Github user yhuai commented on a diff in the pull request:
https://github.com/apache/spark/pull/12781#discussion_r62079118
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/command/tables.scala ---
@@ -452,3 +455,241 @@ case class ShowTablePropertiesCommand(table:
TableIdentifier, propertyKey: Optio
}
}
}
+
+/**
+ * A command to list the column names for a table. This function creates a
+ * [[ShowColumnsCommand]] logical plan.
+ *
+ * The syntax of using this command in SQL is:
+ * {{{
+ * SHOW COLUMNS (FROM | IN) table_identifier [(FROM | IN) database];
+ * }}}
+ */
+case class ShowColumnsCommand(table: TableIdentifier) extends
RunnableCommand {
+ // The result of SHOW COLUMNS has one column called 'result'
+ override val output: Seq[Attribute] = {
+ AttributeReference("result", StringType, nullable = false)() :: Nil
+ }
+
+ override def run(sparkSession: SparkSession): Seq[Row] = {
+ sparkSession.sessionState.catalog.getTableMetadata(table).schema.map {
c =>
+ Row(c.name)
+ }
+ }
+}
+
+/**
+ * A command to list the partition names of a table. If the partition spec
is specified,
+ * partitions that match the spec are returned. [[AnalysisException]]
exception is thrown under
+ * the following conditions:
+ *
+ * 1. If the command is called for a non partitioned table.
+ * 2. If the partition spec refers to the columns that are not defined as
partitioning columns.
+ *
+ * This function creates a [[ShowPartitionsCommand]] logical plan
+ *
+ * The syntax of using this command in SQL is:
+ * {{{
+ * SHOW PARTITIONS [db_name.]table_name [PARTITION(partition_spec)]
+ * }}}
+ */
+case class ShowPartitionsCommand(
+ table: TableIdentifier,
+ spec: Option[TablePartitionSpec]) extends RunnableCommand {
+ // The result of SHOW PARTITIONS has one column called 'result'
+ override val output: Seq[Attribute] = {
+ AttributeReference("result", StringType, nullable = false)() :: Nil
+ }
+
+ private def getPartName(spec: TablePartitionSpec, partColNames:
Seq[String]): String = {
+ partColNames.map { name =>
+ PartitioningUtils.escapePathName(name) + "=" +
PartitioningUtils.escapePathName(spec(name))
+ }.mkString(File.separator)
+ }
+
+ override def run(sparkSession: SparkSession): Seq[Row] = {
+ val catalog = sparkSession.sessionState.catalog
+
+ if (catalog.isTemporaryTable(table)) {
+ throw new AnalysisException(
+ s"SHOW PARTITIONS is not allowed on a temporary table:
${table.unquotedString}")
+ }
+
+ val tab = catalog.getTableMetadata(table)
+
+ /**
+ * Validate and throws an [[AnalysisException]] exception under the
following conditions:
+ * 1. If the table is not partitioned.
+ * 2. If it is a datasource table.
+ * 3. If it is a view or index table.
+ */
+ if (tab.tableType == VIEW ||
+ tab.tableType == INDEX) {
+ throw new AnalysisException(
+ s"SHOW PARTITIONS is not allowed on a view or index table:
${tab.qualifiedName}")
+ }
+
+ if (!DDLUtils.isTablePartitioned(tab)) {
+ throw new AnalysisException(
+ s"SHOW PARTITIONS is not allowed on a table that is not
partitioned: ${tab.qualifiedName}")
+ }
+
+ if (DDLUtils.isDatasourceTable(tab)) {
+ throw new AnalysisException(
+ s"SHOW PARTITIONS is not allowed on a datasource table:
${tab.qualifiedName}")
+ }
+
+ /**
+ * Validate the partitioning spec by making sure all the referenced
columns are
+ * defined as partitioning columns in table definition. An
AnalysisException exception is
+ * thrown if the partitioning spec is invalid.
