In this tutorial I will describe the required steps for setting up a pseudo-distributed, single-node Hadoop cluster backed by the Hadoop Distributed File System, running on Ubuntu Linux.
Hadoop is a framework written in Java for running applications on large clusters of commodity hardware and incorporates features similar to those of the Google File System (GFS) and of the MapReduce computing paradigm. Hadoop’s HDFS is a highly fault-tolerant distributed file system and, like Hadoop in general, 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:
Sun Java 6
Hadoop requires a working Java 1.5+ (aka Java 5) installation. However, using Java 1.6 (aka Java 6) is recommended for running Hadoop. For the sake of this tutorial, I will therefore describe the installation of Java 1.6.
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The full JDK which will be placed in
/usr/lib/jvm/java-6-sun (well, this directory is actually a symlink on
After installation, make a quick check whether Sun’s JDK is correctly set up:
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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).
This will add the user
hduser and the group
hadoop to your local machine.
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
hduser user we created 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 several online guides available.
First, we have to generate an SSH key for the
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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 every time Hadoop interacts with its nodes).
Second, you have to enable SSH access to your local machine with this newly created key.
The final step is to test the SSH setup by connecting to your local machine with the
hduser user. The step is
also needed to save your local machine’s host key fingerprint to the
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
man ssh_config for more information).
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If the SSH connect should fail, these general tips might help:
- Enable debugging with
ssh -vvv localhostand 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
AllowUsers(if this option is active, add the
hduseruser 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.
One problem with IPv6 on Ubuntu is that using
0.0.0.0 for the various networking-related Hadoop configuration
options will result in Hadoop binding to the IPv6 addresses of my Ubuntu box. In my case, I realized that there’s
no practical point in enabling IPv6 on a box when you are not connected to any IPv6 network. Hence, I simply
disabled IPv6 on my Ubuntu machine. Your mileage may vary.
To disable IPv6 on Ubuntu 10.04 LTS, open
/etc/sysctl.conf in the editor of your choice and add the following
lines to the end of the file:
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You have to reboot your machine in order to make the changes take effect.
You can check whether IPv6 is enabled on your machine with the following command:
A return value of 0 means IPv6 is enabled, a value of 1 means disabled (that’s what we want).
You can also disable IPv6 only for Hadoop as documented in
HADOOP-3437. You can do so by adding the following line to
Download Hadoop from the
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
hduser user and
hadoop group, for example:
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(Just to give you the idea, YMMV – personally, I create a symlink from
Add the following lines to the end of the
$HOME/.bashrc file of user
hduser. If you use a shell other than
bash, you should of course update its appropriate configuration files instead of
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You can repeat this exercise also for other users who want to use Hadoop.
Excursus: Hadoop Distributed File System (HDFS)
Before we continue let us briefly learn a bit more about Hadoop’s distributed file system.
The Hadoop Distributed File System (HDFS) is a distributed file system designed to run on commodity hardware. It has many similarities with existing distributed file systems. However, the differences from other distributed file systems are significant. HDFS is highly fault-tolerant and is designed to be deployed on low-cost hardware. HDFS provides high throughput access to application data and is suitable for applications that have large data sets. HDFS relaxes a few POSIX requirements to enable streaming access to file system data. HDFS was originally built as infrastructure for the Apache Nutch web search engine project. HDFS is part of the Apache Hadoop project, which is part of the Apache Lucene project.
The following picture gives an overview of the most important HDFS components.
Our goal in this tutorial is a single-node setup of Hadoop. More information of what we do in this section is available on the Hadoop Wiki.
The only required environment variable we have to configure for Hadoop in this tutorial is
conf/hadoop-env.sh in the editor of your choice (if you used the installation path in this tutorial, the full path
/usr/local/hadoop/conf/hadoop-env.sh) and set the
JAVA_HOME environment variable to the Sun JDK/JRE 6
Note: If you are on a Mac with OS X 10.7 you can use the following line to set up
In this section, we will configure the directory where Hadoop will store its data files, the network ports it listens to, etc. Our setup will use Hadoop’s Distributed File System, 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 parameter – this parameter you
must change to a directory of your choice. We will use the directory
/app/hadoop/tmp in this tutorial. Hadoop’s
default configurations use
hadoop.tmp.dir as the base temporary directory both for the local file system and HDFS,
so don’t be surprised if you see Hadoop creating the specified directory automatically on HDFS at some later point.
