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https://issues.apache.org/jira/browse/CASSANDRA-12417?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15492835#comment-15492835
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Benjamin Lerer commented on CASSANDRA-12417:
--------------------------------------------

[~blambov] For summing floating point numbers, do you think it will make sense 
to use the [Kahan summation 
algorithm|https://en.wikipedia.org/wiki/Kahan_summation_algorithm]?

> 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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