Upload page content

You can upload content for the page named below. If you change the page name, you can also upload content for another page. If the page name is empty, we derive the page name from the file name.

File to load page content from
Page name

Locked History Actions


Infolab Hadoop Cluster

The anatomy of the cluster

  • Name node: ilhadoop1
  • Yarn Resource Manager: ilhadoop1
  • Data nodes:
    • ilh01 - ilh40
    • 320 TB total capacity
  • Yarn Nodes:
    • ilh01 - ilh40
    • 320 total cores
    • 2560 GB RAM


You will need a CSID to use the Hadoop cluster.

You can submit your Hadoop jobs from the following nodes:



Hadoop clusters are usually running their own file system called Hadoop Distributed Filesystem (HDFS). If you run a map/reduce job on the data that you have stored in the HDFS the Yarn Resource Manager will make sure that your job runs on the nodes that have your data stored locally in order to minimize network traffic.

Your HDFS home

Ask your admin to create an HDFS home directory for you. Your HDFS home is going to be:


Let's try and list the contents of that directory:

hadoop fs -ls /user/tommy

Our home directory is empty (even though it was used as a staging area in the example above) so there is no output from the command. Let's try listing all the user homes just to see who is using the HDFS:

tommy@madmax5: ~$ hadoop fs -ls /user/
Found 3 items
drwx------   - hdfs       hadoop          0 2015-10-12 18:54 /user/hdfs
drwxr-xr-x   - tommy     users           0 2015-06-12 15:20 /user/tommy
drwxr-xr-x   - west1      users           0 2015-10-12 22:05 /user/west1

Getting data into HDFS

We have a local directory that contains some XML data that we want to run a mapred job on. Let's create a subdirectory under our HDFS home that will hold those files:

hadoop fs -mkdir xmldata

Please note that the xmldata directory will be created as a subdirectory of /user/your_csid as we have not specified an absolute path. Let's list the content of our home directory to verify it was created.

~/$ fs -ls /user/tommy
Found 1 items
drwxr-xr-x   - tommy users          0 2012-10-17 14:21 /user/tommy/xmldata

If the XML files are in the ~/xmldata directory on the local harddisk, we copy them to the HDFS with the following command:

hadoop fs -put ~/xmldata/* /user/snap/xmldata

You already know how to list the contents of a HDFS directory so we'll leave checking on the files to you.

Getting data out of HDFS

If we want to copy a file from the HDFS back to our "regular" filesystem, we need to run the following command:

hadoop fs -get hdfs_source_filename local_destination_filename

So if we have a file called result.txt in our HDFS home directory, we can copy it to our current directory with the following command:

hadoop fs -get results.txt ./

Please note that whenever we use a HDFS path that does not start with a forward slash / the HDFS command automatically add a prefix /user/your_csid.

Deleting a file

If you want to delete a file from the HDFS you can use the following command:

hadoop fs -rm filename

If we want to delete a directory we can use the following command:

hadoop fs -rm -r directory

HDFS commands

By now you have probably noticed that HDFS command are very similar to the commands that we use on our "regular" file systems. We just have to start every command with the prefix hadoop fs then type in a dash - and the name of the command we use for "regular" files. So instead of chmod 777 somefile we type in hadoop fs -chown 777 somefile. We can get a list of available commands by typing hadoop fs.


Here are a few example map/reduce jobs that you can try running.

A life of Pi

hadoop jar /usr/share/hadoop/hadoop-examples-1.0.3.jar pi 10 1000000

The above example should output something that ends like this:

Job Finished in 33.276 seconds
Estimated value of Pi is 3.14158440000000000000

If running the example above throws an AccessControlException (see below), please let Andrej know about it by e-mail (don't forget to include a full copy of the output).

Number of Maps  = 10
Samples per Map = 1000000
org.apache.hadoop.security.AccessControlException: org.apache.hadoop.security.AccessControlException: Permission denied: user=snap, access=WRITE, inode="user":hdfs:supergroup:rwxr-xr-x
        at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
        at sun.reflect.NativeConstructorAccessorImpl.newInstance(Unknown Source)
        at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(Unknown Source)
        at java.lang.reflect.Constructor.newInstance(Unknown Source)
        at org.apache.hadoop.ipc.RemoteException.instantiateException(RemoteException.java:95)
        at org.apache.hadoop.ipc.RemoteException.unwrapRemoteException(RemoteException.java:57)

At this point, congratulations are in order. If you have not used Hadoop before, you just successfully ran your first Hadoop job.

Some XML extraction

This example expects that there are some XML configuration files on the HDFS and creates come output files. It's here just to get you going with the HDFS...

The XML files are already in the HDFS, but let's copy them to our HDFS home directory first (please replace snap with your CSID):

hadoop fs -cp /tmp/xmldata /user/snap/tmpdata

Now we can run the example:

hadoop jar /usr/share/hadoop/hadoop-examples-1.0.3.jar grep xmldata output 'dfs[a-z.]+'

Again note that since we are not using absolute paths, Hadoop TaskTrackers add /user/snap/ as a path prefix to both xmldata and output in the command above. Perhaps the command itself needs some explanation. We are telling hadoop to use the jar file hadoop-examples. Further more it should use the example called grep from the jar. The example code should find the source files (some XML ocnfiguration files) in the directory xmldata and it should output the results to the directory called output. The last part is a regular expression that grep should search for.

Now that the map/reduce task has finished, we need to copy the files from the output directory in HDFS to a local output directory:

hadoop fs -get output/* ~/output/

Our job prints out all the strings starting with dfs sorted by how often they appeared in the files:

~/ $ cat ~/output/part-00000
2       dfs.audit.logger
1       dfs.data.dir
1       dfs.name.dir
1       dfs.replication
1       dfs.server.namenode.
1       dfsadmin


Apache Pig is a platform running on top of Hadoop that consists of a high-level language for expressing data analysis programs and the infrastructure that runs such programs.

The following example will extract user ids from a standard *nix password file.

First we need to copy the password file to the HDFS (you can use any other file that has a similar structure). You can use the one that is already in the HDFS at /tmp/passwd.

hadoop fs -put passwd /tmp/passwd

The second step is to create a Pig script. Pig scripts are written in a language called Pig Latin. Let's create a file extractid.pig and put the following code into it:

   1 A = load '/tmp/passwd' using PigStorage(':');
   2 B = foreach A generate $0 as id;
   3 store B into 'id.out'; 

The first line loads our passwd file. The second line extract the user ids and the third line writes the results to the directory called id.out.

We can run the Pig script that we have just written by running the following:

pig -x mapreduce extractid.pig

The job should create a file called id.out in our home directory. The listing below shows how to check for the file, transfer it to the local filsystem and display the result:

~/ $ hadoop fs -ls
Found 1 item
drwxrwxrwx   - snap supergroup          0 2012-10-17 17:14 /user/snap/id.out

~/ $ hadoop fs -ls id.out/
Found 2 items
-rw-r--r--   3 snap supergroup          0 2012-10-17 17:14 /user/snap/id.out/_SUCCESS
-rw-r--r--   3 snap supergroup        242 2012-10-17 17:14 /user/snap/id.out/part-m-00000

~/ $ hadoop fs -get id.out ./
~/ $ cat id.out/part-m-00000

Slow execution

If you tried out the examples above you may be thinking but why would I want to use the cluster that takes half a minute for a job awk can do in less than a second on my local computer. Well, the examples are provided only to see if some simple things can run on the cluster and it really does not make any sense to run them on a cluster as the cluster imposes too much overhead for such small tasks. The overhead should however become irrelevant for larger jobs that actually compute something useful.