A typical hard drive is 2 terabytes (2 TB) in size. It takes about five hours to read all the data on a single disk. If we could split the data over 200 hard drives, all 2 TB could be read in about 5 minutes. This is possible because each drive contains only 1/200th of the data, and thus needs to read much less data to finish its part of the task. It is the same principle that is involved with asking 1 person with a single gallon bucket to move a 2000 gallons of water vs. asking 200 people with 200 one gallon buckets to move 2000 gallons of water. If a trip takes one minute, then one person can do the job in 2000 minutes or 33 hours. 200 people, on the other hand, can do the job in 10 minutes. This is the power of distributed, parallel computing, and it is the task undertaken by Hadoop.
One of the goals of Hadoop is to make it possible to do distributed data over multiple drives bought at a typical computer store. We are not talking about some huge mainframe, but about off the shelf drives you might buy at Amazon, Newegg or at Fry's Electronics.
Hadoop is designed to solve problems associated with huge datasets. If you a site gets 100 million visitors each month, each of which leaves a trail of data one megabyte in size, then the site must track 100 million megabytes a month, or over a billion megabytes a year.
Hadoop was partially developed at Google, and they use it to solve many of their problems.
If we are going to distribute data over 100 hard drives, then we need to find a way to reassemble it, or to combine it with other bits of data quickly and easily. The part of Hadoop called MapReduce addresses this issue. Map/Reduce assembles the bits of data across the various hard drives, but it does more than that. It allows developers to create applications that run concurrently across all the nodes of the system, with one set of tasks performed on one machine, another on a second machine and so on. That is the Map part of the equation. Then the reduce part of the equation takes all these distributed pieces of work and assembles the results.
Suppose ten million visitors
The part of Hadoop called HDFS deals with the problem of hardware failure. If we are going to spread out data over 100 hard drives, then the chance of loosing some of it due to hardware failure is higher than it might be on a single machine. The goal is to duplicate the data, so a loss of one hard drive will not destroy the entire terabyte of data.
Most of the rest of this document describes one possible way to set up Hadoop with a minimal configuration for testing Hadoop on a single machine. Hadoop normally runs in multi-user mode, but here we are going to use only a single machine. This kind of setup provides a good way for you to learn the basics of hadoop before you begin deployment across multiple machines.
I have created a set of scripts that can help step you through the Hadoop install. They are stored with a number of other files and programs in the Elvenware repository. Instructions on setting up Mercurial are now on the Mercurial page:
Setting up Java can be a bit of a problem. It is best to get it out of the way first. I have tested with both Sun JDK6 and Sun JDK7, and both seem to work. Below I focus mosting on JDK6, but here is a post on installing JDK7.
NOTE: I have recently come to rely on the simple method found here:
By default, you do not have the right version of Java on Ubuntu Linux. The following commands should fix the situation:
wget https://raw.github.com/flexiondotorg/oab-java6/master/oab-java6.sh -O oab-java6.sh chmod +x oab-java6.sh sudo ./oab-java6.sh
After the endless process outlined above finally terminates, do this:
sudo apt-get install sun-java6-jdk sudo update-alternatives --config java
More information on the scripts shown above is available here:
This should work to setup Java Sun on Mint Linux:
sudo add-apt-repository "deb http://archive.canonical.com/ lucid partner" sudo apt-get update sudo apt-get install sun-java6-jdk sudo update-java-alternatives -s java-6-sun
On one of my system, a day or more after completing the above steps, when I ran sudo apt-get update I got the following error:
W: GPG error: http://ppa.launchpad.net oneiric Release: The following signatures couldn't be verified because the public key is not available: NO_PUBKEY 2EA8F35793D8809A
To elimate the error, I used the menu to go to System Settings | Software Sources | Other Software and unchecked** the references to flexiondotorg. Then I could run **sudo apt-get update without error.
