org.apache.spark.sql.internal.DataFrameWriterImpl.scala Maven / Gradle / Ivy
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* 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.internal
import java.util.Locale
import scala.jdk.CollectionConverters._
import org.apache.spark.annotation.Stable
import org.apache.spark.sql.{DataFrameWriter, Dataset, SaveMode, SparkSession}
import org.apache.spark.sql.catalyst.TableIdentifier
import org.apache.spark.sql.catalyst.analysis.{EliminateSubqueryAliases, NoSuchTableException, UnresolvedIdentifier, UnresolvedRelation}
import org.apache.spark.sql.catalyst.catalog._
import org.apache.spark.sql.catalyst.expressions.Literal
import org.apache.spark.sql.catalyst.plans.logical._
import org.apache.spark.sql.catalyst.util.CaseInsensitiveMap
import org.apache.spark.sql.connector.catalog._
import org.apache.spark.sql.connector.catalog.TableCapability._
import org.apache.spark.sql.connector.catalog.TableWritePrivilege._
import org.apache.spark.sql.connector.expressions.{ClusterByTransform, FieldReference, IdentityTransform, Transform}
import org.apache.spark.sql.errors.QueryCompilationErrors
import org.apache.spark.sql.execution.QueryExecution
import org.apache.spark.sql.execution.command.DDLUtils
import org.apache.spark.sql.execution.datasources.{CreateTable, DataSource, DataSourceUtils, LogicalRelation}
import org.apache.spark.sql.execution.datasources.v2._
import org.apache.spark.sql.internal.SQLConf.PartitionOverwriteMode
import org.apache.spark.sql.sources.BaseRelation
import org.apache.spark.sql.types.StructType
import org.apache.spark.sql.util.CaseInsensitiveStringMap
import org.apache.spark.util.ArrayImplicits._
/**
* Interface used to write a [[Dataset]] to external storage systems (e.g. file systems,
* key-value stores, etc). Use `Dataset.write` to access this.
*
* @since 1.4.0
*/
@Stable
final class DataFrameWriterImpl[T] private[sql](ds: Dataset[T]) extends DataFrameWriter[T] {
format(ds.sparkSession.sessionState.conf.defaultDataSourceName)
private val df = ds.toDF()
/** @inheritdoc */
override def mode(saveMode: SaveMode): this.type = super.mode(saveMode)
/** @inheritdoc */
override def mode(saveMode: String): this.type = super.mode(saveMode)
/** @inheritdoc */
override def format(source: String): this.type = super.format(source)
/** @inheritdoc */
override def option(key: String, value: String): this.type = super.option(key, value)
/** @inheritdoc */
override def option(key: String, value: Boolean): this.type = super.option(key, value)
/** @inheritdoc */
override def option(key: String, value: Long): this.type = super.option(key, value)
/** @inheritdoc */
override def option(key: String, value: Double): this.type = super.option(key, value)
/** @inheritdoc */
override def options(options: scala.collection.Map[String, String]): this.type =
super.options(options)
/** @inheritdoc */
override def options(options: java.util.Map[String, String]): this.type =
super.options(options)
/** @inheritdoc */
@scala.annotation.varargs
override def partitionBy(colNames: String*): this.type = super.partitionBy(colNames: _*)
/** @inheritdoc */
@scala.annotation.varargs
override def bucketBy(numBuckets: Int, colName: String, colNames: String*): this.type =
super.bucketBy(numBuckets, colName, colNames: _*)
/** @inheritdoc */
@scala.annotation.varargs
override def sortBy(colName: String, colNames: String*): this.type =
super.sortBy(colName, colNames: _*)
/** @inheritdoc */
@scala.annotation.varargs
override def clusterBy(colName: String, colNames: String*): this.type =
super.clusterBy(colName, colNames: _*)
/**
* Saves the content of the `DataFrame` at the specified path.
