Running Hadoop on Windows

What is Hadoop?

Hadoop is a an open source Apache project written in Java and designed to provide users with two things: a distributed file system (HDFS) and a method for distributed computation. It’s based on Google’s published Google File System and MapReduce concept which discuss how to build a framework capable of executing intensive computations across tons of computers. Something that might, you know, be helpful in building a giant search index. Read the Hadoop project description and wiki for more information and background on Hadoop.

What’s the big deal about running it on Windows?

Looking for Linux? If you’re looking for a comprehensive guide to getting Hadoop running on Linux, please check out Michael Noll’s excellent guides: Running Hadoop on Ubuntu Linux (Single Node Cluster) and Running Hadoop on Ubuntu Linux (Multi-Node Cluster). This post was inspired by these very informative articles.

Hadoop’s key design goal is to provide storage and computation on lots of homogenous “commodity” machines; usually a fairly beefy machine running Linux. With that goal in mind, the Hadoop team has logically focused on Linux platforms in their development and documentation. Their Quickstart even includes the caveat that “Win32 is supported as a development platform. Distributed operation has not been well tested on Win32, so this is not a production platform.” If you want to use Windows to run Hadoop in pseudo-distributed or distributed mode (more on these modes in a moment), you’re pretty much left on your own. Now, most people will still probably not run Hadoop in production on Windows machines, but the ability to deploy on the most widely used platform in the world is still probably a good idea for allowing Hadoop to be used by many of the developers out there that use Windows on a daily basis.

Caveat Emptor

I’m one of the few that has invested the time to setup an actual distributed Hadoop installation on Windows. I’ve used it for some successful development tests. I have not used this in production. Also, although I can get around in a Linux/Unix environment, I’m no expert so some of the advice below may not be the correct way to configure things. I’m also no security expert. If any of you out there have corrections or advice for me, please let me know in a comment and I’ll get it fixed.

This guide uses Hadoop v0.17 and assumes that you don’t have any previous Hadoop installation. I’ve also done my primary work with Hadoop on Windows XP. Where I’m aware of differences between XP and Vista, I’ve tried to note them. Please comment if something I’ve written is not appropriate for Vista.

Bottom line: your mileage may vary, but this guide should get you started running Hadoop on Windows.

A quick note on distributed Hadoop

Hadoop runs in one of three modes:

  • Standalone: All Hadoop functionality runs in one Java process. This works “out of the box” and is trivial to use on any platform, Windows included.
  • Pseudo-Distributed: Hadoop functionality all runs on the local machine but the various components will run as separate processes. This is much more like “real” Hadoop and does require some configuration as well as SSH. It does not, however, permit distributed storage or processing across multiple machines.
  • Fully Distributed: Hadoop functionality is distributed across a “cluster” of machines. Each machine participates in somewhat different (and occasionally overlapping) roles. This allows multiple machines to contribute processing power and storage to the cluster.

The Hadoop Quickstart can get you started on Standalone mode and Psuedo-Distributed (to some degree). Take a look at that if you’re not ready for Fully Distributed. This guide focuses on the Fully Distributed mode of Hadoop. After all, it’s the most interesting where you’re actually doing real distributed computing.

Pre-Requisites

Java

I’m assuming if you’re interested in running Hadoop that you’re familiar with Java programming and have Java installed on all the machines on which you want to run Hadoop. The Hadoop docs recommend Java 6 and require at least Java 5. Whichever you choose, you need to make sure that you have the same major Java version (5 or 6) installed on each machine. Also, any code you write for running using Hadoop’s MapReduce must be compiled with the version you choose. If you don’t have Java installed, go get it from Sun and install it. I will assume you’re using Java 6 in the rest of this guide.

Cygwin

As I said in the introduction, Hadoop assumes Linux (or a Unix flavor OS) is being used to run Hadoop. This assumption is buried pretty deeply. Various parts of Hadoop are executed using shell scripts that will only work on a Linux shell. It also uses passwordless secure shell (SSH) to communicate between computers in the Hadoop cluster. The best way to do these things on Windows is to make Windows act more like Linux. You can do this using Cygwin, which provides a “Linux-like environment for Windows” that allows you to use Linux-style command line utilities as well as run really useful Linux-centric software like OpenSSH. Go download the latest version of Cygwin. Don’t install it yet. I’ll describe how you need to install it below.

