Github user wzhfy commented on a diff in the pull request:
https://github.com/apache/spark/pull/16228#discussion_r100681997
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/JoinEstimation.scala
---
@@ -0,0 +1,314 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.plans.logical.statsEstimation
+
+import scala.collection.mutable
+
+import org.apache.spark.internal.Logging
+import org.apache.spark.sql.catalyst.CatalystConf
+import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeMap,
AttributeReference, Expression}
+import org.apache.spark.sql.catalyst.planning.ExtractEquiJoinKeys
+import org.apache.spark.sql.catalyst.plans._
+import org.apache.spark.sql.catalyst.plans.logical.{ColumnStat, Join,
Statistics}
+import
org.apache.spark.sql.catalyst.plans.logical.statsEstimation.EstimationUtils._
+import org.apache.spark.sql.types.DataType
+
+
+object JoinEstimation extends Logging {
+ /**
+ * Estimate statistics after join. Return `None` if the join type is not
supported, or we don't
+ * have enough statistics for estimation.
+ */
+ def estimate(conf: CatalystConf, join: Join): Option[Statistics] = {
+ join.joinType match {
+ case Inner | Cross | LeftOuter | RightOuter | FullOuter =>
+ InnerOuterEstimation(conf, join).doEstimate()
+ case LeftSemi | LeftAnti =>
+ LeftSemiAntiEstimation(conf, join).doEstimate()
+ case _ =>
+ logDebug(s"[CBO] Unsupported join type: ${join.joinType}")
+ None
+ }
+ }
+}
+
+case class InnerOuterEstimation(conf: CatalystConf, join: Join) extends
Logging {
+
+ private val leftStats = join.left.stats(conf)
+ private val rightStats = join.right.stats(conf)
+
+ /**
+ * Estimate output size and number of rows after a join operator, and
update output column stats.
+ */
+ def doEstimate(): Option[Statistics] = join match {
+ case _ if !rowCountsExist(conf, join.left, join.right) =>
+ None
+
+ case ExtractEquiJoinKeys(joinType, leftKeys, rightKeys, condition,
left, right) =>
+ // 1. Compute join selectivity
+ val joinKeyPairs = extractJoinKeys(leftKeys, rightKeys)
+ val selectivity = joinSelectivity(joinKeyPairs, leftStats,
rightStats)
+
+ // 2. Estimate the number of output rows
+ val leftRows = leftStats.rowCount.get
+ val rightRows = rightStats.rowCount.get
+ val innerRows = ceil(BigDecimal(leftRows * rightRows) * selectivity)
+
+ // Make sure outputRows won't be too small based on join type.
+ val outputRows = joinType match {
+ case LeftOuter =>
+ // All rows from left side should be in the result.
+ leftRows.max(innerRows)
+ case RightOuter =>
+ // All rows from right side should be in the result.
+ rightRows.max(innerRows)
+ case FullOuter =>
+ // Simulate full outer join as obtaining the number of elements
in the union of two
+ // finite sets: A \cup B = A + B - A \cap B => A FOJ B = A + B -
A IJ B.
+ // But the "inner join" part can be much larger than A \cap B,
making the simulated
+ // result much smaller. To prevent this, we choose the larger
one between the simulated
+ // part and the inner part.
+ (leftRows + rightRows - innerRows).max(innerRows)
+ case _ =>
+ // Don't change for inner or cross join
+ innerRows
+ }
+
+ // 3. Update statistics based on the output of join
+ val intersectedStats = if (selectivity == 0) {
+ AttributeMap[ColumnStat](Nil)
+ } else {
+ updateIntersectedStats(joinKeyPairs, leftStats, rightStats)
+ }
+ val inputAttrStats = AttributeMap(
+ leftStats.attributeStats.toSeq ++ rightStats.attributeStats.toSeq)
+ val attributesWithStat = join.output.filter(a =>
inputAttrStats.contains(a))
+ val (fromLeft, fromRight) =
attributesWithStat.partition(join.left.outputSet.contains(_))
+ val outputStats: Map[Attribute, ColumnStat] = join.joinType match {
+ case LeftOuter =>
+ // Don't update column stats for attributes from left side.
+ fromLeft.map(a => (a, inputAttrStats(a))).toMap ++
+ updateAttrStats(outputRows, fromRight, inputAttrStats,
intersectedStats)
+ case RightOuter =>
+ // Don't update column stats for attributes from right side.
+ updateAttrStats(outputRows, fromLeft, inputAttrStats,
intersectedStats) ++
+ fromRight.map(a => (a, inputAttrStats(a))).toMap
+ case FullOuter =>
+ // Don't update column stats for attributes from both sides.