It is optional as to whether or not to create a user called Hadoop, but I strongly recommend that you do so. This may have security benefits, but we like it primarily because it keeps all the Hadoop related configuration in one place. In the process a custom .bashrc file is set up just for the Hadoop user, which means that we can configure the environment just for him withou muddying up the configuration your primary user account, which can be nice.
This code create a new group called hadoop, adds a user to it, adds the user to the admins group and then switches you over from being yourself to being hadooper. There will be a number of prompts you need to respond to on the way.
sudo addgroup hadoop sudo adduser --ingroup hadoop hadooper sudo usermod -a -G admin hadooper su -l hadooper
I have created a script called CreateUser.sh that performs these actions automatically. See the end of this documentfor more details.\
For Hadoop to work, you have to be able to use SSH to communicate between two machines. In some cases, you will even need to be able to SSH into your current machine. There is generally no real world use for using SSH to communicate with your own machine, but sometimes it can be part of a testing process. In particular, if you are setting up HADOOP to run on a single node for testing or educational purposes, then you will need to SSH into your current machine. I cover these aspects of SSH in a separate file:
Read the appropriate documentation on SSH and then return to this document after you have learned how to use SSH to start an SSH session on your localhost.
Assuming you have completed the above steps, you are now ready to install Hadoop. This can be accomplished by running the addNano.sh script discussed at the end of this document. What follows is a description of that script does.
Here is the download page for Hadoop:
You can also try to download Hadoop by issuing the following command at the Linux command prompt:
The following script downloads and extracts hadoop:
wget http://apache.cs.utah.edu//hadoop/common/hadoop-1.0.1/hadoop-1.0.1.tar.gz tar xzf hadoop-1.0.1 sudo mv hadoop-1.0.1.tar.gz /usr/local/hadoop sudo chown -R hadooper:hadoop /usr/local/hadoop
The scripts starts by downloading a file that is both zipped (gz) and tarred (.tar):
Take a moment to examine this file name, and particular the part at the end. The tar extensions means that many files have been wrapped together in one big file called hadoop-1.0.1-bin.tar. Then gzip compressess that file into a file called hadoop-1.0.1.tar.gz. The following command reverses the process by unzipping the tar file and then extracting (untarring) the contents:
tar xzf hadoop-1.0.1-bin.tar.gz
After the command is run you should see a folder called hadoop-1.0.1. You can usually tell a folder from a file because it is shown in light blue, and because its permissions begin with drwxr-etc.
The script then moves (mv) the folder to /usr/local/hadoop. The command does two things: it moves the folder to a new location, and then renames it by removing the version number. When you are done, you should be able to see the contents of the folder:
charlie@MintBox ~/Downloads $ ls /usr/local/hadoop/ bin hadoop-client-1.0.1.jar ivy.xml sbin build.xml hadoop-core-1.0.1.jar lib share c++ hadoop-examples-1.0.1.jar libexec src CHANGES.txt hadoop-minicluster-1.0.1.jar LICENSE.txt test.sh conf hadoop-test-1.0.1.jar logs webapps contrib hadoop-tools-1.0.1.jar NOTICE.txt hadoop-ant-1.0.1.jar ivy README.txt charlie@MintBox ~/Downloads $
It is now time to set up the environment. There are two useful, but optional, environment variables that we can set up, plus we must set up JAVA_HOME. To set up these ennvironment variables, we could type the following each time we become Hadooper:
The above is preferred, but alternatively, you can explicitly name the version you want to use:
Rather than trying to configure these items by hand each time we become Hadooper, it is better to put them in a file called .bashrc. To oversimplify a somewhat complex subject, I'll say only that the .bashrc file allows us to configure the environment automatically each time we log in to the bash shell. This occurs because .bashrc is run once, just as we are signing in as hadooper. You will recall that to sign in as hadooper, we usually write something like this: su - hadooper.
