Github user cloud-fan commented on a diff in the pull request: https://github.com/apache/spark/pull/16395#discussion_r101643302 --- 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 { + case Some(percent) => 1.0 - percent + // for not-supported condition, set filter selectivity to a conservative estimate 100% + case None => 1.0 + } + case _ => calculateSingleCondition(condition, update) match { + case Some(percent) => percent + // for not-supported condition, set filter selectivity to a conservative estimate 100% + case None => 1.0 + } + } + } + + /** + * 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 ]) { --- End diff -- cc @wzhfy
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