Hadoop is a Java-based programming framework that supports the processing and storage of extremely large datasets on a cluster of inexpensive machines. It was the first major open source project in the big data playing field and is sponsored by the Apache Software Foundation.
Hadoop is comprised of four main layers:
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.
In this tutorial, we’ll install Hadoop in stand-alone mode and run one of the example example MapReduce programs it includes to verify the installation.
To follow this tutorial, you will need:
sudo privileges: You can learn more about how to set up a user with these privileges in our Initial Server Setup with Ubuntu 18.04 guide.
Once you’ve completed this prerequisite, you’re ready to install Hadoop and its dependencies.
Before you begin, you might also like to take a look at An Introduction to Big Data Concepts and Terminology or An Introduction to Hadoop
To get started, we’ll update our package list:
- sudo apt update
Next, we’ll install OpenJDK, the default Java Development Kit on Ubuntu 18.04:
- sudo apt install default-jdk
Once the installation is complete, let’s check the version.
- java -version
Outputopenjdk 10.0.1 2018-04-17
OpenJDK Runtime Environment (build 10.0.1+10-Ubuntu-3ubuntu1)
OpenJDK 64-Bit Server VM (build 10.0.1+10-Ubuntu-3ubuntu1, mixed mode)
This output verifies that OpenJDK has been successfully installed.
With Java in place, we’ll visit the Apache Hadoop Releases page to find the most recent stable release.
Navigate to binary for the release you’d like to install. In this guide, we’ll install Hadoop 3.0.3.
On the next page, right-click and copy the link to the release binary.
On the server, we’ll use
wget to fetch it:
- wget http://www-us.apache.org/dist/hadoop/common/hadoop-3.0.3/hadoop-3.0.3.tar.gz
Note: The Apache website will direct you to the best mirror dynamically, so your URL may not match the URL above.
In order to make sure that the file we downloaded hasn’t been altered, we’ll do a quick check using SHA-256. Return to the releases page, then right-click and copy the link to the checksum file for the release binary you downloaded:
Again, we’ll use
wget on our server to download the file:
- wget https://dist.apache.org/repos/dist/release/hadoop/common/hadoop-3.0.3/hadoop-3.0.3.tar.gz.mds
Then run the verification:
- shasum -a 256 hadoop-3.0.3.tar.gz
Compare this value with the SHA-256 value in the
- cat hadoop-3.0.3.tar.gz.mds
SHA256 = DB96E2C0 D0D5352D 8984892D FAC4E27C 0E682D98 A497B7E0 4EE97C3E 2019277A
You can safely ignore the difference in case and the spaces. 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 apache.org.
Now that we’ve verified that the file wasn’t corrupted or changed, we’ll 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. Use tab-completion or substitute the correct version number in the command below:
- tar -xzvf hadoop-3.0.3.tar.gz
Finally, we’ll move the extracted files into
/usr/local, the appropriate place for locally installed software. Change the version number, if needed, to match the version you downloaded.
- sudo mv hadoop-3.0.3 /usr/local/hadoop
With the software in place, we’re ready to configure its environment.
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 symlink to
/etc/alternatives/java, which is in turn a symlink to default Java binary. We will use
readlink with the
-f flag to follow every symlink in every part of the path, recursively. Then, we’ll use
sed to trim
bin/java from the output to give us the correct value for
To find the default Java path
- readlink -f /usr/bin/java | sed "s:bin/java::"
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. Alternatively, 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.
To begin, open
- sudo nano /usr/local/hadoop/etc/hadoop/hadoop-env.sh
Then, choose one of the following options:
. . .
. . .
. . .
export JAVA_HOME=$(readlink -f /usr/bin/java | sed "s:bin/java::")
. . .
Note: With respect to Hadoop, the value of
hadoop-env.sh overrides any values that are set in the environment by
/etc/profile or in a user’s profile.
Now we should be able to run Hadoop:
OutputUsage: hadoop [OPTIONS] SUBCOMMAND [SUBCOMMAND OPTIONS]
or hadoop [OPTIONS] CLASSNAME [CLASSNAME OPTIONS]
where CLASSNAME is a user-provided Java class
OPTIONS is none or any of:
--config dir Hadoop config directory
--debug turn on shell script debug mode
--help usage information
buildpaths attempt to add class files from build tree
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
SUBCOMMAND is one of:
. . .
The help means we’ve successfully configured Hadoop to run in stand-alone mode. We’ll ensure that it is functioning properly by running the example MapReduce program it ships with. To do so, create a directory called
input in our home directory and copy Hadoop’s configuration files into it to use those files as our data.
- mkdir ~/input
- cp /usr/local/hadoop/etc/hadoop/*.xml ~/input
Next, we can use the following command to run the MapReduce
hadoop-mapreduce-examples program, a Java archive with several options. We’ll invoke its
grep program, one of the many examples included in
hadoop-mapreduce-examples, followed by the input directory,
input and the output directory
grep_example. The MapReduce grep program will count the matches of a literal word or regular expression. Finally, we’ll supply the regular expression
allowed[.]* to find occurrences of the word
allowed within or at the end of a declarative sentence. The expression is case-sensitive, so we wouldn’t find the word if it were capitalized at the beginning of a sentence:
- /usr/local/hadoop/bin/hadoop jar /usr/local/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.0.3.jar grep ~/input ~/grep_example 'allowed[.]*'
When the task completes, it provides a summary of what has been processed and errors it has encountered, but this doesn’t contain the actual results.
Output . . .
File System Counters
FILE: Number of bytes read=1330690
FILE: Number of bytes written=3128841
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
Map input records=2
Map output records=2
Map output bytes=33
Map output materialized bytes=43
Input split bytes=115
Combine input records=0
Combine output records=0
Reduce input groups=2
Reduce shuffle bytes=43
Reduce input records=2
Reduce output records=2
Shuffled Maps =1
Merged Map outputs=1
GC time elapsed (ms)=3
Total committed heap usage (bytes)=478150656
File Input Format Counters
File Output Format Counters
Note: If the output directory already exists, the program will fail, and rather than seeing the summary, the output will look something like:
Output . . .
Results are stored in the output directory and can be checked by running
cat on the output directory:
- cat ~/grep_example/*
The MapReduce task found 19 occurrences of the word
allowed followed by a period and one occurrence where it was not. Running the example program has verified that our stand-alone installation is working properly and that non-privileged users on the system can run Hadoop for exploration or debugging.
In this tutorial, we’ve installed Hadoop in stand-alone mode and verified it by running an example program it provided. To learn how to write your own MapReduce programs, you might want to visit Apache Hadoop’s MapReduce tutorial which walks through the code behind the example. When you’re ready to set up a cluster, see the Apache Foundation Hadoop Cluster Setup guide.
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