Github user cloud-fan commented on a diff in the pull request: https://github.com/apache/spark/pull/16395#discussion_r101641460 --- Diff: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/FilterEstimation.scala --- @@ -0,0 +1,569 @@ +/* + * 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 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] = { + // 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 = 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. + * Returns 1.0 (a conservative filter estimate) if a condition is not supported. + */ + 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 { --- End diff -- so we don't support `Not(And(x, x))`, why?
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