Could also use JVisualVM which is capable of giving some better reports
on benchmarks compared to manually inspecting jstacks.
Keith Turner wrote:
You can try sampling using jstack as a simple and quick way to profile.
Jstack a process writing rfiles ~10 times, with some pause he tween.
Then look at a particular thread writing data across the jstack saves,
do you see the same code being executed in multiple jstacks? If so what
code is that?
Sent from phone. Please excuse typos and brevity.
On Jan 3, 2015 12:46 AM, "Ara Ebrahimi" <[email protected]
<mailto:[email protected]>> wrote:
Hi,
I’m trying to optimize our map/reduce job which generates RFiles
using AccumuloFileOutputFormat. We have a specific time window and
within that time window we need to generate a predefined amount of
simulation data and in terms of number of core we also have an upper
bound we can use. Disks are also fixed at 4 per node and they are
all SSDs. So I can’t employ more machines or more disks or cores to
achieve higher write/s numbers.
So far we’ve managed to utilize 100% of all available cores and the
SSD disks are also highly utilized. I’m trying to reduce processing
time and we are willing to waste more disk space to achieve higher
data generation speed. The data itself is 10s of columns of floating
numbers, all serialized to fixed 9-byte values which doesn’t lend
well to compression. With no compression and replication set to 1 we
can generate the same amount of data in almost half the time. With
snappy it’s almost 10% more data generation time compared to no
compression and almost twice more size on disk for the all the
generated RFiles.
dataBlockSize doesn’t seem to change anything for non-compressed
data. indexBlockSize also didn't change anything (tried 64K vs the
default 128K).
Any other tricks I could employ to achieve higher write/s numbers?
Ara.
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