Hadoop - Streaming

Hadoop streaming is a utility that comes with the Hadoop distribution. This utility allows you to create and run Map/Reduce jobs with any executable or script as the mapper and/or the reducer.

Example Using Python

For Hadoop streaming, we are considering the word-count problem. Any job in Hadoop must have two phases: mapper and reducer. We have written codes for the mapper and the reducer in python script to run it under Hadoop. One can also write the same in Perl and Ruby.

Mapper Phase Code

!/usr/bin/python
import sys
# Input takes from standard input for myline in sys.stdin: 
# Remove whitespace either side myline = myline.strip() 
# Break the line into words words = myline.split() 
# Iterate the words list for myword in words: 
# Write the results to standard output print '%s\t%s' % (myword, 1)

Make sure this file has execution permission (chmod +x /home/ expert/hadoop-1.2.1/mapper.py).

Reducer Phase Code

#!/usr/bin/python
from operator import itemgetter 
import sys 
current_word = ""
current_count = 0 
word = "" 
# Input takes from standard input for myline in sys.stdin: 
# Remove whitespace either side myline = myline.strip() 
# Split the input we got from mapper.py word, count = myline.split('\t', 1) 
# Convert count variable to integer 
   try: 
      count = int(count) 
except ValueError: 
   # Count was not a number, so silently ignore this line continue
if current_word == word: 
   current_count += count 
else: 
   if current_word: 
      # Write result to standard output print '%s\t%s' % (current_word, current_count) 
   current_count = count
   current_word = word
# Do not forget to output the last word if needed! 
if current_word == word: 
   print '%s\t%s' % (current_word, current_count)

Save the mapper and reducer codes in mapper.py and reducer.py in Hadoop home directory. Make sure these files have execution permission (chmod +x mapper.py and chmod +x reducer.py). As python is indentation sensitive so the same code can be download from the below link.

Execution of WordCount Program

$ $HADOOP_HOME/bin/hadoop jar contrib/streaming/hadoop-streaming-1.
2.1.jar \
   -input input_dirs \ 
   -output output_dir \ 
   -mapper <path/mapper.py \ 
   -reducer <path/reducer.py

Where "\" is used for line continuation for clear readability.

For Example,

./bin/hadoop jar contrib/streaming/hadoop-streaming-1.2.1.jar -input myinput -output myoutput -mapper /home/expert/hadoop-1.2.1/mapper.py -reducer /home/expert/hadoop-1.2.1/reducer.py

How Streaming Works

In the above example, both the mapper and the reducer are python scripts that read the input from standard input and emit the output to standard output. The utility will create a Map/Reduce job, submit the job to an appropriate cluster, and monitor the progress of the job until it completes.

When a script is specified for mappers, each mapper task will launch the script as a separate process when the mapper is initialized. As the mapper task runs, it converts its inputs into lines and feed the lines to the standard input (STDIN) of the process. In the meantime, the mapper collects the line-oriented outputs from the standard output (STDOUT) of the process and converts each line into a key/value pair, which is collected as the output of the mapper. By default, the prefix of a line up to the first tab character is the key and the rest of the line (excluding the tab character) will be the value. If there is no tab character in the line, then the entire line is considered as the key and the value is null. However, this can be customized, as per one need.

When a script is specified for reducers, each reducer task will launch the script as a separate process, then the reducer is initialized. As the reducer task runs, it converts its input key/values pairs into lines and feeds the lines to the standard input (STDIN) of the process. In the meantime, the reducer collects the line-oriented outputs from the standard output (STDOUT) of the process, converts each line into a key/value pair, which is collected as the output of the reducer. By default, the prefix of a line up to the first tab character is the key and the rest of the line (excluding the tab character) is the value. However, this can be customized as per specific requirements.

Important Commands

Parameters	Options	Description
-input directory/file-name	Required	Input location for mapper.
-output directory-name	Required	Output location for reducer.
-mapper executable or script or JavaClassName	Required	Mapper executable.
-reducer executable or script or JavaClassName	Required	Reducer executable.
-file file-name	Optional	Makes the mapper, reducer, or combiner executable available locally on the compute nodes.
-inputformat JavaClassName	Optional	Class you supply should return key/value pairs of Text class. If not specified, TextInputFormat is used as the default.
-outputformat JavaClassName	Optional	Class you supply should take key/value pairs of Text class. If not specified, TextOutputformat is used as the default.
-partitioner JavaClassName	Optional	Class that determines which reduce a key is sent to.
-combiner streamingCommand or JavaClassName	Optional	Combiner executable for map output.
-cmdenv name=value	Optional	Passes the environment variable to streaming commands.
-inputreader	Optional	For backwards-compatibility: specifies a record reader class (instead of an input format class).
-verbose	Optional	Verbose output.
-lazyOutput	Optional	Creates output lazily. For example, if the output format is based on FileOutputFormat, the output file is created only on the first call to output.collect (or Context.write).
-numReduceTasks	Optional	Specifies the number of reducers.
-mapdebug	Optional	Script to call when map task fails.
-reducedebug	Optional	Script to call when reduce task fails.

