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From "Peter Schuller (JIRA)" <>
Subject [jira] Commented: (CASSANDRA-1608) Redesigned Compaction
Date Sun, 17 Oct 2010 22:51:25 GMT


Peter Schuller commented on CASSANDRA-1608:

With respect to tracking information, I meant tracking the information necessary to make the
determination as to which rows to supersede or otherwise act upon, rather than keeping track
of what *has* been superseded (which as you point out is dealt with by bloom filters).

In the case of simple criteria (such as seeing a single read spread across more than N sstables)
no such tracking is necessary. But I am concerned with strategies that imply having to keep
significant amounts of data over time, such as anything based on row-level frequency/recency
of access.

In particular, if one hopes to supersede so effectively that hot rows (regardless of sstable
spread) end up in separate sstables with high locality of access, will not this need such
row-level information tracking?

> Redesigned Compaction
> ---------------------
>                 Key: CASSANDRA-1608
>                 URL:
>             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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