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/*
* 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.execution.datasources
import java.util.Locale
import java.util.concurrent.Callable
import org.apache.hadoop.fs.Path
import org.apache.spark.internal.Logging
import org.apache.spark.rdd.RDD
import org.apache.spark.sql._
import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow, QualifiedTableName}
import org.apache.spark.sql.catalyst.CatalystTypeConverters.convertToScala
import org.apache.spark.sql.catalyst.analysis._
import org.apache.spark.sql.catalyst.catalog._
import org.apache.spark.sql.catalyst.expressions
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.catalyst.planning.PhysicalOperation
import org.apache.spark.sql.catalyst.plans.logical.{InsertIntoDir, InsertIntoTable, LogicalPlan, Project}
import org.apache.spark.sql.catalyst.plans.physical.HashPartitioning
import org.apache.spark.sql.catalyst.rules.Rule
import org.apache.spark.sql.execution.{RowDataSourceScanExec, SparkPlan}
import org.apache.spark.sql.execution.command._
import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.sql.sources._
import org.apache.spark.sql.types._
import org.apache.spark.unsafe.types.UTF8String
/**
* Replaces generic operations with specific variants that are designed to work with Spark
* SQL Data Sources.
*
* Note that, this rule must be run after `PreprocessTableCreation` and
* `PreprocessTableInsertion`.
*/
case class DataSourceAnalysis(conf: SQLConf) extends Rule[LogicalPlan] with CastSupport {
def resolver: Resolver = conf.resolver
// Visible for testing.
def convertStaticPartitions(
sourceAttributes: Seq[Attribute],
providedPartitions: Map[String, Option[String]],
targetAttributes: Seq[Attribute],
targetPartitionSchema: StructType): Seq[NamedExpression] = {
assert(providedPartitions.exists(_._2.isDefined))
val staticPartitions = providedPartitions.flatMap {
case (partKey, Some(partValue)) => (partKey, partValue) :: Nil
case (_, None) => Nil
}
// The sum of the number of static partition columns and columns provided in the SELECT
// clause needs to match the number of columns of the target table.
if (staticPartitions.size + sourceAttributes.size != targetAttributes.size) {
throw new AnalysisException(
s"The data to be inserted needs to have the same number of " +
s"columns as the target table: target table has ${targetAttributes.size} " +
s"column(s) but the inserted data has ${sourceAttributes.size + staticPartitions.size} " +
s"column(s), which contain ${staticPartitions.size} partition column(s) having " +
s"assigned constant values.")
}
if (providedPartitions.size != targetPartitionSchema.fields.size) {
throw new AnalysisException(
s"The data to be inserted needs to have the same number of " +
s"partition columns as the target table: target table " +
s"has ${targetPartitionSchema.fields.size} partition column(s) but the inserted " +
s"data has ${providedPartitions.size} partition columns specified.")
}
staticPartitions.foreach {
case (partKey, partValue) =>
if (!targetPartitionSchema.fields.exists(field => resolver(field.name, partKey))) {
throw new AnalysisException(
s"$partKey is not a partition column. Partition columns are " +
s"${targetPartitionSchema.fields.map(_.name).mkString("[", ",", "]")}")
}
}
val partitionList = targetPartitionSchema.fields.map { field =>
val potentialSpecs = staticPartitions.filter {
case (partKey, partValue) => resolver(field.name, partKey)
}
if (potentialSpecs.isEmpty) {
None
} else if (potentialSpecs.size == 1) {
val partValue = potentialSpecs.head._2
Some(Alias(cast(Literal(partValue), field.dataType), field.name)())
} else {
throw new AnalysisException(
s"Partition column ${field.name} have multiple values specified, " +
s"${potentialSpecs.mkString("[", ", ", "]")}. Please only specify a single value.")
