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, you’ll install Hadoop in stand-alone mode and run one of the 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 20.04 guide.
You might also like to take a look at An Introduction to Big Data Concepts and Terminology or An Introduction to Hadoop
Once you’ve completed the prerequisites, log in as your
sudo user to begin.
To get started, you’ll update our package list and install OpenJDK, the default Java Development Kit on Ubuntu 20.04:
- sudo apt update
- sudo apt install default-jdk
Once the installation is complete, let’s check the version.
- java -version
Outputopenjdk version "11.0.13" 2021-10-19
OpenJDK Runtime Environment (build 11.0.13+8-Ubuntu-0ubuntu1.20.04)
OpenJDK 64-Bit Server VM (build 11.0.13+8-Ubuntu-0ubuntu1.20.04, mixed mode, sharing)
This output verifies that OpenJDK has been successfully installed.
With Java in place, you’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 you’ll install Hadoop 3.3.1, but you can substitute the version numbers in this guide with one of your choice.
On the next page, right-click and copy the link to the release binary.
On the server, you’ll use
wget to fetch it:
- wget https://dlcdn.apache.org/hadoop/common/hadoop-3.3.1/hadoop-3.3.1.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 you downloaded hasn’t been altered, you’ll do a quick check using SHA-512, or the Secure Hash Algorithm 512. Return to the releases page, then right-click and copy the link to the checksum file for the release binary you downloaded:
Again, you’ll use
wget on our server to download the file:
- wget https://downloads.apache.org/hadoop/common/hadoop-3.3.1/hadoop-3.3.1.tar.gz.sha512
Then run the verification:
- shasum -a 512 hadoop-3.3.1.tar.gz
Compare this value with the SHA-512 value in the
- cat hadoop-3.3.1.tar.gz.sha512
SHA512 (hadoop-3.3.1.tar.gz) = 2fd0bf74852c797dc864f373ec82ffaa1e98706b309b30d1effa91ac399b477e1accc1ee74d4ccbb1db7da1c5c541b72e4a834f131a99f2814b030fbd043df66
The output of the command you ran against the file you downloaded from the mirror should match the value in the file you downloaded from apache.org.
Now that you’ve verified that the file wasn’t corrupted or changed, you can extract it:
- tar -xzvf hadoop-3.3.1.tar.gz
tar command with the
-x flag to extract,
-z to uncompress,
-v for verbose output, and
-f to specify that you’re extracting from a file.
Finally, you’ll move the extracted files into
/usr/local, the appropriate place for locally installed software:
- sudo mv hadoop-3.3.1 /usr/local/hadoop
With the software in place, you’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. You will use
readlink with the
-f flag to follow every symlink in every part of the path, recursively. Then, you’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, modify the file by choosing one of the following options:
. . .
. . .
. . .
export JAVA_HOME=$(readlink -f /usr/bin/java | sed "s:bin/java::")
. . .
If you have trouble finding these lines, use
CTRL+W to quickly search through the text. Once you’re done, exit with
CTRL+X and save your file.
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 you 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:
. . .
This output means you’ve successfully configured Hadoop to run in stand-alone mode.
You’ll ensure that Hadoop 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, you can use the following command to run the MapReduce
hadoop-mapreduce-examples program, a Java archive with several options:
- /usr/local/hadoop/bin/hadoop jar /usr/local/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.3.1.jar grep ~/input ~/grep_example 'allowed[.]*'
This invokes the
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, the regular expression
allowed[.]* is given to find occurrences of the word
allowed within or at the end of a declarative sentence. The expression is case-sensitive, so you wouldn’t find the word if it were capitalized at the beginning of a sentence.
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=1200956
FILE: Number of bytes written=3656025
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=114
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)=41
Total committed heap usage (bytes)=403800064
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, you’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.
If you’re interested in deploying a full cluster instead of just a stand-alone, see How To Spin Up a Hadoop Cluster with DigitalOcean Droplets.
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