cloud-fan commented on code in PR #36639:
URL: https://github.com/apache/spark/pull/36639#discussion_r880027150
##########
core/src/main/resources/error/error-classes.json:
##########
@@ -1,319 +1,519 @@
{
"AMBIGUOUS_FIELD_NAME" : {
- "message" : [ "Field name <fieldName> is ambiguous and has <n> matching
fields in the struct." ],
+ "message" : [
+ "Field name <fieldName> is ambiguous and has <n> matching fields in the
struct."
+ ],
"sqlState" : "42000"
},
"ARITHMETIC_OVERFLOW" : {
- "message" : [ "<message>.<alternative> If necessary set <config> to
\"false\" (except for ANSI interval type) to bypass this error." ],
+ "message" : [
+ "<message>.<alternative> If necessary set <config> to \"false\" (except
for ANSI interval type) to bypass this error."
+ ],
"sqlState" : "22003"
},
"CANNOT_CAST_DATATYPE" : {
- "message" : [ "Cannot cast <sourceType> to <targetType>." ],
+ "message" : [
+ "Cannot cast <sourceType> to <targetType>."
+ ],
"sqlState" : "22005"
},
"CANNOT_CHANGE_DECIMAL_PRECISION" : {
- "message" : [ "<value> cannot be represented as Decimal(<precision>,
<scale>). If necessary set <config> to \"false\" to bypass this error." ],
+ "message" : [
+ "<value> cannot be represented as Decimal(<precision>, <scale>). If
necessary set <config> to \"false\" to bypass this error."
+ ],
"sqlState" : "22005"
},
"CANNOT_PARSE_DECIMAL" : {
- "message" : [ "Cannot parse decimal" ],
+ "message" : [
+ "Cannot parse decimal"
+ ],
"sqlState" : "42000"
},
"CANNOT_UP_CAST_DATATYPE" : {
- "message" : [ "Cannot up cast <value> from <sourceType> to
<targetType>.\n<details>" ]
+ "message" : [
+ "Cannot up cast <value> from <sourceType> to <targetType>.",
+ "<details>"
+ ]
},
"CAST_INVALID_INPUT" : {
- "message" : [ "The value <value> of the type <sourceType> cannot be cast
to <targetType> because it is malformed. To return NULL instead, use
`try_cast`. If necessary set <config> to \"false\" to bypass this error." ],
+ "message" : [
+ "The value <value> of the type <sourceType> cannot be cast to
<targetType> because it is malformed. To return NULL instead, use `try_cast`.
If necessary set <config> to \"false\" to bypass this error."
+ ],
"sqlState" : "42000"
},
"CAST_OVERFLOW" : {
- "message" : [ "The value <value> of the type <sourceType> cannot be cast
to <targetType> due to an overflow. To return NULL instead, use `try_cast`. If
necessary set <config> to \"false\" to bypass this error." ],
+ "message" : [
+ "The value <value> of the type <sourceType> cannot be cast to
<targetType> due to an overflow. To return NULL instead, use `try_cast`. If
necessary set <config> to \"false\" to bypass this error."
+ ],
"sqlState" : "22005"
},
"CONCURRENT_QUERY" : {
- "message" : [ "Another instance of this query was just started by a
concurrent session." ]
+ "message" : [
+ "Another instance of this query was just started by a concurrent
session."
+ ]
},
"DATETIME_OVERFLOW" : {
- "message" : [ "Datetime operation overflow: <operation>." ],
+ "message" : [
+ "Datetime operation overflow: <operation>."
+ ],
"sqlState" : "22008"
},
"DIVIDE_BY_ZERO" : {
- "message" : [ "Division by zero. To return NULL instead, use `try_divide`.
If necessary set <config> to \"false\" (except for ANSI interval type) to
bypass this error." ],
+ "message" : [
+ "Division by zero. To return NULL instead, use `try_divide`. If
necessary set <config> to \"false\" (except for ANSI interval type) to bypass
this error."