NOTE: Files that begin with a period are "invisible" or "hidden" by default. If we type ls*to get a listing of a directory, we don't see them. To make them "visible," we should type* ls -a:
$ ls andelf bar Downloads examples.desktop $ ls -a . bar .bashrc examples.desktop .sudo_as_admin_successful .. .bash_history .cache .profile andelf .bash_logout Downloads .ssh
Here is the code we want to put in the .bashrc file:
# Set JAVA_HOME: export JAVA_HOME=/usr/lib/jvm/default-java
The first line is a comment, the second sets a variable in the environment of our OS. Our goal, then, is to create a file called .bashrc in our HOME directory, which will be /home/hadooper. Type cd followed by enter with no parameters to move to your home directory, and then to check which directory you are in right now, type pwd:
hadooper@WesternSeas:~$/bin cd hadooper@WesternSeas:~$ pwd /home/hadooper
As you can see, we are now in hadooper's home directory, which is /home/hadooper.That's just where we want to be.
The home directory is where you want to create your .bashrc file. To create it, type:
You may find that .bashrc already exists, and already has the right entries in it, as one of my scripts takes care of that for you. But if you want to do it all by hand, then you should enter the following code into .bashrc, then type Ctrl-O plus enter to save and Ctrl-X to exit:
# Set JAVA_HOME: export JAVA_HOME=/usr/lib/jvm/default-java
At this stage we need to run the .bashrc file so that the environment will be properly set up. The best way to run the file is to temporarilly stop being hadooper, and then sign back in again. To do this, type exit once or twice, until you become the regular user again, that is, until you exit the hadooper shell. Then log back in as hadooper:
su - hadooper
When you log back in your .bashrc file will run automatically, as indeed it will each time you log in from this point forward. That's the whole point of .bashrc, it is a file that is run when you log into the bash shell; you place in that file any code that you want to run at the beginning of a user session. The type of code we placed in .bashrc is typical, in that it is designed to set up the environment. In particular, when your .bashrc file runs, JAVA_HOME variables in the environment. To check this, type echo $JAVA_HOME, etc:
echo $JAVA_HOME /usr/lib/jvm/java-6-sun echo $PATH /usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games:/usr/local/hadoop/bin
As you can see, all the environment variables that we wanted to configure are now set up correctly.
Alternatively, the following can be used to test the status of an environment variable:
We are ready to move on to the next step.\
There are several files found in the confdirectory that you need to configure:
And here is what goes in each file:
fs.default.name hdfs://localhost/ dfs.replication 1 mapred.job.tracker localhost:8021
Also be sure you set up the tmp directory and format the namenode:
sudo mkdir -p /app/hadoop/tmp sudo chown -R hadooper:hadoop /app/hadoop/tmp /usr/local/hadoop/bin/hadoop namenode -format
This section under construction....
To start Hadoop, run the scripts in the bin directory:
Assuming you have navigated to the bin directory, the command to start Hadoop looks liks this:
To see if Hadoop started correctly, use the jps command. The output should look something like this:
$ jps 3216 DataNode 3524 JobTracker 3917 Jps 3759 TaskTracker 2993 NameNode 3443 SecondaryNameNode
There are a set of log files in the hadooper logs directory. To switch to the directory, type something like:
Looking through those log files can help you find the errors that may or may not occur.
Here is a command to view a log file:
There are several different log files in the log directory, you can find them by using thels commind to****look for the files that have .log as an extension:
$ ls *.log hadoop-hadooper-datanode-WesternSeas-VirtualBox.log hadoop-hadooper-jobtracker-WesternSeas-VirtualBox.log hadoop-hadooper-namenode-WesternSeas-VirtualBox.log hadoop-hadooper-secondarynamenode-WesternSeas-VirtualBox.log hadoop-hadooper-tasktracker-WesternSeas-VirtualBox.log
And here is the command to stop Hadoop:
If you shut down all the machines in the cluster, it is a good idea to completely restart.