Hadoop

Go download Hadoop core. I’m writing this guide for version 0.17 and I will assume that’s what you’re using.

More than one Windows PC on a LAN

It should probably go without saying that to follow this guide, you’ll need to have more than one PC. I’m going to assume you have two computers and that they’re both on your LAN. Go ahead and designate one to be the Master and one to be the Slave. These machines together will be your “cluster”. The Master will be responsible for ensuring the Slaves have work to do (such as storing data or running MapReduce jobs). The Master can also do its share of this work as well. If you have more than two PCs, you can always setup Slave2, Slave3 and so on. Some of the steps below will need to be performed on all your cluster machines, some on just Master or Slaves. I’ll note which apply for each step.

Step 1: Configure your hosts file (All machines)

This step isn’t strictly necessary but it will make your life easier down the road if your computers change IPs. It’ll also help you keep things straight in your head as you edit configuration files. Open your Windows hosts file located at c:\windows\system32\drivers\etc\hosts (the file is named “hosts” with no extension) in a text editor and add the following lines (replacing the NNNs with the IP addresses of both master and slave):

master NNN.NNN.NNN.NNN
slave NNN.NNN.NNN.NNN

Save the file.

Step 2: Install Cygwin and Configure OpenSSH sshd (All machines)

Cygwin has a bit of an odd installation process because it lets you pick and choose which libraries of useful Linux-y programs and utilities you want to install. In this case, we’re really installing Cygwin to be able to run shell scripts and OpenSSH. OpenSSH is an implementation of a secure shell (SSH) server (sshd) and client (ssh). If you’re not familiar with SSH, you can think of it as a secure version of telnet. With the ssh command, you can login to another computer running sshd and work with it from the command line. Instead of reinventing the wheel, I’m going to tell you to go here for step-by-step instructions on how to install Cygwin on Windows and get OpenSSH’s sshd server running. You can stop after instruction 6. Like the linked instructions, I’ll assume you’ve installed Cygwin to c:\cygwin though you can install it elsewhere.

If you’re running a firewall on your machine, you’ll need to make sure port 22 is open for incoming SSH connections. As always with firewalls, open your machine up as little as possible. If you’re using Windows firewall, make sure the open port is scoped to your LAN. Microsoft has documentation for how to do all this with Windows Firewall (scroll down to the section titled “Configure Exceptions for Ports”).

Step 3: Configure SSH (All Machines)

Hadoop uses SSH to allow the master computer(s) in a cluster to start and stop processes on the slave computers. One of the nice things about SSH is it supports several modes of secure authentication: you can use passwords or you can use public/private keys to connect without passwords (“passwordless”). Hadoop requires that you setup SSH to do the latter. I’m not going to go into great detail on how this all works, but suffice it to say that you’re going to do the following:

  1. Generate a public-private key pair for your user on each cluster machine.
  2. Exchange each machine user’s public key with each other machine user in the cluster.

Generate public/private key pairs

To generate a key pair, open Cygwin and issue the following commands ($> is the command prompt):
$> ssh-keygen -t dsa -P '' -f ~/.ssh/id_dsa
$> cat ~/.ssh/id_dsa.pub >> ~/.ssh/authorized_keys

Now, you should be able to SSH into your local machine using the following command:
$> ssh localhost

When prompted for your password, enter it. You’ll see something like the following in your Cygwin terminal.

hayes@localhost's password:
Last login: Sun Jun 8 19:47:14 2008 from localhost

hayes@calculon ~
$>

To quit the SSH session and go back to your regular terminal, use:
$> exit

Make sure to do this on all computers in your cluster.

Exchange public keys

Now that you have public and private key pairs on each machine in your cluster, you need to share your public keys around to permit passwordless login from one machine to the other. Once a machine has a public key, it can safely authenticate a request from a remote machine that is encrypted using the private key that matches that public key.

On the master issue the following command in cygwin (where “<slaveusername>” is the username you use to login to Windows on the slave computer):

$> scp ~/.ssh/id_dsa.pub <slaveusername>@slave:~/.ssh/master-key.pub

Enter your password when prompted. This will copy your public key file in use on the master to the slave.

