Github user wzhfy commented on a diff in the pull request:
https://github.com/apache/spark/pull/17148#discussion_r104290248
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/FilterEstimation.scala
---
@@ -101,21 +101,23 @@ case class FilterEstimation(plan: Filter,
catalystConf: CatalystConf) extends Lo
}
case Or(cond1, cond2) =>
- // For ease of debugging, we compute percent1 and percent2 in 2
statements.
val percent1 = calculateFilterSelectivity(cond1, update = false)
val percent2 = calculateFilterSelectivity(cond2, update = false)
(percent1, percent2) match {
case (Some(p1), Some(p2)) => Some(math.min(1.0, p1 + p2 - (p1 *
p2)))
- case (Some(p1), None) => Some(1.0)
- case (None, Some(p2)) => Some(1.0)
- case (None, None) => None
+ case _ => None
}
- case Not(cond) => calculateFilterSelectivity(cond, update = false)
match {
- case Some(percent) => Some(1.0 - percent)
- // for not-supported condition, set filter selectivity to a
conservative estimate 100%
- case None => None
- }
+ case Not(cond) =>
+ if (cond.isInstanceOf[And] || cond.isInstanceOf[Or]) {
+ // Don't support compound Not expression.
--- End diff --
If we over-estimate for a condition `cond1` in `calculateSingleCondition`,
then `Not(cond1)` becomes under-estimation, even if `cond1` is not a compound
condition.
Actually I'm thinking whether it's reasonable to differentiate between
under-estimation and over-estimation. Since we assume uniform distribution, we
really can't be sure if we are over-estimating or not.
E.g. for condition `a=1`, we estimate filter factor as `1/ndv`, it can be
over-estimation if `1` is a value in the "long tail", or be under-estimation if
`1` is the skew value of this column.
In fact, what we do now is just using some empirical formula to compute the
probability that the condition satisfies.
So I suggest that we don't care about "nested Not" or "Not", just do what
we do for other compound conditions as before:
```
case Not(cond) =>
calculateFilterSelectivity(cond, update = false) match {
case Some(percent) => Some(1.0 - percent)
case None => None
}
```
What do you think? @cloud-fan @ron8hu
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