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

    https://github.com/apache/spark/pull/16395#discussion_r100607130
  
    --- Diff: 
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/FilterEstimation.scala
 ---
    @@ -0,0 +1,623 @@
    +/*
    + * 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, 
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
    +    mutableColStats = mutable.Map(stats.attributeStats.map(kv => 
(kv._1.exprId, kv._2)).toSeq: _*)
    +
    +    // 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 filteredRowCount: BigInt =
    +      EstimationUtils.ceil(BigDecimal(stats.rowCount.get) * 
filterSelectivity)
    +    val filteredSizeInBytes: BigInt = EstimationUtils.ceil(BigDecimal(
    +        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
    --- End diff --
    
    we should mention that we will return `1.0` if the condition expression is 
not supported.


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