+ attributesWithStat.map(a => (a, inputAttrStats(a))).toMap
+ case _ =>
+ // Update column stats from both sides for inner or cross join.
+ updateAttrStats(outputRows, attributesWithStat, inputAttrStats,
intersectedStats)
+ }
+ val outputAttrStats = AttributeMap(outputStats.toSeq)
+
+ Some(Statistics(
+ sizeInBytes = getOutputSize(join.output, outputRows,
outputAttrStats),
+ rowCount = Some(outputRows),
+ attributeStats = outputAttrStats,
+ isBroadcastable = false))
+
+ case _ =>
+ // When there is no equi-join condition, we do estimation like
cartesian product.
+ val inputAttrStats = AttributeMap(
+ leftStats.attributeStats.toSeq ++ rightStats.attributeStats.toSeq)
+ // Propagate the original column stats
+ val outputAttrStats = getOutputMap(inputAttrStats, join.output)
+ val outputRows = leftStats.rowCount.get * rightStats.rowCount.get
+ Some(Statistics(
+ sizeInBytes = getOutputSize(join.output, outputRows,
outputAttrStats),
+ rowCount = Some(outputRows),
+ attributeStats = outputAttrStats,
+ isBroadcastable = false))
+ }
+
+ // scalastyle:off
+ /**
+ * The number of rows of A inner join B on A.k1 = B.k1 is estimated by
this basic formula:
+ * T(A IJ B) = T(A) * T(B) / max(V(A.k1), V(B.k1)), where V is the
number of distinct values of
+ * that column. The underlying assumption for this formula is: each
value of the smaller domain
+ * is included in the larger domain.
+ * Generally, inner join with multiple join keys can also be estimated
based on the above
+ * formula:
+ * T(A IJ B) = T(A) * T(B) / (max(V(A.k1), V(B.k1)) * max(V(A.k2),
V(B.k2)) * ... * max(V(A.kn), V(B.kn)))
+ * However, the denominator can become very large and excessively reduce
the result, so we use a
+ * conservative strategy to take only the largest max(V(A.ki), V(B.ki))
as the denominator.
+ */
+ // scalastyle:on
+ def joinSelectivity(
+ joinKeyPairs: Seq[(AttributeReference, AttributeReference)],
+ leftStats: Statistics,
+ rightStats: Statistics): BigDecimal = {
+
+ var ndvDenom: BigInt = -1
+ var i = 0
+ while(i < joinKeyPairs.length && ndvDenom != 0) {
+ val (leftKey, rightKey) = joinKeyPairs(i)
+ // Do estimation if we have enough statistics
+ if (columnStatsExist((leftStats, leftKey), (rightStats, rightKey))) {
+ // Check if the two sides are disjoint
+ val leftKeyStats = leftStats.attributeStats(leftKey)
+ val rightKeyStats = rightStats.attributeStats(rightKey)
+ val lRange = Range(leftKeyStats.min, leftKeyStats.max,
leftKey.dataType)
+ val rRange = Range(rightKeyStats.min, rightKeyStats.max,
rightKey.dataType)
+ if (Range.isIntersected(lRange, rRange)) {
+ // Get the largest ndv among pairs of join keys
+ val maxNdv =
leftKeyStats.distinctCount.max(rightKeyStats.distinctCount)
+ if (maxNdv > ndvDenom) ndvDenom = maxNdv
+ } else {
+ // Set ndvDenom to zero to indicate that this join should have
no output
+ ndvDenom = 0
+ }
+ }
+ i += 1
+ }
+
+ if (ndvDenom < 0) {
+ // There isn't join keys or column stats for any of the join key
pairs, we do estimation like
+ // cartesian product.
+ 1
+ } else if (ndvDenom == 0) {
+ // One of the join key pairs is disjoint, thus the two sides of join
is disjoint.
+ 0
+ } else {
+ 1 / BigDecimal(ndvDenom)
+ }
+ }
+
+ /** Update column stats for output attributes. */
+ private def updateAttrStats(
+ outputRows: BigInt,
+ attributes: Seq[Attribute],
+ oldAttrStats: AttributeMap[ColumnStat],
+ joinKeyStats: AttributeMap[ColumnStat]): AttributeMap[ColumnStat] = {
+ val outputAttrStats = new mutable.HashMap[Attribute, ColumnStat]()
+ val leftRows = leftStats.rowCount.get
+ val rightRows = rightStats.rowCount.get
+ if (outputRows == 0) {
+ // empty output
+ attributes.foreach(a => outputAttrStats.put(a,
emptyColumnStat(a.dataType)))
--- End diff --
we may union this plan with the other plan, then empty column stats is
useful. Now we don't support union estimation because it's difficult to
estimate distinct count after union.
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