UBER Mode in 2.X

UBER mode in 2.X :
Normally mappers and reducers will run by ResourceManager (RM), RM will create separate container for mapper and reducer. Uber configuration, will allow to run mapper and reducers in the same process as the ApplicationMaster (AM).

Uber jobs :
Uber jobs are jobs that are executed within the MapReduce ApplicationMaster rather than communicate with RM to create the mapper and reducer containers. The AM runs the map and reduce tasks within its own process and avoided the overhead of launching and communicate with remote containers.

Why is it so :
If you have a small dataset or you want to run MapReduce on small amount of data, Uber configuration will help you out, by reducing additional time that MapReduce normally spends mapper and reducers phase.

Can I configure/have a Uber for all MapReduce job.?
As of now, map-only jobs and jobs with one reducer are supported.

In other word :
Uber Job occurs when multiple mapper and reducers are combined to use a single container. There are four core settings around the configuration of Uber Jobs in the mapred-site.xml. Configuration options for Uber Jobs:
mapreduce.job.ubertask.enable
mapreduce.job.ubertask.maxmaps
mapreduce.job.ubertask.maxreduces
mapreduce.job.ubertask.maxbytes

Map Reduce Phases

Map -> Combiner -> Partitioner -> Sort -> Shuffle -> Sort -> Reduce

Map phase is done by mappers. Mappers run on unsorted input key/values pairs. Each mapper emits zero, one or multiple output key/value pairs for each input key/value pairs.

Combine phase is done by Combiners. Combiner should combine key/value pairs with the same key together. Each combiner may run zero, once or multiple times.

Shuffle and Sort phase is done by framework. Data from all mappers are grouped by the key, split among reducers and sorted by the key. Each reducer obtains all values associated with the same key. Programmer may supply custom compare function for sorting and Partitioner for data split.

Partitioner decides which Reducer will get a particular key value pair.

Reducer obtains sorted key/[values list] pairs sorted by the key. Value list contains all values with the same key produced by mappers. Each reducer emits zero, one or multiple output key/value pairs for each input key/value pair.

Below diagram is copied from Hadoop book.