[jira] [Commented] (SPARK-3358) PySpark worker fork()ing performance regression in m3.* / PVM instances

["Sean Owen (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-3358?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14243454#comment-14243454 ] 

Sean Owen commented on SPARK-3358:
----------------------------------

Is this then resolved by one of https://github.com/apache/spark/pull/2244 or https://github.com/apache/spark/pull/2259 ?

> PySpark worker fork()ing performance regression in m3.* / PVM instances
> -----------------------------------------------------------------------
>
>                 Key: SPARK-3358
>                 URL: https://issues.apache.org/jira/browse/SPARK-3358
>             Project: Spark
>          Issue Type: Bug
>          Components: PySpark
>    Affects Versions: 1.1.0
>         Environment: m3.* instances on EC2
>            Reporter: Josh Rosen
>
> SPARK-2764 (and some followup commits) simplified PySpark's worker process structure by removing an intermediate pool of processes forked by daemon.py.  Previously, daemon.py forked a fixed-size pool of processes that shared a socket and handled worker launch requests from Java.  After my patch, this intermediate pool was removed and launch requests are handled directly in daemon.py.
> Unfortunately, this seems to have increased PySpark task launch latency when running on m3* class instances in EC2.  Most of this difference can be attributed to m3 instances' more expensive fork() system calls.  I tried the following microbenchmark on m3.xlarge and r3.xlarge instances:
> {code}
> import os
> for x in range(1000):
>   if os.fork() == 0:
>     exit()
> {code}
> On the r3.xlarge instance:
> {code}
> real	0m0.761s
> user	0m0.008s
> sys	0m0.144s
> {code}
> And on m3.xlarge:
> {code}
> real    0m1.699s
> user    0m0.012s
> sys     0m1.008s
> {code}
> I think this is due to HVM vs PVM EC2 instances using different virtualization technologies with different fork costs.
> It may be the case that this performance difference only appears in certain microbenchmarks and is masked by other performance improvements in PySpark, such as improvements to large group-bys.  I'm in the process of re-running spark-perf benchmarks on m3 instances in order to confirm whether this impacts more realistic jobs.



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