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Posted to common-commits@hadoop.apache.org by Apache Wiki <wi...@apache.org> on 2007/10/26 11:10:28 UTC
[Lucene-hadoop Wiki] Update of "Running Hadoop On Ubuntu Linux (Single-Node
Cluster)" by MichaelNoll
Dear Wiki user,
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The following page has been changed by MichaelNoll:
http://wiki.apache.org/lucene-hadoop/Running_Hadoop_On_Ubuntu_Linux_%28Single-Node_Cluster%29
The comment on the change is:
Initial version, based on the tutorial available at http://www.michael-noll.com/
New page:
Preface: This article is based on my tutorial [http://www.michael-noll.com/wiki/Running_Hadoop_On_Ubuntu_Linux_%28Single-Node_Cluster%29 Running Hadoop On Ubuntu Linux (Single-Node Cluster)], and its content has been integrated with my happy permission. Feel free to extend, improve and correct this page as you please! ''--MichaelNoll''
= What we want to do =
In this short tutorial, I will describe the required steps for setting up a single-node [http://lucene.apache.org/hadoop/ Hadoop] cluster using the [http://lucene.apache.org/hadoop/hdfs_design.html Hadoop Distributed File System (HDFS)] on [http://www.ubuntu.com/ Ubuntu Linux].
[http://lucene.apache.org/hadoop/ Hadoop] is a framework written in Java for running applications on large clusters of commodity hardware and incorporates features similar to those of the [http://en.wikipedia.org/wiki/Google_File_System Google File System] and of [http://en.wikipedia.org/wiki/MapReduce MapReduce]. [http://lucene.apache.org/hadoop/hdfs_design.html HDFS] is a highly fault-tolerant distributed file system and like Hadoop designed to be deployed on low-cost hardware. It provides high throughput access to application data and is suitable for applications that have large data sets.
The main goal of this tutorial is to get a ''simple'' Hadoop installation up and running so that you can play around with the software and learn more about it.
This tutorial has been tested with the following software versions:
* [http://www.ubuntu.com/ Ubuntu Linux] 7.04
* [http://lucene.apache.org/hadoop/ Hadoop] 0.14.2, released October 2007 (also works with 0.13.0)
You can find the time of the last document update at the very bottom of this page.
= Prerequisites =
== Sun Java 1.5.0 ==
Hadoop requires a working Java 1.5.x (aka 5.0.x) installation. However, using [http://www.nabble.com/14.1-to-14.2-t4604384.html Java 1.6.x (aka 6.0.x) is recommended] for running Hadoop. For the sake of this tutorial, I will describe the installation of Java 1.5. But if you want Java 1.6 (which you should), simply use the package {{{sun-java6-jdk}}} and adjust the paths described below as needed.
Install Sun's Java Development Kit (JDK) v1.5.0 via {{{Synaptic}}} ({{{System > Administration > Synaptic Package Manager}}}) or via {{{apt-get}}}. Install the package
{{{
sun-java5-jdk
}}}
for the full JDK which will be placed in {{{/usr/lib/jvm/java-1.5.0-sun}}}.
After installation, check if Sun's JDK is on top of {{{/etc/jvm}}}. For example, mine looks like this:
{{{
# /etc/jvm
#
# This file defines the default system JVM search order. Each
# JVM should list their JAVA_HOME compatible directory in this file.
# The default system JVM is the first one available from top to
# bottom.
/usr/lib/jvm/java-1.5.0-sun
/usr/lib/jvm/java-gcj
/usr/lib/jvm/ia32-java-1.5.0-sun
/usr
}}}
== Adding a dedicated Hadoop system user ==
We will use a dedicated Hadoop user account for running Hadoop. While that's not required it is recommended because it helps to separate the Hadoop installation from other software applications and user accounts running on the same machine (think: security, permissions, backups, etc).
{{{
$ sudo addgroup hadoop
$ sudo adduser --ingroup hadoop hadoop
}}}
This will add the user {{{hadoop}}} and the group {{{hadoop}}} to your local machine.
== Configuring SSH ==
Hadoop requires SSH access to manage its nodes, i.e. remote machines plus your local machine if you want to use Hadoop on it (which is what we want to do in this short tutorial). For our single-node setup of Hadoop, we therefore need to configure SSH access to {{{localhost}}} for the {{{hadoop}}} user we create in the previous section.
