Starting Small: Set Up a Hadoop Compute Cluster Using Raspberry Pis

What is Hadoop?

Hadoop is a big data computing framework that generally refers to the main components: the core, HDFS, and MapReduce. There are several other projects under the umbrella as well. For more information, see this interview with Cloudera CSO Mike Olson.

What is a Raspberry Pi?

The Pi is a small, inexpensive ($39) ARM based computer. It is meant primarily as an educational tool.

Is Hadoop on the Pi practical?

Nope! Compute performance is horrendous.

Then why?

It's a great learning opportunity to work with Hadoop and multiple nodes. It's also cool to be able to put your "compute cluster" in a lunch box.
In reality though, this article is much more about setting up your first Hadoop compute cluster than it is about the Pi.

Could I use this guide to setup a non-Raspberry Pi based (Real) Hadoop Cluster?

Absolutely, please do. I'll make notes to that effect throughout.

I've been wanting to do this anyhow, let's get started!

Yeah, that's what I said.

Hardware Used

• 3x Raspberry Pis
• 1x 110v Power Splitter
• 1x 4 port 10/100 Switch
• 1x Powered 7 port USB hub
• 3x 2' Cat 5 cables
• 3x 2' USB "power" cables
• 3x Class 10 16GB SDHC Cards

Total cost: about $170 bucks. I picked it all up at my local Microcenter, including the Pis!

Pi Hadoop Cluster; caution: may be slow.

If you would like a a high performance Hadoop cluster just pick one of these up on the way home:

This will cost at least $59.95. Maybe more. Probably more.

Raspberry Pi Preparation

We'll do the master node by itself first so that this guide can be used for a single or multi-node setup. Note: On a non-Pi installation skip to Networking Preparation (though you may want to update your OS manually).

Initial Config

1. Download the "Raspbian wheezy" image from here and follow these directions to set up your first SD card. Soft-float isn't necessary.
2. Hook up a monitor and keyboard and start up the device by connecting up the USB power.
3. When the Raspberry Pi config tool launches, change the keyboard layout first. If you set passwords, etc. with the wrong KB layout you may have a hard time with that later.
4. Change the timezone, then the locale. (Language, etc.)
5. Change the default user password
6. Configure the Pi to disable the X environment on boot. (boot_behavior->straight to desktop->no)
7. Enable the SSH Daemon (Advanced-> A4 SSH) then exit the raspi-config tool
8. Reboot the Pi. (sudo reboot)

Update The OS

Note: It is assumed from henceforth you are connecting to the Pi or your OS via SSH. You can continue on the direct terminal if you like until we get to multiple nodes.

1. Log on to your Pi using the Raspberry user with the password you set earlier.
2. To pull the newest sources, execute sudo apt-get update
3. To pull upgrades for the OS and packages, execute sudo apt-get upgrade
4. Reboot the pi. (sudo reboot)

Split the Memory/Overclock

1. After logging back into the Pi, execute sudo raspi-config to bring up the Rasperry Pi config tool.
2. Select Option 8, "Advanced Options"
3. Select Option A3, "Memory Split"
4. When prompted for how much memory the GPU should have, enter the minimum, "16" and hit enter.
5. (If you would like to overclock) Select Option 7, "Overclock", hit "OK" to the warning, and select what speed you would like.
6. On the main menu, select "finish" and hit "Yes" to reboot.

Networking Preparation

Each Hadoop node must have a unique name and static IP.  I'll be following the Debian instructions since the Rasbian build is based on Debian. If you're running another distro follow the instructions for that. (Redhat, CentOS, Ubuntu)

First we need to change the machine hostname by editing /etc/hostname

sudo nano /etc/hostname

Note: I'll be using nano throughout the article; I'm sure some of you will be substituting vi instead. :)
Change the hostname to what you would like and save the file; I used "Node 1" throughout this document. If you're setting up the second node make sure to use a different hostname.

Now to change to a static IP by editing /etc/network/interfaces

sudo nano /etc/network/interfaces

Change the iface eth0 to a static IP by replacing "auto eth0" and/or "iface eth0 inet dhcp" with:

iface eth0 inet static
        address 192.168.1.40
        netmask 255.255.255.0
        gateway 192.168.1.1

Note: Substitute the correct address (IP), netmask, and gateway for your environment.

