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aef3397f10
So there's a lot going on here.
Percentile just did not work, even a little, with decimals.
In theory we try to make the int val part of ValCount work, in
ValCountize, but you can't actually use that for everything because
it unconditionally adds bsig.Base even when it shouldn't. But it
doesn't matter that we were returning those values from, say,
(Field).MinForShard, because ValCount.Smaller was not preserving them
when identifying the smaller of two Decimal ValCounts anyway.
And even if it did, the logic in Percentile wouldn't have worked
with passing the raw unscaled integer in as a value to compare
against.
But that's fine because the logic was also more generally wrong.
According to the existing logic, a value is the median value if
exactly as many values are less than it as are greater than it.
This is... not actually very accurate to what we usually mean by
"median". Because some values are *equal* to a given value. So
for instance, say you have the values {1, 1, 1, [a million 2s], 3}.
Our logic would regard 2 as being too high to be the median, because
3 times as many values are lower as are higher.
New interpretation: Imagine a sorted list of all your values, with
N entries. You want the Nth percentile, which is to say, you want N%
of values to be less than the vale you pick, and (100-N)% to be greater.
You can round both of these down. So for instance, if you have 6 values,
and want the median, you want 3 values greater, and 3 values less. To
be picky, we could demand the average of those middle two values, but
we're not in a good position to do that in this implementation.
If the number of desired things less than, or greater than, a target
is 0, we can short-circuit to the minimum or maximum value. This can
happen when nth is close to an end and the number of things is small,
not just at nth=0/nth=100.
So we rework this, and we rework the tests for this behavior to reflect
that logic.
We change executePercentile to be able to return a nil rather than
a weird ValCount in cases where there's no result, such as when
there's no values to compute a percentile of.
We also change the SQL tests to match the new behavior, since some
of them were expecting everything done on a decimal field with values
10-13 to come back as 10.00 as a decimal because that is what the
code returned.
We also propagate these changes to DAX, and along the way, fix up a
TODO item in the DAX copy, and stop skipping the test that was
failing because of that TODO item.
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| .. | ||
| defs.go | ||
| defs_aggregate.go | ||
| defs_between.go | ||
| defs_binops.go | ||
| defs_bool.go | ||
| defs_bulkinsert.go | ||
| defs_cast.go | ||
| defs_copy.go | ||
| defs_create_table.go | ||
| defs_date_functions.go | ||
| defs_delete.go | ||
| defs_distinct.go | ||
| defs_filterpredicates.go | ||
| defs_groupby.go | ||
| defs_having.go | ||
| defs_in.go | ||
| defs_inserts.go | ||
| defs_join.go | ||
| defs_keyed.go | ||
| defs_keyed_insert.go | ||
| defs_like.go | ||
| defs_minmaxnegative.go | ||
| defs_null.go | ||
| defs_orderby.go | ||
| defs_select.go | ||
| defs_set_functions.go | ||
| defs_sql1.go | ||
| defs_string_functions.go | ||
| defs_subquery.go | ||
| defs_timequantum.go | ||
| defs_timestamp_literals.go | ||
| defs_top.go | ||
| defs_unkeyed.go | ||
| defs_unops.go | ||
| defs_views.go | ||
| types.go | ||