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The introduction on the Hadoop project web page (http://hadoop.apache.org/) states: The Apache Hadoop project develops open-source software for reliable, scalable, distributed computing, including: Hadoop Core, our flagship sub-project, provides a distributed filesystem (HDFS) and support for the MapReduce distributed computing metaphor. HBase builds on Hadoop Core to provide a scalable, distributed database. Pig is a high-level data-flow language and execution framework for parallel computation. It is built on top of Hadoop Core. ZooKeeper is a highly available and reliable coordination system. Distributed applications use ZooKeeper to store and mediate updates for critical shared state. Hive is a data warehouse infrastructure built on Hadoop Core that provides data summarization, adhoc querying and analysis of datasets. The Hadoop Core project provides the basic services for building a cloud computing environment with commodity hardware, and the APIs for developing software that will run on that cloud. The two fundamental pieces of Hadoop Core are the MapReduce framework, the cloud computing environment, and he Hadoop Distributed File System (HDFS).

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Note Within the Hadoop Core framework, MapReduce is often referred to as mapred, and HDFS is often

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The Hadoop Core MapReduce framework requires a shared file system. This shared file system does not need to be a system-level file system, as long as there is a distributed file system plug-in available to the framework. While Hadoop Core provides HDFS, HDFS is not required. In Hadoop JIRA (the issue-tracking system), item 4686 is a tracking ticket to separate HDFS into its own Hadoop project. In addition to HDFS, Hadoop Core supports the CloudStore (formerly Kosmos) file system (http://kosmosfs.sourceforge.net/) and Amazon Simple Storage Service (S3) file system (http://aws.amazon.com/s3/). The Hadoop Core framework comes with plug-ins for HDFS, CloudStore, and S3. Users are also free to use any distributed file system that is visible as a system-mounted file system, such as Network File System (NFS), Global File System (GFS), or Lustre. When HDFS is used as the shared file system, Hadoop is able to take advantage of knowledge about which node hosts a physical copy of input data, and will attempt to schedule the task that is to read that data, to run on that machine. This book mainly focuses on using HDFS as the file system.

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The Hadoop Distributed File System (HDFS)MapReduce environment provides the user with a sophisticated framework to manage the execution of map and reduce tasks across a cluster of machines. The user is required to tell the framework the following: The location(s) in the distributed file system of the job input The location(s) in the distributed file system for the job output The input format The output format The class containing the map function Optionally. the class containing the reduce function The JAR file(s) containing the map and reduce functions and any support classes

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