}
}
// We first drop all leading static partitions using dropWhile and check if there is
// any static partition appear after dynamic partitions.
partitionList.dropWhile(_.isDefined).collectFirst {
case Some(_) =>
throw new AnalysisException(
s"The ordering of partition columns is " +
s"${targetPartitionSchema.fields.map(_.name).mkString("[", ",", "]")}. " +
"All partition columns having constant values need to appear before other " +
"partition columns that do not have an assigned constant value.")
}
assert(partitionList.take(staticPartitions.size).forall(_.isDefined))
val projectList =
sourceAttributes.take(targetAttributes.size - targetPartitionSchema.fields.size) ++
partitionList.take(staticPartitions.size).map(_.get) ++
sourceAttributes.takeRight(targetPartitionSchema.fields.size - staticPartitions.size)
projectList
}
override def apply(plan: LogicalPlan): LogicalPlan = plan transform {
case CreateTable(tableDesc, mode, None) if DDLUtils.isDatasourceTable(tableDesc) =>
DDLUtils.checkDataColNames(tableDesc)
CreateDataSourceTableCommand(tableDesc, ignoreIfExists = mode == SaveMode.Ignore)
case CreateTable(tableDesc, mode, Some(query))
if query.resolved && DDLUtils.isDatasourceTable(tableDesc) =>
DDLUtils.checkDataColNames(tableDesc.copy(schema = query.schema))
CreateDataSourceTableAsSelectCommand(tableDesc, mode, query, query.output)
case InsertIntoTable(l @ LogicalRelation(_: InsertableRelation, _, _, _),
parts, query, overwrite, false) if parts.isEmpty =>
InsertIntoDataSourceCommand(l, query, overwrite)
case InsertIntoDir(_, storage, provider, query, overwrite)
if provider.isDefined && provider.get.toLowerCase(Locale.ROOT) != DDLUtils.HIVE_PROVIDER =>
val outputPath = new Path(storage.locationUri.get)
if (overwrite) DDLUtils.verifyNotReadPath(query, outputPath)
InsertIntoDataSourceDirCommand(storage, provider.get, query, overwrite)
case i @ InsertIntoTable(
l @ LogicalRelation(t: HadoopFsRelation, _, table, _), parts, query, overwrite, _) =>
// If the InsertIntoTable command is for a partitioned HadoopFsRelation and
// the user has specified static partitions, we add a Project operator on top of the query
// to include those constant column values in the query result.
//
// Example:
// Let's say that we have a table "t", which is created by
// CREATE TABLE t (a INT, b INT, c INT) USING parquet PARTITIONED BY (b, c)
// The statement of "INSERT INTO TABLE t PARTITION (b=2, c) SELECT 1, 3"
// will be converted to "INSERT INTO TABLE t PARTITION (b, c) SELECT 1, 2, 3".
//
// Basically, we will put those partition columns having a assigned value back
// to the SELECT clause. The output of the SELECT clause is organized as
// normal_columns static_partitioning_columns dynamic_partitioning_columns.
// static_partitioning_columns are partitioning columns having assigned
// values in the PARTITION clause (e.g. b in the above example).
// dynamic_partitioning_columns are partitioning columns that do not assigned
// values in the PARTITION clause (e.g. c in the above example).
val actualQuery = if (parts.exists(_._2.isDefined)) {
val projectList = convertStaticPartitions(
sourceAttributes = query.output,
providedPartitions = parts,
targetAttributes = l.output,
targetPartitionSchema = t.partitionSchema)
Project(projectList, query)
} else {
query
}
// Sanity check
if (t.location.rootPaths.size != 1) {
throw new AnalysisException("Can only write data to relations with a single path.")
}
val outputPath = t.location.rootPaths.head
if (overwrite) DDLUtils.verifyNotReadPath(actualQuery, outputPath)
val mode = if (overwrite) SaveMode.Overwrite else SaveMode.Append
val partitionSchema = actualQuery.resolve(
t.partitionSchema, t.sparkSession.sessionState.analyzer.resolver)
val staticPartitions = parts.filter(_._2.nonEmpty).map { case (k, v) => k -> v.get }
InsertIntoHadoopFsRelationCommand(
outputPath,
staticPartitions,
i.ifPartitionNotExists,
partitionSchema,
t.bucketSpec,
t.fileFormat,
t.options,
actualQuery,
mode,
table,
Some(t.location),
actualQuery.output)
}
}
/**
* Replaces [[UnresolvedCatalogRelation]] with concrete relation logical plans.
