HADOOP-10139. Merging r1562931 from trunk to branch-2
git-svn-id: https://svn.apache.org/repos/asf/hadoop/common/branches/branch-2@1562932 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Arpit Agarwal
parent
4553487c52
commit
7547307e5c
|
|
@ -8,6 +8,9 @@ Release 2.4.0 - UNRELEASED
|
|||
|
||||
IMPROVEMENTS
|
||||
|
||||
HADOOP-10139. Update and improve the Single Cluster Setup document.
|
||||
(Akira Ajisaka via Arpit Agarwal)
|
||||
|
||||
OPTIMIZATIONS
|
||||
|
||||
BUG FIXES
|
||||
|
|
|
|||
|
|
@ -20,174 +20,267 @@ Hadoop MapReduce Next Generation - Setting up a Single Node Cluster.
|
|||
|
||||
%{toc|section=1|fromDepth=0}
|
||||
|
||||
* Mapreduce Tarball
|
||||
* Purpose
|
||||
|
||||
You should be able to obtain the MapReduce tarball from the release.
|
||||
If not, you should be able to create a tarball from the source.
|
||||
|
||||
+---+
|
||||
$ mvn clean install -DskipTests
|
||||
$ cd hadoop-mapreduce-project
|
||||
$ mvn clean install assembly:assembly -Pnative
|
||||
+---+
|
||||
<<NOTE:>> You will need {{{http://code.google.com/p/protobuf}protoc 2.5.0}}
|
||||
installed.
|
||||
|
||||
To ignore the native builds in mapreduce you can omit the <<<-Pnative>>> argument
|
||||
for maven. The tarball should be available in <<<target/>>> directory.
|
||||
|
||||
|
||||
* Setting up the environment.
|
||||
|
||||
Assuming you have installed hadoop-common/hadoop-hdfs and exported
|
||||
<<$HADOOP_COMMON_HOME>>/<<$HADOOP_HDFS_HOME>>, untar hadoop mapreduce
|
||||
tarball and set environment variable <<$HADOOP_MAPRED_HOME>> to the
|
||||
untarred directory. Set <<$HADOOP_YARN_HOME>> the same as <<$HADOOP_MAPRED_HOME>>.
|
||||
|
||||
<<NOTE:>> The following instructions assume you have hdfs running.
|
||||
|
||||
* Setting up Configuration.
|
||||
|
||||
To start the ResourceManager and NodeManager, you will have to update the configs.
|
||||
Assuming your $HADOOP_CONF_DIR is the configuration directory and has the installed
|
||||
configs for HDFS and <<<core-site.xml>>>. There are 2 config files you will have to setup
|
||||
<<<mapred-site.xml>>> and <<<yarn-site.xml>>>.
|
||||
|
||||
** Setting up <<<mapred-site.xml>>>
|
||||
|
||||
Add the following configs to your <<<mapred-site.xml>>>.
|
||||
This document describes how to set up and configure a single-node Hadoop
|
||||
installation so that you can quickly perform simple operations using Hadoop
|
||||
MapReduce and the Hadoop Distributed File System (HDFS).
|
||||
|
||||
* Prerequisites
|
||||
|
||||
** Supported Platforms
|
||||
|
||||
* GNU/Linux is supported as a development and production platform.
|
||||
Hadoop has been demonstrated on GNU/Linux clusters with 2000 nodes.
|
||||
|
||||
* Windows is also a supported platform but the followings steps
|
||||
are for Linux only. To set up Hadoop on Windows, see
|
||||
{{{http://wiki.apache.org/hadoop/Hadoop2OnWindows}wiki page}}.
|
||||
|
||||
** Required Software
|
||||
|
||||
Required software for Linux include:
|
||||
|
||||
[[1]] Java\u2122 must be installed. Recommended Java versions are described
|
||||
at {{{http://wiki.apache.org/hadoop/HadoopJavaVersions}
|
||||
HadoopJavaVersions}}.
|
||||
|
||||
[[2]] ssh must be installed and sshd must be running to use the Hadoop
|
||||
scripts that manage remote Hadoop daemons.
|
||||
|
||||
** Installing Software
|
||||
|
||||
If your cluster doesn't have the requisite software you will need to install
|
||||
it.
