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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 garbage collection


JVM garbage collection can be a challenge if you have a lot of short lived RDDs. JVM needs to go over all the objects to find the ones it needs to garbage collect. The cost of the garbage collection is proportional to the number of objects the GC needs to go through. Therefore, using fewer objects and the data structures that use fewer objects (simpler data structures, such as arrays) helps.

Serialization also shines here as a byte array needs only one object to be garbage collected.

By default, Spark uses 60 percent of the executor memory to cache RDDs and the rest 40 percent for regular objects. Sometimes, you may not need 60 percent for RDDs and can reduce this limit so that more space is available for object creation (less need for GC).

How to do it…

You can set the memory allocated for RDD cache to 40 percent by starting the Spark shell and setting the memory fraction:

$ spark-shell --conf spark.storage.memoryFraction=0.4
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