*
* TODO: we should remove the special handling for hive tables after completely making hive as a
* data source.
*/
class FindDataSourceTable(sparkSession: SparkSession) extends Rule[LogicalPlan] {
private def readDataSourceTable(table: CatalogTable): LogicalPlan = {
val qualifiedTableName = QualifiedTableName(table.database, table.identifier.table)
val catalog = sparkSession.sessionState.catalog
catalog.getCachedPlan(qualifiedTableName, new Callable[LogicalPlan]() {
override def call(): LogicalPlan = {
val pathOption = table.storage.locationUri.map("path" -> CatalogUtils.URIToString(_))
val dataSource =
DataSource(
sparkSession,
// In older version(prior to 2.1) of Spark, the table schema can be empty and should be
// inferred at runtime. We should still support it.
userSpecifiedSchema = if (table.schema.isEmpty) None else Some(table.schema),
partitionColumns = table.partitionColumnNames,
bucketSpec = table.bucketSpec,
className = table.provider.get,
options = table.storage.properties ++ pathOption,
catalogTable = Some(table))
LogicalRelation(dataSource.resolveRelation(checkFilesExist = false), table)
}
})
}
private def readHiveTable(table: CatalogTable): LogicalPlan = {
HiveTableRelation(
table,
// Hive table columns are always nullable.
table.dataSchema.asNullable.toAttributes,
table.partitionSchema.asNullable.toAttributes)
}
override def apply(plan: LogicalPlan): LogicalPlan = plan transform {
case i @ InsertIntoTable(UnresolvedCatalogRelation(tableMeta), _, _, _, _)
if DDLUtils.isDatasourceTable(tableMeta) =>
i.copy(table = readDataSourceTable(tableMeta))
case i @ InsertIntoTable(UnresolvedCatalogRelation(tableMeta), _, _, _, _) =>
i.copy(table = readHiveTable(tableMeta))
case UnresolvedCatalogRelation(tableMeta) if DDLUtils.isDatasourceTable(tableMeta) =>
readDataSourceTable(tableMeta)
case UnresolvedCatalogRelation(tableMeta) =>
readHiveTable(tableMeta)
}
}
/**
* A Strategy for planning scans over data sources defined using the sources API.
*/
case class DataSourceStrategy(conf: SQLConf) extends Strategy with Logging with CastSupport {
import DataSourceStrategy._
def apply(plan: LogicalPlan): Seq[execution.SparkPlan] = plan match {
case PhysicalOperation(projects, filters, l @ LogicalRelation(t: CatalystScan, _, _, _)) =>
pruneFilterProjectRaw(
l,
projects,
filters,
(requestedColumns, allPredicates, _) =>
toCatalystRDD(l, requestedColumns, t.buildScan(requestedColumns, allPredicates))) :: Nil
case PhysicalOperation(projects, filters,
l @ LogicalRelation(t: PrunedFilteredScan, _, _, _)) =>
pruneFilterProject(
l,
projects,
filters,
(a, f) => toCatalystRDD(l, a, t.buildScan(a.map(_.name).toArray, f))) :: Nil
case PhysicalOperation(projects, filters, l @ LogicalRelation(t: PrunedScan, _, _, _)) =>
pruneFilterProject(
l,
projects,
filters,
(a, _) => toCatalystRDD(l, a, t.buildScan(a.map(_.name).toArray))) :: Nil
case l @ LogicalRelation(baseRelation: TableScan, _, _, _) =>
RowDataSourceScanExec(
l.output,
l.output.indices,
Set.empty,
Set.empty,
toCatalystRDD(l, baseRelation.buildScan()),
baseRelation,
None) :: Nil
case _ => Nil
}
// Get the bucket ID based on the bucketing values.