|
||||
|
||||
For example on Ubuntu Linux:
|
||||
|
||||
----
|
||||
$ sudo apt-get install ssh
|
||||
$ sudo apt-get install rsync
|
||||
----
|
||||
|
||||
* Download
|
||||
|
||||
To get a Hadoop distribution, download a recent stable release from one of
|
||||
the {{{http://www.apache.org/dyn/closer.cgi/hadoop/common/}
|
||||
Apache Download Mirrors}}.
|
||||
|
||||
* Prepare to Start the Hadoop Cluster
|
||||
|
||||
Unpack the downloaded Hadoop distribution. In the distribution, edit
|
||||
the file <<<etc/hadoop/hadoop-env.sh>>> to define some parameters as
|
||||
follows:
|
||||
|
||||
----
|
||||
# set to the root of your Java installation
|
||||
export JAVA_HOME=/usr/java/latest
|
||||
|
||||
# Assuming your installation directory is /usr/local/hadoop
|
||||
export HADOOP_PREFIX=/usr/local/hadoop
|
||||
----
|
||||
|
||||
Try the following command:
|
||||
|
||||
----
|
||||
$ bin/hadoop
|
||||
----
|
||||
|
||||
This will display the usage documentation for the hadoop script.
|
||||
|
||||
Now you are ready to start your Hadoop cluster in one of the three supported
|
||||
modes:
|
||||
|
||||
* {{{Standalone Operation}Local (Standalone) Mode}}
|
||||
|
||||
* {{{Pseudo-Distributed Operation}Pseudo-Distributed Mode}}
|
||||
|
||||
* {{{Fully-Distributed Operation}Fully-Distributed Mode}}
|
||||
|
||||
* Standalone Operation
|
||||
|
||||
By default, Hadoop is configured to run in a non-distributed mode, as a
|
||||
single Java process. This is useful for debugging.
|
||||
|
||||
The following example copies the unpacked conf directory to use as input
|
||||
and then finds and displays every match of the given regular expression.
|
||||
Output is written to the given output directory.
|
||||
|
||||
----
|
||||
$ mkdir input
|
||||
$ cp etc/hadoop/*.xml input
|
||||
$ bin/hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-${project.version}.jar grep input output 'dfs[a-z.]+'
|
||||
$ cat output/*
|
||||
----
|
||||
|
||||
* Pseudo-Distributed Operation
|
||||
|
||||
Hadoop can also be run on a single-node in a pseudo-distributed mode where
|
||||
each Hadoop daemon runs in a separate Java process.
|
||||
|
||||
** Configuration
|
||||
|
||||
Use the following:
|
||||
|
||||
etc/hadoop/core-site.xml:
|
||||
|
||||
+---+
|
||||
<configuration>
|
||||
<property>
|
||||
<name>mapreduce.cluster.temp.dir</name>
|
||||
<value></value>
|
||||
<description>No description</description>
|
||||
<final>true</final>
|
||||
</property>
|
||||
|
||||
<property>
|
||||
<name>mapreduce.cluster.local.dir</name>
|
||||
<value></value>
|
||||
<description>No description</description>
|
||||
<final>true</final>
|
||||
<name>fs.defaultFS</name>
|
||||
<value>hdfs://localhost:9000</value>
|
||||
</property>
|
||||
</configuration>
|
||||
+---+
|
||||
|
||||
** Setting up <<<yarn-site.xml>>>
|
||||
|
||||
Add the following configs to your <<<yarn-site.xml>>>
|
||||
etc/hadoop/hdfs-site.xml:
|
||||
|
||||
+---+
|
||||
<configuration>
|
||||
<property>
|
||||
<name>yarn.resourcemanager.resource-tracker.address</name>
|
||||
<value>host:port</value>
|
||||
<description>host is the hostname of the resource manager and
|
||||
port is the port on which the NodeManagers contact the Resource Manager.
|
||||
</description>
|
||||
<name>dfs.replication</name>
|
||||
<value>1</value>
|
||||
</property>
|
||||
</configuration>
|
||||
+---+
|
||||
|
||||
** Setup passphraseless ssh
|
||||
|
||||
Now check that you can ssh to the localhost without a passphrase:
|
||||
|
||||
----
|
||||
$ ssh localhost
|
||||
----
|
||||
|
||||
If you cannot ssh to localhost without a passphrase, execute the
|
||||
following commands:
|
||||
|
||||
----
|
||||
$ ssh-keygen -t dsa -P '' -f ~/.ssh/id_dsa
|
||||
$ cat ~/.ssh/id_dsa.pub >> ~/.ssh/authorized_keys
|
||||
----
|
||||
|
||||
** Execution
|
||||
|
||||
The following instructions are to run a MapReduce job locally.
