Github user gatorsmile commented on a diff in the pull request:
https://github.com/apache/spark/pull/17415#discussion_r108314909
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/FilterEstimation.scala
---
@@ -515,8 +530,135 @@ case class FilterEstimation(plan: Filter,
catalystConf: CatalystConf) extends Lo
Some(percent.toDouble)
}
+ /**
+ * Returns a percentage of rows meeting a binary comparison expression
containing two columns.
+ * In SQL queries, we also see predicate expressions involving two
columns
+ * such as "column-1 (op) column-2" where column-1 and column-2 belong
to same table.
+ * Note that, if column-1 and column-2 belong to different tables, then
it is a join
+ * operator's work, NOT a filter operator's work.
+ *
+ * @param op a binary comparison operator such as =, <, <=, >, >=
+ * @param attrLeft the left Attribute (or a column)
+ * @param attrRight the right Attribute (or a column)
+ * @param update a boolean flag to specify if we need to update
ColumnStat of a given column
+ * for subsequent conditions
+ * @return an optional double value to show the percentage of rows
meeting a given condition
+ */
+ def evaluateBinaryForTwoColumns(
+ op: BinaryComparison,
+ attrLeft: Attribute,
+ attrRight: Attribute,
+ update: Boolean): Option[Double] = {
+
+ if (!colStatsMap.contains(attrLeft)) {
+ logDebug("[CBO] No statistics for " + attrLeft)
+ return None
+ }
+ if (!colStatsMap.contains(attrRight)) {
+ logDebug("[CBO] No statistics for " + attrRight)
+ return None
+ }
+
+ attrLeft.dataType match {
+ case StringType | BinaryType =>
+ // TODO: It is difficult to support other binary comparisons for
String/Binary
+ // type without min/max and advanced statistics like histogram.
+ logDebug("[CBO] No range comparison statistics for String/Binary
type " + attrLeft)
+ return None
+ case _ =>
+ }
+
+ val colStatLeft = colStatsMap(attrLeft)
+ val statsRangeLeft = Range(colStatLeft.min, colStatLeft.max,
attrLeft.dataType)
+ .asInstanceOf[NumericRange]
+ val maxLeft = BigDecimal(statsRangeLeft.max)
+ val minLeft = BigDecimal(statsRangeLeft.min)
+ val ndvLeft = BigDecimal(colStatLeft.distinctCount)
+
+ val colStatRight = colStatsMap(attrRight)
+ val statsRangeRight = Range(colStatRight.min, colStatRight.max,
attrRight.dataType)
+ .asInstanceOf[NumericRange]
+ val maxRight = BigDecimal(statsRangeRight.max)
+ val minRight = BigDecimal(statsRangeRight.min)
+ val ndvRight = BigDecimal(colStatRight.distinctCount)
+
+ // determine the overlapping degree between predicate range and
column's range
+ val (noOverlap: Boolean, completeOverlap: Boolean) = op match {
+ case _: EqualTo =>
+ ((maxLeft < minRight) || (maxRight < minLeft),
+ (minLeft == minRight) && (maxLeft == maxRight))
+ case _: LessThan =>
+ (minLeft >= maxRight, maxLeft <= minRight)
+ case _: LessThanOrEqual =>
+ (minLeft >= maxRight, maxLeft <= minRight)
+ case _: GreaterThan =>
+ (maxLeft <= minRight, minLeft >= maxRight)
+ case _: GreaterThanOrEqual =>
+ (maxLeft < minRight, minLeft > maxRight)
+ }
+
+ var percent = BigDecimal(1.0)
+ if (noOverlap) {
+ percent = 0.0
+ } else if (completeOverlap) {
+ percent = 1.0
+ } else {
+ // For partial overlap, we use an empirical value 1/3 as suggested
by the book
+ // "Database Systems, the complete book".
+ percent = 1.0/3.0
+
+ if (update) {
+ // Need to adjust new min/max after the filter condition is applied
+
+ val ndvLeft = BigDecimal(colStatLeft.distinctCount)
+ var newNdvLeft = (ndvLeft * percent).setScale(0,
RoundingMode.HALF_UP).toBigInt()
+ if (newNdvLeft < 1) newNdvLeft = 1
+ val ndvRight = BigDecimal(colStatLeft.distinctCount)
+ var newNdvRight = (ndvRight * percent).setScale(0,
RoundingMode.HALF_UP).toBigInt()
+ if (newNdvRight < 1) newNdvRight = 1
+
+ var newMaxLeft = colStatLeft.max
+ var newMinLeft = colStatLeft.min
+ var newMaxRight = colStatRight.max
+ var newMinRight = colStatRight.min
+
+ op match {
+ case _: EqualTo =>
+ // need to set new min to the larger min value, and
+ // set the new max to the smaller max value.
+ if (minLeft < minRight) newMinLeft = colStatRight.min
+ else newMinRight = colStatLeft.min
+ if (maxLeft < maxRight) newMaxRight = colStatLeft.max
+ else newMaxLeft = colStatRight.max
+
+ case _: GreaterThan | _: GreaterThanOrEqual =>
+ // the left side should be greater than the right side.
+ // If not, we need to adjust it to narrow the range.
+ if (minLeft < minRight) newMinLeft = colStatRight.min
+ if (maxLeft < maxRight) newMaxRight = colStatLeft.max
+
+ case _: LessThan | _: LessThanOrEqual =>
+ // the left side should be less than the right side.
+ // If not, we need to adjust it to narrow the range.
+ if (minLeft > minRight) newMinRight = colStatLeft.min
+ if (maxLeft > maxRight) newMaxLeft = colStatRight.max
+ }
+
+ val newStatsLeft = colStatLeft.copy(distinctCount = newNdvLeft,
min = newMinLeft,
+ max = newMaxLeft, nullCount = 0)
+ colStatsMap(attrLeft) = newStatsLeft
+ val newStatsRight = colStatRight.copy(distinctCount = newNdvRight,
min = newMinRight,
+ max = newMaxRight, nullCount = 0)
--- End diff --
`nullCount ` might not be simply set to zero if we also support
`EqualNullSafe `
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