Github user rxin commented on a diff in the pull request:
https://github.com/apache/spark/pull/12645#discussion_r60839130
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/command/createDataSourceTables.scala
---
@@ -0,0 +1,452 @@
+/*
+ * 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.command
+
+import java.util.regex.Pattern
+
+import scala.collection.mutable
+import scala.util.control.NonFatal
+
+import org.apache.spark.internal.Logging
+import org.apache.spark.sql._
+import org.apache.spark.sql.catalyst.TableIdentifier
+import org.apache.spark.sql.catalyst.analysis.EliminateSubqueryAliases
+import org.apache.spark.sql.catalyst.catalog.{CatalogColumn,
CatalogStorageFormat, CatalogTable, CatalogTableType}
+import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
+import org.apache.spark.sql.execution.datasources.{BucketSpec, DataSource,
HadoopFsRelation, LogicalRelation}
+import org.apache.spark.sql.internal.HiveSerDe
+import org.apache.spark.sql.sources.InsertableRelation
+import org.apache.spark.sql.types._
+
+/**
+ * A command used to create a data source table.
+ *
+ * Note: This is different from [[CreateTable]]. Please check the syntax
for difference.
+ * This is not intended for temporary tables.
+ *
+ * The syntax of using this command in SQL is:
+ * {{{
+ * CREATE TABLE [IF NOT EXISTS] [db_name.]table_name
+ * [(col1 data_type [COMMENT col_comment], ...)]
+ * USING format OPTIONS ([option1_name "option1_value", option2_name
"option2_value", ...])
+ * }}}
+ */
+case class CreateDataSourceTableCommand(
+ tableIdent: TableIdentifier,
+ userSpecifiedSchema: Option[StructType],
+ provider: String,
+ options: Map[String, String],
+ ignoreIfExists: Boolean,
+ managedIfNoPath: Boolean)
+ extends RunnableCommand {
+
+ override def run(sqlContext: SQLContext): Seq[Row] = {
+ // Since we are saving metadata to metastore, we need to check if
metastore supports
+ // the table name and database name we have for this query.
MetaStoreUtils.validateName
+ // is the method used by Hive to check if a table name or a database
name is valid for
+ // the metastore.
+ if (!CreateDataSourceTableUtils.validateName(tableIdent.table)) {
+ throw new AnalysisException(s"Table name ${tableIdent.table} is not
a valid name for " +
+ s"metastore. Metastore only accepts table name containing
characters, numbers and _.")
+ }
+ if (tableIdent.database.isDefined &&
+ !CreateDataSourceTableUtils.validateName(tableIdent.database.get)) {
+ throw new AnalysisException(s"Database name
${tableIdent.database.get} is not a valid name " +
+ s"for metastore. Metastore only accepts database name containing "
+
+ s"characters, numbers and _.")
+ }
+
+ val tableName = tableIdent.unquotedString
+ val sessionState = sqlContext.sessionState
+
+ if (sessionState.catalog.tableExists(tableIdent)) {
+ if (ignoreIfExists) {
+ return Seq.empty[Row]
+ } else {
+ throw new AnalysisException(s"Table $tableName already exists.")
+ }
+ }
+
+ var isExternal = true
+ val optionsWithPath =
+ if (!options.contains("path") && managedIfNoPath) {
+ isExternal = false
+ options + ("path" ->
sessionState.catalog.defaultTablePath(tableIdent))
+ } else {
+ options
+ }
+
+ // Create the relation to validate the arguments before writing the
metadata to the metastore.
+ DataSource(
+ sqlContext = sqlContext,
+ userSpecifiedSchema = userSpecifiedSchema,
+ className = provider,
+ bucketSpec = None,
+ options = optionsWithPath).resolveRelation()
+
+ CreateDataSourceTableUtils.createDataSourceTable(
+ sqlContext = sqlContext,
+ tableIdent = tableIdent,
+ userSpecifiedSchema = userSpecifiedSchema,
+ partitionColumns = Array.empty[String],
+ bucketSpec = None,
+ provider = provider,
+ options = optionsWithPath,
+ isExternal = isExternal)
+
+ Seq.empty[Row]
+ }
+}
+
+/**
+ * A command used to create a data source table using the result of a
query.
+ *
+ * Note: This is different from [[CreateTableAsSelect]]. Please check the
syntax for difference.
+ * This is not intended for temporary tables.
+ *
+ * The syntax of using this command in SQL is:
+ * {{{
+ * CREATE TABLE [IF NOT EXISTS] [db_name.]table_name
+ * USING format OPTIONS ([option1_name "option1_value", option2_name
"option2_value", ...])
