HADOOP-10139. Update and improve the Single Cluster Setup document. (Contributed by Akira Ajisaka)
git-svn-id: https://svn.apache.org/repos/asf/hadoop/common/trunk@1562931 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Arpit Agarwal
parent
3d9ad8e3b6
commit
f874b20061
@ -301,6 +301,9 @@ Release 2.4.0 - UNRELEASED
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IMPROVEMENTS
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IMPROVEMENTS
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HADOOP-10139. Update and improve the Single Cluster Setup document.
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(Akira Ajisaka via Arpit Agarwal)
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OPTIMIZATIONS
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OPTIMIZATIONS
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BUG FIXES
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BUG FIXES
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@ -20,174 +20,267 @@ Hadoop MapReduce Next Generation - Setting up a Single Node Cluster.
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%{toc|section=1|fromDepth=0}
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%{toc|section=1|fromDepth=0}
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* Mapreduce Tarball
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* Purpose
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You should be able to obtain the MapReduce tarball from the release.
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This document describes how to set up and configure a single-node Hadoop
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If not, you should be able to create a tarball from the source.
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installation so that you can quickly perform simple operations using Hadoop
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MapReduce and the Hadoop Distributed File System (HDFS).
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+---+
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$ mvn clean install -DskipTests
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* Prerequisites
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$ cd hadoop-mapreduce-project
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$ mvn clean install assembly:assembly -Pnative
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** Supported Platforms
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+---+
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<<NOTE:>> You will need {{{http://code.google.com/p/protobuf}protoc 2.5.0}}
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* GNU/Linux is supported as a development and production platform.
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installed.
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Hadoop has been demonstrated on GNU/Linux clusters with 2000 nodes.
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To ignore the native builds in mapreduce you can omit the <<<-Pnative>>> argument
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* Windows is also a supported platform but the followings steps
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for maven. The tarball should be available in <<<target/>>> directory.
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are for Linux only. To set up Hadoop on Windows, see
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{{{http://wiki.apache.org/hadoop/Hadoop2OnWindows}wiki page}}.
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* Setting up the environment.
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** Required Software
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Assuming you have installed hadoop-common/hadoop-hdfs and exported
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Required software for Linux include:
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<<$HADOOP_COMMON_HOME>>/<<$HADOOP_HDFS_HOME>>, untar hadoop mapreduce
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tarball and set environment variable <<$HADOOP_MAPRED_HOME>> to the
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[[1]] Java\u2122 must be installed. Recommended Java versions are described
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untarred directory. Set <<$HADOOP_YARN_HOME>> the same as <<$HADOOP_MAPRED_HOME>>.
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at {{{http://wiki.apache.org/hadoop/HadoopJavaVersions}
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HadoopJavaVersions}}.
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<<NOTE:>> The following instructions assume you have hdfs running.
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[[2]] ssh must be installed and sshd must be running to use the Hadoop
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* Setting up Configuration.
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scripts that manage remote Hadoop daemons.
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To start the ResourceManager and NodeManager, you will have to update the configs.
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** Installing Software
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Assuming your $HADOOP_CONF_DIR is the configuration directory and has the installed
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configs for HDFS and <<<core-site.xml>>>. There are 2 config files you will have to setup
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If your cluster doesn't have the requisite software you will need to install
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<<<mapred-site.xml>>> and <<<yarn-site.xml>>>.
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it.
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** Setting up <<<mapred-site.xml>>>
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For example on Ubuntu Linux:
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Add the following configs to your <<<mapred-site.xml>>>.
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----
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$ sudo apt-get install ssh
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$ sudo apt-get install rsync
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----
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* Download
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To get a Hadoop distribution, download a recent stable release from one of
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the {{{http://www.apache.org/dyn/closer.cgi/hadoop/common/}
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Apache Download Mirrors}}.
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* Prepare to Start the Hadoop Cluster
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Unpack the downloaded Hadoop distribution. In the distribution, edit
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the file <<<etc/hadoop/hadoop-env.sh>>> to define some parameters as
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follows:
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----
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# set to the root of your Java installation
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export JAVA_HOME=/usr/java/latest
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# Assuming your installation directory is /usr/local/hadoop
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export HADOOP_PREFIX=/usr/local/hadoop
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----
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Try the following command:
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----
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$ bin/hadoop
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----
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This will display the usage documentation for the hadoop script.
