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Posted to reviews@spark.apache.org by lins05 <gi...@git.apache.org> on 2017/02/24 06:17:28 UTC
[GitHub] spark pull request #17051: [SPARK-17075][SQL] Follow up: fix file line endin...
GitHub user lins05 opened a pull request:
https://github.com/apache/spark/pull/17051
[SPARK-17075][SQL] Follow up: fix file line ending and improve the tests
## What changes were proposed in this pull request?
Fixed the line ending of `FilterEstimation.scala`. Also improved the tests to cover more cases.
## How was this patch tested?
Existing unit tests.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/lins05/spark fix-cbo-filter-file-encoding
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/17051.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #17051
----
commit ee6d9915b26254db176a5aa34c1d59e304e201e0
Author: Shuai Lin <li...@gmail.com>
Date: 2017-02-24T05:59:41Z
[SPARK-17075][SQL] Follow up: fix file line ending and improve the tests.
commit 0f56d0f1003268e4945ec5a427bbcc4bb7061a49
Author: Shuai Lin <li...@gmail.com>
Date: 2017-02-24T05:58:37Z
Use transformExpressionsDown to rewrite the filter.
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[GitHub] spark issue #17051: [SPARK-17075][SQL] Follow up: fix file line ending and i...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the issue:
https://github.com/apache/spark/pull/17051
Test PASSed.
Refer to this link for build results (access rights to CI server needed):
https://amplab.cs.berkeley.edu/jenkins//job/SparkPullRequestBuilder/73414/
Test PASSed.
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[GitHub] spark pull request #17051: [SPARK-17075][SQL] Follow up: fix file line endin...
Posted by cloud-fan <gi...@git.apache.org>.
Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/17051#discussion_r102887733
--- Diff: sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/statsEstimation/FilterEstimationSuite.scala ---
@@ -398,6 +398,27 @@ class FilterEstimationSuite extends StatsEstimationTestBase {
// For all other SQL types, we compare the entire object directly.
assert(filteredStats.attributeStats(ar) == expectedColStats)
}
- }
+ // If the filter has a binary operator (including those nested inside
+ // AND/OR/NOT), swap the sides of the attribte and the literal, reverse the
+ // operator, and then check again.
+ val rewrittenFilter = filterNode transformExpressionsDown {
+ case op @ EqualTo(ar: AttributeReference, l: Literal) =>
--- End diff --
nit: `case b @ BinaryComparison(ar: AttributeReference, l: Literal) => b.withNewChildren(l, ar)`
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[GitHub] spark issue #17051: [SPARK-17075][SQL] Follow up: fix file line ending and i...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the issue:
https://github.com/apache/spark/pull/17051
Merged build finished. Test FAILed.
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[GitHub] spark pull request #17051: [SPARK-17075][SQL] Follow up: fix file line endin...
Posted by lins05 <gi...@git.apache.org>.
Github user lins05 commented on a diff in the pull request:
https://github.com/apache/spark/pull/17051#discussion_r102888024
--- Diff: sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/statsEstimation/FilterEstimationSuite.scala ---
@@ -398,6 +398,27 @@ class FilterEstimationSuite extends StatsEstimationTestBase {
// For all other SQL types, we compare the entire object directly.
assert(filteredStats.attributeStats(ar) == expectedColStats)
}
- }
+ // If the filter has a binary operator (including those nested inside
+ // AND/OR/NOT), swap the sides of the attribte and the literal, reverse the
+ // operator, and then check again.
+ val rewrittenFilter = filterNode transformExpressionsDown {
+ case op @ EqualTo(ar: AttributeReference, l: Literal) =>
--- End diff --
\U0001f44d
I tried to find something like this but failed to, so I resorted to the current code. Thanks for the tip!
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[GitHub] spark pull request #17051: [SPARK-17075][SQL] Follow up: fix file line endin...
Posted by lins05 <gi...@git.apache.org>.
Github user lins05 commented on a diff in the pull request:
https://github.com/apache/spark/pull/17051#discussion_r102887355
--- Diff: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/FilterEstimation.scala ---
@@ -1,511 +1,509 @@
-/*
- * 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))
- val validQuerySet = hSetBigdec.filter(e => e >= statsRange.min && e <= statsRange.max)
- // We use hSetBigdecToAnyMap to help us find the original hSet value.
- val hSetBigdecToAnyMap: Map[BigDecimal, Any] =
- hSet.map(e => BigDecimal(e.toString) -> e).toMap
-
- if (validQuerySet.isEmpty) {
- return Some(0.0)
- }
-
- // Need to save new min/max using the external type value of the literal
- val newMax = convertBoundValue(attrRef.dataType, hSetBigdecToAnyMap(validQuerySet.max))
- val newMin = convertBoundValue(attrRef.dataType, 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]
-
- // determine the overlapping degree between predicate range and column's range
- val literalValueBD = BigDecimal(literal.value.toString)
- 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)
- }
-
- // Need to save new min/max using the external type value of the literal
- val newValue = convertBoundValue(attrRef.dataType, literal.value)
-
- if (update) {
- op match {
- case _: GreaterThan => newMin = newValue
- case _: GreaterThanOrEqual => newMin = newValue
- case _: LessThan => newMax = newValue
- case _: LessThanOrEqual => newMax = newValue
- }
-
- newNdv = math.max(math.round(ndv.toDouble * percent), 1)
- val newStats = aColStat.copy(distinctCount = newNdv, min = newMin,
- max = newMax, nullCount = 0)
-
- mutableColStats += (attrRef.exprId -> newStats)
- }
- }
-
- Some(percent)
- }
-
-}
+/*
--- End diff --
The diff shown here is because I also removed an unused import. I'll revert it to make review easier.