Hadoop - Multi Node Cluster

This chapter explains the setup of the Hadoop Multi-Node cluster on a distributed environment.

As the whole cluster cannot be demonstrated, we are explaining the Hadoop cluster environment using three systems (one master and two slaves); given below are their IP addresses.

• Hadoop Master: 192.168.1.15 (hadoop-master)
• Hadoop Slave: 192.168.1.16 (hadoop-slave-1)
• Hadoop Slave: 192.168.1.17 (hadoop-slave-2)

Follow the steps given below to have Hadoop Multi-Node cluster setup.

Installing Java

Java is the main prerequisite for Hadoop. First of all, you should verify the existence of java in your system using “java -version”. The syntax of java version command is given below.

$ java -version

If everything works fine it will give you the following output.

java version "1.7.0_71" 
Java(TM) SE Runtime Environment (build 1.7.0_71-b13) 
Java HotSpot(TM) Client VM (build 25.0-b02, mixed mode)

If java is not installed in your system, then follow the given steps for installing java.

Step 1

Download java (JDK - X64.tar.gz) by visiting the following link http://www.oracle.com/technetwork/java/javase/downloads/jdk7-downloads-1880260.html

Then jdk-7u71-linux-x64.tar.gz will be downloaded into your system.

Step 2

Generally you will find the downloaded java file in Downloads folder. Verify it and extract the jdk-7u71-linux-x64.gz file using the following commands.

$ cd Downloads/
$ ls
jdk-7u71-Linux-x64.gz
$ tar zxf jdk-7u71-Linux-x64.gz
$ ls
jdk1.7.0_71 jdk-7u71-Linux-x64.gz

Step 3

To make java available to all the users, you have to move it to the location “/usr/local/”. Open the root, and type the following commands.

$ su
password:
# mv jdk1.7.0_71 /usr/local/
# exit

Step 4

For setting up PATH and JAVA_HOME variables, add the following commands to ~/.bashrc file.

export JAVA_HOME=/usr/local/jdk1.7.0_71
export PATH=PATH:$JAVA_HOME/bin

Now verify the java -version command from the terminal as explained above. Follow the above process and install java in all your cluster nodes.

Creating User Account

Create a system user account on both master and slave systems to use the Hadoop installation.

# useradd hadoop 
# passwd hadoop

Mapping the nodes

You have to edit hosts file in /etc/ folder on all nodes, specify the IP address of each system followed by their host names.

# vi /etc/hosts
enter the following lines in the /etc/hosts file.
192.168.1.109 hadoop-master 
192.168.1.145 hadoop-slave-1 
192.168.56.1 hadoop-slave-2

Configuring Key Based Login

Setup ssh in every node such that they can communicate with one another without any prompt for password.

# su hadoop 
$ ssh-keygen -t rsa 
$ ssh-copy-id -i ~/.ssh/id_rsa.pub tutorialspoint@hadoop-master 
$ ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop_tp1@hadoop-slave-1 
$ ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop_tp2@hadoop-slave-2 
$ chmod 0600 ~/.ssh/authorized_keys 
$ exit

Installing Hadoop

In the Master server, download and install Hadoop using the following commands.

# mkdir /opt/hadoop 
# cd /opt/hadoop/ 
# wget http://apache.mesi.com.ar/hadoop/common/hadoop-1.2.1/hadoop-1.2.0.tar.gz 
# tar -xzf hadoop-1.2.0.tar.gz 
# mv hadoop-1.2.0 hadoop
# chown -R hadoop /opt/hadoop 
# cd /opt/hadoop/hadoop/

Configuring Hadoop

You have to configure Hadoop server by making the following changes as given below.

core-site.xml

Open the core-site.xml file and edit it as shown below.

<configuration>
   <property> 
      <name>fs.default.name</name> 
      <value>hdfs://hadoop-master:9000/</value> 
   </property> 
   <property> 
      <name>dfs.permissions</name> 
      <value>false</value> 
   </property> 
</configuration>

hdfs-site.xml

Open the hdfs-site.xml file and edit it as shown below.