+ */
+ if (spec.isDefined) {
+ val badColumns =
spec.get.keySet.filterNot(tab.partitionColumns.map(_.name).contains)
+ if (badColumns.nonEmpty) {
+ val badCols = badColumns.mkString("[", ", ", "]")
+ throw new AnalysisException(
+ s"Non-partitioning column(s) $badCols are specified for SHOW
PARTITIONS")
+ }
+ }
+
+ val partNames = catalog.listPartitions(table, spec).map { p =>
+ getPartName(p.spec, tab.partitionColumnNames)
+ }
+
+ partNames.map(Row(_))
+ }
+}
+
+case class ShowCreateTableCommand(table: TableIdentifier) extends
RunnableCommand {
+ override val output: Seq[Attribute] = Seq(
+ AttributeReference("createtab_stmt", StringType, nullable = false)()
+ )
+
+ override def run(sparkSession: SparkSession): Seq[Row] = {
+ val catalog = sparkSession.sessionState.catalog
+
+ if (catalog.isTemporaryTable(table)) {
+ throw new AnalysisException(
+ s"SHOW CREATE TABLE cannot be applied to temporary table")
+ }
+
+ if (!catalog.tableExists(table)) {
+ throw new AnalysisException(s"Table $table doesn't exist")
+ }
+
+ val tableMetadata = catalog.getTableMetadata(table)
+
+ val stmt = if (DDLUtils.isDatasourceTable(tableMetadata)) {
+ showCreateDataSourceTable(tableMetadata)
+ } else {
+ throw new UnsupportedOperationException(
+ "SHOW CREATE TABLE only supports Spark SQL data source tables.")
+ }
+
+ Seq(Row(stmt))
+ }
+
+ private def showCreateDataSourceTable(metadata: CatalogTable): String = {
+ val builder = StringBuilder.newBuilder
+
+ builder ++= s"CREATE TABLE ${table.quotedString} "
+ showDataSourceTableDataCols(metadata, builder)
+ showDataSourceTableOptions(metadata, builder)
+ showDataSourceTableNonDataColumns(metadata, builder)
+
+ builder.toString()
+ }
+
+ private def showDataSourceTableDataCols(metadata: CatalogTable, builder:
StringBuilder): Unit = {
+ val props = metadata.properties
+ val schemaParts = for {
+ numParts <- props.get("spark.sql.sources.schema.numParts").toSeq
+ index <- 0 until numParts.toInt
+ } yield props.getOrElse(
+ s"spark.sql.sources.schema.part.$index",
+ throw new AnalysisException(
+ s"Corrupted schema in catalog: $numParts parts expected, but part
$index is missing."
+ )
+ )
+
+ if (schemaParts.nonEmpty) {
+ val fields =
DataType.fromJson(schemaParts.mkString).asInstanceOf[StructType].fields
+ val colTypeList = fields.map(f => s"${quoteIdentifier(f.name)}
${f.dataType.sql}")
+ builder ++= colTypeList.mkString("(", ", ", ")")
+ }
+
+ builder ++= "\n"
+ }
+
+ private def showDataSourceTableOptions(metadata: CatalogTable, builder:
StringBuilder): Unit = {
+ val props = metadata.properties
+
+ builder ++= s"USING ${props("spark.sql.sources.provider")}\n"
+
+ val dataSourceOptions = metadata.storage.serdeProperties.filterNot {
+ case (key, value) =>
+ // If it's a managed table, omit PATH option. Spark SQL always
creates external table
+ // when the table creation DDL contains the PATH option.
+ key.toLowerCase == "path" && metadata.tableType == MANAGED
+ }.map {
+ case (key, value) => s"${quoteIdentifier(key)}
'${escapeSingleQuotedString(value)}'"
+ }
+
+ if (dataSourceOptions.nonEmpty) {
+ builder ++= "OPTIONS (\n"
+ builder ++= dataSourceOptions.mkString(" ", ",\n ", "\n")
+ builder ++= ")\n"
+ }
+ }
+
+ private def showDataSourceTableNonDataColumns(
+ metadata: CatalogTable, builder: StringBuilder): Unit = {
+ val props = metadata.properties
+
+ def getColumnNamesByType(colType: String, typeName: String):
Seq[String] = {
+ (for {
+ numCols <-
props.get(s"spark.sql.sources.schema.num${colType.capitalize}Cols").toSeq
+ index <- 0 until numCols.toInt
+ } yield props.getOrElse(
+ s"spark.sql.sources.schema.${colType}Col.$index",
+ throw new AnalysisException(
+ s"Corrupted $typeName in catalog: $numCols parts expected, but
part $index is missing."
+ )
+ )).map(quoteIdentifier)
+ }
+
+ val partCols = getColumnNamesByType("part", "partitioning columns")
+ if (partCols.nonEmpty) {
+ builder ++= s"PARTITIONED BY ${partCols.mkString("(", ", ", ")")}\n"
+ }
--- End diff --
We should also have utility functions for reconstructing partition columns,
bucketing columns, and sorting columns from strings.
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