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Now you are ready to start your Hadoop cluster in one of the three supported
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modes:
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* {{{Standalone Operation}Local (Standalone) Mode}}
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* {{{Pseudo-Distributed Operation}Pseudo-Distributed Mode}}
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* {{{Fully-Distributed Operation}Fully-Distributed Mode}}
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* Standalone Operation
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By default, Hadoop is configured to run in a non-distributed mode, as a
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single Java process. This is useful for debugging.
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The following example copies the unpacked conf directory to use as input
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and then finds and displays every match of the given regular expression.
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Output is written to the given output directory.
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----
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$ mkdir input
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$ cp etc/hadoop/*.xml input
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$ bin/hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-${project.version}.jar grep input output 'dfs[a-z.]+'
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$ cat output/*
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----
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* Pseudo-Distributed Operation
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Hadoop can also be run on a single-node in a pseudo-distributed mode where
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each Hadoop daemon runs in a separate Java process.
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** Configuration
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Use the following:
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etc/hadoop/core-site.xml:
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+---+
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+---+
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<configuration>
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<property>
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<property>
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<name>mapreduce.cluster.temp.dir</name>
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<name>fs.defaultFS</name>
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<value></value>
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<value>hdfs://localhost:9000</value>
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<description>No description</description>
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<final>true</final>
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</property>
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<property>
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<name>mapreduce.cluster.local.dir</name>
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<value></value>
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<description>No description</description>
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<final>true</final>
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</property>
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</property>
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</configuration>
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+---+
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+---+
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** Setting up <<<yarn-site.xml>>>
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etc/hadoop/hdfs-site.xml:
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Add the following configs to your <<<yarn-site.xml>>>
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+---+
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+---+
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<configuration>
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<property>
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<property>
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<name>yarn.resourcemanager.resource-tracker.address</name>
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<name>dfs.replication</name>
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<value>host:port</value>
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<value>1</value>
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<description>host is the hostname of the resource manager and
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port is the port on which the NodeManagers contact the Resource Manager.
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</description>
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</property>
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</property>
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</configuration>
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+---+
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** Setup passphraseless ssh
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Now check that you can ssh to the localhost without a passphrase:
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----
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$ ssh localhost
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----
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If you cannot ssh to localhost without a passphrase, execute the
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following commands:
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----
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$ ssh-keygen -t dsa -P '' -f ~/.ssh/id_dsa
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$ cat ~/.ssh/id_dsa.pub >> ~/.ssh/authorized_keys
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----
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** Execution
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The following instructions are to run a MapReduce job locally.
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If you want to execute a job on YARN, see {{YARN on Single Node}}.
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[[1]] Format the filesystem:
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----
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$ bin/hdfs namenode -format
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----
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[[2]] Start NameNode daemon and DataNode daemon:
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----
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$ sbin/start-dfs.sh
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----
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The hadoop daemon log output is written to the <<<${HADOOP_LOG_DIR}>>>
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directory (defaults to <<<${HADOOP_HOME}/logs>>>).
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[[3]] Browse the web interface for the NameNode; by default it is
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available at:
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* NameNode - <<<http://localhost:50070/>>>
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[[4]] Make the HDFS directories required to execute MapReduce jobs:
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----
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$ bin/hdfs dfs -mkdir /user
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$ bin/hdfs dfs -mkdir /user/<username>
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----
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[[5]] Copy the input files into the distributed filesystem:
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----
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$ bin/hdfs dfs -put etc/hadoop input
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----
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[[6]] Run some of the examples provided:
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----
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$ bin/hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-${project.version}.jar grep input output 'dfs[a-z.]+'
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----
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[[7]] Examine the output files:
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Copy the output files from the distributed filesystem to the local
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filesystem and examine them:
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----
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$ bin/hdfs dfs -get output output
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$ cat output/*
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----
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or
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View the output files on the distributed filesystem:
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----
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$ bin/hdfs dfs -cat output/*
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----
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[[8]] When you're done, stop the daemons with:
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----
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$ sbin/stop-dfs.sh
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----
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** YARN on Single Node
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You can run a MapReduce job on YARN in a pseudo-distributed mode by setting
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a few parameters and running ResourceManager daemon and NodeManager daemon
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in addition.
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The following instructions assume that 1. ~ 4. steps of
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{{{Execution}the above instructions}} are already executed.