Now we create the directory and set the required ownerships and permissions:
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If you forget to set the required ownerships and permissions, you will see a
java.io.IOException when you try to
format the name node in the next section).
Add the following snippets between the
<configuration> ... </configuration> tags in the respective configuration
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Formatting the HDFS filesystem via the NameNode
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.
To format the filesystem (which simply initializes the directory specified by the
dfs.name.dir variable), run the
The output will look like this:
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Starting your single-node cluster
Run the command:
This will startup a Namenode, Datanode, Jobtracker and a Tasktracker on your machine.
The output will look like this:
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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 How to debug MapReduce programs.
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You can also check with
netstat if Hadoop is listening on the configured ports.
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If there are any errors, examine the log files in the
Stopping your single-node cluster
Run the command
to stop all the daemons running on your machine.
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Running a MapReduce job
We will now run your first Hadoop MapReduce 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 occurred, separated by a tab. More information of what happens behind the scenes is available at the Hadoop Wiki.
Download example input data
We will use three ebooks from Project Gutenberg for this example:
- The Outline of Science, Vol. 1 (of 4) by J. Arthur Thomson
- The Notebooks of Leonardo Da Vinci
- Ulysses by James Joyce
Download each ebook as text files in
Plain Text UTF-8 encoding and store the files in a local temporary directory
of choice, for example
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Restart the Hadoop cluster
Restart your Hadoop cluster if it’s not running already.
Copy local example data to HDFS
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Run the MapReduce job
Now, we actually run the WordCount example job.
This command will read all the files in the HDFS directory
/user/hduser/gutenberg, process it, and store the result
in the HDFS directory
Exception in thread "main" java.io.IOException: Error opening job jar: hadoop*examples*.jar at org.apache.hadoop.util.RunJar.main (RunJar.java: 90) Caused by: java.util.zip.ZipException: error in opening zip fileIn this case, re-run the command with the full name of the Hadoop Examples JAR file, for example:
hduser@ubuntu:/usr/local/hadoop$ bin/hadoop jar hadoop-examples-1.0.3.jar wordcount /user/hduser/gutenberg /user/hduser/gutenberg-output
Example output of the previous command in the console:
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Check if the result is successfully stored in HDFS directory
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If you want to modify some Hadoop settings on the fly like increasing the number of Reduce tasks, you can use the
An important note about mapred.map.tasks: Hadoop does not honor mapred.map.tasks beyond considering it a hint. But it accepts the user specified mapred.reduce.tasks and doesn’t manipulate that. You cannot force mapred.map.tasks but you can specify mapred.reduce.tasks.
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
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.
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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
The command fs -getmerge will simply concatenate any files it finds in the directory you specify. This means that the merged file might (and most likely will) not be sorted.
Hadoop Web Interfaces
Hadoop comes with several web interfaces which are by default (see
conf/hadoop-default.xml) available at these
- http://localhost:50070/ – web UI of the NameNode daemon
- http://localhost:50030/ – web UI of the JobTracker daemon
- http://localhost:50060/ – web UI of the TaskTracker daemon
These web interfaces provide concise information about what’s happening in your Hadoop cluster. You might want to give them a try.
NameNode Web Interface (HDFS layer)
The name node web UI shows you a cluster summary including information about total/remaining capacity, live and dead nodes. Additionally, it allows you to browse the HDFS namespace and view the contents of its files in the web browser. It also gives access to the local machine’s Hadoop log files.
By default, it’s available at http://localhost:50070/.
JobTracker Web Interface (MapReduce layer)
The JobTracker web UI provides information about general job statistics of the Hadoop cluster, running/completed/failed jobs and a job history log file. It also gives access to the ‘‘local machine’s’’ Hadoop log files (the machine on which the web UI is running on).
By default, it’s available at http://localhost:50030/.
TaskTracker Web Interface (MapReduce layer)
The task tracker web UI shows you running and non-running tasks. It also gives access to the ‘‘local machine’s’’ Hadoop log files.
By default, it’s available at http://localhost:50060/.
If you’re feeling comfortable, you can continue your Hadoop experience with my follow-up tutorial 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%, heh).
From yours truly:
From other people:
Only important changes to this article are listed here:
- 2011-07-17: Renamed the Hadoop user from
hduserbased on readers’ feedback. This should make the distinction between the local Hadoop user (now
hduser), the local Hadoop group (
hadoop), and the Hadoop CLI tool (
hadoop) more clear.