Install/Deploy Instructions for Tez

Replace x.y.z with the tez release number that you are using. E.g. 0.5.0

  1. Deploy Apache Hadoop using either the 2.2.0 release or a compatible 2.x version.
    • One thing to note though when compiling Tez is that you will need to change the value of the hadoop.version property in the top-level pom.xml to match the version of the hadoop branch being used.
  2. Build tez using mvn clean package -DskipTests=true -Dmaven.javadoc.skip=true
    • This assumes that you have already installed JDK6 or later, Maven 3 or later and Protocol Buffers (protoc compiler) 2.5 or later
    • If you prefer to run the unit tests, remove skipTests from the command above.
    • If you use Eclipse IDE, you can import the projects using “Import/Maven/Existing Maven Projects”. Eclipse does not automatically generate Java sources or include the generated sources into the projects. Please build using maven as described above and then use Project Properties to include “target/generatedsources/java” as a source directory into the “Java Build Path” for these projects: tez-api, tez-mapreduce, tez-runtime-internals and tez-runtime-library. This needs to be done just once after importing the project.
  3. Copy the relevant tez tarball into HDFS, and configure tez-site.xml
    • A tez tarball containing tez and hadoop libraries will be found at tez-dist/target/tez-x.y.z-SNAPSHOT.tar.gz
    • Assuming that the tez jars are put in /apps/ on HDFS, the command would be hadoop dfs -mkdir /apps/tez-x.y.z-SNAPSHOT hadoop dfs -copyFromLocal tez-dist/target/tez-x.y.z-SNAPSHOT-archive.tar.gz /apps/tez-x.y.z-SNAPSHOT/
    • tez-site.xml configuration.
      • Set tez.lib.uris to point to the tar.gz uploaded to HDFS. Assuming the steps mentioned so far were followed, set tez.lib.uris to "${fs.defaultFS}/apps/tez-x.y.z-SNAPSHOT/tez-x.y.z-SNAPSHOT.tar.gz"
      • Ensure tez.use.cluster.hadoop-libs is not set in tez-site.xml, or if it is set, the value should be false
  4. Optional: If running existing MapReduce jobs on Tez. Modify mapred-site.xml to change “mapreduce.framework.name” property from its default value of “yarn” to “yarn-tez”
  5. Configure the client node to include the tez-libraries in the hadoop classpath
    • Extract the tez minimal tarball created in step 2 to a local directory (assuming TEZ_JARS is where the files will be decompressed for the next steps) tar -xvzf tez-dist/target/tez-x.y.z-minimal.tar.gz -C $TEZ_JARS
    • set TEZ_CONF_DIR to the location of tez-site.xml
    • Add $TEZ_CONF_DIR, ${TEZ_JARS}/* and ${TEZ_JARS}/lib/* to the application classpath. For example, doing it via the standard Hadoop tool chain would use the following command to set up the application classpath: export HADOOP_CLASSPATH=${TEZ_CONF_DIR}:${TEZ_JARS}/*:${TEZ_JARS}/lib/*
    • Please note the “*” which is an important requirement when setting up classpaths for directories containing jar files.
  6. There is a basic example of using an MRR job in the tez-examples.jar. Refer to OrderedWordCount.java in the source code. To run this example:

    $HADOOP_PREFIX/bin/hadoop jar tez-examples.jar orderedwordcount <input> <output>
    

    This will use the TEZ DAG ApplicationMaster to run the ordered word count job. This job is similar to the word count example except that it also orders all words based on the frequency of occurrence.

    Tez DAGs could be run separately as different applications or serially within a single TEZ session. There is a different variation of orderedwordcount in tez-tests that supports the use of Sessions and handling multiple input-output pairs. You can use it to run multiple DAGs serially on different inputs/outputs.

    $HADOOP_PREFIX/bin/hadoop jar tez-tests.jar testorderedwordcount <input1> <output1> <input2> <output2> <input3> <output3> ...
    

    The above will run multiple DAGs for each input-output pair.

    To use TEZ sessions, set -DUSE_TEZ_SESSION=true

    $HADOOP_PREFIX/bin/hadoop jar tez-tests.jar testorderedwordcount -DUSE_TEZ_SESSION=true <input1> <output1> <input2> <output2>
    
  7. Submit a MR job as you normally would using something like:

    $HADOOP_PREFIX/bin/hadoop jar hadoop-mapreduce-client-jobclient-3.0.0-SNAPSHOT-tests.jar sleep -mt 1 -rt 1 -m 1 -r 1
    

    This will use the TEZ DAG ApplicationMaster to run the MR job. This can be verified by looking at the AM’s logs from the YARN ResourceManager UI. This needs mapred-site.xml to have “mapreduce.framework.name” set to “yarn-tez”

Hadoop Installation dependent Install/Deploy Instructions

The above install instructions use Tez with pre-packaged Hadoop libraries included in the package and is the recommended method for installation. If its needed to make Tez use the existing cluster Hadoop libraries then follow this alternate machanism to setup Tez to use Hadoop libraries from the cluster. Step 3 above changes as follows. Also subsequent steps would use tez-dist/target/tez-x.y.z-minimal.tar.gz instead of tez-dist/target/tez-x.y.z.tar.gz - A tez build without Hadoop dependencies will be available at tez-dist/target/tez-x.y.z-minimal.tar.gz - Assuming that the tez jars are put in /apps/ on HDFS, the command would be “hadoop fs -mkdir /apps/tez-x.y.z” “hadoop fs -copyFromLocal tez-dist/target/tez-x.y.z-minimal.tar.gz /apps/tez-x.y.z” - tez-site.xml configuration - Set tez.lib.uris to point to the paths in HDFS containing the tez jars. Assuming the steps mentioned so far were followed, set tez.lib.uris to "${fs.defaultFS}/apps/tez-x.y.z/tez-x.y.z-minimal.tar.gz - set tez.use.cluster.hadoop-libs to true