I assume that you have SSH up and running on your machine and configured it to allow SSH public key authentication. If not, there are [http://ubuntuguide.org/ several guides] available.
First, we have to generate an SSH key for the <tt>hadoop</tt> user.
{{{
noll@ubuntu:~$ su - hadoop
hadoop@ubuntu:~$ ssh-keygen -t rsa -P ""
Generating public/private rsa key pair.
Enter file in which to save the key (/home/hadoop/.ssh/id_rsa):
Created directory '/home/hadoop/.ssh'.
Your identification has been saved in /home/hadoop/.ssh/id_rsa.
Your public key has been saved in /home/hadoop/.ssh/id_rsa.pub.
The key fingerprint is:
9d:47:ab:d7:22:54:f0:f9:b9:3b:64:93:12:75:81:27 hadoop@ubuntu
hadoop@ubuntu:~$
}}}
The second line will create an RSA key pair with an empty password. Generally, using an empty password is not recommended, but in this case it is needed to unlock the key without your interaction (you don't want to enter the passphrase everytime Hadoop interacts with its nodes).
Second, you have to enable SSH access to your local machine with this newly created key.
{{{
hadoop@ubuntu:~$ cat $HOME/.ssh/id_rsa.pub >> $HOME/.ssh/authorized_keys
}}}
The final step is to test the SSH setup by connecting to your local machine with the {{{hadoop}}} user. The step is also needed to save your local machine's host key fingerprint to the {{{hadoop}}} user's {{{known_hosts}}} file. If you have any special SSH configuration for your local machine like a non-standard SSH port, you can define host-specific SSH options in {{{$HOME/.ssh/config}}} (see {{{man ssh_config}}} for more information).
{{{
hadoop@ubuntu:~$ ssh localhost
The authenticity of host 'localhost (127.0.0.1)' can't be established.
RSA key fingerprint is 76:d7:61:86:ea:86:8f:31:89:9f:68:b0:75:88:52:72.
Are you sure you want to continue connecting (yes/no)? yes
Warning: Permanently added 'localhost' (RSA) to the list of known hosts.
Ubuntu 7.04
...
hadoop@ubuntu:~$
}}}
If the SSH connect should fail, these general tips might help:
* Enable debugging with {{{ssh -vvv localhost}}} and investigate the error in detail.
* Check the SSH server configuration in {{{/etc/ssh/sshd_config}}}, in particular the options {{{PubkeyAuthentication}}} (which should be set to {{{yes}}}) and {{{AllowUsers}}} (if this option is active, add the <tt>hadoop</tt> user to it). If you made any changes to the SSH server configuration file, you can force a configuration reload with {{{sudo /etc/init.d/ssh reload}}}.
== Disabling IPv6 ==
I have not found out yet how to configure Hadoop to listen on ''all IPv4'' (again: IPv4) network interfaces. Using {{{0.0.0.0}}} for the various networking-related Hadoop configuration options will result in Hadoop binding to the ''IPv6'' addresses on my Ubuntu box.
As a workaround (and realizing that there's no practical point in enabling IPv6 on a box when you are not connected to any IPv6 network), I simply disabled IPv6 on my Ubuntu machine.
To disable IPv6 on Ubuntu Linux, open {{{/etc/modprobe.d/blacklist}}} in the editor of your choice and add the following lines to the end of the file:
{{{
# disable IPv6
blacklist ipv6
}}}
You have to reboot your machine in order to make the changes take effect.
= Hadoop =
== Installation ==
You have to [http://www.apache.org/dyn/closer.cgi/lucene/hadoop/ download Hadoop] from the [http://www.apache.org/dyn/closer.cgi/lucene/hadoop/ Apache Download Mirrors] and extract the contents of the Hadoop package to a location of your choice. I picked {{{/usr/local/hadoop}}}. Make sure to change the owner of all the files to the {{{hadoop}}} user and group, for example:
{{{
$ cd /usr/local
$ sudo tar xzf hadoop-0.14.2.tar.gz
$ sudo mv hadoop-0.14.2 hadoop
$ sudo chown -R hadoop:hadoop hadoop
}}}
(just to give you the idea, YMMV - personally, I create a symlink from {{{hadoop-0.14.2}}} to {{{hadoop}}})
== Configuration ==
Our goal in this tutorial is a single-node setup of Hadoop. More information of what we do in this section is available at GettingStartedWithHadoop.