*
* @since 1.4.0
*/
def save(path: String): Unit = {
if (!df.sparkSession.sessionState.conf.legacyPathOptionBehavior &&
extraOptions.contains("path")) {
throw QueryCompilationErrors.pathOptionNotSetCorrectlyWhenWritingError()
}
saveInternal(Some(path))
}
/**
* Saves the content of the `DataFrame` as the specified table.
*
* @since 1.4.0
*/
def save(): Unit = saveInternal(None)
private def saveInternal(path: Option[String]): Unit = {
if (source.toLowerCase(Locale.ROOT) == DDLUtils.HIVE_PROVIDER) {
throw QueryCompilationErrors.cannotOperateOnHiveDataSourceFilesError("write")
}
assertNotBucketed("save")
val maybeV2Provider = lookupV2Provider()
if (maybeV2Provider.isDefined) {
val provider = maybeV2Provider.get
val sessionOptions = DataSourceV2Utils.extractSessionConfigs(
provider, df.sparkSession.sessionState.conf)
val optionsWithPath = getOptionsWithPath(path)
val finalOptions = sessionOptions.filter { case (k, _) => !optionsWithPath.contains(k) } ++
optionsWithPath.originalMap
val dsOptions = new CaseInsensitiveStringMap(finalOptions.asJava)
def getTable: Table = {
// If the source accepts external table metadata, here we pass the schema of input query
// and the user-specified partitioning to `getTable`. This is for avoiding
// schema/partitioning inference, which can be very expensive.
// If the query schema is not compatible with the existing data, the behavior is undefined.
// For example, writing file source will success but the following reads will fail.
if (provider.supportsExternalMetadata()) {
provider.getTable(
df.schema.asNullable,
partitioningAsV2.toArray,
dsOptions.asCaseSensitiveMap())
} else {
DataSourceV2Utils.getTableFromProvider(provider, dsOptions, userSpecifiedSchema = None)
}
}
import org.apache.spark.sql.execution.datasources.v2.DataSourceV2Implicits._
val catalogManager = df.sparkSession.sessionState.catalogManager
mode match {
case SaveMode.Append | SaveMode.Overwrite =>
val (table, catalog, ident) = provider match {
case supportsExtract: SupportsCatalogOptions =>
val ident = supportsExtract.extractIdentifier(dsOptions)
val catalog = CatalogV2Util.getTableProviderCatalog(
supportsExtract, catalogManager, dsOptions)
(catalog.loadTable(ident), Some(catalog), Some(ident))
case _: TableProvider =>
val t = getTable
if (t.supports(BATCH_WRITE)) {
(t, None, None)
} else {
// Streaming also uses the data source V2 API. So it may be that the data source
// implements v2, but has no v2 implementation for batch writes. In that case, we
// fall back to saving as though it's a V1 source.
return saveToV1Source(path)
}
}
val relation = DataSourceV2Relation.create(table, catalog, ident, dsOptions)
checkPartitioningMatchesV2Table(table)
if (mode == SaveMode.Append) {
runCommand(df.sparkSession) {
AppendData.byName(relation, df.logicalPlan, finalOptions)
}
} else {
// Truncate the table. TableCapabilityCheck will throw a nice exception if this
// isn't supported
runCommand(df.sparkSession) {
OverwriteByExpression.byName(
relation, df.logicalPlan, Literal(true), finalOptions)
}
}
case createMode =>
provider match {
case supportsExtract: SupportsCatalogOptions =>
val ident = supportsExtract.extractIdentifier(dsOptions)
val catalog = CatalogV2Util.getTableProviderCatalog(
supportsExtract, catalogManager, dsOptions)
val tableSpec = UnresolvedTableSpec(
properties = Map.empty,
provider = Some(source),
optionExpression = OptionList(Seq.empty),
location = extraOptions.get("path"),
comment = extraOptions.get(TableCatalog.PROP_COMMENT),
serde = None,
external = false)
runCommand(df.sparkSession) {
CreateTableAsSelect(
UnresolvedIdentifier(
catalog.name +: ident.namespace.toImmutableArraySeq :+ ident.name),
partitioningAsV2,
df.queryExecution.analyzed,
tableSpec,
finalOptions,
ignoreIfExists = createMode == SaveMode.Ignore)
}
case _: TableProvider =>
if (getTable.supports(BATCH_WRITE)) {
throw QueryCompilationErrors.writeWithSaveModeUnsupportedBySourceError(
source, createMode.name())
} else {
// Streaming also uses the data source V2 API. So it may be that the data source
// implements v2, but has no v2 implementation for batch writes. In that case, we
// fallback to saving as though it's a V1 source.
