Github user JoshRosen commented on the pull request:
https://github.com/apache/spark/pull/2030#issuecomment-52606422
Benchmarked as of 0d8ed5b and the results aren't conclusively faster than
`master`; the good news is that we've narrowed the gap that I saw earlier
between `master` and v1.0.2 for small jobs.
Each bar here represents a test where I ran 100 back-to-back jobs, each
with 10 tasks, and varied the size of the taskâs closure (each bar is the
average of 10 runs, ignoring the first run to allow for JIT / warmup). The
closure sizes (x-axis) are empty (well, whatever the minimum size was), 1
megabyte, and 10 megabytes; y-axis is time (seconds). This is running on 10
r3.2xlarge nodes in EC2. The test code is based off of my modified version of
spark-perf
(https://github.com/JoshRosen/spark-perf/commit/0e768b2e03bfb3eeb421397e6e0fe93082879ef8)
Or, in tabular form, the means:
and standard deviations:
Keep in mind that this is running 100 back-to-back jobs; for example,
v1.0.2 averaged 9ms per job for the small jobs.
I'll run these benchmarks again tomorrow morning when I'm less tired to
make sure I haven't inadvertently misconfigured anything.
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