=== hadoop-env.sh ===
The only required environment variable we have to configure for Hadoop in this tutorial is {{{JAVA_HOME}}}. Open {{{<HADOOP_INSTALL>/conf/hadoop-env.sh}}} in the editor of your choice (if you used the installation path in this tutorial, the full path is {{{/usr/local/hadoop/conf/hadoop-env.sh}}}) and set the {{{JAVA_HOME}}} environment variable to the Sun JDK/JRE 1.5.0 directory.
Change
{{{
# The java implementation to use. Required.
# export JAVA_HOME=/usr/lib/j2sdk1.5-sun
}}}
to
{{{
# The java implementation to use. Required.
export JAVA_HOME=/usr/lib/jvm/java-1.5.0-sun
}}}
If you chose to use Java 1.6, remember to put the correct paths in here!
=== hadoop-site.xml ===
Any site-specific configuration of Hadoop is configured in {{{<HADOOP_INSTALL>/conf/hadoop-site.xml}}}. Here we will configure the directory where Hadoop will store its data files, the ports it listens to, etc. Our setup will use Hadoop's Distributed File System, [http://lucene.apache.org/hadoop/hdfs_design.html HDFS], even though our little "cluster" only contains our single local machine.
You can leave the settings below as is with the exception of the {{{hadoop.tmp.dir}}} variable which you have to change to the directory of your choice, for example {{{/usr/local/hadoop-datastore/hadoop-${user.name}}}}. Hadoop will expand {{{${user.name}}}} to the system user which is running Hadoop, so in our case this will be {{{hadoop}}} and thus the final path will be {{{/usr/local/hadoop-datastore/hadoop-hadoop}}}.
'''Note:''' Depending on your choice of location, you might have to create the directory manually with {{{sudo mkdir /your/path; sudo chown hadoop:hadoop /your/path}}} in case the {{{hadoop}}} user does not have the required permissions to do so (otherwise, you will see a {{{java.io.IOException}}} when you try to format the name node in the next section).
{{{
<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<!-- Put site-specific property overrides in this file. -->
<configuration>
<property>
<name>hadoop.tmp.dir</name>
<value>/your/path/to/hadoop/tmp/dir/hadoop-${user.name}</value>
<description>A base for other temporary directories.</description>
</property>
<property>
<name>fs.default.name</name>
<value>hdfs://localhost:54310</value>
<description>The name of the default file system. A URI whose
scheme and authority determine the FileSystem implementation. The
uri's scheme determines the config property (fs.SCHEME.impl) naming
the FileSystem implementation class. The uri's authority is used to
determine the host, port, etc. for a filesystem.</description>
</property>
<property>
<name>mapred.job.tracker</name>
<value>localhost:54311</value>
<description>The host and port that the MapReduce job tracker runs
at. If "local", then jobs are run in-process as a single map
and reduce task.
</description>
</property>
<property>
<name>dfs.replication</name>
<value>1</value>
<description>Default block replication.
The actual number of replications can be specified when the file is created.
The default is used if replication is not specified in create time.
</description>
</property>
</configuration>
}}}
See GettingStartedWithHadoop and the documentation in [http://lucene.apache.org/hadoop/api/overview-summary.html Hadoop's API Overview] if you have any questions about Hadoop's configuration options.
== Formatting the name node ==
The first step to starting up your Hadoop installation is formatting the Hadoop filesystem which is implemented on top of the local filesystem of your "cluster" (which includes only your local machine if you followed this tutorial). You need to do this the first time you set up a Hadoop cluster. Do not format a running Hadoop filesystem, this will cause all your data to be erased.
To format the filesystem (which simply initializes the directory specified by the {{{dfs.name.dir}}} variable), run the command
{{{
hadoop@ubuntu:~$ <HADOOP_INSTALL>/hadoop/bin/hadoop namenode -format
}}}
The output will look like this:
{{{
hadoop@ubuntu:/usr/local/hadoop$ bin/hadoop namenode -format
07/09/21 12:00:25 INFO dfs.NameNode: STARTUP_MSG:
/***********************************************************
STARTUP_MSG: Starting NameNode
STARTUP_MSG: host = ubuntu/127.0.0.1
STARTUP_MSG: args = [-format]
***********************************************************/
07/09/21 12:00:25 INFO dfs.Storage: Storage directory [...] has been successfully formatted.
