Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/16228#discussion_r100303159
--- 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
--- End diff --
the `nullCount` will be inaccurate after this, right?
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