+ * AS SELECT ...
+ * }}}
+ */
+case class CreateDataSourceTableAsSelectCommand(
+ tableIdent: TableIdentifier,
+ provider: String,
+ partitionColumns: Array[String],
+ bucketSpec: Option[BucketSpec],
+ mode: SaveMode,
+ options: Map[String, String],
+ query: LogicalPlan)
+ extends RunnableCommand {
+
+ override def run(sqlContext: SQLContext): Seq[Row] = {
+ // Since we are saving metadata to metastore, we need to check if
metastore supports
+ // the table name and database name we have for this query.
MetaStoreUtils.validateName
+ // is the method used by Hive to check if a table name or a database
name is valid for
+ // the metastore.
+ if (!CreateDataSourceTableUtils.validateName(tableIdent.table)) {
+ throw new AnalysisException(s"Table name ${tableIdent.table} is not
a valid name for " +
+ s"metastore. Metastore only accepts table name containing
characters, numbers and _.")
+ }
+ if (tableIdent.database.isDefined &&
+ !CreateDataSourceTableUtils.validateName(tableIdent.database.get)) {
+ throw new AnalysisException(s"Database name
${tableIdent.database.get} is not a valid name " +
+ s"for metastore. Metastore only accepts database name containing "
+
+ s"characters, numbers and _.")
+ }
+
+ val tableName = tableIdent.unquotedString
+ val sessionState = sqlContext.sessionState
+ var createMetastoreTable = false
+ var isExternal = true
+ val optionsWithPath =
+ if (!options.contains("path")) {
+ isExternal = false
+ options + ("path" ->
sessionState.catalog.defaultTablePath(tableIdent))
+ } else {
+ options
+ }
+
+ var existingSchema = None: Option[StructType]
+ if (sqlContext.sessionState.catalog.tableExists(tableIdent)) {
+ // Check if we need to throw an exception or just return.
+ mode match {
+ case SaveMode.ErrorIfExists =>
+ throw new AnalysisException(s"Table $tableName already exists. "
+
+ s"If you are using saveAsTable, you can set SaveMode to
SaveMode.Append to " +
+ s"insert data into the table or set SaveMode to
SaveMode.Overwrite to overwrite" +
+ s"the existing data. " +
+ s"Or, if you are using SQL CREATE TABLE, you need to drop
$tableName first.")
+ case SaveMode.Ignore =>
+ // Since the table already exists and the save mode is Ignore,
we will just return.
+ return Seq.empty[Row]
+ case SaveMode.Append =>
+ // Check if the specified data source match the data source of
the existing table.
+ val dataSource = DataSource(
+ sqlContext = sqlContext,
+ userSpecifiedSchema = Some(query.schema.asNullable),
+ partitionColumns = partitionColumns,
+ bucketSpec = bucketSpec,
+ className = provider,
+ options = optionsWithPath)
+ // TODO: Check that options from the resolved relation match the
relation that we are
+ // inserting into (i.e. using the same compression).
+
+ EliminateSubqueryAliases(
+ sessionState.catalog.lookupRelation(tableIdent)) match {
+ case l @ LogicalRelation(_: InsertableRelation | _:
HadoopFsRelation, _, _) =>
+ existingSchema = Some(l.schema)
+ case o =>
+ throw new AnalysisException(s"Saving data in ${o.toString}
is not supported.")
+ }
+ case SaveMode.Overwrite =>
+ sqlContext.sql(s"DROP TABLE IF EXISTS $tableName")
+ // Need to create the table again.
+ createMetastoreTable = true
+ }
+ } else {
+ // The table does not exist. We need to create it in metastore.
+ createMetastoreTable = true
+ }
+
+ val data = Dataset.ofRows(sqlContext, query)
+ val df = existingSchema match {
+ // If we are inserting into an existing table, just use the existing
schema.
+ case Some(s) => data.selectExpr(s.fieldNames: _*)
+ case None => data
+ }
+
+ // Create the relation based on the data of df.
+ val dataSource = DataSource(
+ sqlContext,
+ className = provider,
+ partitionColumns = partitionColumns,
+ bucketSpec = bucketSpec,
+ options = optionsWithPath)
+
+ val result = dataSource.write(mode, df)
+
+ if (createMetastoreTable) {
+ // We will use the schema of resolved.relation as the schema of the
table (instead of
+ // the schema of df). It is important since the nullability may be
changed by the relation
+ // provider (for example, see
org.apache.spark.sql.parquet.DefaultSource).