+ ],
"sqlState" : "22012"
},
"DUPLICATE_KEY" : {
- "message" : [ "Found duplicate keys <keyColumn>" ],
+ "message" : [
+ "Found duplicate keys <keyColumn>"
+ ],
"sqlState" : "23000"
},
"FAILED_EXECUTE_UDF" : {
- "message" : [ "Failed to execute user defined function (<functionName>:
(<signature>) => <result>)" ]
+ "message" : [
+ "Failed to execute user defined function (<functionName>: (<signature>)
=> <result>)"
+ ]
},
"FAILED_RENAME_PATH" : {
- "message" : [ "Failed to rename <sourcePath> to <targetPath> as
destination already exists" ],
+ "message" : [
+ "Failed to rename <sourcePath> to <targetPath> as destination already
exists"
+ ],
"sqlState" : "22023"
},
"FAILED_SET_ORIGINAL_PERMISSION_BACK" : {
- "message" : [ "Failed to set original permission <permission> back to the
created path: <path>. Exception: <message>" ]
+ "message" : [
+ "Failed to set original permission <permission> back to the created
path: <path>. Exception: <message>"
+ ]
},
"FORBIDDEN_OPERATION" : {
- "message" : [ "The operation <statement> is not allowed on <objectType>:
<objectName>" ]
+ "message" : [
+ "The operation <statement> is not allowed on <objectType>: <objectName>"
+ ]
},
"GRAPHITE_SINK_INVALID_PROTOCOL" : {
- "message" : [ "Invalid Graphite protocol: <protocol>" ]
+ "message" : [
+ "Invalid Graphite protocol: <protocol>"
+ ]
},
"GRAPHITE_SINK_PROPERTY_MISSING" : {
- "message" : [ "Graphite sink requires '<property>' property." ]
+ "message" : [
+ "Graphite sink requires '<property>' property."
+ ]
},
"GROUPING_COLUMN_MISMATCH" : {
- "message" : [ "Column of grouping (<grouping>) can't be found in grouping
columns <groupingColumns>" ],
+ "message" : [
+ "Column of grouping (<grouping>) can't be found in grouping columns
<groupingColumns>"
+ ],
"sqlState" : "42000"
},
"GROUPING_ID_COLUMN_MISMATCH" : {
- "message" : [ "Columns of grouping_id (<groupingIdColumn>) does not match
grouping columns (<groupByColumns>)" ],
+ "message" : [
+ "Columns of grouping_id (<groupingIdColumn>) does not match grouping
columns (<groupByColumns>)"
+ ],
"sqlState" : "42000"
},
"GROUPING_SIZE_LIMIT_EXCEEDED" : {
- "message" : [ "Grouping sets size cannot be greater than <maxSize>" ]
+ "message" : [
+ "Grouping sets size cannot be greater than <maxSize>"
+ ]
},
"INCOMPARABLE_PIVOT_COLUMN" : {
- "message" : [ "Invalid pivot column <columnName>. Pivot columns must be
comparable." ],
+ "message" : [
+ "Invalid pivot column <columnName>. Pivot columns must be comparable."
+ ],
"sqlState" : "42000"
},
"INCOMPATIBLE_DATASOURCE_REGISTER" : {
- "message" : [ "Detected an incompatible DataSourceRegister. Please remove
the incompatible library from classpath or upgrade it. Error: <message>" ]
+ "message" : [
+ "Detected an incompatible DataSourceRegister. Please remove the
incompatible library from classpath or upgrade it. Error: <message>"
+ ]
},
"INCONSISTENT_BEHAVIOR_CROSS_VERSION" : {
- "message" : [ "You may get a different result due to the upgrading to" ],
+ "message" : [
+ "You may get a different result due to the upgrading to"
+ ],
"subClass" : {
"DATETIME_PATTERN_RECOGNITION" : {
- "message" : [ " Spark >= 3.0: \nFail to recognize <pattern> pattern in
the DateTimeFormatter. 1) You can set <config> to \"LEGACY\" to restore the
behavior before Spark 3.0. 2) You can form a valid datetime pattern with the
guide from https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html"; ]
+ "message" : [
+ " Spark >= 3.0: ",
+ "Fail to recognize <pattern> pattern in the DateTimeFormatter. 1)
You can set <config> to \"LEGACY\" to restore the behavior before Spark 3.0. 2)
You can form a valid datetime pattern with the guide from
https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html";
+ ]
},
"FORMAT_DATETIME_BY_NEW_PARSER" : {
- "message" : [ " Spark >= 3.0: \nFail to format it to <resultCandidate>
in the new formatter. You can set\n<config> to \"LEGACY\" to restore the
behavior before\nSpark 3.0, or set to \"CORRECTED\" and treat it as an invalid
datetime string.\n" ]
+ "message" : [
+ " Spark >= 3.0: ",
+ "Fail to format it to <resultCandidate> in the new formatter. You
can set <config> to \"LEGACY\" to restore the behavior before Spark 3.0, or set
to \"CORRECTED\" and treat it as an invalid datetime string."
+ ]
},
"PARSE_DATETIME_BY_NEW_PARSER" : {
- "message" : [ " Spark >= 3.0: \nFail to parse <datetime> in the new
parser. You can set <config> to \"LEGACY\" to restore the behavior before Spark
3.0, or set to \"CORRECTED\" and treat it as an invalid datetime string." ]
+ "message" : [
+ " Spark >= 3.0: ",
+ "Fail to parse <datetime> in the new parser. You can set <config> to
\"LEGACY\" to restore the behavior before Spark 3.0, or set to \"CORRECTED\"
and treat it as an invalid datetime string."