Make sure you can resolve DNS queries correctly by editing your /etc/resolv.conf:

sudo nano /etc/resolv.conf

Change the content to match below, substituting the correct information for your environment:

domain company.com
search company.com
nameserver 192.168.1.20
nameserver 192.168.1.30

domain=domain suffix for this machine
search=appends when FQDN not specified
nameserver=list DNS servers in order of precedence

We could restart networking etc, but for the sake of simplicity we'll bounce the box:

sudo reboot

Install Java

If you're going for performance you can install the SunJDK, but getting that to work requires a bit of extra effort. Since we're on the Pi and performance isn't our goal, I'll be using OpenJDK which installs easily. If you're installing on a "real" machine/VM, you may want to diverge a bit here and go for the real deal (Probably Java ver 6).
After connecting to the new IP via SSH again, execute:

sudo apt-get install openjdk-7-jdk

Ensure that 7 is the version you want. If not substitute the package accordingly.

Create and Config Hadoop User and Groups

Create the hadoop group:

sudo addgroup hadoop

Create hadoop user "hduser" and place it in the hadoop group; make sure you remember the password!

sudo adduser --ingroup hadoop hduser

Give hduser the ability to sudo:

sudo adduser hduser sudo

Now logout:

logout

Log back in as the newly created hduser. From hence forth everything will be executed as that user.

Setup SSH Certs

SSH is used between Hadoop nodes and services (even on the same node) to coordinate work. These SSH sessions will be run as the Hadoop user, "hduser" that we created earlier. We need to generate a keypair to use for EACH node. Let's do this one first:

Create the certs for use with ssh:

ssh-keygen -t rsa -P ""

When prompted, save the key to the default directory (will be /home/USERNAME/.ssh/id_rsa). If done correctly you will be shown the fingerprint and randomart image.

Copy the public key into the user's authorized keys store (~ represents the home directory of the current user, >> appends to that file to preserve any keys already there) 

cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys

SSH to localhost and add to the list of known hosts.

ssh localhost

When prompted, type "yes" to add to the list of known hosts.

Download/Install Hadoop!

Note: As of this writing, the newest version of the 1.x branch (which we're covering) is 1.2. (Ahem, 1.2.1, they're quick) You should check to see what the newest stable release is and change the download link below accordingly. Versions and mirrors can be found here.

Download to home dir:

cd ~
wget http://mirror.catn.com/pub/apache/hadoop/core/hadoop-1.2.0/hadoop-1.2.0.tar.gz

Unzip to the /usr/local dir:

sudo tar vxzf hadoop-1.2.0.tar.gz -C /usr/local

Change the dir name of the unzipped hadoop dir. Note that if your version # is different you'll need to adjust the command

cd /usr/local
sudo mv hadoop-1.2.0/ hadoop

Set the hadoop dir owner to be the hadoop user (hduser):

sudo chown -R hduser:hadoop hadoop

Configure the User Environment

Now we'll config the environment for the user (hduser) to run Hadoop. This assumes you're logged on as that user.
Change to the home dir and open the .bashrc file for editing:

cd ~
nano .bashrc

Add the following to the .bashrc file. It doesn't matter where: (note if you're using Sun Java on a real box you need to put in the right dir for JAVA_HOME)

export JAVA_HOME=/usr/lib/jvm/java-7-openjdk-armhf
export HADOOP_INSTALL=/usr/local/hadoop
export PATH=$PATH:$HADOOP_INSTALL/bin

Bounce the box (you could actually just log off/on, but what the hell eh?):

sudo reboot

Configure Hadoop!

(Finally!) It's time to setup Hadoop on the machine. With these steps we'll set up a single node and we'll discuss multiple node configuration afterward.
First let's ensure your Hadoop install is in place correctly. After logging in as hduser, execute:

hadoop version

you should see something like: (differs depending on version)

Hadoop 1.2.0
Subversion https://svn.apache.org/repos/asf/hadoop/common/branches/branch-1.2 -r 1479473
Compiled by hortonfo on Mon May  6 06:59:37 UTC 2013
From source with checksum 2e0dac51ede113c1f2ca8e7d82fb3405
This command was run using /usr/local/hadoop/hadoop-core-1.2.0.jar

