karenfeng commented on a change in pull request #34168:
URL: https://github.com/apache/spark/pull/34168#discussion_r723749452
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/errors/QueryExecutionErrors.scala
##########
@@ -1297,74 +1297,87 @@ object QueryExecutionErrors {
}
def serDeInterfaceNotFoundError(e: NoClassDefFoundError): Throwable = {
- new ClassNotFoundException("The SerDe interface removed since Hive
2.3(HIVE-15167)." +
- " Please migrate your custom SerDes to Hive 2.3. See HIVE-15167 for more
details.", e)
+ new SparkClassNotFoundException(
+ errorClass = "SERDE_INTERFACE_NOT_FOUND",
+ messageParameters = Array.empty, e)
}
def convertHiveTableToCatalogTableError(
e: SparkException, dbName: String, tableName: String): Throwable = {
- new SparkException(s"${e.getMessage}, db: $dbName, table: $tableName", e)
+ new SparkException(
+ errorClass = "CANNOT_CONVERT_HIVE_TABLE_TO_CATALOG_TABLE",
+ messageParameters = Array(e.getMessage, dbName, tableName), e)
}
def cannotRecognizeHiveTypeError(
e: ParseException, fieldType: String, fieldName: String): Throwable = {
new SparkException(
- s"Cannot recognize hive type string: $fieldType, column: $fieldName", e)
+ errorClass = "CANNOT_RECOGNIZE_HIVE_TYPE",
+ messageParameters = Array(fieldType, fieldName), e)
}
def getTablesByTypeUnsupportedByHiveVersionError(): Throwable = {
- new UnsupportedOperationException("Hive 2.2 and lower versions don't
support " +
- "getTablesByType. Please use Hive 2.3 or higher version.")
+ new SparkUnsupportedOperationException(
+ errorClass = "GET_TABLES_BY_TYPE_UNSUPPORTED_BY_HIVE_VERSION",
+ messageParameters = Array.empty
+ )
}
def dropTableWithPurgeUnsupportedError(): Throwable = {
- new UnsupportedOperationException("DROP TABLE ... PURGE")
+ new SparkUnsupportedOperationException(
+ errorClass = "DROP_TABLE_WITH_PURGE_UNSUPPORTED",
+ messageParameters = Array.empty
+ )
}
def alterTableWithDropPartitionAndPurgeUnsupportedError(): Throwable = {
- new UnsupportedOperationException("ALTER TABLE ... DROP PARTITION ...
PURGE")
+ new SparkUnsupportedOperationException(
+ errorClass = "ALTER_TABLE_WITH_DROP_PARTITION_AND_PURGE_UNSUPPORTED",
+ messageParameters = Array.empty
+ )
}
def invalidPartitionFilterError(): Throwable = {
- new UnsupportedOperationException(
- """Partition filter cannot have both `"` and `'` characters""")
+ new SparkUnsupportedOperationException(
+ errorClass = "INVALID_PARTITION_FILTER",
+ messageParameters = Array.empty
+ )
}
def getPartitionMetadataByFilterError(e: InvocationTargetException):
Throwable = {
- new RuntimeException(
- s"""
- |Caught Hive MetaException attempting to get partition metadata by
filter
- |from Hive. You can set the Spark configuration setting
- |${SQLConf.HIVE_METASTORE_PARTITION_PRUNING_FALLBACK_ON_EXCEPTION} to
true to work around
- |this problem, however this will result in degraded performance.
Please
- |report a bug: https://issues.apache.org/jira/browse/SPARK
- """.stripMargin.replaceAll("\n", " "), e)
+ new SparkRuntimeException(
+ errorClass = "CANNOT_GET_PARTITION_METADATA_BY_FILTER",
+ messageParameters = Array(
+
SQLConf.HIVE_METASTORE_PARTITION_PRUNING_FALLBACK_ON_EXCEPTION.toString),
+ e)
}
def unsupportedHiveMetastoreVersionError(version: String, key: String):
Throwable = {
- new UnsupportedOperationException(s"Unsupported Hive Metastore version
($version). " +
- s"Please set $key with a valid version.")
+ new SparkUnsupportedOperationException(
+ errorClass = "UNSUPPORTED_HIVE_METASTORE_VERSION",
+ messageParameters = Array(version, key))
}
def loadHiveClientCausesNoClassDefFoundError(
cnf: NoClassDefFoundError,
execJars: Seq[URL],
key: String,
e: InvocationTargetException): Throwable = {
- new ClassNotFoundException(
- s"""
- |$cnf when creating Hive client using classpath:
${execJars.mkString(", ")}\n
- |Please make sure that jars for your version of hive and hadoop are
included in the
- |paths passed to $key.
