![[Note]](../common/images/admon/note.png) | Note |
|---|---|
This feature is a technical preview and considered under development. Do not use this feature in your production systems. If you have questions regarding this feature, contact Support by logging a case on our Hortonworks Support Portal at https://support.hortonworks.com. |
An Uber Job is when multiple mappers and reducers are combined to use a single
Container. There are four core settings around the configuration of Uber Jobs
found in the mapred-site.xml options presented in the following
table.
Configuration options for Uber Jobs
| Property | Description |
| mapreduce.job.ubertask.enable |
Whether to enable the small-jobs "ubertask" optimization, which runs "sufficiently small" jobs sequentially within a single JVM. "Small" is defined by the following maxmaps, maxreduces, and maxbytes settings. Users can override this value. Default = false |
| mapreduce.job.ubertask.maxmaps |
The threshold for the number of maps beyond which a job is considered too large for the ubertasking optimization. Users can override this value, but only downward. Default = 9 |
| mapreduce.job.ubertask.maxreduces |
The threshold for the number of reduces beyond which a job is considered too large for the ubertasking optimization. CURRENTLY THE CODE CANNOT SUPPORT MORE THAN ONE REDUCE and will ignore larger values (zero is a valid maximum value, however). Users can override this value, but only downward. Default = 1 |
| mapreduce.job.ubertask.maxbytes |
The threshold for the number of input bytes beyond which a job is
considered too large for the ubertasking optimization. If no value
is specified, dfs.block.size is used as the default. Be sure to
specify a default value in Default = HDFS Block Size |