Good. Now let's configure. First up we'll set up the environment file that defines some overall runtime parameters:

nano /usr/local/hadoop/conf/hadoop-env.sh

Add the following lines to set the Java Home parameter (specifies where Java is) and how much memory the Hadoop processes are allowed to use. There should be sample lines in your existing hadoop-env.sh that you can just un-comment and modify.
Note: If you're using a "real" machine rather than a Pi, change the heapsize to a number appropriate to your machine (physical mem - (OS + caching overhead)) and ensure you have the right directory for JAVA_HOME because it will likely be different. If you have a Raspberry Pi version "A" rather than "B", you'll heapsize will need to be much lower since the RAM is halved.

export JAVA_HOME=/usr/lib/jvm/java-7-openjdk-armhf
export HADOOP_HEAPSIZE=272

Now let's configure core-site.xml. This file defines critical operational parameters for a Hadoop site.
Note: You'll notice we're using "localhost" in the configuration. That will change when we go to multi-node, but we'll leave it as localhost for a demonstration of an exportable local only configuration.

nano /usr/local/hadoop/conf/core-site.xml

Add the following lines. If there is already information in the file make sure you respect XML format rules. The "description" field is not required, but I've included it to help this tutorial make sense.

<configuration>
  <property>
    <name>hadoop.tmp.dir</name>
    <value>/fs/hadoop/tmp</value>    <description>Sets the operating directory for Hadoop data.
    </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>
</configuration>

It's time to configure the mapred-site.xml. This file determines where the mapreduce job tracker(s) run. Again, we're using localhost for now and we'll change it when we go to multi-node in a bit.

nano /usr/local/hadoop/conf/mapred-site.xml

Add the following lines:

<configuration>
  <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>
</configuration>

Now on to hdfs-site.xml. This file configures the HDFS parameters.

nano /usr/local/hadoop/conf/hdfs-site.xml

Add the following lines:
Note: The dfs.replication value sets how many copies of any given file should exist in the configured HDFS site. We'll do 1 for now since there is only one node, but again when we configure multiple nodes we'll change this.

<configuration>
  <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>

Create the working directory and set permissions

sudo mkdir -p /fs/hadoop/tmp
sudo chown hduser:hadoop /fs/hadoop/tmp
sudo chmod 750 /fs/hadoop/tmp/

Format the working directory

/usr/local/hadoop/bin/hadoop namenode -format

Start Hadoop and Run Your First Job

Let's roll, this will be fun.
Start it up:

cd /usr/local/hadoop
bin/start-all.sh

Now let's make sure it's running successfully. First we'll use the jps command (Java PS) to determine what Java processes are running:

jps

You should see the following processes: (ignore the number in front, that's a process ID)

4863 Jps
4003 SecondaryNameNode
4192 TaskTracker
3893 DataNode
3787 NameNode
4079 JobTracker

If there are any missing processes, you'll need to review logs. By default, logs are located in:

/usr/local/hadoop/logs

There are two types of log files: .out and .log. .out files detail process information while .log files are the logging output from the process. Generally .log files are used for troubleshooting. The logfile name standard is:

hadoop-(username)-(processtype)-(machinename).log

i.e.

hadoop-hduser-jobtracker-node1.log

In this, like many other articles, we'll use the included wordcount example. For the wordcount example you will need plain text large enough to be interesting. For this purpose, you can download plain text books from Project Gutenberg.

After downloading 1 or more books (make sure you selected "Plain Text UTF-8" format) copy them to the local filesystem on your Hadoop node using SSH (via SCP or similar). In my example I've copied the books to /tmp/books.

Now let's copy the books onto the HDFS filesystem by using the Hadoop dfs command.

cd /usr/local/hadoop
bin/hadoop dfs -copyFromLocal /tmp/books /fs/hduser/books

After copying in the book(s), execute the wordcount example:

bin/hadoop jar hadoop*examples*.jar wordcount /fs/hduser/books /fs/hduser/books-output

You'll see the job run; it may take awhile... remember these Pis don't perform all that well. Upon completion you should see something like this:

13/06/17 15:46:54 INFO mapred.JobClient: Job complete: job_201306170244_0001
13/06/17 15:46:55 INFO mapred.JobClient: Counters: 29
13/06/17 15:46:55 INFO mapred.JobClient:   Job Counters
13/06/17 15:46:55 INFO mapred.JobClient:     Launched reduce tasks=1
13/06/17 15:46:55 INFO mapred.JobClient:     SLOTS_MILLIS_MAPS=566320
13/06/17 15:46:55 INFO mapred.JobClient:     Total time spent by all reduces waiting after reserving slots (ms)=0
13/06/17 15:46:55 INFO mapred.JobClient:     Total time spent by all maps waiting after reserving slots (ms)=0
13/06/17 15:46:55 INFO mapred.JobClient:     Launched map tasks=1
13/06/17 15:46:55 INFO mapred.JobClient:     Data-local map tasks=1
13/06/17 15:46:55 INFO mapred.JobClient:     SLOTS_MILLIS_REDUCES=112626
13/06/17 15:46:55 INFO mapred.JobClient:   File Output Format Counters
13/06/17 15:46:55 INFO mapred.JobClient:     Bytes Written=229278
...