On the slave, issue the following command in cygwin:

$> cat ~/.ssh/master-key.pub >> ~/.ssh/authorized_keys

This will append your public key to the set of authorized keys the slave accepts for authentication purposes.

Back on the master, test this out by issuing the following command in cygwin:

$> ssh <slaveusername>@slave

If all is well, you should be logged into the slave computer with no password required.

Repeat this process in reverse, copying the slave’s public key to the master. Also, make sure to exchange public keys between the master and any other slaves that may be in your cluster.

Configure SSH to use default usernames (optional)

If all of your cluster machines are using the same username, you can safely skip this step. If not, read on.

Most Hadoop tutorials suggest that you setup a user specific to Hadoop. If you want to do that, you certainly can. Why setup a specific user for Hadoop? Well, in addition to being more secure from a file permissions and security perspective, when Hadoop uses SSH to issue commands from one machine to another it will automatically try to login to the remote machine using the same user as the current machine. If you have different users on different machines, the SSH login performed by Hadoop will fail. However, most of us on Windows typically use our machines with a single user and would probably prefer not to have to setup a new user on each machine just for Hadoop.

The way to allow Hadoop to work with multiple users is by configuring SSH to automatically select the appropriate user when Hadoop issues its SSH command. (You’ll also need to edit the hadoop-env.sh config file, but that comes later in this guide.) You can do this by editing the file named “config” (no extension) located in the same “.ssh” directory where you stored your public and private keys for authentication. Cygwin stores this directory under “c:\cygwin\home\<windowsusername>\.ssh”.

On the master, create a file called config and add the following lines (replacing “<slaveusername>” with the username you’re using on the Slave machine:

Host slave
User <slaveusername>

If you have more slaves in your cluster, add Host and User lines for those as well.

On each slave, create a file called config and add the following lines (replacing “<masterusername>” with the username you’re using on the Master machine:

Host master
User <masterusername>

Now test this out. On the master, go to cygwin and issue the following command:

$> ssh slave

You should be automatically logged into the slave machine with no username and no password required. Make sure to exit out of your ssh session.

For more information on this configuration file’s format and what it does, go here or run man ssh_config in cygwin.

Step 4: Extract Hadoop (All Machines)

If you haven’t downloaded Hadoop 0.17, go do that now. The file will have a “.tar.gz” extension which is not natively understood by Windows. You’ll need something like WinRAR to extract it. (If anyone knows something easier than WinRAR for extracting tarred-gzipped files on Windows, please leave a comment.)

Once you’ve got an extraction utility, extract it directly into c:\cygwin\usr\local. (Assuming you installed Cygwin to c:\cygwin as described above.)

The extracted folder will be named hadoop-0.17.0. Rename it to hadoop. All further steps assume you’re in this hadoop directory and will use relative paths for configuration files and shell scripts.

Step 5: Configure hadoop-env.sh (All Machines)

The conf/hadoop-env.sh file is a shell script that sets up various environment variables that Hadoop needs to run. Open conf/hadoop-env.sh in a text editor. Look for the line that starts with “#export JAVA_HOME”. Change that line to something like the following:

export JAVA_HOME=c:\\Program\ Files\\Java\\jdk1.6.0_06

This should be the home directory of your Java installation. Note that you need to remove the leading “#” (comment) symbol and that you need to escape both backslashes and spaces with a backslash.

Next, locate the line that starts with “#export HADOOP_IDENT_STRING”. Change it to something like the following:

export HADOOP_IDENT_STRING=MYHADOOP

Where MYHADOOP can be anything you want to identify your Hadoop cluster with. Just make sure each machine in your cluster uses the same value.

To test these changes issue the following commands in cygwin:

$> cd /usr/local/hadoop
$> bin/hadoop version

You should see output similar to this:

Hadoop 0.17.0
Subversion http://svn.apache.org/repos/asf/hadoop/core/branches/branch-0.17 -r 656523
Compiled by hadoopqa on Thu May 15 07:22:55 UTC 2008

If you see output like this:

bin/hadoop: line 166: c:\Program Files\Java\jdk1.6.0_05/bin/java: No such file or directory
bin/hadoop: line 251: c:\Program Files\Java\jdk1.6.0_05/bin/java: No such file or directory
bin/hadoop: line 251: exec: c:\Program Files\Java\jdk1.6.0_05/bin/java: cannot execute: No such file or directory

This means that your Java home directory is wrong. Go back and make sure you specified the correct directory and used the appropriate escaping.

