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https://issues.apache.org/jira/browse/CASSANDRA-1608?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12923191#action_12923191
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Ryan King commented on CASSANDRA-1608:
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I think if we just start with tracking which sstables are accessed together,
then prioritizing those for being compacted together we can improve our
locality with minimal overhead.
Fixed size (or variable within a range) SSTables could then also help. If we
achieve the fixed size by splitting by token range when doing a compaction, we
would have much more predictable read time for both skinny rows and wide rows.
I'm not sure how well this would react to a rapidly changing workload, though.
> Redesigned Compaction
> ---------------------
>
> Key: CASSANDRA-1608
> URL: https://issues.apache.org/jira/browse/CASSANDRA-1608
> Project: Cassandra
> Issue Type: Improvement
> Components: Core
> Reporter: Chris Goffinet
> Fix For: 0.7.1
>
>
> After seeing the I/O issues in CASSANDRA-1470, I've been doing some more
> thinking on this subject that I wanted to lay out.
> I propose we redo the concept of how compaction works in Cassandra. At the
> moment, compaction is kicked off based on a write access pattern, not read
> access pattern. In most cases, you want the opposite. You want to be able to
> track how well each SSTable is performing in the system. If we were to keep
> statistics in-memory of each SSTable, prioritize them based on most accessed,
> and bloom filter hit/miss ratios, we could intelligently group sstables that
> are being read most often and schedule them for compaction. We could also
> schedule lower priority maintenance on SSTable's not often accessed.
> I also propose we limit the size of each SSTable to a fix sized, that gives
> us the ability to better utilize our bloom filters in a predictable manner.
> At the moment after a certain size, the bloom filters become less reliable.
> This would also allow us to group data most accessed. Currently the size of
> an SSTable can grow to a point where large portions of the data might not
> actually be accessed as often.
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