beliefer commented on a change in pull request #31757:
URL: https://github.com/apache/spark/pull/31757#discussion_r590966655
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/errors/QueryCompilationErrors.scala
##########
@@ -754,4 +756,335 @@ private[spark] object QueryCompilationErrors {
new AnalysisException(s"Invalid partitionExprs specified: $sortOrders For
range " +
"partitioning use REPARTITION_BY_RANGE instead.")
}
+
+ def partitionColumnNotSpecifiedError(format: String, partitionColumn:
String): Throwable = {
+ new AnalysisException(s"Failed to resolve the schema for $format for " +
+ s"the partition column: $partitionColumn. It must be specified
manually.")
+ }
+
+ def dataSchemaNotSpecifiedError(format: String): Throwable = {
+ new AnalysisException(s"Unable to infer schema for $format. It must be
specified manually.")
+ }
+
+ def dataPathNotExistError(path: String): Throwable = {
+ new AnalysisException(s"Path does not exist: $path")
+ }
+
+ def outputModeUnsupportedByDataSourceError(
+ className: String, outputMode: OutputMode): Throwable = {
+ new AnalysisException(s"Data source $className does not support
$outputMode output mode")
+ }
+
+ def notSpecifySchemaForSchemaRelationProviderError(className: String):
Throwable = {
+ new AnalysisException(s"A schema needs to be specified when using
$className.")
+ }
+
+ def userSpecifiedSchemaMismatchActualSchemaError(
+ schema: StructType, actualSchema: StructType): Throwable = {
+ new AnalysisException(
+ s"""
+ |The user-specified schema doesn't match the actual schema:
+ |user-specified: ${schema.toDDL}, actual: ${actualSchema.toDDL}. If
you're using
+ |DataFrameReader.schema API or creating a table, please do not
specify the schema.
+ |Or if you're scanning an existed table, please drop it and re-create
it.
+ """.stripMargin)
+ }
+
+ def dataSchemaNotSpecifiedError(format: String, fileCatalog: String):
Throwable = {
+ new AnalysisException(
+ s"Unable to infer schema for $format at $fileCatalog. It must be
specified manually")
+ }
+
+ def invalidDataSourceError(className: String): Throwable = {
+ new AnalysisException(s"$className is not a valid Spark SQL Data Source.")
+ }
+
+ def cannotSaveIntervalIntoExternalStorageError(): Throwable = {
+ new AnalysisException("Cannot save interval data type into external
storage.")
+ }
+
+ def unableResolveAttributeError(name: String, data: LogicalPlan): Throwable
= {
+ new AnalysisException(
+ s"Unable to resolve $name given [${data.output.map(_.name).mkString(",
")}]")
+ }
+
+ def orcNotUsedWithHiveEnabledError(): Throwable = {
+ new AnalysisException(
+ s"""
+ |Hive built-in ORC data source must be used with Hive support enabled.
+ |Please use the native ORC data source by setting
'spark.sql.orc.impl' to 'native'
+ """.stripMargin)
+ }
+
+ def failedFindAvroDataSourceError(provider: String): Throwable = {
+ new AnalysisException(
+ s"""
+ |Failed to find data source: $provider. Avro is built-in but external
data
+ |source module since Spark 2.4. Please deploy the application as per
+ |the deployment section of "Apache Avro Data Source Guide".
+ """.stripMargin.replaceAll("\n", " "))
+ }
+
+ def failedFindKafkaDataSourceError(provider: String): Throwable = {
+ new AnalysisException(
+ s"""
+ |Failed to find data source: $provider. Please deploy the application
as
+ |per the deployment section of "Structured Streaming + Kafka
Integration Guide".
+ """.stripMargin.replaceAll("\n", " "))
+ }
+
+ def findMultipleDataSourceError(provider: String, sourceNames: Seq[String]):
Throwable = {
+ new AnalysisException(
+ s"""
+ |Multiple sources found for $provider (${sourceNames.mkString(", ")}),
+ | please specify the fully qualified class name.
+ """.stripMargin)
+ }
+
+ def writeEmptySchemasUnsupportedByDataSourceError(): Throwable = {
+ new AnalysisException(
+ s"""
+ |Datasource does not support writing empty or nested empty schemas.
+ |Please make sure the data schema has at least one or more column(s).
+ """.stripMargin)
+ }
+
+ def insertMisMatchedColumnNumberError(
+ targetAttributes: Seq[Attribute],
+ sourceAttributes: Seq[Attribute],
+ staticPartitionsSize: Int): Throwable = {
+ new AnalysisException(
+ s"""
+ |The data to be inserted needs to have the same number of columns as
the
+ |target table: target table has ${targetAttributes.size} column(s)
but the
+ |inserted data has ${sourceAttributes.size + staticPartitionsSize}
column(s),
+ |which contain $staticPartitionsSize partition column(s) having
assigned
+ |constant values.
+ """.stripMargin)
+ }
+
+ def insertMisMatchedPartitionNumberError(
+ targetPartitionSchema: StructType,
+ providedPartitionsSize: Int): Throwable = {
+ new AnalysisException(
+ s"""
+ |The data to be inserted needs to have the same number of partition
columns
+ |as the target table: target table has
${targetPartitionSchema.fields.size}
+ |partition column(s) but the inserted data has $providedPartitionsSize
+ |partition columns specified.
+ """.stripMargin.replaceAll("\n", " "))
+ }
+
+ def invalidPartitionColumnError(
+ partKey: String, targetPartitionSchema: StructType): Throwable = {
+ new AnalysisException(
+ s"""
+ |$partKey is not a partition column. Partition columns are
+ |${targetPartitionSchema.fields.map(_.name).mkString("[", ",", "]")}
+ """.stripMargin)
+ }
+
+ def specifyMultipleValuesForPartitionColumnError(
+ field: StructField, potentialSpecs: Map[String, String]): Throwable = {
+ new AnalysisException(
+ s"""
+ |Partition column ${field.name} have multiple values specified,
+ |${potentialSpecs.mkString("[", ", ", "]")}. Please only specify a
single value.
+ """.stripMargin)
+ }
+
+ def invalidPartitionColumnHavingConstantValueError(
+ targetPartitionSchema: StructType): Throwable = {
+ new AnalysisException(
+ s"""
+ |The ordering of partition columns is
+ |${targetPartitionSchema.fields.map(_.name).mkString("[", ",", "]")}
+ |All partition columns having constant values need to appear before
other
+ |partition columns that do not have an assigned constant value.
+ """.stripMargin)
+ }
+
+ def cannotWriteDataToRelationWithSinglePathError(): Throwable = {
Review comment:
I doubt this change. It seems we should preserve it.
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