<configuration>
   <property> 
      <name>dfs.data.dir</name> 
      <value>/opt/hadoop/hadoop/dfs/name/data</value> 
      <final>true</final> 
   </property> 

   <property> 
      <name>dfs.name.dir</name> 
      <value>/opt/hadoop/hadoop/dfs/name</value> 
      <final>true</final> 
   </property> 

   <property> 
      <name>dfs.replication</name> 
      <value>1</value> 
   </property> 
</configuration>

mapred-site.xml

Open the mapred-site.xml file and edit it as shown below.

<configuration>
   <property> 
      <name>mapred.job.tracker</name> 
      <value>hadoop-master:9001</value> 
   </property> 
</configuration>

hadoop-env.sh

Open the hadoop-env.sh file and edit JAVA_HOME, HADOOP_CONF_DIR, and HADOOP_OPTS as shown below.

Note: Set the JAVA_HOME as per your system configuration.

export JAVA_HOME=/opt/jdk1.7.0_17 export HADOOP_OPTS=-Djava.net.preferIPv4Stack=true export HADOOP_CONF_DIR=/opt/hadoop/hadoop/conf

Installing Hadoop on Slave Servers

Install Hadoop on all the slave servers by following the given commands.

# su hadoop 
$ cd /opt/hadoop 
$ scp -r hadoop hadoop-slave-1:/opt/hadoop 
$ scp -r hadoop hadoop-slave-2:/opt/hadoop

Configuring Hadoop on Master Server

Open the master server and configure it by following the given commands.

# su hadoop 
$ cd /opt/hadoop/hadoop

Configuring Master Node

$ vi etc/hadoop/masters
hadoop-master

Configuring Slave Node

$ vi etc/hadoop/slaves
hadoop-slave-1 
hadoop-slave-2

Format Name Node on Hadoop Master

# su hadoop 
$ cd /opt/hadoop/hadoop 
$ bin/hadoop namenode –format

11/10/14 10:58:07 INFO namenode.NameNode: STARTUP_MSG: /************************************************************ 
STARTUP_MSG: Starting NameNode 
STARTUP_MSG: host = hadoop-master/192.168.1.109 
STARTUP_MSG: args = [-format] 
STARTUP_MSG: version = 1.2.0 
STARTUP_MSG: build = 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 
STARTUP_MSG: java = 1.7.0_71 ************************************************************/ 11/10/14 10:58:08 INFO util.GSet: Computing capacity for map BlocksMap editlog=/opt/hadoop/hadoop/dfs/name/current/edits
………………………………………………….
………………………………………………….
…………………………………………………. 11/10/14 10:58:08 INFO common.Storage: Storage directory /opt/hadoop/hadoop/dfs/name has been successfully formatted. 11/10/14 10:58:08 INFO namenode.NameNode: 
SHUTDOWN_MSG: /************************************************************ SHUTDOWN_MSG: Shutting down NameNode at hadoop-master/192.168.1.15 ************************************************************/

Starting Hadoop Services

The following command is to start all the Hadoop services on the Hadoop-Master.

$ cd $HADOOP_HOME/sbin
$ start-all.sh

Adding a New DataNode in the Hadoop Cluster

Given below are the steps to be followed for adding new nodes to a Hadoop cluster.

Networking

Add new nodes to an existing Hadoop cluster with some appropriate network configuration. Assume the following network configuration.

For New node Configuration:

IP address : 192.168.1.103 
netmask : 255.255.255.0
hostname : slave3.in

Adding User and SSH Access

Add a User

On a new node, add "hadoop" user and set password of Hadoop user to "hadoop123" or anything you want by using the following commands.

useradd hadoop
passwd hadoop

Setup Password less connectivity from master to new slave.

Execute the following on the master

mkdir -p $HOME/.ssh 
chmod 700 $HOME/.ssh 
ssh-keygen -t rsa -P '' -f $HOME/.ssh/id_rsa 
cat $HOME/.ssh/id_rsa.pub >> $HOME/.ssh/authorized_keys 
chmod 644 $HOME/.ssh/authorized_keys
Copy the public key to new slave node in hadoop user $HOME directory
scp $HOME/.ssh/id_rsa.pub hadoop@192.168.1.103:/home/hadoop/

Execute the following on the slaves

Login to hadoop. If not, login to hadoop user.

su hadoop ssh -X hadoop@192.168.1.103

Copy the content of public key into file "$HOME/.ssh/authorized_keys"and then change the permission for the same by executing the following commands.

cd $HOME
mkdir -p $HOME/.ssh 
chmod 700 $HOME/.ssh
cat id_rsa.pub >>$HOME/.ssh/authorized_keys 
chmod 644 $HOME/.ssh/authorized_keys

Check ssh login from the master machine. Now check if you can ssh to the new node without a password from the master.

ssh hadoop@192.168.1.103 or hadoop@slave3

Set Hostname of New Node

You can set hostname in file /etc/sysconfig/network

On new slave3 machine
NETWORKING=yes 
HOSTNAME=slave3.in

To make the changes effective, either restart the machine or run hostname command to a new machine with the respective hostname (restart is a good option).

