Github user wzhfy commented on a diff in the pull request:
https://github.com/apache/spark/pull/16395#discussion_r96182085
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/FilterEstimation.scala
---
@@ -0,0 +1,620 @@
+/*
+ * 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 java.sql.{Date, Timestamp}
+
+import scala.collection.immutable.{HashSet, Map}
+import scala.collection.mutable
+
+import org.apache.spark.internal.Logging
+import org.apache.spark.sql.catalyst.CatalystConf
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans.logical._
+import org.apache.spark.sql.catalyst.util.DateTimeUtils
+import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.types.UTF8String
+
+/**
+ * @param plan a LogicalPlan node that must be an instance of Filter
+ * @param catalystConf a configuration showing if CBO is enabled
+ */
+case class FilterEstimation(plan: Filter, catalystConf: CatalystConf)
extends Logging {
+
+ /**
+ * We use a mutable colStats because we need to update the corresponding
ColumnStat
+ * for a column after we apply a predicate condition. For example, A
column c has
+ * [min, max] value as [0, 100]. In a range condition such as (c > 40
AND c <= 50),
+ * we need to set the column's [min, max] value to [40, 100] after we
evaluate the
+ * first condition c > 40. We need to set the column's [min, max] value
to [40, 50]
+ * after we evaluate the second condition c <= 50.
+ */
+ private var mutableColStats: mutable.Map[ExprId, ColumnStat] =
mutable.Map.empty
+
+ /**
+ * Returns an option of Statistics for a Filter logical plan node.
+ * For a given compound expression condition, this method computes
filter selectivity
+ * (or the percentage of rows meeting the filter condition), which
+ * is used to compute row count, size in bytes, and the updated
statistics after a given
+ * predicated is applied.
+ *
+ * @return Option[Statistics] When there is no statistics collected, it
returns None.
+ */
+ def estimate: Option[Statistics] = {
+ val stats: Statistics = plan.child.stats(catalystConf)
+ if (stats.rowCount.isEmpty) return None
+
+ // save a mutable copy of colStats so that we can later change it
recursively
+ val statsExprIdMap: Map[ExprId, ColumnStat] =
+ stats.attributeStats.map(kv => (kv._1.exprId, kv._2))
+ mutableColStats = mutable.Map.empty ++= statsExprIdMap
+
+ // estimate selectivity of this filter predicate
+ val filterSelectivity: Double = calculateConditions(plan.condition)
+
+ // attributeStats has mapping Attribute-to-ColumnStat.
+ // mutableColStats has mapping ExprId-to-ColumnStat.
+ // We use an ExprId-to-Attribute map to facilitate the mapping
Attribute-to-ColumnStat
+ val expridToAttrMap: Map[ExprId, Attribute] =
+ stats.attributeStats.map(kv => (kv._1.exprId, kv._1))
+ // copy mutableColStats contents to an immutable AttributeMap.
+ val mutableAttributeStats: mutable.Map[Attribute, ColumnStat] =
+ mutableColStats.map(kv => expridToAttrMap(kv._1) -> kv._2)
+ val newColStats = AttributeMap(mutableAttributeStats.toSeq)
+
+ val filteredRowCountValue: BigInt =
+ EstimationUtils.ceil(BigDecimal(stats.rowCount.get) *
filterSelectivity)
+ val filteredSizeInBytes: BigInt = EstimationUtils.ceil(BigDecimal(
+ EstimationUtils.getOutputSize(plan.output, newColStats,
filteredRowCountValue)
+ ))
+
+ Some(stats.copy(sizeInBytes = filteredSizeInBytes, rowCount =
Some(filteredRowCountValue),
+ attributeStats = newColStats))
+ }
+
+ /**
+ * Returns a percentage of rows meeting a compound condition in Filter
node.
+ * A compound condition is decomposed into multiple single conditions
linked with AND, OR, NOT.
+ * For logical AND conditions, we need to update stats after a condition
estimation
+ * so that the stats will be more accurate for subsequent estimation.
This is needed for
+ * range condition such as (c > 40 AND c <= 50)
+ * For logical OR conditions, we do not update stats after a condition
estimation.