saveToV1Source(path)
}
}
}
} else {
saveToV1Source(path)
}
}
private def getOptionsWithPath(path: Option[String]): CaseInsensitiveMap[String] = {
if (path.isEmpty) {
extraOptions
} else {
extraOptions + ("path" -> path.get)
}
}
private def saveToV1Source(path: Option[String]): Unit = {
partitioningColumns.foreach { columns =>
extraOptions = extraOptions + (
DataSourceUtils.PARTITIONING_COLUMNS_KEY ->
DataSourceUtils.encodePartitioningColumns(columns))
}
clusteringColumns.foreach { columns =>
extraOptions = extraOptions + (
DataSourceUtils.CLUSTERING_COLUMNS_KEY ->
DataSourceUtils.encodePartitioningColumns(columns))
}
val optionsWithPath = getOptionsWithPath(path)
// Code path for data source v1.
runCommand(df.sparkSession) {
DataSource(
sparkSession = df.sparkSession,
className = source,
partitionColumns = partitioningColumns.getOrElse(Nil),
options = optionsWithPath.originalMap).planForWriting(mode, df.logicalPlan)
}
}
/**
* Inserts the content of the `DataFrame` to the specified table. It requires that
* the schema of the `DataFrame` is the same as the schema of the table.
*
* @note Unlike `saveAsTable`, `insertInto` ignores the column names and just uses position-based
* resolution. For example:
*
* @note SaveMode.ErrorIfExists and SaveMode.Ignore behave as SaveMode.Append in `insertInto` as
* `insertInto` is not a table creating operation.
*
* {{{
* scala> Seq((1, 2)).toDF("i", "j").write.mode("overwrite").saveAsTable("t1")
* scala> Seq((3, 4)).toDF("j", "i").write.insertInto("t1")
* scala> Seq((5, 6)).toDF("a", "b").write.insertInto("t1")
* scala> sql("select * from t1").show
* +---+---+
* | i| j|
* +---+---+
* | 5| 6|
* | 3| 4|
* | 1| 2|
* +---+---+
* }}}
*
* Because it inserts data to an existing table, format or options will be ignored.