// Restriction: Bucket pruning works iff the bucketing column has one and only one column.
def getBucketId(bucketColumn: Attribute, numBuckets: Int, value: Any): Int = {
val mutableRow = new SpecificInternalRow(Seq(bucketColumn.dataType))
mutableRow(0) = cast(Literal(value), bucketColumn.dataType).eval(null)
val bucketIdGeneration = UnsafeProjection.create(
HashPartitioning(bucketColumn :: Nil, numBuckets).partitionIdExpression :: Nil,
bucketColumn :: Nil)
bucketIdGeneration(mutableRow).getInt(0)
}
// Based on Public API.
private def pruneFilterProject(
relation: LogicalRelation,
projects: Seq[NamedExpression],
filterPredicates: Seq[Expression],
scanBuilder: (Seq[Attribute], Array[Filter]) => RDD[InternalRow]) = {
pruneFilterProjectRaw(
relation,
projects,
filterPredicates,
(requestedColumns, _, pushedFilters) => {
scanBuilder(requestedColumns, pushedFilters.toArray)
})
}
// Based on Catalyst expressions. The `scanBuilder` function accepts three arguments:
//
// 1. A `Seq[Attribute]`, containing all required column attributes. Used to handle relation
// traits that support column pruning (e.g. `PrunedScan` and `PrunedFilteredScan`).
//
// 2. A `Seq[Expression]`, containing all gathered Catalyst filter expressions, only used for
// `CatalystScan`.
//
// 3. A `Seq[Filter]`, containing all data source `Filter`s that are converted from (possibly a
// subset of) Catalyst filter expressions and can be handled by `relation`. Used to handle
// relation traits (`CatalystScan` excluded) that support filter push-down (e.g.
// `PrunedFilteredScan` and `HadoopFsRelation`).
//
// Note that 2 and 3 shouldn't be used together.
private def pruneFilterProjectRaw(
relation: LogicalRelation,
projects: Seq[NamedExpression],
filterPredicates: Seq[Expression],
scanBuilder: (Seq[Attribute], Seq[Expression], Seq[Filter]) => RDD[InternalRow]): SparkPlan = {
val projectSet = AttributeSet(projects.flatMap(_.references))
val filterSet = AttributeSet(filterPredicates.flatMap(_.references))
val candidatePredicates = filterPredicates.map { _ transform {
case a: AttributeReference => relation.attributeMap(a) // Match original case of attributes.
}}
val (unhandledPredicates, pushedFilters, handledFilters) =
selectFilters(relation.relation, candidatePredicates)
// Combines all Catalyst filter `Expression`s that are either not convertible to data source
// `Filter`s or cannot be handled by `relation`.
val filterCondition = unhandledPredicates.reduceLeftOption(expressions.And)
if (projects.map(_.toAttribute) == projects &&
projectSet.size == projects.size &&
filterSet.subsetOf(projectSet)) {
// When it is possible to just use column pruning to get the right projection and
// when the columns of this projection are enough to evaluate all filter conditions,
// just do a scan followed by a filter, with no extra project.
val requestedColumns = projects
// Safe due to if above.
.asInstanceOf[Seq[Attribute]]
// Match original case of attributes.
.map(relation.attributeMap)
val scan = RowDataSourceScanExec(
relation.output,
requestedColumns.map(relation.output.indexOf),
pushedFilters.toSet,
handledFilters,
scanBuilder(requestedColumns, candidatePredicates, pushedFilters),
relation.relation,
relation.catalogTable.map(_.identifier))
filterCondition.map(execution.FilterExec(_, scan)).getOrElse(scan)
} else {
// A set of column attributes that are only referenced by pushed down filters. We can
// eliminate them from requested columns.
