Improve the I/O subsystem for ROW-READ stage
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Key: CASSANDRA-1576
URL: https://issues.apache.org/jira/browse/CASSANDRA-1576
Project: Cassandra
Issue Type: Improvement
Components: Core
Affects Versions: 0.7 beta 2, 0.6.5
Reporter: Chris Goffinet
I did some profiling awhile ago, and noticed that there is quite a bit of
overhead that is happening in the ROW-READ stage of Cassandra. My testing was
on 0.6 branch. Jonathan mentioned there is endpoint snitch caching in 0.7. One
of the pain points is that we do synchronize I/O in our threads. I have
observed through profiling and other benchmarks, that even having a very
powerful machine (16-core Nehalem, 32GB of RAM), the amount of overhead of
going through to the page cache can still be between 2-3ms (with mmap). I
observed at least 800 microseconds more overhead if not using mmap. There is
definitely overhead in this stage. I propose we seriously consider moving to
doing Asynchronous I/O in each of these threads instead.
Imagine the following scenario:
3ms with mmap to read from page cache + 1.1ms of function call overhead
(observed google iterators in 0.6, could be much better in 0.7)
That's 4.1ms per message. With 32 threads, at best the machine is only going to
be able to serve:
7,804 messages/s.
This number also means that all your data has to be in page cache. If you start
to dip into any set of data that isn't in cache, this number is going to drop
substantially, even if your hit rate was 99%.
Anyone with a serious data set that is greater than the total page cache of the
cluster, is going to be victim of major slowdowns as soon as any requests come
in needing to fetch data not in cache. If you run without the Direct I/O patch,
and you actually have a pretty good write load, you can expect your cluster to
fall victim even more with page cache thrashing as new SSTables are read/writen
using compaction.
All of these scenarios mentioned above were seen at Digg with 45-node cluster,
16-core machines with a dataset larger than total page cache.
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