07/09/21 12:00:25 INFO dfs.NameNode: SHUTDOWN_MSG:
/***********************************************************
SHUTDOWN_MSG: Shutting down NameNode at ubuntu/127.0.0.1
***********************************************************/
hadoop@ubuntu:/usr/local/hadoop$
}}}
== Starting your single-node cluster ==
Run the command:
{{{
hadoop@ubuntu:~$ <HADOOP_INSTALL>/bin/start-all.sh
}}}
This will startup a Namenode, Datanode, Jobtracker and a Tasktracker on your machine.
The output will look like this:
{{{
hadoop@ubuntu:/usr/local/hadoop$ bin/start-all.sh
starting namenode, logging to /usr/local/hadoop/bin/../logs/hadoop-hadoop-namenode-ubuntu.out
localhost: starting datanode, logging to /usr/local/hadoop/bin/../logs/hadoop-hadoop-datanode-ubuntu.out
localhost: starting secondarynamenode, logging to /usr/local/hadoop/bin/../logs/hadoop-hadoop-secondarynamenode-ubuntu.out
starting jobtracker, logging to /usr/local/hadoop/bin/../logs/hadoop-hadoop-jobtracker-ubuntu.out
localhost: starting tasktracker, logging to /usr/local/hadoop/bin/../logs/hadoop-hadoop-tasktracker-ubuntu.out
hadoop@ubuntu:/usr/local/hadoop$
}}}
A nifty tool for checking whether the expected Hadoop processes are running is {{{jps}}} (part of Sun's Java since v1.5.0). See also HowToDebugMapReducePrograms.
{{{
hadoop@sea:/usr/local/hadoop/$ jps
19811 TaskTracker
19674 SecondaryNameNode
19735 JobTracker
19497 NameNode
20879 TaskTracker$Child
21810 Jps
}}}
You can also check with {{{netstat}}} if Hadoop is listening on the configured ports.
{{{
hadoop@ubuntu:~$ sudo netstat -plten | grep java
tcp 0 0 0.0.0.0:50050 0.0.0.0:* LISTEN 1001 86234 23634/java
tcp 0 0 127.0.0.1:54310 0.0.0.0:* LISTEN 1001 85800 23317/java
tcp 0 0 127.0.0.1:54311 0.0.0.0:* LISTEN 1001 86383 23543/java
tcp 0 0 0.0.0.0:50090 0.0.0.0:* LISTEN 1001 86119 23478/java
tcp 0 0 0.0.0.0:50060 0.0.0.0:* LISTEN 1001 86233 23634/java
tcp 0 0 0.0.0.0:50030 0.0.0.0:* LISTEN 1001 86393 23543/java
tcp 0 0 0.0.0.0:50070 0.0.0.0:* LISTEN 1001 85964 23317/java
tcp 0 0 0.0.0.0:50010 0.0.0.0:* LISTEN 1001 86045 23389/java
tcp 0 0 0.0.0.0:50075 0.0.0.0:* LISTEN 1001 86102 23389/java
hadoop@ubuntu:~$
}}}
If there are any errors, examine the log files in the {{{<HADOOP_INSTALL>/logs/}}} directory.
== Stopping your single-node cluster ==
Run the command
{{{
hadoop@ubuntu:~$ <HADOOP_INSTALL>/bin/stop-all.sh
}}}
to stop all the daemons running on your machine.
Exemplary output:
{{{
hadoop@ubuntu:/usr/local/hadoop$ bin/stop-all.sh
stopping jobtracker
localhost: Ubuntu 7.04
localhost: stopping tasktracker
stopping namenode
localhost: Ubuntu 7.04
localhost: stopping datanode
localhost: Ubuntu 7.04
localhost: stopping secondarynamenode
hadoop@ubuntu:/usr/local/hadoop$
}}}
== Running a MapReduce job ==
We will now run your first HadoopMapReduce job. We will use the WordCount example job which reads text files and counts how often words occur. The input is text files and the output is text files, each line of which contains a word and the count of how often it occured, separated by a tab. More information of what happens behind the scenes is available at the WordCount article.
