println(_))
rdd.foreachPartition(t => t.foreach(println(_)))
println(rdd.max())
println(rdd.min(...