Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/16395#discussion_r102864512
--- 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)) {
+ 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 facilitate 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.
+ val hSetBigdec = hSet.map(e => BigDecimal(e.toString))
--- End diff --
we should filter out null values first
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