Running Hadoop MapReduce on Alluxio

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This guide describes how to get Alluxio running with Apache Hadoop MapReduce, so that you can easily run your MapReduce programs with files stored on Alluxio. The below instructions assume Hadoop 2.7 is installed; certain paths may differ for other versions.

Initial Setup

The prerequisite for this guide includes

  • You have Java.
  • You have set up an Alluxio cluster.
  • In order to run some simple map-reduce examples, we also recommend you download the map-reduce examples jar based on your hadoop version, or if you are using Hadoop 1, this examples jar.

Configuring Hadoop

Add the following three properties to the core-site.xml of your Hadoop installation:

  <description>The Alluxio FileSystem (Hadoop 1.x and 2.x)</description>
  <description>The Alluxio AbstractFileSystem (Hadoop 2.x)</description>

This will allow your MapReduce jobs to recognize URIs with Alluxio scheme (i.e., alluxio://) in their input and output files.


export HADOOP_CLASSPATH=/path/to/alluxio/client/alluxio-enterprise-1.8.0-client.jar:${HADOOP_CLASSPATH}

This ensures Alluxio client jar available for the MapReduce job client that creates and submits jobs to interact with URIs with Alluxio scheme.

Distributing the Alluxio Client Jar

In order for the MapReduce applications to be able to read and write files in Alluxio, the Alluxio client jar must be distributed on the classpath of the application across different nodes.

This guide on how to include 3rd party libraries from Cloudera describes several ways to distribute the jars. From that guide, the recommended way to distributed the Alluxio client jar is to use the distributed cache, via the -libjars command line option. Another way to distribute the client jar is to manually distribute it to all the Hadoop nodes. Below are instructions for the two main alternatives:

1.Using the -libjars command line option. You can use the -libjars command line option when using hadoop jar ..., specifying /path/to/alluxio/client/alluxio-enterprise-1.8.0-client.jar as the argument of -libjars. Hadoop will place the jar in the Hadoop DistributedCache, making it available to all the nodes. For example, the following command adds the Alluxio client jar to the -libjars option:

$ bin/hadoop jar libexec/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.3.jar wordcount -libjars /path/to/alluxio/client/alluxio-enterprise-1.8.0-client.jar <INPUT FILES> <OUTPUT DIRECTORY>

2.Distributing the client jars to all nodes manually. To install Alluxio on each node, place the client jar /path/to/alluxio/client/alluxio-enterprise-1.8.0-client.jar in the $HADOOP_HOME/lib (may be $HADOOP_HOME/share/hadoop/common/lib for different versions of Hadoop) directory of every MapReduce node, and then restart Hadoop. Alternatively, add this jar to mapreduce.application.classpath system property for your Hadoop deployment to ensure this jar is on the classpath. Note that the jars must be installed again for each update to a new release. On the other hand, when the jar is already on every node, then the -libjars command line option is not needed.

Check MapReduce with Alluxio integration (Supports Hadoop 2.X)

Before running MapReduce on Alluxio, you might want to make sure that your configuration has been setup correctly for integrating with Alluxio. The MapReduce integration checker can help you achieve this.

When you have a running Hadoop cluster (or standalone), you can run the following command in the Alluxio project directory:

$ integration/checker/bin/ mapreduce

You can use -h to display helpful information about the command. This command will report potential problems that might prevent you from running MapReduce on Alluxio.

Running Hadoop wordcount with Alluxio Locally

Assuming Hadoop and Alluxio are already running, add a sample file to Alluxio to run wordcount on. From your Alluxio directory:

$ bin/alluxio fs copyFromLocal conf/ /wordcount/input.txt

This command will copy the conf/ file into the Alluxio namespace with the path /wordcount/input.txt.

Now we can run a MapReduce job (using Hadoop 2.7.3 as example) for wordcount.

$ bin/hadoop jar libexec/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.3.jar wordcount -libjars /path/to/alluxio/client/alluxio-enterprise-1.8.0-client.jar alluxio://localhost:19998/wordcount/input.txt alluxio://localhost:19998/wordcount/output

After this job completes, the result of the wordcount will be in the /wordcount/output directory in Alluxio. You can see the resulting files by running:

$ bin/alluxio fs ls /wordcount/output
$ bin/alluxio fs cat /wordcount/output/part-r-00000