|
||||
If you want to execute a job on YARN, see {{YARN on Single Node}}.
|
||||
|
||||
[[1]] Format the filesystem:
|
||||
|
||||
----
|
||||
$ bin/hdfs namenode -format
|
||||
----
|
||||
|
||||
[[2]] Start NameNode daemon and DataNode daemon:
|
||||
|
||||
----
|
||||
$ sbin/start-dfs.sh
|
||||
----
|
||||
|
||||
The hadoop daemon log output is written to the <<<${HADOOP_LOG_DIR}>>>
|
||||
directory (defaults to <<<${HADOOP_HOME}/logs>>>).
|
||||
|
||||
[[3]] Browse the web interface for the NameNode; by default it is
|
||||
available at:
|
||||
|
||||
* NameNode - <<<http://localhost:50070/>>>
|
||||
|
||||
[[4]] Make the HDFS directories required to execute MapReduce jobs:
|
||||
|
||||
----
|
||||
$ bin/hdfs dfs -mkdir /user
|
||||
$ bin/hdfs dfs -mkdir /user/<username>
|
||||
----
|
||||
|
||||
[[5]] Copy the input files into the distributed filesystem:
|
||||
|
||||
----
|
||||
$ bin/hdfs dfs -put etc/hadoop input
|
||||
----
|
||||
|
||||
[[6]] Run some of the examples provided:
|
||||
|
||||
----
|
||||
$ bin/hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-${project.version}.jar grep input output 'dfs[a-z.]+'
|
||||
----
|
||||
|
||||
[[7]] Examine the output files:
|
||||
|
||||
Copy the output files from the distributed filesystem to the local
|
||||
filesystem and examine them:
|
||||
|
||||
----
|
||||
$ bin/hdfs dfs -get output output
|
||||
$ cat output/*
|
||||
----
|
||||
|
||||
or
|
||||
|
||||
View the output files on the distributed filesystem:
|
||||
|
||||
----
|
||||
$ bin/hdfs dfs -cat output/*
|
||||
----
|
||||
|
||||
[[8]] When you're done, stop the daemons with:
|
||||
|
||||
----
|
||||
$ sbin/stop-dfs.sh
|
||||
----
|
||||
|
||||
** YARN on Single Node
|
||||
|
||||
You can run a MapReduce job on YARN in a pseudo-distributed mode by setting
|
||||
a few parameters and running ResourceManager daemon and NodeManager daemon
|
||||
in addition.
|
||||
|
||||
The following instructions assume that 1. ~ 4. steps of
|
||||
{{{Execution}the above instructions}} are already executed.
|
||||
|
||||
[[1]] Configure parameters as follows:
|
||||
|
||||
etc/hadoop/mapred-site.xml:
|
||||
|
||||
+---+
|
||||
<configuration>
|
||||
<property>
|
||||
<name>yarn.resourcemanager.scheduler.address</name>
|
||||
<value>host:port</value>
|
||||
<description>host is the hostname of the resourcemanager and port is the port
|
||||
on which the Applications in the cluster talk to the Resource Manager.