*
* @since 1.4.0
*/
def insertInto(tableName: String): Unit = {
import df.sparkSession.sessionState.analyzer.{AsTableIdentifier, NonSessionCatalogAndIdentifier, SessionCatalogAndIdentifier}
import org.apache.spark.sql.connector.catalog.CatalogV2Implicits._
assertNotBucketed("insertInto")
if (partitioningColumns.isDefined) {
throw QueryCompilationErrors.partitionByDoesNotAllowedWhenUsingInsertIntoError()
}
val session = df.sparkSession
val canUseV2 = lookupV2Provider().isDefined
session.sessionState.sqlParser.parseMultipartIdentifier(tableName) match {
case NonSessionCatalogAndIdentifier(catalog, ident) =>
insertInto(catalog, ident)
case SessionCatalogAndIdentifier(catalog, ident)
if canUseV2 && ident.namespace().length <= 1 =>
insertInto(catalog, ident)
case AsTableIdentifier(tableIdentifier) =>
insertInto(tableIdentifier)
case other =>
throw QueryCompilationErrors.cannotFindCatalogToHandleIdentifierError(other.quoted)
}
}
private def insertInto(catalog: CatalogPlugin, ident: Identifier): Unit = {
import org.apache.spark.sql.connector.catalog.CatalogV2Implicits._
val table = catalog.asTableCatalog.loadTable(ident, getWritePrivileges.toSet.asJava) match {
case _: V1Table =>
return insertInto(TableIdentifier(ident.name(), ident.namespace().headOption))
case t =>
DataSourceV2Relation.create(t, Some(catalog), Some(ident))
}
val command = mode match {
case SaveMode.Append | SaveMode.ErrorIfExists | SaveMode.Ignore =>
AppendData.byPosition(table, df.logicalPlan, extraOptions.toMap)
case SaveMode.Overwrite =>
val conf = df.sparkSession.sessionState.conf
val dynamicPartitionOverwrite = table.table.partitioning.length > 0 &&
conf.partitionOverwriteMode == PartitionOverwriteMode.DYNAMIC
if (dynamicPartitionOverwrite) {
OverwritePartitionsDynamic.byPosition(table, df.logicalPlan, extraOptions.toMap)
} else {
OverwriteByExpression.byPosition(table, df.logicalPlan, Literal(true), extraOptions.toMap)
}
}
runCommand(df.sparkSession) {
command
}
}
private def insertInto(tableIdent: TableIdentifier): Unit = {
runCommand(df.sparkSession) {
InsertIntoStatement(
table = UnresolvedRelation(tableIdent).requireWritePrivileges(getWritePrivileges),
partitionSpec = Map.empty[String, Option[String]],
Nil,
query = df.logicalPlan,
overwrite = mode == SaveMode.Overwrite,
ifPartitionNotExists = false)
}
}
private def getWritePrivileges: Seq[TableWritePrivilege] = mode match {
case SaveMode.Overwrite => Seq(INSERT, DELETE)
case _ => Seq(INSERT)
}
private def getBucketSpec: Option[BucketSpec] = {
isBucketed()
numBuckets.map { n =>
BucketSpec(n, bucketColumnNames.get, sortColumnNames.getOrElse(Nil))
}
}
/**
* Saves the content of the `DataFrame` as the specified table.
*
* In the case the table already exists, behavior of this function depends on the
* save mode, specified by the `mode` function (default to throwing an exception).
* When `mode` is `Overwrite`, the schema of the `DataFrame` does not need to be
* the same as that of the existing table.
*
* When `mode` is `Append`, if there is an existing table, we will use the format and options of
* the existing table. The column order in the schema of the `DataFrame` doesn't need to be same
* as that of the existing table. Unlike `insertInto`, `saveAsTable` will use the column names to
* find the correct column positions. For example:
*
* {{{
* scala> Seq((1, 2)).toDF("i", "j").write.mode("overwrite").saveAsTable("t1")
* scala> Seq((3, 4)).toDF("j", "i").write.mode("append").saveAsTable("t1")
* scala> sql("select * from t1").show
* +---+---+
* | i| j|
* +---+---+
* | 1| 2|
* | 4| 3|
* +---+---+
* }}}
*
* In this method, save mode is used to determine the behavior if the data source table exists in
* Spark catalog. We will always overwrite the underlying data of data source (e.g. a table in
* JDBC data source) if the table doesn't exist in Spark catalog, and will always append to the
* underlying data of data source if the table already exists.
*
* When the DataFrame is created from a non-partitioned `HadoopFsRelation` with a single input
* path, and the data source provider can be mapped to an existing Hive builtin SerDe (i.e. ORC
* and Parquet), the table is persisted in a Hive compatible format, which means other systems
* like Hive will be able to read this table. Otherwise, the table is persisted in a Spark SQL
* specific format.
