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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