This script, called MasterCleanAndRestart.sh is for the master machine:
echo "Stopping Hadoop" bash /usr/local/hadoop/bin/stop-mapred.sh bash /usr/local/hadoop/bin/stop-dfs.sh echo "Refreshing and reformatting file system" bash CleanAndRestart.sh echo "Run ClearAndRestart on slave machines" read -p "Press [Enter] key to re-start hadoop..." bash /usr/local/hadoop/bin/start-dfs.sh bash /usr/local/hadoop/bin/start-mapred.sh
This script called CleanAndRestart.sh is for the client, or slave machines:
# Any time you shut down Hadoop altogether, and particularly if you # shut down the machine it is on, you really ought to clean out the out the # temp files and reformat the drive for the distributed file system, # which means you lose all your data. sudo rm -r /app/hadoop/tmp/ sudo mkdir -p /app/hadoop/tmp sudo chown -R hadooper:hadoop /app/hadoop/tmp /usr/local/hadoop/bin/hadoop namenode -format
Begin by starting to Linux VMs on the same machine. In this example, let's pretend that your first VM is named BoxPrimary, and the second VM is named Box02. In giving them these names, we are implicitly deciding that BoxPrimary will be our primary, or master, hadoop server. It will be the job-tracker and the name-node. All the other machines we add to our hadoop network will face toward this machine. You might have assignment your machines different names, but that is an implementation detail. The point is that you have two Linux VMs running, and that you have decided one of them will be the primary hadoop server.
Edit the /etc/hosts file in both virtual machines so that the two machines can ping one another:
The actual names and IP addresses you use for the these two boxes will likely depend on the names you originally gave to your two Linux VMs, as well as the IP addresses assigned to them by the DHCP server. Remember that you can type ipconfig to find out the current IP address of your server. Furthermore, you need not use the same name in the hosts file as you gave to your VM when you created it, though it might be less confusing if you did indeed give them the same name. To test the configuration, go to the command line of Box02 and type ping BoxPrimary.Then go to the command line of BoxPrimary and type ping Box02. When you are done, press Ctrl-Cto end the ping session:
$ ping Box02 PING Box02 (192.168.56.102) 56(84) bytes of data. 64 bytes from Box02 (192.168.56.123): icmp_req=1 ttl=64 time=2.55 ms 64 bytes from Box02 (192.168.56.123): icmp_req=2 ttl=64 time=0.667 ms
Now become Hadooper and copy the SSH public key from the first machine to the second
ssh-copy-id -i $HOME/.ssh/id_rsa.pub hadooper@Box02
When I entered the code shown above, the result looked like this:
ssh-copy-id -i $HOME/.ssh/id_rsa.pub hadooper@Box02 The authenticity of host 'Box02 (192.168.0.124)' can't be established. ECDSA key fingerprint is f9:6e:01:0e:34:d7:3b:6c:3a:bd:78:92:69:21:90:70. Are you sure you want to continue connecting (yes/no)? yes Warning: Permanently added 'Box02,192.168.0.124' (ECDSA) to the list of known hosts. hadooper@Box02's password: Now try logging into the machine, with "ssh 'hadooper@westernseas'", and check in: ~/.ssh/authorized_keys to make sure we haven't added extra keys that you weren't expecting.
Following the hint displayed above, I tried to SSH into Box02:
$ ssh hadooper@Box02 Welcome to Ubuntu 11.10 (GNU/Linux 3.0.0-16-generic i686) * Documentation: https://help.ubuntu.com/ 0 packages can be updated. 0 updates are security updates. Last login: Sun Mar 4 13:20:13 2012 from localhost
The key thing to notice in the code shown above is that that I was never prompted for a password. That is the way things should be when an SSH public/private key pair is set up correctly.
