GitHub user mgaido91 opened a pull request:
https://github.com/apache/spark/pull/21949
[SPARK-24957][SQL][BACKPORT-2.2] Average with decimal followed by
aggregation returns wrong result
## What changes were proposed in this pull request?
When we do an average, the result is computed dividing the sum of the
values by their count. In the case the result is a DecimalType, the way we are
casting/managing the precision and scale is not really optimized and it is not
coherent with what we do normally.
In particular, a problem can happen when the Divide operand returns a
result which contains a precision and scale different by the ones which are
expected as output of the Divide operand. In the case reported in the JIRA, for
instance, the result of the Divide operand is a Decimal(38, 36), while the
output data type for Divide is 38, 22. This is not an issue when the Divide is
followed by a CheckOverflow or a Cast to the right data type, as these
operations return a decimal with the defined precision and scale. Despite in
the Average operator we do have a Cast, this may be bypassed if the result of
Divide is the same type which it is casted to, hence the issue reported in the
JIRA may arise.
The PR proposes to use the normal rules/handling of the arithmetic
operators with Decimal data type, so we both reuse the existing code (having a
single logic for operations between decimals) and we fix this problem as the
result is always guarded by CheckOverflow.
## How was this patch tested?
added UT
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/mgaido91/spark SPARK-24957_2.2
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/21949.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #21949
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commit 1f817a058887090ebf41c510a3b6086a062433d6
Author: Marco Gaido <marcogaido91@...>
Date: 2018-07-29T10:53:09Z
[SPARK-24957][SQL] Average with decimal followed by aggregation returns
wrong result
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