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https://issues.apache.org/jira/browse/CASSANDRA-7282?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Benedict updated CASSANDRA-7282:
--------------------------------
Attachment: profile.yaml
run1.svg
Ok, so I ran a more realistic workload with the attached profile.yaml, 50/50
read/writes, with reads favouring recently written partitions following an
extreme distribution. i.e. the following stress command:
./tools/bin/cassandra-stress user profile=profile.yaml ops\(insert=5,read=5\)
n=20000000 -pop seq=1..10M read-lookback=extreme\(1..1M,2\) -rate threads=200
-mode cql3 native prepared
This is still a workload geared towards exhibiting favourable behaviour, but it
is certainly a larger than memory workload.
The graph comparing the results (run1.svg) attached demonstrates it is still
showing a clear improvement, of around 10% throughput, reduced latencies,
reduced total GC work. It also results in less frequent flushes, presumably due
to it requiring slightly less memory than CSLM.
> Faster Memtable map
> -------------------
>
> Key: CASSANDRA-7282
> URL: https://issues.apache.org/jira/browse/CASSANDRA-7282
> Project: Cassandra
> Issue Type: Improvement
> Components: Core
> Reporter: Benedict
> Assignee: Benedict
> Labels: performance
> Fix For: 3.0
>
> Attachments: profile.yaml, reads.svg, run1.svg, writes.svg
>
>
> Currently we maintain a ConcurrentSkipLastMap of DecoratedKey -> Partition in
> our memtables. Maintaining this is an O(lg(n)) operation; since the vast
> majority of users use a hash partitioner, it occurs to me we could maintain a
> hybrid ordered list / hash map. The list would impose the normal order on the
> collection, but a hash index would live alongside as part of the same data
> structure, simply mapping into the list and permitting O(1) lookups and
> inserts.
> I've chosen to implement this initial version as a linked-list node per item,
> but we can optimise this in future by storing fatter nodes that permit a
> cache-line's worth of hashes to be checked at once, further reducing the
> constant factor costs for lookups.
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