~~ Licensed under the Apache License, Version 2.0 (the "License");
~~ you may not use this file except in compliance with the License.
~~ You may obtain a copy of the License at
~~
~~   http://www.apache.org/licenses/LICENSE-2.0
~~
~~ Unless required by applicable law or agreed to in writing, software
~~ distributed under the License is distributed on an "AS IS" BASIS,
~~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
~~ See the License for the specific language governing permissions and
~~ limitations under the License. See accompanying LICENSE file.

  ---
  Apache Hadoop ${project.version}
  ---
  ---
  ${maven.build.timestamp}
  
Apache Hadoop 0.23

  Apache Hadoop 0.23 consists of significant improvements over the previous 
  stable release (hadoop-0.20.205).

  Here is a short overview of the improvments to both HDFS and MapReduce.

  * {HDFS Federation}

  In order to scale the name service horizontally, federation uses multiple 
  independent Namenodes/Namespaces. The Namenodes are federated, that is, the 
  Namenodes are independent and don't require coordination with each other. 
  The datanodes are used as common storage for blocks by all the Namenodes. 
  Each datanode registers with all the Namenodes in the cluster. Datanodes 
  send periodic heartbeats and block reports and handles commands from the 
  Namenodes.

  More details are available in the 
  {{{./hadoop-project-dist/hadoop-hdfs/Federation.html}HDFS Federation}}
  document.

  * {MapReduce NextGen aka YARN aka MRv2}

  The new architecture introduced in hadoop-0.23, divides the two major 
  functions of the JobTracker: resource management and job life-cycle management 
  into separate components.

  The new ResourceManager manages the global assignment of compute resources to 
  applications and the per-application ApplicationMaster manages the 
  application‚Äôs scheduling and coordination. 

  An application is either a single job in the sense of classic MapReduce jobs 
  or a DAG of such jobs. 

  The ResourceManager and per-machine NodeManager daemon, which manages the 
  user processes on that machine, form the computation fabric. 

  The per-application ApplicationMaster is, in effect, a framework specific 
  library and is tasked with negotiating resources from the ResourceManager and 
  working with the NodeManager(s) to execute and monitor the tasks.

  More details are available in the 
  {{{./hadoop-yarn/hadoop-yarn-site/YARN.html}YARN}}
  document.

Getting Started

  The Hadoop documentation includes the information you need to get started using
  Hadoop. Begin with the
  {{{./hadoop-project-dist/hadoop-common/SingleCluster.html}Single Node Setup}} which
  shows you how to set up a single-node Hadoop installation. Then move on to the
  {{{./hadoop-project-dist/hadoop-common/ClusterSetup.html}Cluster Setup}} to learn how
  to set up a multi-node Hadoop installation.