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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 FREE CHAPTER 2. Storage 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

The inner workings of HDFS

In Chapter 1, Introduction, we gave a very high-level overview of HDFS; we will now explore it in a little more detail. As mentioned in that chapter, HDFS can be viewed as a filesystem, though one with very specific performance characteristics and semantics. It's implemented with two main server processes: the NameNode and the DataNodes, configured in a master/slave setup. If you view the NameNode as holding all the filesystem metadata and the DataNodes as holding the actual filesystem data (blocks), then this is a good starting point. Every file placed onto HDFS will be split into multiple blocks that might reside on numerous DataNodes, and it's the NameNode that understands how these blocks can be combined to construct the files.

Cluster startup

Let's explore the various responsibilities of these nodes and the communication between them by assuming we have an HDFS cluster that was previously shut down and then examining the startup behavior.

NameNode...

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