Github user cloud-fan commented on a diff in the pull request: https://github.com/apache/spark/pull/16395#discussion_r101937726 --- Diff: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/FilterEstimation.scala --- @@ -0,0 +1,531 @@ +/* + * 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 + } + } + } + + /** + * This method converts a Literal value of numeric type to a BigDecimal value. + * In order to avoid type casting error such as Java int to Java long, we need to + * convert a numeric integer value to String, and then convert it to long, + * and then convert it to BigDecimal. + * + * @param attrDataType the column data type + * @param litValue the literal value + * @return a BigDecimal value + */ + def numericLiteralToBigDecimal( + attrDataType: DataType, + litValue: Any) + : BigDecimal = { + attrDataType match { + case _: IntegralType => + BigDecimal(litValue.toString) + + case _: FractionalType => + BigDecimal(litValue.toString.toDouble) + + case DateType => + val dateLiteral = + DateTimeUtils.fromJavaDate(litValue.asInstanceOf[Date]).toString + BigDecimal(java.lang.Long.valueOf(dateLiteral)) + + case TimestampType => + val tsLiteral = + DateTimeUtils.fromJavaTimestamp(litValue.asInstanceOf[Timestamp]).toString + BigDecimal(java.lang.Long.valueOf(tsLiteral)) + } + } + + /** + * 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 litRange = Range(Some(literal.value), Some(literal.value), literal.dataType) + val inBoundary: Boolean = Range.isIntersected(statsRange, litRange) + + if (inBoundary) { + + if (update) { + // We update ColumnStat structure after apply this equality predicate. + // Set distinctCount to 1. Set nullCount to 0. + val newStats = attrRef.dataType match { + case _: NumericType => + val newValue = Some(literal.value) + aColStat.copy(distinctCount = 1, min = newValue, + max = newValue, nullCount = 0) + case DateType => + val dateValue = Some(literal.value) + aColStat.copy(distinctCount = 1, min = dateValue, + max = dateValue, nullCount = 0) + case TimestampType => + val tsValue = Some(literal.value) + aColStat.copy(distinctCount = 1, min = tsValue, + max = tsValue, nullCount = 0) + case _ => aColStat.copy(distinctCount = 1, 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)) { + logDebug("[CBO] No statistics for " + attrRef) + return None + } + + val aColStat = mutableColStats(attrRef.exprId) + val ndv = aColStat.distinctCount + val aType = attrRef.dataType + var newNdv: Long = 0 + + // use [min, max] to filter the original hSet + aType match { + case _: NumericType | DateType | TimestampType => + val statsRange = + Range(aColStat.min, aColStat.max, aType).asInstanceOf[NumericRange] + + // To faciliate finding the min and max values in hSet, we map hSet values to BigDecimal. + // Using hSetBigdec, we can find the min and max values quickly in the ordered hSetBigdec. + // We use hSetBigdecToAnyMap to help us find the original hSet value. + val hSetBigdecToAnyMap: Map[BigDecimal, Any] = + hSet.map(e => numericLiteralToBigDecimal(aType, e) -> e).toMap + val hSetBigdec = hSet.map(e => numericLiteralToBigDecimal(aType, e)) + val validQuerySet = hSetBigdec.filter(e => e >= statsRange.min && e <= statsRange.max) + + if (validQuerySet.isEmpty) { + return Some(0.0) + } + + val newMax = Some(hSetBigdecToAnyMap(validQuerySet.max)) + val newMin = Some(hSetBigdecToAnyMap(validQuerySet.min)) + // newNdv should not be greater than the old ndv. For example, column has only 2 values + // 1 and 6. The predicate column IN (1, 2, 3, 4, 5). validQuerySet.size is 5. + newNdv = math.min(validQuerySet.size.toLong, ndv.longValue()) + if (update) { + val newStats = aColStat.copy(distinctCount = newNdv, min = newMin, + max = newMax, nullCount = 0) + mutableColStats += (attrRef.exprId -> newStats) + } + + // We assume the whole set since there is no min/max information for String/Binary type + case StringType | BinaryType => + newNdv = math.min(hSet.size.toLong, ndv.longValue()) + if (update) { + val newStats = aColStat.copy(distinctCount = newNdv, nullCount = 0) + mutableColStats += (attrRef.exprId -> newStats) + } + } + + // return the filter selectivity. Without advanced statistics such as histograms, + // we have to assume uniform distribution. + Some(math.min(1.0, newNdv.toDouble / ndv.toDouble)) + } + + /** + * Returns a percentage of rows meeting a binary comparison expression. + * This method evaluate expression for Numeric columns only. + * + * @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 + */ + def evaluateBinaryForNumeric( + op: BinaryComparison, + attrRef: AttributeReference, + literal: Literal, + update: Boolean) + : Option[Double] = { + + var percent = 1.0 + val aColStat = mutableColStats(attrRef.exprId) + val ndv = aColStat.distinctCount + val statsRange = + Range(aColStat.min, aColStat.max, attrRef.dataType).asInstanceOf[NumericRange] + + val literalValueBD = + numericLiteralToBigDecimal(attrRef.dataType, literal.value) + + // determine the overlapping degree between predicate range and column's range + val (noOverlap: Boolean, completeOverlap: Boolean) = op match { + case _: LessThan => + (literalValueBD <= statsRange.min, literalValueBD > statsRange.max) + case _: LessThanOrEqual => + (literalValueBD < statsRange.min, literalValueBD >= statsRange.max) + case _: GreaterThan => + (literalValueBD >= statsRange.max, literalValueBD < statsRange.min) + case _: GreaterThanOrEqual => + (literalValueBD > statsRange.max, literalValueBD <= statsRange.min) + } + + if (noOverlap) { + percent = 0.0 + } else if (completeOverlap) { + percent = 1.0 + } else { + // this is partial overlap case + var newMax = aColStat.max + var newMin = aColStat.min + var newNdv = ndv + val literalToDouble = literalValueBD.toDouble + val maxToDouble = BigDecimal(statsRange.max).toDouble + val minToDouble = BigDecimal(statsRange.min).toDouble + + // Without advanced statistics like histogram, we assume uniform data distribution. + // We just prorate the adjusted range over the initial range to compute filter selectivity. + // For ease of computation, we convert all relevant numeric values to Double. + percent = op match { + case _: LessThan => + (literalToDouble - minToDouble) / (maxToDouble - minToDouble) + case _: LessThanOrEqual => + if (literalValueBD == BigDecimal(statsRange.min)) 1.0 / ndv.toDouble + else (literalToDouble - minToDouble) / (maxToDouble - minToDouble) + case _: GreaterThan => + (maxToDouble - literalToDouble) / (maxToDouble - minToDouble) + case _: GreaterThanOrEqual => + if (literalValueBD == BigDecimal(statsRange.max)) 1.0 / ndv.toDouble + else (maxToDouble - literalToDouble) / (maxToDouble - minToDouble) + } + + if (update) { + op match { + case _: GreaterThan => newMin = Some(literal.value) + case _: GreaterThanOrEqual => newMin = Some(literal.value) + case _: LessThan => newMax = Some(literal.value) + case _: LessThanOrEqual => newMax = Some(literal.value) + } + + newNdv = math.max(math.round(ndv.toDouble * percent), 1) + val newStats = aColStat.copy(distinctCount = newNdv, min = newMin, + max = newMax, nullCount = 0) --- End diff -- same problem here
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