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https://issues.apache.org/jira/browse/CASSANDRA-12417?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15492876#comment-15492876
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Branimir Lambov commented on CASSANDRA-12417:
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Yes, Kahan's should do if you want to avoid the division. Make sure it's not
optimized away (try averaging 1e20, 1, -1e20).
> Built-in AVG aggregate is much less useful than it should be
> ------------------------------------------------------------
>
> Key: CASSANDRA-12417
> URL: https://issues.apache.org/jira/browse/CASSANDRA-12417
> Project: Cassandra
> Issue Type: Bug
> Components: CQL
> Reporter: Branimir Lambov
> Assignee: Alex Petrov
>
> For fixed-size integer types overflow is all but guaranteed to happen,
> yielding incorrect result. While for sum it is somewhat acceptable as the
> result cannot fit the type, this is not the case for average.
> As the result of average is always within the scope of the source type,
> failing to produce it only signifies a bad implementation. Yes, one can solve
> this by type-casting, but do we really want to always have to be telling
> people that the correct spelling of the average function is
> {{cast(avg(cast(value as bigint))) as int)}}, especially if this is so
> trivial to fix?
> Additionally, the straightforward addition we use for floating point versions
> is not a good choice numerically for larger numbers of values. We should
> switch to a more stable version, e.g. iterative mean using {{avg = avg +
> (value - avg) / count}}.
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