Nishkam Ravi commented on YARN-2041:

Unlike FIFO, whose performance deteriorates consistently across multiple 
benchmarks as value of yarn.nodemanager.resource.memory-mb is increased from 
16GB to 40GB, Capacity scheduler performs well for all benchmarks except for 

For TeraValidate in single-job mode:

Exec. time with Fair: 38 sec (yarn.nodemanager.resource.memory-mb = 16GB)
Exec. time with Fair: 38 sec (yarn.nodemanager.resource.memory-mb = 40GB)
Exec. time with Capacity: 51 sec (yarn.nodemanager.resource.memory-mb = 16GB)
Exec. time with Capacity: 100 sec (yarn.nodemanager.resource.memory-mb = 40GB)

Also, in multi-job mode, Capacity seems to be behaving like FIFO. Scheduling 
one job at a time for execution. 

> Hard to co-locate MR2 and Spark jobs on the same cluster in YARN
> ----------------------------------------------------------------
>                 Key: YARN-2041
>                 URL: https://issues.apache.org/jira/browse/YARN-2041
>             Project: Hadoop YARN
>          Issue Type: Improvement
>          Components: nodemanager
>    Affects Versions: 2.3.0
>            Reporter: Nishkam Ravi
> Performance of MR2 jobs falls drastically as YARN config parameter 
> yarn.nodemanager.resource.memory-mb  is increased beyond a certain value. 
> Performance of Spark falls drastically as the value of 
> yarn.nodemanager.resource.memory-mb is decreased beyond a certain value for a 
> large data set.
> This makes it hard to co-locate MR2 and Spark jobs in YARN.
> The experiments are being conducted on a 6-node cluster. The following 
> workloads are being run: TeraGen, TeraSort, TeraValidate, WordCount, 
> ShuffleText and PageRank.
> Will add more details to this JIRA over time.

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