Github user cloud-fan commented on a diff in the pull request:

    https://github.com/apache/spark/pull/16395#discussion_r102862209
  
    --- Diff: 
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/FilterEstimation.scala
 ---
    @@ -0,0 +1,511 @@
    +/*
    + * 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._
    +
    +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, 
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] = {
    +    // We first copy child node's statistics and then modify it based on 
filter selectivity.
    +    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
    +    mutableColStats = mutable.Map(stats.attributeStats.map(kv => 
(kv._1.exprId, kv._2)).toSeq: _*)
    +
    +    // estimate selectivity of this filter predicate
    +    val filterSelectivity: Double = 
calculateFilterSelectivity(plan.condition) match {
    +      case Some(percent) => percent
    +      // for not-supported condition, set filter selectivity to a 
conservative estimate 100%
    +      case None => 1.0
    +    }
    +
    +    // 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 filteredRowCount: BigInt =
    +      EstimationUtils.ceil(BigDecimal(stats.rowCount.get) * 
filterSelectivity)
    +    val filteredSizeInBytes =
    +      EstimationUtils.getOutputSize(plan.output, filteredRowCount, 
newColStats)
    +
    +    Some(stats.copy(sizeInBytes = filteredSizeInBytes, rowCount = 
Some(filteredRowCount),
    +      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 double value to show the percentage of rows meeting a given 
condition.
    +   *         It returns None if the condition is not supported.
    +   */
    +  def calculateFilterSelectivity(condition: Expression, update: Boolean = 
true): Option[Double] = {
    +
    +    condition match {
    +      case And(cond1, cond2) =>
    +        (calculateFilterSelectivity(cond1, update), 
calculateFilterSelectivity(cond2, update))
    +        match {
    +          case (Some(p1), Some(p2)) => Some(p1 * p2)
    +          case (Some(p1), None) => Some(p1)
    +          case (None, Some(p2)) => Some(p2)
    +          case (None, None) => None
    +        }
    +
    +      case Or(cond1, cond2) =>
    +        // For ease of debugging, we compute percent1 and percent2 in 2 
statements.
    +        val percent1 = calculateFilterSelectivity(cond1, update = false)
    +        val percent2 = calculateFilterSelectivity(cond2, update = false)
    +        (percent1, percent2) match {
    +          case (Some(p1), Some(p2)) => Some(math.min(1.0, p1 + p2 - (p1 * 
p2)))
    +          case (Some(p1), None) => Some(1.0)
    +          case (None, Some(p2)) => Some(1.0)
    +          case (None, None) => None
    +        }
    +
    +      case Not(cond) => calculateFilterSelectivity(cond, update = false) 
match {
    +        case Some(percent) => Some(1.0 - percent)
    +        // for not-supported condition, set filter selectivity to a 
conservative estimate 100%
    +        case None => None
    +      }
    +
    +      case _ =>
    +        calculateSingleCondition(condition, update)
    +    }
    +  }
    +
    +  /**
    +   * 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.forall(e => e.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)
    +
    +      case IsNull(ar: AttributeReference) =>
    +        evaluateIsNull(ar, isNull = true, update)
    +
    +      case IsNotNull(ar: AttributeReference) =>
    +        evaluateIsNull(ar, isNull = false, update)
    +
    +      case _ =>
    +        // TODO: it's difficult to support string operators without 
advanced statistics.
    +        // Hence, these string operators Like(_, _) | Contains(_, _) | 
StartsWith(_, _)
    +        // | EndsWith(_, _) are not supported yet
    +        logDebug("[CBO] Unsupported filter condition: " + condition)
    +        None
    +    }
    +  }
    +
    +  /**
    +   * Returns a percentage of rows meeting "IS NULL" or "IS NOT NULL" 
condition.
    +   *
    +   * @param attrRef an AttributeReference (or a column)
    +   * @param isNull set to true for "IS NULL" condition.  set to false for 
"IS NOT NULL" condition
    +   * @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
    +   *         It returns None if no statistics collected for a given column.
    +   */
    +  def evaluateIsNull(
    +      attrRef: AttributeReference,
    +      isNull: Boolean,
    +      update: Boolean)
    +    : Option[Double] = {
    +    if (!mutableColStats.contains(attrRef.exprId)) {
    +      logDebug("[CBO] No statistics for " + attrRef)
    +      return None
    +    }
    +    val aColStat = mutableColStats(attrRef.exprId)
    +    val rowCountValue = plan.child.stats(catalystConf).rowCount.get
    +    val nullPercent: BigDecimal =
    +      if (rowCountValue == 0) 0.0
    +      else BigDecimal(aColStat.nullCount) / BigDecimal(rowCountValue)
    +
    +    if (update) {
    +      val newStats =
