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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

Faster average computations with aggregate

In the previous section, we saw how we can use map and reduce to calculate averages. Let's now look at faster average computations with the aggregate function. You can refer to the documentation mentioned in the previous section.

The aggregate is a function that takes three arguments, none of which are optional.

The first one is the zeroValue argument, where we put in the base case of the aggregated results.

The second argument is the sequential operator (seqOp), which allows you to stack and aggregate values on top of zeroValue. You can start with zeroValue, and the seqOp function that you feed into aggregate takes values from your RDD, and stacks or aggregates it on top of zeroValue.

The last argument is combOp, which stands for combination operation, where we simply take the zeroValue argument that is now aggregated through the...

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