*
* @since 1.4.0
*/
def saveAsTable(tableName: String): Unit = {
import df.sparkSession.sessionState.analyzer.{AsTableIdentifier, NonSessionCatalogAndIdentifier, SessionCatalogAndIdentifier}
import org.apache.spark.sql.connector.catalog.CatalogV2Implicits._
val session = df.sparkSession
val canUseV2 = lookupV2Provider().isDefined || (df.sparkSession.sessionState.conf.getConf(
SQLConf.V2_SESSION_CATALOG_IMPLEMENTATION).isDefined &&
!df.sparkSession.sessionState.catalogManager.catalog(CatalogManager.SESSION_CATALOG_NAME)
.isInstanceOf[DelegatingCatalogExtension])
session.sessionState.sqlParser.parseMultipartIdentifier(tableName) match {
case nameParts @ NonSessionCatalogAndIdentifier(catalog, ident) =>
saveAsTable(catalog.asTableCatalog, ident, nameParts)
case nameParts @ SessionCatalogAndIdentifier(catalog, ident)
if canUseV2 && ident.namespace().length <= 1 =>
saveAsTable(catalog.asTableCatalog, ident, nameParts)
case AsTableIdentifier(tableIdentifier) =>
saveAsTable(tableIdentifier)
case other =>
throw QueryCompilationErrors.cannotFindCatalogToHandleIdentifierError(other.quoted)
}
}
private def saveAsTable(
catalog: TableCatalog, ident: Identifier, nameParts: Seq[String]): Unit = {
val tableOpt = try Option(catalog.loadTable(ident, getWritePrivileges.toSet.asJava)) catch {
case _: NoSuchTableException => None
}
val command = (mode, tableOpt) match {
case (_, Some(_: V1Table)) =>
return saveAsTable(TableIdentifier(ident.name(), ident.namespace().headOption))
case (SaveMode.Append, Some(table)) =>
checkPartitioningMatchesV2Table(table)
val v2Relation = DataSourceV2Relation.create(table, Some(catalog), Some(ident))
AppendData.byName(v2Relation, df.logicalPlan, extraOptions.toMap)
case (SaveMode.Overwrite, _) =>
val tableSpec = UnresolvedTableSpec(
properties = Map.empty,
provider = Some(source),
optionExpression = OptionList(Seq.empty),
location = extraOptions.get("path"),
comment = extraOptions.get(TableCatalog.PROP_COMMENT),
serde = None,
external = false)
ReplaceTableAsSelect(
UnresolvedIdentifier(nameParts),
partitioningAsV2,
df.queryExecution.analyzed,
tableSpec,
writeOptions = extraOptions.toMap,
orCreate = true) // Create the table if it doesn't exist
case (other, _) =>
// We have a potential race condition here in AppendMode, if the table suddenly gets
// created between our existence check and physical execution, but this can't be helped
// in any case.
val tableSpec = UnresolvedTableSpec(
properties = Map.empty,
provider = Some(source),
optionExpression = OptionList(Seq.empty),
location = extraOptions.get("path"),
comment = extraOptions.get(TableCatalog.PROP_COMMENT),
serde = None,
external = false)
CreateTableAsSelect(
UnresolvedIdentifier(nameParts),
partitioningAsV2,
df.queryExecution.analyzed,
tableSpec,
writeOptions = extraOptions.toMap,
other == SaveMode.Ignore)
}
runCommand(df.sparkSession) {
command
}
}
private def saveAsTable(tableIdent: TableIdentifier): Unit = {
val catalog = df.sparkSession.sessionState.catalog
val qualifiedIdent = catalog.qualifyIdentifier(tableIdent)
val tableExists = catalog.tableExists(qualifiedIdent)
(tableExists, mode) match {
case (true, SaveMode.Ignore) =>
// Do nothing
case (true, SaveMode.ErrorIfExists) =>
throw QueryCompilationErrors.tableAlreadyExistsError(qualifiedIdent)
case (true, SaveMode.Overwrite) =>
// Get all input data source or hive relations of the query.