=== Download example input data ===
We will use three ebooks from Project Gutenberg for this example:
* [http://www.gutenberg.org/etext/20417 The Outline of Science, Vol. 1 (of 4) by J. Arthur Thomson]
* [http://www.gutenberg.org/etext/5000 The Notebooks of Leonardo Da Vinci]
* [http://www.gutenberg.org/etext/4300 Ulysses by James Joyce]
Download each ebook as plain text files in {{{us-ascii}}} encoding and store the uncompressed files in a temporary directory of choice, for example {{{/tmp/gutenberg}}}.
{{{
hadoop@ubuntu:~$ ls -l /tmp/gutenberg/
total 3592
-rw-r--r-- 1 hadoop hadoop 674425 2007-01-22 12:56 20417-8.txt
-rw-r--r-- 1 hadoop hadoop 1423808 2006-08-03 16:36 7ldvc10.txt
-rw-r--r-- 1 hadoop hadoop 1561677 2004-11-26 09:48 ulyss12.txt
hadoop@ubuntu:~$
}}}
=== Restart the Hadoop cluster ===
Restart your Hadoop cluster if it's not running already.
{{{
hadoop@ubuntu:~$ <HADOOP_INSTALL>/bin/start-all.sh
}}}
=== Copy local example data to HDFS ===
Before we run the actual MapReduce job, we first have to copy the files from our local file system to Hadoop's [http://lucene.apache.org/hadoop/hdfs_design.html HDFS]. See ImportantConcepts for more information about this step.
{{{
hadoop@ubuntu:/usr/local/hadoop$ bin/hadoop dfs -copyFromLocal /tmp/gutenberg gutenberg
hadoop@ubuntu:/usr/local/hadoop$ bin/hadoop dfs -ls
Found 1 items
/user/hadoop/gutenberg <dir>
hadoop@ubuntu:/usr/local/hadoop$ bin/hadoop dfs -ls gutenberg
Found 3 items
/user/hadoop/gutenberg/20417-8.txt <r 1> 674425
/user/hadoop/gutenberg/7ldvc10.txt <r 1> 1423808
/user/hadoop/gutenberg/ulyss12.txt <r 1> 1561677
}}}
=== Run the MapReduce job ===
Now, we actually run the WordCount examle job.
{{{
hadoop@ubuntu:/usr/local/hadoop$ bin/hadoop jar hadoop-0.14.2-examples.jar wordcount gutenberg gutenberg-output
}}}
This command will read all the files in the HDFS directory {{{gutenberg}}}, process it, and store the result in the HDFS directory {{{gutenberg-output}}}.
Exemplary output of the previous command in the console:
{{{
hadoop@ubuntu:/usr/local/hadoop$ bin/hadoop jar hadoop-0.14.2-examples.jar wordcount gutenberg gutenberg-output
07/09/21 13:00:30 INFO mapred.FileInputFormat: Total input paths to process : 3
07/09/21 13:00:31 INFO mapred.JobClient: Running job: job_200709211255_0001
07/09/21 13:00:32 INFO mapred.JobClient: map 0% reduce 0%
07/09/21 13:00:42 INFO mapred.JobClient: map 66% reduce 0%
07/09/21 13:00:47 INFO mapred.JobClient: map 100% reduce 22%
07/09/21 13:00:54 INFO mapred.JobClient: map 100% reduce 100%
07/09/21 13:00:55 INFO mapred.JobClient: Job complete: job_200709211255_0001
07/09/21 13:00:55 INFO mapred.JobClient: Counters: 12
07/09/21 13:00:55 INFO mapred.JobClient: Job Counters
07/09/21 13:00:55 INFO mapred.JobClient: Launched map tasks=3
07/09/21 13:00:55 INFO mapred.JobClient: Launched reduce tasks=1
07/09/21 13:00:55 INFO mapred.JobClient: Data-local map tasks=3
07/09/21 13:00:55 INFO mapred.JobClient: Map-Reduce Framework
07/09/21 13:00:55 INFO mapred.JobClient: Map input records=77637
07/09/21 13:00:55 INFO mapred.JobClient: Map output records=628439
07/09/21 13:00:55 INFO mapred.JobClient: Map input bytes=3659910
07/09/21 13:00:55 INFO mapred.JobClient: Map output bytes=6061344
07/09/21 13:00:55 INFO mapred.JobClient: Combine input records=628439
07/09/21 13:00:55 INFO mapred.JobClient: Combine output records=103910
