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Learning Hadoop 2

You're reading from   Learning Hadoop 2 Design and implement data processing, lifecycle management, and analytic workflows with the cutting-edge toolbox of Hadoop 2

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Product type Paperback
Published in Feb 2015
Publisher Packt
ISBN-13 9781783285518
Length 382 pages
Edition 1st Edition
Tools
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Toc

Table of Contents (13) Chapters Close

Preface 1. Introduction 2. Storage FREE CHAPTER 3. Processing – MapReduce and Beyond 4. Real-time Computation with Samza 5. Iterative Computation with Spark 6. Data Analysis with Apache Pig 7. Hadoop and SQL 8. Data Lifecycle Management 9. Making Development Easier 10. Running a Hadoop Cluster 11. Where to Go Next Index

Cluster tuning


In addition to the previous comments specific to a cluster run on EMR, there are some general thoughts to keep in mind when running workloads on any type of cluster. This will, of course, be more explicit when running outside of EMR as it often abstracts some of the details.

JVM considerations

You should be running the 64-bit version of a JVM and using the server mode. This can take longer to produce optimized code, but it also uses more aggressive strategies and will re-optimize code over time. This makes it a much better fit for long-running services, such as Hadoop processes.

Ensure that you allocate enough memory to the JVM to prevent overly-frequent Garbage Collection (GC) pauses. The concurrent mark-and-sweep collector is currently the most tested and recommended for Hadoop. The Garbage First (G1) collector has become the GC option of choice in numerous other workloads since its introduction with JDK7, so it's worth monitoring recommended best practice as it evolves. These...

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