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 wiki page.

Required Software

Required software for Linux include:

1. Java™ must be installed. Recommended Java versions are described at 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

  # set to the root of your Java installationexport JAVA_HOME=/usr/java/latest# Assuming your installation directory is /usr/local/hadoopexport HADOOP_PREFIX=/usr/local/hadoop

  $ bin/hadoop

  $ mkdir input$ cp etc/hadoop/*.xml input$ bin/hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.4.1.jar grep input output 'dfs[a-z.]+'$ cat output/*

Configuration

Use the following:

etc/hadoop/core-site.xml:

<configuration><property><name>fs.defaultFS</name><value>hdfs://localhost:9000</value></property>
</configuration>

etc/hadoop/hdfs-site.xml:

<configuration><property><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:此步骤报很多警告，但不影响执行结果。

     $ 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-2.4.1.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 the above instructions are already executed.

1. Configure parameters as follows:

   etc/hadoop/mapred-site.xml:

   <configuration><property><name>mapreduce.framework.name</name><value>yarn</value></property>
   </configuration>

   etc/hadoop/yarn-site.xml:

   <configuration><property><name>yarn.nodemanager.aux-services</name><value>mapreduce_shuffle</value></property>
   </configuration>

2. Start ResourceManager daemon and NodeManager daemon:

     $ sbin/start-yarn.sh

3. Browse the web interface for the ResourceManager; by default it is available at:
   • ResourceManager - http://localhost:8088/
4. Run a MapReduce job.
5. When you're done, stop the daemons with:

     $ sbin/stop-yarn.sh