Once you have setup SSH so that you can pop over to Box02 without being prompted for a password, you want to do the same thing in reverse; that is, you want to set things up so that you can ssh from Box02 to BoxPrimary. To begin, log into Box02, and then run the same command you ran in BoxPrimary:
ssh-copy-id -i $HOME/.ssh/id_rsa.pub hadooper@BoxPrimary
Now check to make sure you can ssh into BoxPrimary without entering a password:
You need to edit core-site.xml and madred-site.xml and change the URL from localhost to BoxPrimary in both BoxPrimary and Box02:
Make this change also in mapred-site.xml:
<property> <name>mapred.job.tracker</name> <value>BoxPrimary:54311</value> </property>
In BoxPrimary, go ahead and start the server
$ jps 10581 Jps 10581 Jps 10510 SecondaryNam10100 NameNode
jps 6281 J5956 DataNode
If you have trouble, check the logs in Box02:
Errors can include something like this: INFO org.apache.hadoop.ipc.Client: Retrying connect to server:Errors of that type probably means there is something wrong in the /etc/hosts file either on PrimaryBox or Box02, or perhaps in both places.
If everything is working, then http://BoxPrimary:50070/dfshealth.jsp will show at least one live node
This script is called RunApp.sh.It is designed to run one of the example applications that ship with hadoop. It is probably a good idea to completely restart the system between tests, as described above in the Restart section.
"/usr/local/hadoop/bin/hadoop" echo $HADOOP DFS=$HADOOP" dfs" JAR=$HADOOP" jar" GUTENBERG="/user/hadooper/gutenberg" OUTPUT=$GUTENBERG"-output" echo $DFS $DFS -rmr $GUTENBERG $DFS -rmr $OUTPUT $DFS -copyFromLocal $HOME/gutenberg $GUTENBERG $DFS -ls /user/hadooper $DFS -ls $GUTENBERG $JAR /usr/local/hadoop/hadoop-examples-1.0.1.jar wordcount $GUTENBERG $OUTPUT read -p "Press [Enter] key to see the results" /usr/local/hadoop/bin/hadoop dfs -cat /user/hadooper/gutenberg-o/usr/local/hadoop/bin/hadoop dfs -cat /user/hadooper/gutenberg-output/part-r-00000HADOOP=
If you download the mercurial sources linked to Elvenware you will find a number of the scripts shown on this page in the Python/CreateHadoopFiles directory. Here is how to use them.
When you download the files from elvenware repository, it is often helpful to copy the hadoop files into a folder called bin:
If you are already in bin, then you can use this command to copy the files from the downloaded repsoitory into your current directory, so long as you have the most recent version of the repository in /home/hadooper/andelf:
cp /home/hadooper/andelf/Python/CreateHadoopFiles/src/* .
Then you need to run one or more of the scripts. To prepare them, first make them executable:
chmod +x *.sh
You can now run the scripts by typing ./SomeScript.sh. For instance, you might do this to execute the script called GetBooks:
After running the script, you should find that James Joyce's Ulysses and other books have been downloaded and stored in the following folder:
Considered as a whole, the Hadooper scripts from the repository are designed to to perform certain key steps, such as downloading and installing Hadoop, or setting up SSH, or running the application.
Here is a good order for running the scripts:
After creating the users will automatically be running as the user Hadoop. Now:
There is some manual configuration you will need to do with the host files and copying the .ssh keys back to the primary or master box. After that, you are essentially ready to run. I would go through this process after you bring up all the nodes, and before you first run an application:
As long as you don't shut down the machines, you shouldn't have to bother with running ./MasterCleanAndRestart.sh or./CleanAndRestart.sh inbetween test runs The problems I've had seem to occur when the file systems get out of sync on the various data nodes. Once that happens, I think it is simplest to start over, but below in the Linkx section you can see a note about namespaceids.
The other problem:: I have had the whole process of running the application stop during the Reduce phase at some point such as 22%. There is a very long pause, maybe five minutes, and then you get an error about too many something or others being created, and then the process finishes normally. When I encountered this error, it meant that I had one or or more of my hosts files incorrectly configured, by which I mean I simply had a type in the name of one of the hosts, or had left out a host, or had typed in an IP address incorrectly. Nothing tricky, but just wrong.