How to use TEZ
=======================

Tez provides an ApplicationMaster that can run any arbritary DAG of tasks. It also
provides a translation layer to run MR or MRR jobs using the MR APIs. This translation
layer is not fully feature compatible so if you do see any issues with running your
existing MR jobs on TEZ, please file jiras.

Install/Deploy Instructions
===========================

1) Deploy Apache Hadoop either using the 2.2.0 release or a compatible 2.x version.
2) Build tez using "mvn clean install -DskipTests=true -Dmaven.javadoc.skip=true"
   - If you prefer to run the unit tests, remove skipTests from the command above.
   - If you would like to create a tarball of the release, use
     "mvn clean package -Dtar -DskipTests=true -Dmaven.javadoc.skip=true"
3) Copy the tez jars and their dependencies into HDFS.
   - The tez jars and dependencies will be found in tez-dist/target/tez-0.4.0-incubating-SNAPSHOT/tez-0.4.0-incubating-SNAPSHOT
     if you run the intial command mentioned in step 2.
   - Assuming that the tez jars are put in /apps/ on HDFS, the command would be
     "hadoop dfs -put tez-dist/target/tez-0.4.0-incubating-SNAPSHOT/tez-0.4.0-incubating-SNAPSHOT /apps/"
   - Please do not upload the tarball to HDFS, upload only the jars.
4) Configure tez-site.xml to set tez.lib.uris to point to the paths in HDFS containing
   the jars. Please note that the paths are not searched recursively so for <basedir>
   and <basedir>/lib/, you will need to configure the 2 paths as a comma-separated list.
   - Assuming you followed step 3, the value would be:
      "${fs.default.name}/apps/tez-0.4.0-incubating-SNAPSHOT,${fs.default.name}/apps/tez-0.4.0-incubating-SNAPSHOT/lib/"
5) Modify mapred-site.xml to change "mapreduce.framework.name" property from its
   default value of "yarn" to "yarn-tez"
6) set HADOOP_CLASSPATH to have the following paths in it:
   - TEZ_CONF_DIR - location of tez-site.xml
   - TEZ_JARS and TEZ_JARS/libs - location of the tez jars and dependencies.
7) Submit a MR job as you normally would using something like:

$HADOOP_PREFIX/bin/hadoop jar hadoop-mapreduce-client-jobclient-2.2.0-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.

8) There is a basic example of using an MRR job in the tez-mapreduce-examples.jar. Refer to OrderedWordCount.java
in the source code. To run this example:

$HADOOP_PREFIX/bin/hadoop jar tez-mapreduce-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.

There are multiple variations to run orderedwordcount. You can use it to run multiple
DAGs serially on different inputs/outputs. These DAGs could be run separately as
different applications or serially within a single TEZ session.

$HADOOP_PREFIX/bin/hadoop jar tez-mapreduce-examples.jar orderedwordcount <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-mapreduce-examples.jar orderedwordcount -DUSE_TEZ_SESSION=true <input1> <output1> <input2> <output2>