Step 6: Configure hadoop-site.xml (All Machines)

The conf/hadoop-site.xml file is basically a properties file that lets you configure all sorts of HDFS and MapReduce parameters on a per-machine basis. I’m not going to go into detail here about what each property does, but there are 3 that you need to configure on all machines: fs.default.name, mapred.job.tracker and dfs.replication. You can just copy the XML below into your conf/hadoop-site.xml file.

<?xml version=”1.0″?>
<?xml-stylesheet type=”text/xsl” href=”configuration.xsl”?>
<!– Put site-specific property overrides in this file. –>
<configuration>
<property>
<name>fs.default.name</name>
<value>hdfs://master:47110</value>
</property>
<property>
<name>mapred.job.tracker</name>
<value>master:47111</value>
</property>
<property>
<name>dfs.replication</name>
<value>2</value>
</property>
</configuration>

For more information about what these configuration properties (and others) do, see the Hadoop cluster setup docs and the hadoop-default.xml documentation.

Step 7: Configure slaves file (Master only)

The conf/slaves file tells the master where it can find slaves to do work. Open yours in a text editor. It will probably have one line which says “localhost”. Replace that with the following:

master
slave

Step 8: Firewall Configuration (All Machines)

If you’re using Windows Firewall, you will need to ensure that the appropriate ports are open so that the slaves can make HTTP requests for information from the master. (This is different from the port 22 needed for SSH.) The list of ports for which you should make exceptions are as follows: 47110, 47111, 50010, 50030, 50060, 50070, 50075, 50090. These should all be open on the master for request coming from your local network. For more information about these ports, see the Hadoop default configuration file documentation.

You should also make sure that Java applications are allowed by the firewall to connect to the network on all your machines including the slaves.

Step 9: Starting your cluster (Master Only)

To start your cluster, make sure you’re in cygwin on the master and have changed to your hadoop installation directory. To fully start your cluster, you’ll need to start DFS first and then MapReduce.

Starting DFS

Issue the following command:

$> bin/start-dfs.sh

You should see output somewhat like the following (note that I have 2 slaves in my cluster which has a cluster ID of Appozite, your mileage will vary somewhat):

starting namenode, logging to /usr/local/hadoop/bin/../logs/hadoop-Appozite-namenode-calculon.out
master: starting datanode, logging to /usr/local/hadoop/bin/../logs/hadoop-Appozite-datanode-calculon.out
slave: starting datanode, logging to /usr/local/hadoop/bin/../logs/hadoop-Appozite-datanode-hayes-daviss-macbo
ok-pro.local.out
slave2: starting datanode, logging to /usr/local/hadoop/bin/../logs/hadoop-Appozite-datanode-XTRAPUFFYJR.out
master: starting secondarynamenode, logging to /usr/local/hadoop/bin/../logs/hadoop-Appozite-secondarynamenode
-calculon.out

To see if your distributed file system is actually running across multiple machines, you can open the Hadoop DFS web interface which will be running on your master on port 50070. You can probably open it by clicking this link: http://localhost:50070. Below is a screenshot of my cluster. As you can see, there are 3 nodes with a total of 712.27 GB of space. (Click the image to see the larger version.)

Starting MapReduce

To start the MapReduce part of Hadoop, issue the following command:

$> bin/start-mapred.sh

You should see output similar to the following (again noting that I’ve got 3 nodes in my cluster):

starting jobtracker, logging to /usr/local/hadoop/bin/../logs/hadoop-Appozite-jobtracker-calculon.out
master: starting tasktracker, logging to /usr/local/hadoop/bin/../logs/hadoop-Appozite-tasktracker-calculon.ou
t
slave: starting tasktracker, logging to /usr/local/hadoop/bin/../logs/hadoop-Appozite-tasktracker-hayes-daviss
-macbook-pro.local.out
slave2: starting tasktracker, logging to /usr/local/hadoop/bin/../logs/hadoop-Appozite-tasktracker-XTRAPUFFYJR
.out

You can view your MapReduce setup using the MapReduce monitoring web app that comes with Hadoop which runs on port 50030 of your master node. You can probably open it by clicking this like: http://localhost:50030. Below is a screenshot from my browser. There’s not much exciting to see here until you have an actual MapReduce job running.

