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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
Toc

Table of Contents (19) Chapters close

Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Getting Started with Apache Spark 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

Leveraging speculation


Like MapReduce, Spark uses speculation to spawn additional tasks if it suspects a task is running on a straggler node. A good use case would be to think of a situation when 95 percent or 99 percent of your job finishes really fast and then gets stuck (we have all been there).

How to do it...

There are a few settings you can use to control speculation. The examples are provided only to show how to change values. Mostly, just turning on speculation is good enough:

  1. Setting spark.speculation (the default is false):
$ spark-shell -conf spark.speculation=true
  1. Setting spark.speculation.interval (the default is 100 milliseconds) (denotes the rate at which Spark examines tasks to see whether speculation is needed): 
$ spark-shell -conf spark.speculation.interval=200
  1. Setting spark.speculation.multiplier (the default is 1.5) (denotes how many times a task has to be slower than median to be a candidate for speculation):
$ spark-shell -conf spark.speculation.multiplier=1.5
  1. Setting spark...
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