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Hands-On Big Data Analytics with PySpark

You're reading from   Hands-On Big Data Analytics with PySpark Analyze large datasets and discover techniques for testing, immunizing, and parallelizing Spark jobs

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Product type Paperback
Published in Mar 2019
Publisher Packt
ISBN-13 9781838644130
Length 182 pages
Edition 1st Edition
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Authors (3):
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James Cross James Cross
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James Cross
Bartłomiej Potaczek Bartłomiej Potaczek
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Bartłomiej Potaczek
Rudy Lai Rudy Lai
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Rudy Lai
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Table of Contents (15) Chapters Close

Preface 1. Installing Pyspark and Setting up Your Development Environment FREE CHAPTER 2. Getting Your Big Data into the Spark Environment Using RDDs 3. Big Data Cleaning and Wrangling with Spark Notebooks 4. Aggregating and Summarizing Data into Useful Reports 5. Powerful Exploratory Data Analysis with MLlib 6. Putting Structure on Your Big Data with SparkSQL 7. Transformations and Actions 8. Immutable Design 9. Avoiding Shuffle and Reducing Operational Expenses 10. Saving Data in the Correct Format 11. Working with the Spark Key/Value API 12. Testing Apache Spark Jobs 13. Leveraging the Spark GraphX API 14. Other Books You May Enjoy

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(...
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