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Apache Spark 2.x Cookbook

You're reading from  Apache Spark 2.x Cookbook

Product type Book
Published in May 2017
Publisher
ISBN-13 9781787127265
Pages 294 pages
Edition 1st Edition
Languages
Author (1):
Rishi Yadav Rishi Yadav
Profile icon Rishi Yadav

Table of Contents (19) Chapters

Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Getting Started with Apache Spark Developing Applications with Spark Spark SQL Working with External Data Sources Spark Streaming Getting Started with Machine Learning Supervised Learning with MLlib — Regression Supervised Learning with MLlib — Classification Unsupervised Learning Recommendations Using Collaborative Filtering Graph Processing Using GraphX and GraphFrames 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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