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Spark Cookbook

You're reading from   Spark Cookbook With over 60 recipes on Spark, covering Spark Core, Spark SQL, Spark Streaming, MLlib, and GraphX libraries this is the perfect Spark book to always have by your side

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Product type Paperback
Published in Jul 2015
Publisher
ISBN-13 9781783987061
Length 226 pages
Edition 1st Edition
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Author (1):
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Rishi Yadav Rishi Yadav
Author Profile Icon Rishi Yadav
Rishi Yadav
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Table of Contents (14) Chapters Close

Preface 1. Getting Started with Apache Spark 2. Developing Applications with Spark FREE CHAPTER 3. External Data Sources 4. Spark SQL 5. Spark Streaming 6. Getting Started with Machine Learning Using MLlib 7. Supervised Learning with MLlib – Regression 8. Supervised Learning with MLlib – Classification 9. Unsupervised Learning with MLlib 10. Recommender Systems 11. Graph Processing Using GraphX 12. Optimizations and Performance Tuning Index

Optimizing the level of parallelism


Optimizing the level of parallelism is very important to fully utilize the cluster capacity. In the case of HDFS, it means that the number of partitions is the same as the number of InputSplits, which is mostly the same as the number of blocks.

In this recipe, we will cover different ways to optimize the number of partitions.

How to do it…

Specify the number of partitions when loading a file into RDD with the following steps:

  1. Start the Spark shell:

    $ spark-shell
    
  2. Load the RDD with a custom number of partitions as a second parameter:

    scala> sc.textFile("hdfs://localhost:9000/user/hduser/words",10)
    

Another approach is to change the default parallelism by performing the following steps:

  1. Start the Spark shell with the new value of default parallelism:

    $ spark-shell --conf spark.default.parallelism=10
    
  2. Check the default value of parallelism:

    scala> sc.defaultParallelism
    

Note

You can also reduce the number of partitions using an RDD method called coalesce(numPartitions...

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