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Apache Spark 2.x Cookbook

You're reading from   Apache Spark 2.x Cookbook Over 70 cloud-ready recipes for distributed Big Data processing and analytics

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Product type Paperback
Published in May 2017
Publisher
ISBN-13 9781787127265
Length 294 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 (13) Chapters Close

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

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 input splits, which is mostly the same as the number of blocks. The default block size in HDFS is 128 MB, and that works well in case of Spark as well. 

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
  1. 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

Note

Have the number of partitions two to three times the number of cores to...

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