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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

Detecting a shuffle in a process

In this section, we will learn how to detect a shuffle in a process.

In this section, we will cover the following topics:

  • Loading randomly partitioned data
  • Issuing repartition using a meaningful partition key
  • Understanding how shuffle occurs by explaining a query

We will load randomly partitioned data to see how and where the data is loaded. Next, we will issue a partition using a meaningful partition key. We will then repartition data to the proper executors using the deterministic and meaningful key. In the end, we will explain our queries by using the explain() method and understand the shuffle. Here, we have a very simple test.

We will create a DataFrame with some data. For example, we created an InputRecord with some random UID and user_1, and another input with random ID in user_1, and the last record for user_2. Let's imagine that...

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