How to run MR on TEZ
=======================

Tez provides an ApplicationMaster that can run MR or MRR jobs. There is a
translation layer implemented ( may have bugs so please file JIRAs if you
come across any issues ) that allows a user to run an MR job against the TEZ
DAG ApplicationMaster.

NOTE: Due to some quirks, all jobs will show an IOException and a failure at
the end. Please look at the YARN ResourceManager UI to check the correct result
of the job.

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

1) Deploy Apache Hadoop using the 3.0.0-SNAPSHOT from trunk.
   - You can also use the 2.1.0 branch. For this, the only change will be to build
     tez with the -Dhadoop.version set to the correct version matching the hadoop
     branch being used.
2) Copy the tez jars and their dependencies into HDFS.
3) 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.
3) Modify mapred-site.xml to change "mapreduce.framework.name" property from its
   default value of "yarn" to "yarn-tez"
4) 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.
5) 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.

6) 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.