October 2015
Intermediate to advanced
50 pages
1h 15m
English
Content preview from Hadoop with Python
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Chapter 1. Hadoop Distributed File System (HDFS)
The Hadoop Distributed File System (HDFS) is a Java-based distributed, scalable, and portable filesystem designed to span large clusters of commodity servers. The design of HDFS is based on GFS, the Google File System, which is described in a paper published by Google. Like many other distributed filesystems, HDFS holds a large amount of data and provides transparent access to many clients distributed across a network. Where HDFS excels is in its ability to store very large files in a reliable and scalable manner.
HDFS is designed to store a lot of information, typically petabytes (for very large files), gigabytes, and terabytes. This is accomplished by using a block-structured filesystem. Individual files are split into fixed-size blocks that are stored on machines across the cluster. Files made of several blocks generally do not have all of their blocks stored on a single machine.
HDFS ensures reliability by replicating blocks and distributing the replicas across the cluster. The default replication factor is three, meaning that each block exists three times on the cluster. Block-level replication enables data availability even when machines fail.
This chapter begins by introducing the core concepts of HDFS and explains how to interact with the filesystem using the native built-in commands. After a few examples, a Python client library is introduced that enables HDFS to be accessed programmatically from within Python applications. ...
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