07/09/21 13:00:55 INFO mapred.JobClient: Reduce input groups=85096
07/09/21 13:00:55 INFO mapred.JobClient: Reduce input records=103910
07/09/21 13:00:55 INFO mapred.JobClient: Reduce output records=85096
hadoop@ubuntu:/usr/local/hadoop$
}}}
Check if the result is successfully stored in HDFS directory {{{gutenberg-output}}}:
{{{
hadoop@ubuntu:/usr/local/hadoop$ bin/hadoop dfs -ls
Found 2 items
/user/hadoop/gutenberg <dir>
/user/hadoop/gutenberg-output <dir>
hadoop@wind:/usr/local/hadoop$ bin/hadoop dfs -ls gutenberg-output
Found 1 items
/user/hadoop/gutenberg-output/part-00000 <r 1> 903193
hadoop@ubuntu:/usr/local/hadoop$
}}}
=== Retrieve the job result from HDFS ===
To inspect the file, you can copy it from HDFS to the local file system. Alternatively, you can use the command
{{{
hadoop@ubuntu:/usr/local/hadoop$ bin/hadoop dfs -cat gutenberg-output/part-00000
}}}
to read the file directly from HDFS without copying it to the local file system. In this tutorial, we will copy the results to the local file system though.
{{{
hadoop@ubuntu:/usr/local/hadoop$ mkdir /tmp/gutenberg-output
hadoop@ubuntu:/usr/local/hadoop$ bin/hadoop dfs -copyToLocal gutenberg-output/part-00000 /tmp/gutenberg-output
hadoop@ubuntu:/usr/local/hadoop$ head /tmp/gutenberg-output/part-00000
"(Lo)cra" 1
"1490 1
"1498," 1
"35" 1
"40," 1
"A 2
"AS-IS". 2
"A_ 1
"Absoluti 1
"Alack! 1
hadoop@ubuntu:/usr/local/hadoop$
}}}
Note that in this specific output the quote signs (") enclosing the words in the {{{head}}} output above have not been inserted by Hadoop. They are the result of the word tokenizer used in the WordCount example, and in this case they matched the beginning of a quote in the ebook texts. Just inspect the {{{part-00000}}} file further to see it for yourself.
== Hadoop Web Interfaces ==
Hadoop comes with several web interfaces which are by default (see {{{conf/hadoop-default.xml}}}) available at these locations:
* http://localhost:50030/ - web UI for MapReduce job tracker(s)
* http://localhost:50060/ - web UI for task tracker(s)
* http://localhost:50070/ - web UI for HDFS name node(s)
These web interfaces provide concise information about what's happening in your Hadoop cluster. You might want to give them a try.
= What's next? =
If you're feeling comfortable, you can continue your Hadoop experience with the follow-up tutorial [http://www.michael-noll.com/wiki/Running_Hadoop_On_Ubuntu_Linux_%28Multi-Node_Cluster%29 Running Hadoop On Ubuntu Linux (Multi-Node Cluster)] where I describe how to build a Hadoop ''multi-node'' cluster with two Ubuntu boxes (this will increase your current cluster size by 100% :-P).
In addition, there is a [http://www.michael-noll.com/wiki/Writing_An_Hadoop_MapReduce_Program_In_Python tutorial] on [http://www.michael-noll.com/wiki/Writing_An_Hadoop_MapReduce_Program_In_Python how to code a simple MapReduce job]] in the Python programming language which can serve as the basis for writing your own MapReduce programs.
= Comments =
* The [http://www.michael-noll.com/wiki/Running_Hadoop_On_Ubuntu_Linux_%28Multi-Node_Cluster%29 multi-node cluster tutorial] and the [http://www.michael-noll.com/wiki/Writing_An_Hadoop_MapReduce_Program_In_Python Python MapReduce tutorial] are not yet integrated into this wiki, still working on that ''-- MichaelNoll''