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[[1]] Configure parameters as follows:
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etc/hadoop/mapred-site.xml:
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+---+
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<configuration>
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<property>
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<property>
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<name>yarn.resourcemanager.scheduler.address</name>
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<name>mapreduce.framework.name</name>
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<value>host:port</value>
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<value>yarn</value>
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<description>host is the hostname of the resourcemanager and port is the port
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on which the Applications in the cluster talk to the Resource Manager.
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</description>
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</property>
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</property>
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</configuration>
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+---+
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<property>
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etc/hadoop/yarn-site.xml:
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<name>yarn.resourcemanager.scheduler.class</name>
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<value>org.apache.hadoop.yarn.server.resourcemanager.scheduler.capacity.CapacityScheduler</value>
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<description>In case you do not want to use the default scheduler</description>
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</property>
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<property>
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<name>yarn.resourcemanager.address</name>
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<value>host:port</value>
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<description>the host is the hostname of the ResourceManager and the port is the port on
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which the clients can talk to the Resource Manager. </description>
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</property>
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<property>
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<name>yarn.nodemanager.local-dirs</name>
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<value></value>
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<description>the local directories used by the nodemanager</description>
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</property>
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<property>
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<name>yarn.nodemanager.address</name>
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<value>0.0.0.0:port</value>
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<description>the nodemanagers bind to this port</description>
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</property>
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<property>
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<name>yarn.nodemanager.resource.memory-mb</name>
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<value>10240</value>
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<description>the amount of memory on the NodeManager in GB</description>
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</property>
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<property>
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<name>yarn.nodemanager.remote-app-log-dir</name>
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<value>/app-logs</value>
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<description>directory on hdfs where the application logs are moved to </description>
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</property>
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<property>
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<name>yarn.nodemanager.log-dirs</name>
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<value></value>
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<description>the directories used by Nodemanagers as log directories</description>
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</property>
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+---+
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<configuration>
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<property>
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<property>
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<name>yarn.nodemanager.aux-services</name>
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<name>yarn.nodemanager.aux-services</name>
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<value>mapreduce_shuffle</value>
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<value>mapreduce_shuffle</value>
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<description>shuffle service that needs to be set for Map Reduce to run </description>
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</property>
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</property>
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</configuration>
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+---+
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+---+
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* Setting up <<<capacity-scheduler.xml>>>
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[[2]] Start ResourceManager daemon and NodeManager daemon:
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Make sure you populate the root queues in <<<capacity-scheduler.xml>>>.
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----
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$ sbin/start-yarn.sh
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----
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+---+
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[[3]] Browse the web interface for the ResourceManager; by default it is
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<property>
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available at:
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<name>yarn.scheduler.capacity.root.queues</name>
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<value>unfunded,default</value>
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</property>
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<property>
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* ResourceManager - <<<http://localhost:8088/>>>
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<name>yarn.scheduler.capacity.root.capacity</name>
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<value>100</value>
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</property>
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<property>
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[[4]] Run a MapReduce job.
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<name>yarn.scheduler.capacity.root.unfunded.capacity</name>
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<value>50</value>
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</property>
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<property>
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[[5]] When you're done, stop the daemons with:
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<name>yarn.scheduler.capacity.root.default.capacity</name>
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<value>50</value>
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</property>
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+---+
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* Running daemons.
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----
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$ sbin/stop-yarn.sh
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----
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Assuming that the environment variables <<$HADOOP_COMMON_HOME>>, <<$HADOOP_HDFS_HOME>>, <<$HADOO_MAPRED_HOME>>,
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* Fully-Distributed Operation
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<<$HADOOP_YARN_HOME>>, <<$JAVA_HOME>> and <<$HADOOP_CONF_DIR>> have been set appropriately.
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Set $<<$YARN_CONF_DIR>> the same as $<<HADOOP_CONF_DIR>>
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Run ResourceManager and NodeManager as:
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For information on setting up fully-distributed, non-trivial clusters
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see {{{./ClusterSetup.html}Cluster Setup}}.
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+---+
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$ cd $HADOOP_MAPRED_HOME
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$ sbin/yarn-daemon.sh start resourcemanager
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$ sbin/yarn-daemon.sh start nodemanager
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+---+
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You should be up and running. You can run randomwriter as:
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+---+
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$ $HADOOP_COMMON_HOME/bin/hadoop jar hadoop-examples.jar randomwriter out
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+---+
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Good luck.
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Reference in New Issue
Block a user