Testing it out

Now that you’ve got your Hadoop cluster up and running, executing MapReduce jobs or writing to and reading from DFS are no different on Windows than any other platform so long as you use cygwin to execute commands. At this point, I’ll refer you to Michael Noll’s Hadoop on Ubuntu Linux tutorial for an explanation on how to run a large enough MapReduce job to take advantage of your cluster. (Note that he’s using Hadoop 0.16.0 instead of 0.17.0, so you’ll replace “0.16.0″ with “0.17.0″ where applicable.) Follow his instructions and you should be good to go. The Hadoop site also offers a MapReduce tutorial to you can get started writing your own jobs in Java. If you’re interested in writing MapReduce jobs in other languages that take advantage of Hadoop, check out the Hadoop Streaming documentation.

How to stop your cluster

When you’re ready to stop your cluster, it’s simple. Just top MapReduce and then DFS.

To stop MapReduce, issue the following command on the master:

$>bin/stop-mapred.sh

You should see output similar to the following:

stopping jobtracker
slave: stopping tasktracker
master: stopping tasktracker
slave2: stopping tasktracker

To stop DFS, issue the following command on the master:

$> bin/stop-dfs.sh

You should see output similar to the following:

stopping namenode
master: stopping datanode
slave: stopping datanode
slave2: stopping datanode
master: stopping secondarynamenode

And that’s it

I hope this helps anyone out there trying to run Hadoop on Windows. If any of you have corrections, questions or suggestions please comment and let me know. Happy Hadooping!

Update 6/18/2008: Fixed link to Hadoop Admin screenshot. Thanks to Robert Towne for pointing out the bad link.

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26 comments

  1. Any issue with using Virtual PCs (VMs) to create the cluster? Obviously it wouldn’t yield performance benefit but would provide a simple test environment…

  2. @Charles Hill: You might be better off doing “pseudo distributed” Hadoop where each part of Hadoop runs in a different process. However, if you’re looking to experiment with distributed Hadoop and are short multiple machines it *should* work fine to run it across VMs (though I’ve not actually tried it). I suppose you could also do it in an enterprise environment where you have several beefy boxes running your VMs. Let me know how it goes.

  3. good info, thanks for posting this.

    btw, your link to the hadoop admin page is not linked to anything

    http://hayesdavis.net/wp-content/uploads/2008/06/hadoop-mapreduce-admin.png

  4. @robert towne: Thanks for letting me know about the bad link. It’s fixed now.

  5. Hi Hayes!

    Thanks for your tutorial – it was a great help for me. I set up a mixed Linux/Win Hadoop cluster and everything is working fine. I have a SSH related question and maybe you can answer it.

    Is it really necessary that each node in the cluster has the pubulic RSA key of all the other nodes added to it’s authorized keys? I just added the master’s public RSA kay to all the slave nodes and everything is working. But working doesn’t imply that things are running as fast as they could. Maybe there would be a speed gain if the slave nodes could login to each other with SSH.

    cheers
    Johannes

  6. Johannes,

    I thought the same thing myself. The Hadoop docs are somewhat unclear on that point so I was going the “better safe than sorry” route. However, after running it for a while, I’m pretty sure the master only uses SSH to send commands to the slaves and never vice-versa. Might be a good question for one of the Hadoop mailing lists.

  7. when I run the bin/hadoop version command I get following error.

    bin/hadoop: line 2: $’\r’: command not found bin/hadoop: line 17: $’\r’: command not foundbin/hadoop: line 18: $’\r’: command not found

    Can you please help me.
    ranahosur@gmail.com

  8. Good work…
    Was struggling with running a hadoop cluster on winidows

  9. Hi, I used your method to install hadoop 0.17.1 on two Windows 2003 R2 64 bit boxes, ssh works, but the dfs namenode service can’t start, it seems Hadoop is really not well tested on Windows.