+ CreateDataSourceTableUtils.createDataSourceTable(
+ sqlContext = sqlContext,
+ tableIdent = tableIdent,
+ userSpecifiedSchema = Some(result.schema),
+ partitionColumns = partitionColumns,
+ bucketSpec = bucketSpec,
+ provider = provider,
+ options = optionsWithPath,
+ isExternal = isExternal)
+ }
+
+ // Refresh the cache of the table in the catalog.
+ sessionState.catalog.refreshTable(tableIdent)
+ Seq.empty[Row]
+ }
+}
+
+object CreateDataSourceTableUtils extends Logging {
+ /**
+ * Checks if the given name conforms the Hive standard ("[a-zA-z_0-9]+"),
+ * i.e. if this name only contains characters, numbers, and _.
+ *
+ * This method is intended to have the same behavior of
+ * org.apache.hadoop.hive.metastore.MetaStoreUtils.validateName.
+ */
+ def validateName(name: String): Boolean = {
+ val tpat = Pattern.compile("[\\w_]+")
+ val matcher = tpat.matcher(name)
+
+ matcher.matches()
+ }
+
+ def createDataSourceTable(
+ sqlContext: SQLContext,
+ tableIdent: TableIdentifier,
+ userSpecifiedSchema: Option[StructType],
+ partitionColumns: Array[String],
+ bucketSpec: Option[BucketSpec],
+ provider: String,
+ options: Map[String, String],
+ isExternal: Boolean): Unit = {
+ val tableProperties = new mutable.HashMap[String, String]
+ tableProperties.put("spark.sql.sources.provider", provider)
+
+ // Saves optional user specified schema. Serialized JSON schema
string may be too long to be
+ // stored into a single metastore SerDe property. In this case, we
split the JSON string and
+ // store each part as a separate SerDe property.
+ userSpecifiedSchema.foreach { schema =>
+ val threshold =
sqlContext.sessionState.conf.schemaStringLengthThreshold
+ val schemaJsonString = schema.json
+ // Split the JSON string.
+ val parts = schemaJsonString.grouped(threshold).toSeq
+ tableProperties.put("spark.sql.sources.schema.numParts",
parts.size.toString)
+ parts.zipWithIndex.foreach { case (part, index) =>
+ tableProperties.put(s"spark.sql.sources.schema.part.$index", part)
+ }
+ }
+
+ if (userSpecifiedSchema.isDefined && partitionColumns.length > 0) {
+ tableProperties.put("spark.sql.sources.schema.numPartCols",
partitionColumns.length.toString)
+ partitionColumns.zipWithIndex.foreach { case (partCol, index) =>
+ tableProperties.put(s"spark.sql.sources.schema.partCol.$index",
partCol)
+ }
+ }
+
+ if (userSpecifiedSchema.isDefined && bucketSpec.isDefined) {
+ val BucketSpec(numBuckets, bucketColumnNames, sortColumnNames) =
bucketSpec.get
+
+ tableProperties.put("spark.sql.sources.schema.numBuckets",
numBuckets.toString)
+ tableProperties.put("spark.sql.sources.schema.numBucketCols",
+ bucketColumnNames.length.toString)
+ bucketColumnNames.zipWithIndex.foreach { case (bucketCol, index) =>
+ tableProperties.put(s"spark.sql.sources.schema.bucketCol.$index",
bucketCol)
+ }
+
+ if (sortColumnNames.nonEmpty) {
+ tableProperties.put("spark.sql.sources.schema.numSortCols",
+ sortColumnNames.length.toString)
+ sortColumnNames.zipWithIndex.foreach { case (sortCol, index) =>
+ tableProperties.put(s"spark.sql.sources.schema.sortCol.$index",
sortCol)
+ }
+ }
+ }
+
+ if (userSpecifiedSchema.isEmpty && partitionColumns.length > 0) {
+ // The table does not have a specified schema, which means that the
schema will be inferred
+ // when we load the table. So, we are not expecting partition
columns and we will discover
+ // partitions when we load the table. However, if there are
specified partition columns,
+ // we simply ignore them and provide a warning message.
+ logWarning(
+ s"The schema and partitions of table $tableIdent will be inferred
when it is loaded. " +
+ s"Specified partition columns
(${partitionColumns.mkString(",")}) will be ignored.")