- """.stripMargin.replaceAll("\n", " "), e)
+ new SparkClassNotFoundException(
+ errorClass = "LOAD_HIVE_CLIENT_CAUSES_NO_CLASS_DEFINITION_FOUND",
+ messageParameters = Array(cnf.toString, execJars.mkString(", "), key), e)
}
def cannotFetchTablesOfDatabaseError(dbName: String, e: Exception):
Throwable = {
- new SparkException(s"Unable to fetch tables of db $dbName", e)
+ new SparkException(
+ errorClass = "CANNOT_FETCH DATABASE_TABLES",
Review comment:
Missing underscore here
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/errors/QueryExecutionErrors.scala
##########
@@ -1297,74 +1297,87 @@ object QueryExecutionErrors {
}
def serDeInterfaceNotFoundError(e: NoClassDefFoundError): Throwable = {
- new ClassNotFoundException("The SerDe interface removed since Hive
2.3(HIVE-15167)." +
- " Please migrate your custom SerDes to Hive 2.3. See HIVE-15167 for more
details.", e)
+ new SparkClassNotFoundException(
+ errorClass = "SERDE_INTERFACE_NOT_FOUND",
+ messageParameters = Array.empty, e)
}
def convertHiveTableToCatalogTableError(
e: SparkException, dbName: String, tableName: String): Throwable = {
- new SparkException(s"${e.getMessage}, db: $dbName, table: $tableName", e)
+ new SparkException(
+ errorClass = "CANNOT_CONVERT_HIVE_TABLE_TO_CATALOG_TABLE",
+ messageParameters = Array(e.getMessage, dbName, tableName), e)
}
def cannotRecognizeHiveTypeError(
e: ParseException, fieldType: String, fieldName: String): Throwable = {
new SparkException(
- s"Cannot recognize hive type string: $fieldType, column: $fieldName", e)
+ errorClass = "CANNOT_RECOGNIZE_HIVE_TYPE",
+ messageParameters = Array(fieldType, fieldName), e)
}
def getTablesByTypeUnsupportedByHiveVersionError(): Throwable = {
- new UnsupportedOperationException("Hive 2.2 and lower versions don't
support " +
- "getTablesByType. Please use Hive 2.3 or higher version.")
+ new SparkUnsupportedOperationException(
+ errorClass = "GET_TABLES_BY_TYPE_UNSUPPORTED_BY_HIVE_VERSION",
+ messageParameters = Array.empty
+ )
}
def dropTableWithPurgeUnsupportedError(): Throwable = {
- new UnsupportedOperationException("DROP TABLE ... PURGE")
+ new SparkUnsupportedOperationException(
+ errorClass = "DROP_TABLE_WITH_PURGE_UNSUPPORTED",
+ messageParameters = Array.empty
+ )
}
def alterTableWithDropPartitionAndPurgeUnsupportedError(): Throwable = {
- new UnsupportedOperationException("ALTER TABLE ... DROP PARTITION ...
PURGE")
+ new SparkUnsupportedOperationException(
+ errorClass = "ALTER_TABLE_WITH_DROP_PARTITION_AND_PURGE_UNSUPPORTED",
+ messageParameters = Array.empty
+ )
}
def invalidPartitionFilterError(): Throwable = {
- new UnsupportedOperationException(
- """Partition filter cannot have both `"` and `'` characters""")
+ new SparkUnsupportedOperationException(
+ errorClass = "INVALID_PARTITION_FILTER",
+ messageParameters = Array.empty
+ )
}
def getPartitionMetadataByFilterError(e: InvocationTargetException):
Throwable = {
- new RuntimeException(
- s"""
- |Caught Hive MetaException attempting to get partition metadata by
filter
- |from Hive. You can set the Spark configuration setting
- |${SQLConf.HIVE_METASTORE_PARTITION_PRUNING_FALLBACK_ON_EXCEPTION} to
true to work around
- |this problem, however this will result in degraded performance.
Please
- |report a bug: https://issues.apache.org/jira/browse/SPARK
- """.stripMargin.replaceAll("\n", " "), e)
+ new SparkRuntimeException(
+ errorClass = "CANNOT_GET_PARTITION_METADATA_BY_FILTER",
Review comment:
We just introduced `INTERNAL_ERROR`; can you use that instead? This will
be useful for minimizing clutter.
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