[jira] [Commented] (CASSANDRA-12417) Built-in AVG aggregate is much less useful than it should be

["Benjamin Lerer (JIRA)" <ji...@apache.org>]

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

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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