val handledSet = {
val handledPredicates = filterPredicates.filterNot(unhandledPredicates.contains)
val unhandledSet = AttributeSet(unhandledPredicates.flatMap(_.references))
AttributeSet(handledPredicates.flatMap(_.references)) --
(projectSet ++ unhandledSet).map(relation.attributeMap)
}
// Don't request columns that are only referenced by pushed filters.
val requestedColumns =
(projectSet ++ filterSet -- handledSet).map(relation.attributeMap).toSeq
val scan = RowDataSourceScanExec(
relation.output,
requestedColumns.map(relation.output.indexOf),
pushedFilters.toSet,
handledFilters,
scanBuilder(requestedColumns, candidatePredicates, pushedFilters),
relation.relation,
relation.catalogTable.map(_.identifier))
execution.ProjectExec(
projects, filterCondition.map(execution.FilterExec(_, scan)).getOrElse(scan))
}
}
/**
* Convert RDD of Row into RDD of InternalRow with objects in catalyst types
*/
private[this] def toCatalystRDD(
relation: LogicalRelation,
output: Seq[Attribute],
rdd: RDD[Row]): RDD[InternalRow] = {
if (relation.relation.needConversion) {
execution.RDDConversions.rowToRowRdd(rdd, output.map(_.dataType))
} else {
rdd.asInstanceOf[RDD[InternalRow]]
}
}
/**
* Convert RDD of Row into RDD of InternalRow with objects in catalyst types
*/
private[this] def toCatalystRDD(relation: LogicalRelation, rdd: RDD[Row]): RDD[InternalRow] = {
toCatalystRDD(relation, relation.output, rdd)
}
}
object DataSourceStrategy {
/**
* Tries to translate a Catalyst [[Expression]] into data source [[Filter]].
*
* @return a `Some[Filter]` if the input [[Expression]] is convertible, otherwise a `None`.
*/
protected[sql] def translateFilter(predicate: Expression): Option[Filter] = {
predicate match {
case expressions.EqualTo(a: Attribute, Literal(v, t)) =>
Some(sources.EqualTo(a.name, convertToScala(v, t)))
case expressions.EqualTo(Literal(v, t), a: Attribute) =>
Some(sources.EqualTo(a.name, convertToScala(v, t)))
case expressions.EqualNullSafe(a: Attribute, Literal(v, t)) =>
Some(sources.EqualNullSafe(a.name, convertToScala(v, t)))
case expressions.EqualNullSafe(Literal(v, t), a: Attribute) =>
Some(sources.EqualNullSafe(a.name, convertToScala(v, t)))
case expressions.GreaterThan(a: Attribute, Literal(v, t)) =>
Some(sources.GreaterThan(a.name, convertToScala(v, t)))
case expressions.GreaterThan(Literal(v, t), a: Attribute) =>
Some(sources.LessThan(a.name, convertToScala(v, t)))
case expressions.LessThan(a: Attribute, Literal(v, t)) =>
Some(sources.LessThan(a.name, convertToScala(v, t)))
case expressions.LessThan(Literal(v, t), a: Attribute) =>
Some(sources.GreaterThan(a.name, convertToScala(v, t)))
case expressions.GreaterThanOrEqual(a: Attribute, Literal(v, t)) =>
Some(sources.GreaterThanOrEqual(a.name, convertToScala(v, t)))
case expressions.GreaterThanOrEqual(Literal(v, t), a: Attribute) =>
Some(sources.LessThanOrEqual(a.name, convertToScala(v, t)))
case expressions.LessThanOrEqual(a: Attribute, Literal(v, t)) =>
Some(sources.LessThanOrEqual(a.name, convertToScala(v, t)))
case expressions.LessThanOrEqual(Literal(v, t), a: Attribute) =>
Some(sources.GreaterThanOrEqual(a.name, convertToScala(v, t)))
case expressions.InSet(a: Attribute, set) =>
val toScala = CatalystTypeConverters.createToScalaConverter(a.dataType)
Some(sources.In(a.name, set.toArray.map(toScala)))
// Because we only convert In to InSet in Optimizer when there are more than certain
// items. So it is possible we still get an In expression here that needs to be pushed
// down.
