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      StandAlone

      How to Install Hadoop in Stand-Alone Mode on Debian 9


      Introduction

      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 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.

      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 example MapReduce programs it includes to verify the installation.

      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

      Prerequisites

      To follow this tutorial, you will need:

      Step 1 — Installing Hadoop

      To install Hadoop, first 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.

      Screenshot of the Hadoop releases page highlighting the link to the latest stable binary

      On the next page, right-click and copy the link to the release binary.

      Screenshot of the Hadoop mirror page

      On your server, 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 ensure that the file you downloaded hasn’t been altered, 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:

      Screenshot highlighting the .mds file

      Again, use wget on your 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:

      • sha256sum hadoop-3.0.3.tar.gz

      Output

      db96e2c0d0d5352d8984892dfac4e27c0e682d98a497b7e04ee97c3e2019277a hadoop-3.0.3.tar.gz

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

      • cat hadoop-3.0.3.tar.gz.mds | grep SHA256

      ~/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 you ran against the file we 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, use the tar command with the -x flag to extract, -z to uncompress, -v for verbose output, and -f to specify that you’re extracting the archive 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, 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.

      Step 3 — Running Hadoop

      Let’s make sure Hadoop runs. Execute the following command to launch Hadoop and display its help options:

      • /usr/local/hadoop/bin/hadoop

      You’ll see the following output, which lets you know you’ve successfully configured Hadoop to run in stand-alone mode.

      Output

      Usage: 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: . . .

      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 your 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’ll 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.

      Execute the following command:

      • /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-Reduce Framework 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 Spilled Records=4 Shuffled Maps =1 Failed Shuffles=0 Merged Map outputs=1 GC time elapsed (ms)=3 Total committed heap usage (bytes)=478150656 Shuffle Errors BAD_ID=0 CONNECTION=0 IO_ERROR=0 WRONG_LENGTH=0 WRONG_MAP=0 WRONG_REDUCE=0 File Input Format Counters Bytes Read=147 File Output Format Counters Bytes Written=34

      The results are stored in the ~/grep_example directory.

      If this output directory already exists, the program will fail, and rather than seeing the summary, you’ll see something like this:

      Output

      . . . at java.base/java.lang.reflect.Method.invoke(Method.java:564) at org.apache.hadoop.util.RunJar.run(RunJar.java:244) at org.apache.hadoop.util.RunJar.main(RunJar.java:158)

      Check the results by running cat on the output directory:

      You'll see this output:

      Output

      19 allowed. 1 allowed

      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.

      Conclusion

      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, visit Apache Hadoop's MapReduce tutorial which walks through the code behind the example you used in this tutorial. When you're ready to set up a cluster, see the Apache Foundation Hadoop Cluster Setup guide.



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