+ *
+ * @param condition the compound logical expression
+ * @param update a boolean flag to specify if we need to update
ColumnStat of a column
+ * for subsequent conditions
+ * @return a doube value to show the percentage of rows meeting a given
condition
+ */
+ def calculateConditions(
+ condition: Expression,
+ update: Boolean = true)
+ : Double = {
+
+ condition match {
+ case And(cond1, cond2) =>
+ val p1 = calculateConditions(cond1, update)
+ val p2 = calculateConditions(cond2, update)
+ p1 * p2
+
+ case Or(cond1, cond2) =>
+ val p1 = calculateConditions(cond1, update = false)
+ val p2 = calculateConditions(cond2, update = false)
+ math.min(1.0, p1 + p2 - (p1 * p2))
+
+ case Not(cond) => calculateSingleCondition(cond, update = false)
match {
+ case Some(percent) => 1.0 - percent
+ case None => 1.0
+ }
+ case _ => calculateSingleCondition(condition, update) match {
+ case Some(percent) => percent
+ case None => 1.0
+ // for not-supported condition, set filter selectivity to a
conservative estimate 100%
+ }
+ }
+ }
+
+ /**
+ * Returns a percentage of rows meeting a single condition in Filter
node.
+ * Currently we only support binary predicates where one side is a
column,
+ * and the other is a literal.
+ *
+ * @param condition a single logical expression
+ * @param update a boolean flag to specify if we need to update
ColumnStat of a column
+ * for subsequent conditions
+ * @return Option[Double] value to show the percentage of rows meeting a
given condition.
+ * It returns None if the condition is not supported.
+ */
+ def calculateSingleCondition(
+ condition: Expression,
+ update: Boolean)
+ : Option[Double] = {
+ condition match {
+ // For evaluateBinary method, we assume the literal on the right
side of an operator.
+ // So we will change the order if not.
+
+ // EqualTo does not care about the order
+ case op @ EqualTo(ar: AttributeReference, l: Literal) =>
+ evaluateBinary(op, ar, l, update)
+ case op @ EqualTo(l: Literal, ar: AttributeReference) =>
+ evaluateBinary(op, ar, l, update)
+
+ case op @ LessThan(ar: AttributeReference, l: Literal) =>
+ evaluateBinary(op, ar, l, update)
+ case op @ LessThan(l: Literal, ar: AttributeReference) =>
+ evaluateBinary(GreaterThan(ar, l), ar, l, update)
+
+ case op @ LessThanOrEqual(ar: AttributeReference, l: Literal) =>
+ evaluateBinary(op, ar, l, update)
+ case op @ LessThanOrEqual(l: Literal, ar: AttributeReference) =>
+ evaluateBinary(GreaterThanOrEqual(ar, l), ar, l, update)
+
+ case op @ GreaterThan(ar: AttributeReference, l: Literal) =>
+ evaluateBinary(op, ar, l, update)
+ case op @ GreaterThan(l: Literal, ar: AttributeReference) =>
+ evaluateBinary(LessThan(ar, l), ar, l, update)
+
+ case op @ GreaterThanOrEqual(ar: AttributeReference, l: Literal) =>
+ evaluateBinary(op, ar, l, update)
+ case op @ GreaterThanOrEqual(l: Literal, ar: AttributeReference) =>
+ evaluateBinary(LessThanOrEqual(ar, l), ar, l, update)
+
+ case In(ar: AttributeReference, expList) if
!expList.exists(!_.isInstanceOf[Literal]) =>
+ // Expression [In (value, seq[Literal])] will be replaced with
optimized version
+ // [InSet (value, HashSet[Literal])] in Optimizer, but only for
list.size > 10.
+ // Here we convert In into InSet anyway, because they share the
same processing logic.
+ val hSet = expList.map(e => e.eval())
+ evaluateInSet(ar, HashSet() ++ hSet, update)
+
+ case InSet(ar: AttributeReference, set) =>
+ evaluateInSet(ar, set, update)
+
+ // It's difficult to estimate IsNull after outer joins. Hence,
+ // we support IsNull and IsNotNull only when the child is a leaf
node (table).
+ case IsNull(ar: AttributeReference) =>
+ if (plan.child.isInstanceOf[LeafNode ]) {
+ evaluateIsNull(ar, true, update)
+ } else {
+ None
+ }
+
+ case IsNotNull(ar: AttributeReference) =>
+ if (plan.child.isInstanceOf[LeafNode ]) {
+ evaluateIsNull(ar, false, update)
--- End diff --
isNull = false to make it clear
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