    +        if (isNull) aColStat.copy(distinctCount = 0, min = None, max = 
None)
    +        else aColStat.copy(nullCount = 0)
    +
    +      mutableColStats += (attrRef.exprId -> newStats)
    +    }
    +
    +    val percent =
    +      if (isNull) {
    +        nullPercent.toDouble
    +      }
    +      else {
    +        /** ISNOTNULL(column) */
    +        1.0 - nullPercent.toDouble
    +      }
    +
    +    Some(percent)
    +  }
    +
    +  /**
    +   * Returns a percentage of rows meeting a binary comparison expression.
    +   *
    +   * @param op a binary comparison operator uch as =, <, <=, >, >=
    +   * @param attrRef an AttributeReference (or a column)
    +   * @param literal a literal value (or constant)
    +   * @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
    +    *         It returns None if no statistics exists for a given column 
or wrong value.
    +   */
    +  def evaluateBinary(
    +      op: BinaryComparison,
    +      attrRef: AttributeReference,
    +      literal: Literal,
    +      update: Boolean)
    +    : Option[Double] = {
    +    if (!mutableColStats.contains(attrRef.exprId)) {
    +      logDebug("[CBO] No statistics for " + attrRef)
    +      return None
    +    }
    +
    +    op match {
    +      case EqualTo(l, r) => evaluateEqualTo(attrRef, literal, update)
    +      case _ =>
    +        attrRef.dataType match {
    +          case _: NumericType | DateType | TimestampType =>
    +            evaluateBinaryForNumeric(op, attrRef, literal, update)
    +          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 " + attrRef)
    +            None
    +        }
    +    }
    +  }
    +
    +  /**
    +   * For a SQL data type, its internal data type may be different from its 
external type.
    +   * For DateType, its internal type is Int, and its external data type is 
Java Date type.
    +   * The min/max values in ColumnStat are saved in their corresponding 
external type.
    +   *
    +   * @param attrDataType the column data type
    +   * @param litValue the literal value
    +   * @return a BigDecimal value
    +   */
    +  def convertBoundValue(attrDataType: DataType, litValue: Any): 
Option[Any] = {
    +    attrDataType match {
    +      case DateType =>
    +        Some(DateTimeUtils.toJavaDate(litValue.toString.toInt))
    +      case TimestampType =>
    +        Some(DateTimeUtils.toJavaTimestamp(litValue.toString.toLong))
    +      case StringType | BinaryType =>
    +        None
    +      case _ =>
    +        Some(litValue)
    +    }
    +  }
    +
    +  /**
    +   * Returns a percentage of rows meeting an equality (=) expression.
    +   * This method evaluates the equality predicate for all data types.
    +   *
    +   * @param attrRef an AttributeReference (or a column)
    +   * @param literal a literal value (or constant)
    +   * @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 evaluateEqualTo(
    +      attrRef: AttributeReference,
    +      literal: Literal,
    +      update: Boolean)
    +    : Option[Double] = {
    +
    +    val aColStat = mutableColStats(attrRef.exprId)
    +    val ndv = aColStat.distinctCount
    +
    +    // decide if the value is in [min, max] of the column.
    +    // We currently don't store min/max for binary/string type.
    +    // Hence, we assume it is in boundary for binary/string type.
    +    val statsRange = Range(aColStat.min, aColStat.max, attrRef.dataType)
    +    val inBoundary: Boolean = Range.rangeContainsLiteral(statsRange, 
literal)
    +
    +    if (inBoundary) {
    +
    +      if (update) {
    +        // We update ColumnStat structure after apply this equality 
predicate.
    +        // Set distinctCount to 1.  Set nullCount to 0.
    +        // Need to save new min/max using the external type value of the 
literal
    +        val newValue = convertBoundValue(attrRef.dataType, literal.value)
    +        val newStats = aColStat.copy(distinctCount = 1, min = newValue,
    +          max = newValue, nullCount = 0)
    +        mutableColStats += (attrRef.exprId -> newStats)
    +      }
    +
    +      Some(1.0 / ndv.toDouble)
    +    } else {
    +      Some(0.0)
    +    }
    +
    +  }
    +
    +  /**
    +   * Returns a percentage of rows meeting "IN" operator expression.
    +   * This method evaluates the equality predicate for all data types.
    +   *
    +   * @param attrRef an AttributeReference (or a column)
    +   * @param hSet a set of literal values
    +   * @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
    +   *         It returns None if no statistics exists for a given column.
    +   */
    +
    +  def evaluateInSet(
    +      attrRef: AttributeReference,
    +      hSet: Set[Any],
    +      update: Boolean)
    +    : Option[Double] = {
    +    if (!mutableColStats.contains(attrRef.exprId)) {
    --- End diff --
    
    we can have a method for this logic


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