  10. Hi, we should format the file system before issuing the bin/start-dfs.sh using bin/hadoop namenode -format

  11. I cannot see the Hadoop DFS web interface by going to http://localhost:50070

    Before I start a Hadoop cluster do I need to format a new distributed filesystem using following command:
    $ bin/hadoop namenode -format

    Then start the HDFS with the following command, run on the designated NameNode:
    $ bin/start-dfs.sh

  12. When i run the bin/hadoop verison
    i got golloiwng error:
    bin/hadoop: line 2: $’\r’: command not found bin/hadoop: line 7: $’\r’: command not foundbin/hadoop: line 18: $’\r’: command not found

    Its looks like my that file gets some ‘\r’ and unable to execute command. Can someone help me out?

    One guy asked this question bfr but there is no reply.

    Thanks

  13. wasim:
    #1) You need to first install “Cygwin”. Which is a unix emulator for Windows. Search Google for Cygwin.
    #2) After u install cygwin, you will have a utility available under Cygwin prompt called “dos2unix”. This converts all Dos “\r” to appropriate Unix equivalents. So to run it do the following in a Cygwin prompt:
    cd /cygdrive/c/hadoop-0.17.2.1/bin
    dos2unix hadoop
    dos2unix hadoop-config.sh
    dos2unix ../conf/hadoop-env.sh

    You should now be able to execute your Hadoop process.

    Good Luck…

  14. Hi Hayes, very good tutorial

    You should update it to mention that before running the cluster you MUST format the namenode with the following command

    bin/hadoop namenode -format

  15. One thing I’d like to mention is that while extracting the Hadoop tar file using “smart LF/CR” option in Winzip creates DOS versions of the script which refuses to run on Cygwin bash 3.2. Workarounds (use one):
    1. DOnt use the “Smart LF/CR” option in winzip
    2. Use tar xvf to extract the files
    3. use -o igncr to run bash (painful option)

  16. If you include package “gzip” when downloading the cygwin packages, you can afterward use the “gunzip” command to extract the hadoop “*.tar.gz” file for “Step 4: Extract Hadoop (All Machines)”.

    Run the followings in a cygwin window:
    1. Move the “hadoop*.tar.gz’ to /usr/local
    2. Run command: gzunzip hadoop*.tar.gz (you will see a hadoop*.tar file)
    3. Run command: tar xvf hadoop*.tar

  17. Absolutely great post.
    Thanks a lot – you saved me quite a hassle as hadoop’s docs are as good as a map of Africa when you’re stuck in the middle of Alaska… :)

    Cheers!

  18. I just recently made a tutorial on how to set-up hadoop development environment with Eclipse plug-in on the Windows machine.

    http://v-lad.org/Tutorials/Hadoop/00%20-%20Intro.html

  19. Remove every old data folder where datanode store can store data from your system and the use command bin/hadoop namenode -format

  20. Can you please tell when configuring SSH to use default usernames what changes have to be made on hadoop-env.sh. I have made a config file and can do ssh. But when i use command bin/start-all.sh it there it still search in differnt directory for hadoop-deamon.sh file

  21. When I was half way through the article, I thought this must be one of those 10 page guides but to my pleasant surprise you managed to sum it up quite clearly and concisely.
    Thank you for the your post. :)

  22. I’ve started playing with this, as an experiment to try distributing across remote computers, does anyone know if I could use my dreamhost account as a client?

  23. I have followed this,altough trying a pseudo cluster first, and have come to a roadblock when I try to start up hadoop:

    $ bin/start-all.sh
    starting namenode, logging to /cygdrive/c/apache/hadoop/hadoop-0.20.1/bin/../logs/hadoop-brian-namenode-xx.out

    localhost:/bin/bash: /cygdrive/c/apache/hadoop/hadoop-0.20.1/bin/hadoop-daemon.sh: No such file or directory

    The error seems related to a carriage return after logging into the same machine()’localhost’ through ssh, running hadoop as a pseudo-cluster

  24. Excellent post, thanks!

    You asked for something better than WinRAR to extract tar.gz files. 7-zip will work fine:
    http://www.7-zip.org/

  25. @Carl Rosenberger. I tried 7-zip and it untarred it to .gz format, but when I tried to unzip .gz file, it unzipped maybe 1% of the file. Don’t know if 7-zip has a bug or it just does not plainly support tar.gz which I doubt. Later on switched to tar from cygwin and it worked beautifully

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