case expressions.In(a: Attribute, list) if !list.exists(!_.isInstanceOf[Literal]) =>
val hSet = list.map(e => e.eval(EmptyRow))
val toScala = CatalystTypeConverters.createToScalaConverter(a.dataType)
Some(sources.In(a.name, hSet.toArray.map(toScala)))
case expressions.IsNull(a: Attribute) =>
Some(sources.IsNull(a.name))
case expressions.IsNotNull(a: Attribute) =>
Some(sources.IsNotNull(a.name))
case expressions.And(left, right) =>
// See SPARK-12218 for detailed discussion
// It is not safe to just convert one side if we do not understand the
// other side. Here is an example used to explain the reason.
// Let's say we have (a = 2 AND trim(b) = 'blah') OR (c > 0)
// and we do not understand how to convert trim(b) = 'blah'.
// If we only convert a = 2, we will end up with
// (a = 2) OR (c > 0), which will generate wrong results.
// Pushing one leg of AND down is only safe to do at the top level.
// You can see ParquetFilters' createFilter for more details.
for {
leftFilter <- translateFilter(left)
rightFilter <- translateFilter(right)
} yield sources.And(leftFilter, rightFilter)
case expressions.Or(left, right) =>
for {
leftFilter <- translateFilter(left)
rightFilter <- translateFilter(right)
} yield sources.Or(leftFilter, rightFilter)
case expressions.Not(child) =>
translateFilter(child).map(sources.Not)
case expressions.StartsWith(a: Attribute, Literal(v: UTF8String, StringType)) =>
Some(sources.StringStartsWith(a.name, v.toString))
case expressions.EndsWith(a: Attribute, Literal(v: UTF8String, StringType)) =>
Some(sources.StringEndsWith(a.name, v.toString))
case expressions.Contains(a: Attribute, Literal(v: UTF8String, StringType)) =>
Some(sources.StringContains(a.name, v.toString))
case _ => None
}
}
/**
* Selects Catalyst predicate [[Expression]]s which are convertible into data source [[Filter]]s
* and can be handled by `relation`.
*
* @return A triplet of `Seq[Expression]`, `Seq[Filter]`, and `Seq[Filter]` . The first element
* contains all Catalyst predicate [[Expression]]s that are either not convertible or
* cannot be handled by `relation`. The second element contains all converted data source
* [[Filter]]s that will be pushed down to the data source. The third element contains
* all [[Filter]]s that are completely filtered at the DataSource.
*/
protected[sql] def selectFilters(
relation: BaseRelation,
predicates: Seq[Expression]): (Seq[Expression], Seq[Filter], Set[Filter]) = {
// For conciseness, all Catalyst filter expressions of type `expressions.Expression` below are
// called `predicate`s, while all data source filters of type `sources.Filter` are simply called
// `filter`s.
// A map from original Catalyst expressions to corresponding translated data source filters.
// If a predicate is not in this map, it means it cannot be pushed down.
val translatedMap: Map[Expression, Filter] = predicates.flatMap { p =>
translateFilter(p).map(f => p -> f)
}.toMap
val pushedFilters: Seq[Filter] = translatedMap.values.toSeq
// Catalyst predicate expressions that cannot be converted to data source filters.
val nonconvertiblePredicates = predicates.filterNot(translatedMap.contains)
// Data source filters that cannot be handled by `relation`. An unhandled filter means
// the data source cannot guarantee the rows returned can pass the filter.
// As a result we must return it so Spark can plan an extra filter operator.
val unhandledFilters = relation.unhandledFilters(translatedMap.values.toArray).toSet
val unhandledPredicates = translatedMap.filter { case (p, f) =>
unhandledFilters.contains(f)
}.keys
val handledFilters = pushedFilters.toSet -- unhandledFilters
(nonconvertiblePredicates ++ unhandledPredicates, pushedFilters, handledFilters)
}
}
