diff options
Diffstat (limited to 'sql')
3 files changed, 29 insertions, 29 deletions
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/rules.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/rules.scala index 5eb2f0a9ff..f14c63c19f 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/rules.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/rules.scala @@ -72,29 +72,20 @@ case class PreprocessDDL(conf: SQLConf) extends Rule[LogicalPlan] { def apply(plan: LogicalPlan): LogicalPlan = plan transform { // When we CREATE TABLE without specifying the table schema, we should fail the query if - // bucketing information is specified, as we can't infer bucketing from data files currently, - // and we should ignore the partition columns if it's specified, as we will infer it later, at - // runtime. + // bucketing information is specified, as we can't infer bucketing from data files currently. + // Since the runtime inferred partition columns could be different from what user specified, + // we fail the query if the partitioning information is specified. case c @ CreateTable(tableDesc, _, None) if tableDesc.schema.isEmpty => if (tableDesc.bucketSpec.isDefined) { failAnalysis("Cannot specify bucketing information if the table schema is not specified " + "when creating and will be inferred at runtime") } - - val partitionColumnNames = tableDesc.partitionColumnNames - if (partitionColumnNames.nonEmpty) { - // The table does not have a specified schema, which means that the schema will be inferred - // at runtime. So, we are not expecting partition columns and we will discover partitions - // at runtime. However, if there are specified partition columns, we simply ignore them and - // provide a warning message. - logWarning( - s"Specified partition columns (${partitionColumnNames.mkString(",")}) will be " + - s"ignored. The schema and partition columns of table ${tableDesc.identifier} will " + - "be inferred.") - c.copy(tableDesc = tableDesc.copy(partitionColumnNames = Nil)) - } else { - c + if (tableDesc.partitionColumnNames.nonEmpty) { + failAnalysis("It is not allowed to specify partition columns when the table schema is " + + "not defined. When the table schema is not provided, schema and partition columns " + + "will be inferred.") } + c // Here we normalize partition, bucket and sort column names, w.r.t. the case sensitivity // config, and do various checks: diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/command/DDLSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/command/DDLSuite.scala index e6ae42258d..b343454b12 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/command/DDLSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/command/DDLSuite.scala @@ -265,7 +265,7 @@ class DDLSuite extends QueryTest with SharedSQLContext with BeforeAndAfterEach { userSpecifiedPartitionCols.map(p => s"PARTITIONED BY ($p)").getOrElse("") val schemaClause = userSpecifiedSchema.map(s => s"($s)").getOrElse("") val uri = path.toURI - sql( + val sqlCreateTable = s""" |CREATE TABLE $tabName $schemaClause |USING parquet @@ -273,11 +273,18 @@ class DDLSuite extends QueryTest with SharedSQLContext with BeforeAndAfterEach { | path '$uri' |) |$partitionClause - """.stripMargin) - val tableMetadata = spark.sessionState.catalog.getTableMetadata(TableIdentifier(tabName)) + """.stripMargin + if (userSpecifiedSchema.isEmpty && userSpecifiedPartitionCols.nonEmpty) { + val e = intercept[AnalysisException](sql(sqlCreateTable)).getMessage + assert(e.contains( + "not allowed to specify partition columns when the table schema is not defined")) + } else { + sql(sqlCreateTable) + val tableMetadata = spark.sessionState.catalog.getTableMetadata(TableIdentifier(tabName)) - assert(expectedSchema == tableMetadata.schema) - assert(expectedPartitionCols == tableMetadata.partitionColumnNames) + assert(expectedSchema == tableMetadata.schema) + assert(expectedPartitionCols == tableMetadata.partitionColumnNames) + } } } diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveExternalCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveExternalCatalog.scala index 2586d11a6c..7f50e38d30 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveExternalCatalog.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveExternalCatalog.scala @@ -622,24 +622,26 @@ object HiveExternalCatalog { def getSchemaFromTableProperties(metadata: CatalogTable): StructType = { val errorMessage = "Could not read schema from the hive metastore because it is corrupted." val props = metadata.properties - props.get(DATASOURCE_SCHEMA).map { schema => + val schema = props.get(DATASOURCE_SCHEMA) + if (schema.isDefined) { // Originally, we used `spark.sql.sources.schema` to store the schema of a data source table. // After SPARK-6024, we removed this flag. // Although we are not using `spark.sql.sources.schema` any more, we need to still support. - DataType.fromJson(schema).asInstanceOf[StructType] - } getOrElse { - props.get(DATASOURCE_SCHEMA_NUMPARTS).map { numParts => - val parts = (0 until numParts.toInt).map { index => + DataType.fromJson(schema.get).asInstanceOf[StructType] + } else { + val numSchemaParts = props.get(DATASOURCE_SCHEMA_NUMPARTS) + if (numSchemaParts.isDefined) { + val parts = (0 until numSchemaParts.get.toInt).map { index => val part = metadata.properties.get(s"$DATASOURCE_SCHEMA_PART_PREFIX$index").orNull if (part == null) { throw new AnalysisException(errorMessage + - s" (missing part $index of the schema, $numParts parts are expected).") + s" (missing part $index of the schema, ${numSchemaParts.get} parts are expected).") } part } // Stick all parts back to a single schema string. DataType.fromJson(parts.mkString).asInstanceOf[StructType] - } getOrElse { + } else { throw new AnalysisException(errorMessage) } } |