|
||||
</description>
|
||||
<name>mapreduce.framework.name</name>
|
||||
<value>yarn</value>
|
||||
</property>
|
||||
</configuration>
|
||||
+---+
|
||||
|
||||
<property>
|
||||
<name>yarn.resourcemanager.scheduler.class</name>
|
||||
<value>org.apache.hadoop.yarn.server.resourcemanager.scheduler.capacity.CapacityScheduler</value>
|
||||
<description>In case you do not want to use the default scheduler</description>
|
||||
</property>
|
||||
|
||||
<property>
|
||||
<name>yarn.resourcemanager.address</name>
|
||||
<value>host:port</value>
|
||||
<description>the host is the hostname of the ResourceManager and the port is the port on
|
||||
which the clients can talk to the Resource Manager. </description>
|
||||
</property>
|
||||
|
||||
<property>
|
||||
<name>yarn.nodemanager.local-dirs</name>
|
||||
<value></value>
|
||||
<description>the local directories used by the nodemanager</description>
|
||||
</property>
|
||||
|
||||
<property>
|
||||
<name>yarn.nodemanager.address</name>
|
||||
<value>0.0.0.0:port</value>
|
||||
<description>the nodemanagers bind to this port</description>
|
||||
</property>
|
||||
|
||||
<property>
|
||||
<name>yarn.nodemanager.resource.memory-mb</name>
|
||||
<value>10240</value>
|
||||
<description>the amount of memory on the NodeManager in GB</description>
|
||||
</property>
|
||||
|
||||
<property>
|
||||
<name>yarn.nodemanager.remote-app-log-dir</name>
|
||||
<value>/app-logs</value>
|
||||
<description>directory on hdfs where the application logs are moved to </description>
|
||||
</property>
|
||||
|
||||
<property>
|
||||
<name>yarn.nodemanager.log-dirs</name>
|
||||
<value></value>
|
||||
<description>the directories used by Nodemanagers as log directories</description>
|
||||
</property>
|
||||
etc/hadoop/yarn-site.xml:
|
||||
|
||||
+---+
|
||||
<configuration>
|
||||
<property>
|
||||
<name>yarn.nodemanager.aux-services</name>
|
||||
<value>mapreduce_shuffle</value>
|
||||
<description>shuffle service that needs to be set for Map Reduce to run </description>
|
||||
</property>
|
||||
</configuration>
|
||||
+---+
|
||||
|
||||
* Setting up <<<capacity-scheduler.xml>>>
|
||||
[[2]] Start ResourceManager daemon and NodeManager daemon:
|
||||
|
||||
Make sure you populate the root queues in <<<capacity-scheduler.xml>>>.
|
||||
----
|
||||
$ sbin/start-yarn.sh
|
||||
----
|
||||
|
||||
+---+
|
||||
<property>
|
||||
<name>yarn.scheduler.capacity.root.queues</name>
|
||||
<value>unfunded,default</value>
|
||||
</property>
|
||||
[[3]] Browse the web interface for the ResourceManager; by default it is
|
||||
available at:
|
||||
|
||||
<property>
|
||||
<name>yarn.scheduler.capacity.root.capacity</name>
|
||||
<value>100</value>
|
||||
</property>
|
||||
* ResourceManager - <<<http://localhost:8088/>>>
|
||||
|
||||
<property>
|
||||
<name>yarn.scheduler.capacity.root.unfunded.capacity</name>
|
||||
<value>50</value>
|
||||
</property>
|
||||
[[4]] Run a MapReduce job.
|
||||
|
||||
<property>
|
||||
<name>yarn.scheduler.capacity.root.default.capacity</name>
|
||||
<value>50</value>
|
||||
</property>
|
||||
+---+
|
||||
[[5]] When you're done, stop the daemons with:
|
||||
|
||||
* Running daemons.
|
||||
----
|
||||
$ sbin/stop-yarn.sh
|
||||
----
|
||||
|
||||
Assuming that the environment variables <<$HADOOP_COMMON_HOME>>, <<$HADOOP_HDFS_HOME>>, <<$HADOO_MAPRED_HOME>>,
|
||||
<<$HADOOP_YARN_HOME>>, <<$JAVA_HOME>> and <<$HADOOP_CONF_DIR>> have been set appropriately.
|
||||
Set $<<$YARN_CONF_DIR>> the same as $<<HADOOP_CONF_DIR>>
|
||||
* Fully-Distributed Operation
|
||||
|
||||
Run ResourceManager and NodeManager as:
|
||||
|
||||
+---+
|
||||
$ cd $HADOOP_MAPRED_HOME
|
||||
$ sbin/yarn-daemon.sh start resourcemanager
|
||||
$ sbin/yarn-daemon.sh start nodemanager
|
||||
+---+
|
||||
|
||||
You should be up and running. You can run randomwriter as:
|
||||
|
||||
+---+
|
||||
$ $HADOOP_COMMON_HOME/bin/hadoop jar hadoop-examples.jar randomwriter out
|
||||
+---+
|
||||
|
||||
Good luck.
|
||||
For information on setting up fully-distributed, non-trivial clusters
|
||||
see {{{./ClusterSetup.html}Cluster Setup}}.
|
||||
|
|
|
|||
Loading…
Reference in New Issue