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* This patch passes all tests.
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jenkins retest this please
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cc @ron8hu @cloud-fan
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thanks, merging to master!
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[GitHub] spark pull request #17051: [SPARK-17075][SQL] Follow up: fix file line endin...
Posted by lins05 <gi...@git.apache.org>.
Github user lins05 commented on a diff in the pull request:
https://github.com/apache/spark/pull/17051#discussion_r102888839
--- Diff: sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/statsEstimation/FilterEstimationSuite.scala ---
@@ -398,6 +398,27 @@ class FilterEstimationSuite extends StatsEstimationTestBase {
// For all other SQL types, we compare the entire object directly.
assert(filteredStats.attributeStats(ar) == expectedColStats)
}
- }
+ // If the filter has a binary operator (including those nested inside
+ // AND/OR/NOT), swap the sides of the attribte and the literal, reverse the
+ // operator, and then check again.
+ val rewrittenFilter = filterNode transformExpressionsDown {
+ case op @ EqualTo(ar: AttributeReference, l: Literal) =>
--- End diff --
Emm, we not only switch the side of the attr and the literal, but also reversed the operator, e.g. `LessThan` would be changed to `GreaterThan`. So I guess we can't use `withNewChildren` here.
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Posted by lins05 <gi...@git.apache.org>.
Github user lins05 commented on a diff in the pull request:
https://github.com/apache/spark/pull/17051#discussion_r102887655
--- Diff: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/FilterEstimation.scala ---
@@ -1,511 +1,509 @@
-/*
- * 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))
- val validQuerySet = hSetBigdec.filter(e => e >= statsRange.min && e <= statsRange.max)
- // We use hSetBigdecToAnyMap to help us find the original hSet value.
- val hSetBigdecToAnyMap: Map[BigDecimal, Any] =
- hSet.map(e => BigDecimal(e.toString) -> e).toMap
-
- if (validQuerySet.isEmpty) {
- return Some(0.0)
- }
-
- // Need to save new min/max using the external type value of the literal
- val newMax = convertBoundValue(attrRef.dataType, hSetBigdecToAnyMap(validQuerySet.max))
- val newMin = convertBoundValue(attrRef.dataType, 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]
-
- // determine the overlapping degree between predicate range and column's range
- val literalValueBD = BigDecimal(literal.value.toString)
- 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)
- }
-
- // Need to save new min/max using the external type value of the literal
- val newValue = convertBoundValue(attrRef.dataType, literal.value)
-
- if (update) {
- op match {
- case _: GreaterThan => newMin = newValue
- case _: GreaterThanOrEqual => newMin = newValue
- case _: LessThan => newMax = newValue
- case _: LessThanOrEqual => newMax = newValue
- }
-
- newNdv = math.max(math.round(ndv.toDouble * percent), 1)
- val newStats = aColStat.copy(distinctCount = newNdv, min = newMin,
- max = newMax, nullCount = 0)
-
- mutableColStats += (attrRef.exprId -> newStats)
- }
- }
-
- Some(percent)
- }
-
-}
+/*
--- End diff --
Done. But that seems not affecting the diffs.
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[GitHub] spark pull request #17051: [SPARK-17075][SQL] Follow up: fix file line endin...
Posted by cloud-fan <gi...@git.apache.org>.
Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/17051#discussion_r102887105
--- Diff: sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/FilterEstimation.scala ---
@@ -1,511 +1,509 @@
-/*
- * 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))
- val validQuerySet = hSetBigdec.filter(e => e >= statsRange.min && e <= statsRange.max)
- // We use hSetBigdecToAnyMap to help us find the original hSet value.
- val hSetBigdecToAnyMap: Map[BigDecimal, Any] =
- hSet.map(e => BigDecimal(e.toString) -> e).toMap
-
- if (validQuerySet.isEmpty) {
- return Some(0.0)
- }
-
- // Need to save new min/max using the external type value of the literal
- val newMax = convertBoundValue(attrRef.dataType, hSetBigdecToAnyMap(validQuerySet.max))
- val newMin = convertBoundValue(attrRef.dataType, 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]
-
- // determine the overlapping degree between predicate range and column's range
- val literalValueBD = BigDecimal(literal.value.toString)
- 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)
- }
-
- // Need to save new min/max using the external type value of the literal
- val newValue = convertBoundValue(attrRef.dataType, literal.value)
-
- if (update) {
- op match {
- case _: GreaterThan => newMin = newValue
- case _: GreaterThanOrEqual => newMin = newValue
- case _: LessThan => newMax = newValue
- case _: LessThanOrEqual => newMax = newValue
- }
-
- newNdv = math.max(math.round(ndv.toDouble * percent), 1)
- val newStats = aColStat.copy(distinctCount = newNdv, min = newMin,
- max = newMax, nullCount = 0)
-
- mutableColStats += (attrRef.exprId -> newStats)
- }
- }
-
- Some(percent)
- }
-
-}
+/*
--- End diff --
please make sure the only change is line ending
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