The results from the run, if you're curious, should be in the last part of the command you executed earlier. (/fs/hduser/books-output in our case) Update: to check the output use the hadoop dfs command a la:

hduser@rasdoop1 /usr/local/hadoop $ bin/hadoop dfs -ls /fs/hduser/books-output
Found 3 items
-rw-r--r--   3 hduser supergroup          0 2015-05-05 05:00 /fs/hduser/books-output/_SUCCESS
drwxr-xr-x   - hduser supergroup          0 2015-05-05 04:35 /fs/hduser/books-output/_logs
-rw-r--r--   3 hduser supergroup     926451 2015-05-05 04:58 /fs/hduser/books-output/part-r-00000

hduser@rasdoop1 /usr/local/hadoop $bin/hadoop dfs -cat /fs/hduser/books-output/part-r-00000
...

Congrats! You just ran your first Hadoop job, welcome to the world of (tiny)big data!

Multiple Nodes

Hadoop has been successfully run on thousands of nodes; this is where we derive the power of the platform. The first step to getting to many nodes is getting to 2, so let's do that.

Set up the Secondary Pi

I'll cover cloning a node for the third Pi, so for this one we'll assume you've set up another Pi the same as the first using the instructions above. After that, we just need make appropriate changes to the configuration files. Important Note: I'll be referring to the nodes as Node 1 and Node 2 and making the assumption that you have named them that. Feel free to use different names, just make sure you substitute them in below. Name resolution will be an issue; we'll touch on that below.

Node 1 will run:

• NameNode
• Secondary NameNode (In a large production cluster this would be somewhere else)
• DataNode
• JobTracker (In a large production cluster this would be somewhere else)
• TaskTracker

Node 2 will run:

• DataNode
• TaskTracker

Normally this is where I planned on writing up an overview of the different pieces of Hadoop, but upon researching I found an article that exceeded what I planned to write it by such a magnitude I figured why bother, I'll just link it here. Excellent post by Brad Hedlund, be sure to check it out.
On Node 1, edit masters

nano /usr/local/hadoop/conf/masters

remove "localhost" and add the first node FQDN and save the file:

Node1.domain.ext

Note that conf/masters does NOT determine which node holds "master" roles, i.e. NameNode, JobTracker, etc. It only defines which node will attempt to contact the nodes in slaves (below) to initiate start-up.
On Node 1, edit slaves

nano /usr/local/hadoop/conf/slaves

remove "localhost" add the internal FQDN of all nodes in the cluster and save the file:

Node1.domain.ext
Node2.domain.ext

On ALL (both) cluster members edit core-site.xml

nano /usr/local/hadoop/conf/core-site.xml

and change the fs.default.name value to reflect the location of the NameNode:

<property>
 <name>fs.default.name</name>
 <value>hdfs://node1:54310</value>
</property>

On ALL (both) cluster members edit mapred-site.xml

nano /usr/local/hadoop/conf/mapred-site.xml

and change the mapred.job.tracker value to reflect the location of the JobTracker:

<property>
 <name>mapred.job.tracker</name>
 <value>node1:54311</value>
</property>

On ALL (both) cluster members edit hdfs-site.xml

nano /usr/local/hadoop/conf/hdfs-site.xml

and change the dfs.replication value to reflect the location of the JobTracker:

<property>
 <name>dfs.replication</name>
 <value>2</value>
</property>

The dfs.replication value determines how many copies of any given piece of data will exist in the HDFS site. By default this is set to 3 which is optimal for many small->medium clusters. In this case we set it to the number of nodes we'll have right now, 2.

Now we need to ensure the master node can talk to the slave node(s) by adding the master key to the authorized keys file on each node (just 1 for now)
On Node 1, cat your public key and copy it to the clipboard or some other medium for transfer to the slave node

cat ~/.ssh/id_rsa.pub

On Node 2

nano ~/.ssh/authorized_keys

and paste in the key from the master and save the file to allow it to take commands.

