How to Configure Hadoop on Ubuntu Xenial

Hadoop Overview

Hadoop is an open source framework. It is provided by Apache to process and analyze very huge volume of data. It is written in Java and currently used by Google, Facebook, LinkedIn, Yahoo, Twitter etc. Similar to data residing in a local file system of personal computer system, in Hadoop, data resides in a distributed file system which is called as a Hadoop Distributed File system. Hadoop is suitable for:

  • Big Data Analysis: Big Data tends to be distributed and unstructured in nature. Hadoop processes the logic (not the actual data) that flows to the computing nodes, less network bandwidth is consumed. This concept is called as data locality concept which helps increase efficiency of Hadoop based applications.
  • Scalability: Hadoop clusters can easily be scaled to any extent by adding additional cluster nodes
  • Fault Tolerance: Hadoop ecosystem has a provision to replicate the input data on to other cluster nodes. That way, in the event of a cluster node failure, data processing can still proceed by using data stored on another cluster node.

Hadoop clusters are relatively complex to set up, so the project includes a stand-alone mode which is suitable for learning about Hadoop, performing simple operations, and debugging.

Hadoop is comprised of four main layers:

  • Hadoop Common is the collection of utilities and libraries that support other Hadoop modules.

  • HDFS, which stands for Hadoop Distributed File System, is responsible for persisting data to disk.

  • YARN, short for Yet Another Resource Negotiator, is the “operating system” for HDFS.

  • MapReduce is the original processing model for Hadoop clusters. It distributes work within the cluster or map, then organizes and reduces the results from the nodes into a response to a query. Many other processing models are available for the 3.x version of Hadoop.

For more information, refer to the official Apache Hadoop documentation.


Installing the Default JRE/JDK

1 . Update the package index

apt-get update

2 . Install Java. Specifically, this command will install the Java Runtime Environment (JRE).

apt-get install default-jre

There is another default Java installation called the JDK (Java Development Kit). The JDK is usually only needed if you are going to compile Java programs or if the software that will use Java specifically requires it.

The JDK does contain the JRE, so there are no disadvantages if you install the JDK instead of the JRE, except for the larger file size. You can install the JDK with the following command: apt-get install default-jdk

3 . Once the installation is complete, let’s check the version.

java -version

which returns

OpenJDK Runtime Environment (build 1.8.0_181-8u181-b13-0ubuntu0.16.04.1-b13)
OpenJDK 64-Bit Server VM (build 25.181-b13, mixed mode)

Installing Hadoop in Standalone Mode

Visit the Apache Hadoop Releases page to select the most recent stable release. We will install Hadoop version 3.1.1

1 . Copy the link to the release binary.


2 . Use wget to fetch it:


In order to make sure that the file we downloaded has not been altered, we’ll do a quick check using SHA-256. Return to the releases page, then click and copy the link to the checksum file for the release binary you downloaded:


3 . Copy the link displayed in the browser and use wget on our server to download the file


4 . Run the verification

shasum -a 256 hadoop-3.1.1.tar.gz

which returns

f837fe260587f71629aad1f4fb6719274e948111dc96ffc5a8e26f27deac5602  hadoop-3.1.1.tar.gz

5 . Compare this value with the SHA-256 value in the .mds file:

cat hadoop-3.1.1.tar.gz.mds

The output of the command we ran against the file we downloaded from the mirror should match the value in the file we downloaded from

6 . Use the tar command with the -x flag to extract, -z to uncompress, -v for verbose output, and -f to specify that we’re extracting from a file.

tar -xzvf hadoop-3.1.1.tar.gz

7 . Move the extracted files into /usr/local. Change the version number to match the version you downloaded.

mv hadoop-3.1.1 /usr/local/hadoop

Configuring Hadoop's Java Home

Hadoop requires that you set the path to Java, either as an environment variable or in the Hadoop configuration file.

The path to Java, /usr/bin/java is a symbolic link to /etc/alternatives/java, which is a symbolic link to the default Java binary.

Use readlink command with the -f option to follow every symbolic link in every part of the path, recursively. Then, use sed to trim bin/java from the output to give us the correct value for JAVA_HOME.

To find the default Java path, launch:

readlink -f /usr/bin/java | sed "s:bin/java::"

which returns


You can copy this output to set Hadoop’s Java home to this specific version, which ensures that if the default Java changes, this value will not. Or, you can use the readlink command dynamically in the file so that Hadoop will automatically use whatever Java version is set as the system default.

1 . Open

sudo nano /usr/local/hadoop/etc/hadoop/

Note: With respect to Hadoop, the value of JAVA_HOME in overrides any values that are set in the environment by /etc/profile or in a user’s profile.

2 . Select one of the following options:

Option 1: Static Value

. . .
export JAVA_HOME=//usr/lib/jvm/java-8-openjdk-amd64/jre/
. . .

Option 2: Dynamic Value

. . .
export JAVA_HOME=$(readlink -f /usr/bin/java | sed "s:bin/java::")
 . . .

Running Hadoop

Run Hadoop


which returns

  where CLASSNAME is a user-provided Java class

  OPTIONS is none or any of:

buildpaths                       attempt to add class files from build tree
--config dir                     Hadoop config directory
--debug                          turn on shell script debug mode
--help                           usage information
hostnames list[,of,host,names]   hosts to use in slave mode
hosts filename                   list of hosts to use in slave mode
loglevel level                   set the log4j level for this command
workers                          turn on worker mode

The help means we’ve successfully configured Hadoop to run in stand-alone mode. To make sure Hadoop is running properly, we will use an example of a MapReduce program.

MapReduce Example

MapReduce is a programming framework that allows us to perform distributed and parallel processing on large data sets in a distributed environment.

MapReduce consists of two distinct tasks – Map and Reduce. As the name MapReduce suggests, reducer phase takes place after mapper phase has been completed.

1 . Create a directory called input in our home directory

mkdir ~/input

2 . Copy Hadoop’s configuration files into it to use those files as our data.

cp /usr/local/hadoop/etc/hadoop/*.xml ~/input

3 . Download Hadoop example


4 . Unzip the file


5 . Move the jar_files to /usr/local/hadoop/share/hadoop/mapreduce/hadoop/

mv hadoop-mapreduce-examples-3.0.3.jar /usr/local/hadoop/share/hadoop/mapreduce/.

Important: Verify that your /etc/hosts file is properly setup and contains at least the following: localhost <your hostname>

6 . Use the following command to run the MapReduce hadoop-mapreduce-examples program. We’ll invoke its grep program which counts the matches of a literal word or regular expression (e.g., allowed).

/usr/local/hadoop/bin/hadoop jar /usr/local/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.0.3.jar grep ~/input ~/grep_example 'allowed[.]*'

Results are stored in the output directory and can be checked by running cat on the output directory:

cat ~/grep_example/*

which returns

19  allowed
1   allowed

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