Github user gatorsmile commented on a diff in the pull request: https://github.com/apache/spark/pull/14482#discussion_r73471840 --- Diff: sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/rules.scala --- @@ -62,6 +66,122 @@ private[sql] class ResolveDataSource(sparkSession: SparkSession) extends Rule[Lo } /** + * Preprocess some DDL plans, e.g. [[CreateTable]], to do some normalization and checking. + */ +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. + 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 + } + + // Here we normalize partition, bucket and sort column names, w.r.t. the case sensitivity + // config, and do various checks: + // * column names in table definition can't be duplicated. + // * partition, bucket and sort column names must exist in table definition. + // * partition, bucket and sort column names can't be duplicated. + // * can't use all table columns as partition columns. + // * partition columns' type must be AtomicType. + // * sort columns' type must be orderable. + case c @ CreateTable(tableDesc, mode, query) if c.childrenResolved => + val schema = if (query.isDefined) query.get.schema else tableDesc.schema + checkDuplication(schema.map(_.name), "table definition of " + tableDesc.identifier) + + val partitionColsChecked = checkPartitionColumns(schema, tableDesc) + val bucketColsChecked = checkBucketColumns(schema, partitionColsChecked) + c.copy(tableDesc = bucketColsChecked) + } + + private def checkPartitionColumns(schema: StructType, tableDesc: CatalogTable): CatalogTable = { + val normalizedPartitionCols = tableDesc.partitionColumnNames.map { colName => + normalizeColumnName(tableDesc.identifier, schema, colName, "partition") + } + checkDuplication(normalizedPartitionCols, "partition") + + if (schema.nonEmpty && normalizedPartitionCols.length == schema.length) { + failAnalysis("Cannot use all columns for partition columns") + } + + schema.filter(f => normalizedPartitionCols.contains(f.name)).map(_.dataType).foreach { + case _: AtomicType => // OK + case other => failAnalysis(s"Cannot use ${other.simpleString} for partition column") + } + + tableDesc.copy(partitionColumnNames = normalizedPartitionCols) + } + + private def checkBucketColumns(schema: StructType, tableDesc: CatalogTable): CatalogTable = { + tableDesc.bucketSpec match { + case Some(BucketSpec(numBuckets, bucketColumnNames, sortColumnNames)) => + val normalizedBucketCols = bucketColumnNames.map { colName => + normalizeColumnName(tableDesc.identifier, schema, colName, "bucket") + } + checkDuplication(normalizedBucketCols, "bucket") + + val normalizedSortCols = sortColumnNames.map { colName => + normalizeColumnName(tableDesc.identifier, schema, colName, "sort") + } + checkDuplication(normalizedSortCols, "sort") + + schema.filter(f => normalizedSortCols.contains(f.name)).map(_.dataType).foreach { + case dt if RowOrdering.isOrderable(dt) => // OK + case other => failAnalysis(s"Cannot use ${other.simpleString} for sorting column") + } + + tableDesc.copy( + bucketSpec = Some(BucketSpec(numBuckets, normalizedBucketCols, normalizedSortCols)) + ) + + case None => tableDesc + } + } + + private def checkDuplication(colNames: Seq[String], colType: String): Unit = { + if (colNames.distinct.length != colNames.length) { + val duplicateColumns = colNames.groupBy(identity).collect { --- End diff -- Great! Thanks!
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