Name Lookup

The Hadoop nodes should be able to resolve the names of the nodes in the cluster. To accomplish this we have two options: the right way and the quick way.

The right way: DNS

The best way to enable name lookup works correctly is by adding the nodes and their IPs to your internal DNS. Not only does this facilitate standard Hadoop operation, but it makes it possible to browse around the Hadoop status web pages (more on that below) from a non-node member, which I suspect you'll want to do. Those sites are automatically created by Hadoop all the hyperlinks use the node DNS names, so you need to be able to resolve those names from a "client" machine. This can scale too; with the right tool set it's very easy to automate DNS updates when spinning up a node.

To do this option, add your Hadoop hostnames to the DNS zone they reside in.

The quick way: HOSTS

If you don't have access to update your internal DNS zone, you'll need to use the hosts files on the nodes. This will allow the nodes to talk to each other via name. There are scenarios where you may want to automate the updating of node based HOSTS files for performance or fault tolerance reasons as well, but I won't go into that here.

To use this option do the following:

On ALL (both) cluster members edit /etc/hosts

nano /etc/hosts

and add each of your nodes with the appropriate IP addresses (mine are only examples)

192.168.1.40    node1
192.168.1.41    node2

Now that the configuration is done we need to format the HDFS filesystem. Note: THIS IS DESTRUCTIVE. Anything currently on your single node Hadoop system will be deleted.
On Node 1

bin/hadoop namenode -format

When that is done, let's start the cluster:

cd /usr/local/hadoop
bin/start-all.sh

Just as before, use the jps command to check processes, but this time on both nodes.
On Node 1

jps

You should see the following processes: (ignore the number in front, that's a process ID)

4863 Jps
4003 SecondaryNameNode
4192 TaskTracker
3893 DataNode
3787 NameNode
4079 JobTracker

On Node 2

jps

You should see the following processes: (ignore the number in front, that's a process ID)

6365 TaskTracker
7248 Jps
6279 DataNode

If there are problems review the logs. (for detailed location see reference above) Note that by default each node contains its own logs.

Now let's run the wordcount example in a cluster. To facilitate this you'll need enough data to split, so when downloading UTF-8 books from Project Gutenberg get at least 6 very long books (that should do with default settings).

Copy them to the local filesystem on your master Hadoop node using SSH (via SCP or similar). In my example I've copied the books to /tmp/books.

Now let's copy the books onto the HDFS filesystem by using the Hadoop dfs command. This will automatically replicate data to all nodes.

cd /usr/local/hadoop
bin/hadoop dfs -copyFromLocal /tmp/books /fs/hduser/books

After copying in the book(s) wait a couple minutes for all blocks to replicate (more on monitoring below) then execute the wordcount example:

bin/hadoop jar hadoop*examples*.jar wordcount /fs/hduser/books /fs/hduser/books-output

Monitoring HDFS/Jobs

Hadoop includes a great set of management web sites that will allow you to monitor jobs, check logfiles, browse the filesystem, etc. Let's take a moment to examine the three sites.

JobTracker
By default, the job tracker site can be found at http://(jobtrackernodename.domain.ext):50030/jobtracker.jsp ; i.e. http://node1.company.com:50030/jobtracker.jsp

On the Job Tracker you can see information regarding the cluster, running map and reduce tasks, node status, running and completed jobs, and you can also drill into specific node and task status.

DFSHealth
By default, the DFSHealth site can be found at http://(NameNodename.domain.ext):50070/dfshealth.jsp ; i.e. http://node1.company.com:50070/dfshealth.jsp

The DFSHealth site allows you to browse the HDFS system, view nodes, look at space consumption, and check NameNode logs.

TaskTracker
By default, the task tracker site can be found at http://(nodename.domain.ext):50060/tasktracker.jsp ; i.e. http://node2.company.com:50060/tasktracker.jsp

The Task Tracker site runs on each node that can run a task to provide more detailed information about what task is running on that node at the time.

Cloning a Node and Adding it to the Cluster

Now we'll explore how to get our third node into the cluster the quickest way possible: cloning the drive. This will cover, at a high level, the SD card cloning process for the Pi. If you're using a real machine replace the drive cloning steps with your favorite (*cough*Clonzilla*cough*) cloning software, or copy the virtual drive if you are using VMs.