On slave3 node machine:

hostname slave3.in

Update /etc/hosts on all machines of the cluster with the following lines:

192.168.1.102 slave3.in slave3

Now try to ping the machine with hostnames to check whether it is resolving to IP or not.

On new node machine:

ping master.in

Start the DataNode on New Node

Start the datanode daemon manually using $HADOOP_HOME/bin/hadoop-daemon.sh script. It will automatically contact the master (NameNode) and join the cluster. We should also add the new node to the conf/slaves file in the master server. The script-based commands will recognize the new node.

Login to new node

su hadoop or ssh -X hadoop@192.168.1.103

Start HDFS on a newly added slave node by using the following command

./bin/hadoop-daemon.sh start datanode

Check the output of jps command on a new node. It looks as follows.

$ jps
7141 DataNode
10312 Jps

Removing a DataNode from the Hadoop Cluster

We can remove a node from a cluster on the fly, while it is running, without any data loss. HDFS provides a decommissioning feature, which ensures that removing a node is performed safely. To use it, follow the steps as given below:

Step 1

Login to master.

Login to master machine user where Hadoop is installed.

$ su hadoop

Step 2

Change cluster configuration.

An exclude file must be configured before starting the cluster. Add a key named dfs.hosts.exclude to our $HADOOP_HOME/etc/hadoop/hdfs-site.xml file. The value associated with this key provides the full path to a file on the NameNode's local file system which contains a list of machines which are not permitted to connect to HDFS.

For example, add these lines to etc/hadoop/hdfs-site.xml file.

<property> 
   <name>dfs.hosts.exclude</name> 
   <value>/home/hadoop/hadoop-1.2.1/hdfs_exclude.txt</value> 
   <description>DFS exclude</description> 
</property>

Step 3

Determine hosts to decommission.

Each machine to be decommissioned should be added to the file identified by the hdfs_exclude.txt, one domain name per line. This will prevent them from connecting to the NameNode. Content of the "/home/hadoop/hadoop-1.2.1/hdfs_exclude.txt" file is shown below, if you want to remove DataNode2.

slave2.in

Step 4

Force configuration reload.

Run the command "$HADOOP_HOME/bin/hadoop dfsadmin -refreshNodes" without the quotes.

$ $HADOOP_HOME/bin/hadoop dfsadmin -refreshNodes

This will force the NameNode to re-read its configuration, including the newly updated ‘excludes’ file. It will decommission the nodes over a period of time, allowing time for each node's blocks to be replicated onto machines which are scheduled to remain active.

On slave2.in, check the jps command output. After some time, you will see the DataNode process is shutdown automatically.

Step 5

Shutdown nodes.

After the decommission process has been completed, the decommissioned hardware can be safely shut down for maintenance. Run the report command to dfsadmin to check the status of decommission. The following command will describe the status of the decommission node and the connected nodes to the cluster.

$ $HADOOP_HOME/bin/hadoop dfsadmin -report

Step 6

Edit excludes file again.

Once the machines have been decommissioned, they can be removed from the ‘excludes’ file. Running "$HADOOP_HOME/bin/hadoop dfsadmin -refreshNodes" again will read the excludes file back into the NameNode; allowing the DataNodes to rejoin the cluster after the maintenance has been completed, or additional capacity is needed in the cluster again, etc.

Special Note: If the above process is followed and the tasktracker process is still running on the node, it needs to be shut down. One way is to disconnect the machine as we did in the above steps. The Master will recognize the process automatically and will declare as dead. There is no need to follow the same process for removing the tasktracker because it is NOT much crucial as compared to the DataNode. DataNode contains the data that you want to remove safely without any loss of data.

The tasktracker can be run/shutdown on the fly by the following command at any point of time.

$ $HADOOP_HOME/bin/hadoop-daemon.sh stop tasktracker $HADOOP_HOME/bin/hadoop-daemon.sh start tasktracker

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