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From "Alex Petrov (JIRA)" <>
Subject [jira] [Updated] (CASSANDRA-12417) Built-in AVG aggregate is much less useful than it should be
Date Fri, 23 Sep 2016 11:56:20 GMT


Alex Petrov updated CASSANDRA-12417:
    Status: Patch Available  (was: Open)

To improve summation precision for {{float}} and {{double}} implementations, I've used the
summation with Kahan's algorithm. {{BigDecimal}} implementation is unchanged.

For {{avg}}, all whole numbers are using single implementation (long arithmetics, falling
back to {{BigInteger}} when needed) for sum calculation in order to avoid situations when
the sum is large, although resulting number does not overflow the current type boundary. For
floating point numbers, logic is similar, using Kahan's algorithm for calculating sum, switching
to {{BigDecimal}} on infinity/overflow.

|[trunk|] |[utest|]

> Built-in AVG aggregate is much less useful than it should be
> ------------------------------------------------------------
>                 Key: CASSANDRA-12417
>                 URL:
>             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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