+ }
+
+ val tableType = if (isExternal) {
+ tableProperties.put("EXTERNAL", "TRUE")
+ CatalogTableType.EXTERNAL_TABLE
+ } else {
+ tableProperties.put("EXTERNAL", "FALSE")
+ CatalogTableType.MANAGED_TABLE
+ }
+
+ val maybeSerDe = HiveSerDe.sourceToSerDe(provider,
sqlContext.sessionState.conf)
+ val dataSource =
+ DataSource(
+ sqlContext,
+ userSpecifiedSchema = userSpecifiedSchema,
+ partitionColumns = partitionColumns,
+ bucketSpec = bucketSpec,
+ className = provider,
+ options = options)
+
+ def newSparkSQLSpecificMetastoreTable(): CatalogTable = {
+ CatalogTable(
+ identifier = tableIdent,
+ tableType = tableType,
+ schema = Nil,
+ storage = CatalogStorageFormat(
+ locationUri = None,
+ inputFormat = None,
+ outputFormat = None,
+ serde = None,
+ serdeProperties = options
+ ),
+ properties = tableProperties.toMap)
+ }
+
+ def newHiveCompatibleMetastoreTable(
+ relation: HadoopFsRelation,
+ serde: HiveSerDe): CatalogTable = {
+ assert(partitionColumns.isEmpty)
+ assert(relation.partitionSchema.isEmpty)
+
+ CatalogTable(
+ identifier = tableIdent,
+ tableType = tableType,
+ storage = CatalogStorageFormat(
+ locationUri =
Some(relation.location.paths.map(_.toUri.toString).head),
+ inputFormat = serde.inputFormat,
+ outputFormat = serde.outputFormat,
+ serde = serde.serde,
+ serdeProperties = options
+ ),
+ schema = relation.schema.map { f =>
+ CatalogColumn(f.name, f.dataType.catalogString)
+ },
+ properties = tableProperties.toMap,
+ viewText = None)
+ }
+
+ // TODO: Support persisting partitioned data source relations in Hive
compatible format
+ val qualifiedTableName = tableIdent.quotedString
+ val skipHiveMetadata = options.getOrElse("skipHiveMetadata",
"false").toBoolean
+ val (hiveCompatibleTable, logMessage) = (maybeSerDe,
dataSource.resolveRelation()) match {
+ case _ if skipHiveMetadata =>
+ val message =
+ s"Persisting partitioned data source relation
$qualifiedTableName into " +
+ "Hive metastore in Spark SQL specific format, which is NOT
compatible with Hive."
+ (None, message)
+
+ case (Some(serde), relation: HadoopFsRelation)
+ if relation.location.paths.length == 1 &&
relation.partitionSchema.isEmpty =>
+ val hiveTable = newHiveCompatibleMetastoreTable(relation, serde)
+ val message =
+ s"Persisting data source relation $qualifiedTableName with a
single input path " +
+ s"into Hive metastore in Hive compatible format. Input path: "
+
+ s"${relation.location.paths.head}."
+ (Some(hiveTable), message)
+
+ case (Some(serde), relation: HadoopFsRelation) if
relation.partitionSchema.nonEmpty =>
+ val message =
+ s"Persisting partitioned data source relation
$qualifiedTableName into " +
+ "Hive metastore in Spark SQL specific format, which is NOT
compatible with Hive. " +
+ "Input path(s): " + relation.location.paths.mkString("\n",
"\n", "")
+ (None, message)
+
+ case (Some(serde), relation: HadoopFsRelation) =>
+ val message =
+ s"Persisting data source relation $qualifiedTableName with
multiple input paths into " +
+ "Hive metastore in Spark SQL specific format, which is NOT
compatible with Hive. " +
+ s"Input paths: " + relation.location.paths.mkString("\n",
"\n", "")
+ (None, message)
+
+ case (Some(serde), _) =>
+ val message =
+ s"Data source relation $qualifiedTableName is not a " +
+ s"${classOf[HadoopFsRelation].getSimpleName}. Persisting it
into Hive metastore " +
+ "in Spark SQL specific format, which is NOT compatible with
Hive."
+ (None, message)
+
+ case _ =>
+ val message =
+ s"Couldn't find corresponding Hive SerDe for data source
provider $provider. " +
+ s"Persisting data source relation $qualifiedTableName into
Hive metastore in " +
+ s"Spark SQL specific format, which is NOT compatible with
Hive."
+ (None, message)
+ }
+
+ (hiveCompatibleTable, logMessage) match {
+ case (Some(table), message) =>
+ // We first try to save the metadata of the table in a Hive
compatible way.
+ // If Hive throws an error, we fall back to save its metadata in
the Spark SQL
+ // specific way.
+ try {
+ logInfo(message)
+ sqlContext.sessionState.catalog.createTable(table,
ignoreIfExists = false)
+ } catch {
+ case NonFatal(e) =>
--- End diff --
in what cases would we go into this path?
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