val srcRelations = df.logicalPlan.collect {
case LogicalRelation(src: BaseRelation, _, _, _) => src
case relation: HiveTableRelation => relation.tableMeta.identifier
}
val tableRelation = df.sparkSession.table(qualifiedIdent).queryExecution.analyzed
EliminateSubqueryAliases(tableRelation) match {
// check if the table is a data source table (the relation is a BaseRelation).
case LogicalRelation(dest: BaseRelation, _, _, _) if srcRelations.contains(dest) =>
throw QueryCompilationErrors.cannotOverwriteTableThatIsBeingReadFromError(
qualifiedIdent)
// check hive table relation when overwrite mode
case relation: HiveTableRelation
if srcRelations.contains(relation.tableMeta.identifier) =>
throw QueryCompilationErrors.cannotOverwriteTableThatIsBeingReadFromError(
qualifiedIdent)
case _ => // OK
}
// Drop the existing table
catalog.dropTable(qualifiedIdent, ignoreIfNotExists = true, purge = false)
createTable(qualifiedIdent)
// Refresh the cache of the table in the catalog.
catalog.refreshTable(qualifiedIdent)
case _ => createTable(qualifiedIdent)
}
}
private def createTable(tableIdent: TableIdentifier): Unit = {
val storage = DataSource.buildStorageFormatFromOptions(extraOptions.toMap)
val tableType = if (storage.locationUri.isDefined) {
CatalogTableType.EXTERNAL
} else {
CatalogTableType.MANAGED
}
val properties = if (clusteringColumns.isEmpty) {
Map.empty[String, String]
} else {
Map(ClusterBySpec.toPropertyWithoutValidation(
ClusterBySpec.fromColumnNames(clusteringColumns.get)))
}
val tableDesc = CatalogTable(
identifier = tableIdent,
tableType = tableType,
storage = storage,
schema = new StructType,
provider = Some(source),
partitionColumnNames = partitioningColumns.getOrElse(Nil),
bucketSpec = getBucketSpec,
properties = properties)
runCommand(df.sparkSession)(
CreateTable(tableDesc, mode, Some(df.logicalPlan)))
}
/** Converts the provided partitioning and bucketing information to DataSourceV2 Transforms. */
private def partitioningAsV2: Seq[Transform] = {
val partitioning = partitioningColumns.map { colNames =>
colNames.map(name => IdentityTransform(FieldReference(name)))
}.getOrElse(Seq.empty[Transform])
val bucketing =
getBucketSpec.map(spec => CatalogV2Implicits.BucketSpecHelper(spec).asTransform).toSeq
val clustering = clusteringColumns.map { colNames =>
ClusterByTransform(colNames.map(FieldReference(_)))
}
partitioning ++ bucketing ++ clustering
}
/**
* For V2 DataSources, performs if the provided partitioning matches that of the table.
* Partitioning information is not required when appending data to V2 tables.
*/
private def checkPartitioningMatchesV2Table(existingTable: Table): Unit = {
val v2Partitions = partitioningAsV2
if (v2Partitions.isEmpty) return
require(v2Partitions.sameElements(existingTable.partitioning()),
"The provided partitioning or clustering columns do not match the existing table's.\n" +
s" - provided: ${v2Partitions.mkString(", ")}\n" +
s" - table: ${existingTable.partitioning().mkString(", ")}")
}
/**
* Wrap a DataFrameWriter action to track the QueryExecution and time cost, then report to the
* user-registered callback functions.
*/
private def runCommand(session: SparkSession)(command: LogicalPlan): Unit = {
val qe = new QueryExecution(session, command, df.queryExecution.tracker)
qe.assertCommandExecuted()
}
private def lookupV2Provider(): Option[TableProvider] = {
DataSource.lookupDataSourceV2(source, df.sparkSession.sessionState.conf) match {
// TODO(SPARK-28396): File source v2 write path is currently broken.
case Some(_: FileDataSourceV2) => None
case other => other
}
}
}