You should always clone a slave node unless you intend on making a new cluster. To this end, do the following:

1. Shut down Node 2 (sudo init 0) and remove the SD card.
2. Insert the SD card into a PC to so we can clone it; I'm using a Windows 7 machine so if you're using Linux we'll diverge here. (I'll meet you at the pass!)
3. Capture an image to your hdd using your favorite SDcard imaging software. I use either Roadkill's Disk Image or dd for windows. 
4. Remove the Node 2 SD card and place it back into Node 2. Do not power up yet.
5. Insert a new SD card of the same size to your imaging PC.
6. Delete any partitions on the card to ensure error-free imaging. I use Diskpart , Select Disk x , Clean , exit where "x" is the disk number. Use list disk to find it... don't screw up or you'll wipe out your HDD!
7. Image the new SD card with the image from Node 2 and remove it from the PC when complete.





8. Insert the newly cloned SD card to Node 3. Power up that node and leave Node 2 off for now or the IPs will conflict.

Log into the new node (same IP as the cloned node) via ssh with the hduser (Hadoop user) account and perform the following tasks:

Change the hostname:

sudo nano /etc/hostname

For this example change "Node2" to "Node3" and save the file.

Change the IP address:

sudo nano /etc/network/interfaces

For this example change "192.168.1.41" to "192.168.1.42" and save the file. Switch to your own IP address range if necessary.

If you're using HOSTS for name resolution, add Node 3 with the proper IP address to the hosts file

sudo nano /etc/hosts

Repeat this on the master node and for the sake of completeness the other node(s) should probably be updated as well. In a large scale deployment this would all be automated.
If you are using DNS, make sure to add the Node3 entry to your zone.

Reboot Node 3 to enact the IP and hostname changes

sudo reboot

Power up Node 2 again at this time if you had it powered down. Log back into Node 3 (now with the new IP!) with the hduser account via SSH.

Generate a new key:

ssh-keygen -t rsa -P ""

Copy the key to its own trusted store:

cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys

If you aren't already, log into Node1 (Master) as hduser via SSH and do the following:
Add Node 3 to the slaves list

nano /usr/local/hadoop/conf/slaves

SSH to Node 3 to add it to known hosts

ssh rasdoop3

When prompted re: continue connecting type yes

An ALL nodes edit hdfs-site.xml

nano /usr/local/hadoop/conf/hdfs-site.xml

Change it to replicate to 3 nodes. Note if you add more nodes to the cluster after this you would most likely NOT increase this above 3. It's not 1:1 that we're going for, it's the number of replicas we want in the entire cluster. Once you're running a cluster with > 3 nodes you'll generally know if you want dfs.replication set to more than 3.

<configuration>
  <property>
    <name>dfs.replication</name>
    <value>3</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>

Now we need to format the filesystem. It is possible, but slightly more complicated, to clone/add a node without re-formatting the whole HDFS cluster, but that's a topic for another blog post. Since we have virtually no data here, we'll address all concerns by wiping clean:
On Node 1:

cd /usr/local/hadoop
bin/hadoop namenode -format

Now we should be able to start up all three nodes. Let's give it a shot!
On Node 1:

bin/start-all.sh

Note: If you have issues starting up the Datanodes you may need to delete the sub-directories under the directory listed in core-site.xml as "hadoop.tmp.dir" and then re-format again (see above). 

As for startup troubleshooting and running the wordcount example, copy out the instructions above after the initial cluster setup. There should be nothing different from running with two nodes save the fact that you may need more books to get it working on all 3 nodes.

Postmortem/Additional Reading

You did it! You've now got the experience of setting up a Hadoop cluster under your belt. There's a ton of directions to go from here; this relatively new tech is changing the way a lot of companies view data and business intelligence.

There is so much great community content out there; here's just a small list of the references I used and some additional reading.

References:

Raspberry Pi forums: Hadoop + HDFS + MR on Pi cluster - works!
Michael G. Noll: Running Hadoop on Ubuntu Linux Single-Node Cluster
Michael G. Noll: Running Hadoop on Ubuntu Linux Multi-Note Cluster
Hadoop Wiki: Getting Started with Hadoop
Hadoop Wiki: Wordcount Example
University of Glasgow's Raspberry Pi Hadoop Project
Hadoop Wiki: Cluster Setup
Hadoop Wiki: Java Versions
All Things Hadoop: Tips, Tricks and Pointers When Setting Up..
Hadoop Wiki: FAQ
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