+ ]
},
"READ_ANCIENT_DATETIME" : {
- "message" : [ " Spark >= 3.0: \nreading dates before 1582-10-15 or
timestamps before 1900-01-01T00:00:00Z\nfrom <format> files can be ambiguous,
as the files may be written by\nSpark 2.x or legacy versions of Hive, which
uses a legacy hybrid calendar\nthat is different from Spark 3.0+'s Proleptic
Gregorian calendar.\nSee more details in SPARK-31404. You can set the SQL
config <config> or\nthe datasource option <option> to \"LEGACY\" to rebase the
datetime values\nw.r.t. the calendar difference during reading. To read the
datetime values\nas it is, set the SQL config <config> or the datasource option
<option>\nto \"CORRECTED\".\n" ]
+ "message" : [
+ " Spark >= 3.0: ",
+ "reading dates before 1582-10-15 or timestamps before
1900-01-01T00:00:00Z from <format> files can be ambiguous, as the files may be
written by Spark 2.x or legacy versions of Hive, which uses a legacy hybrid
calendar that is different from Spark 3.0+'s Proleptic Gregorian calendar. ",
+ "See more details in SPARK-31404. ",
+ "You can set the SQL config <config> or the datasource option
<option> to \"LEGACY\" to rebase the datetime values w.r.t. the calendar
difference during reading. ",
+ "To read the datetime values as it is, set the SQL config <config>
or the datasource option <option> to \"CORRECTED\"."
+ ]
},
"WRITE_ANCIENT_DATETIME" : {
- "message" : [ " Spark >= 3.0: \nwriting dates before 1582-10-15 or
timestamps before 1900-01-01T00:00:00Z\ninto <format> files can be dangerous,
as the files may be read by Spark 2.x\nor legacy versions of Hive later, which
uses a legacy hybrid calendar that\nis different from Spark 3.0+'s Proleptic
Gregorian calendar. See more\ndetails in SPARK-31404. You can set <config> to
\"LEGACY\" to rebase the\ndatetime values w.r.t. the calendar difference during
writing, to get maximum\ninteroperability. Or set <config> to \"CORRECTED\" to
write the datetime\nvalues as it is, if you are sure that the written files
will only be read by\nSpark 3.0+ or other systems that use Proleptic Gregorian
calendar.\n" ]
+ "message" : [
+ " Spark >= 3.0: ",
+ "writing dates before 1582-10-15 or timestamps before
1900-01-01T00:00:00Z into <format> files can be dangerous, as the files may be
read by Spark 2.x or legacy versions of Hive later, which uses a legacy hybrid
calendar that is different from Spark 3.0+'s Proleptic Gregorian calendar. ",
+ "See more details in SPARK-31404. ",
+ "You can set <config> to \"LEGACY\" to rebase the datetime values
w.r.t. the calendar difference during writing, to get maximum interoperability.
",
+ "Or set <config> to \"CORRECTED\" to write the datetime values as it
is, if you are sure that the written files will only be read by Spark 3.0+ or
other systems that use Proleptic Gregorian calendar."
+ ]
}
}
},
"INDEX_OUT_OF_BOUNDS" : {
- "message" : [ "Index <indexValue> must be between 0 and the length of the
ArrayData." ],
+ "message" : [
+ "Index <indexValue> must be between 0 and the length of the ArrayData."
+ ],
"sqlState" : "22023"
},
"INTERNAL_ERROR" : {
- "message" : [ "<message>" ]
+ "message" : [
+ "<message>"
+ ]
},
"INVALID_ARRAY_INDEX" : {
- "message" : [ "The index <indexValue> is out of bounds. The array has
<arraySize> elements. If necessary set <config> to \"false\" to bypass this
error." ]
+ "message" : [
+ "The index <indexValue> is out of bounds. The array has <arraySize>
elements. If necessary set <config> to \"false\" to bypass this error."
+ ]
},
"INVALID_ARRAY_INDEX_IN_ELEMENT_AT" : {
- "message" : [ "The index <indexValue> is out of bounds. The array has
<arraySize> elements. To return NULL instead, use `try_element_at`. If
necessary set <config> to \"false\" to bypass this error." ]
+ "message" : [
+ "The index <indexValue> is out of bounds. The array has <arraySize>
elements. To return NULL instead, use `try_element_at`. If necessary set
<config> to \"false\" to bypass this error."
+ ]
},
"INVALID_BUCKET_FILE" : {
- "message" : [ "Invalid bucket file: <path>" ]
+ "message" : [
+ "Invalid bucket file: <path>"
+ ]
},
"INVALID_FIELD_NAME" : {
- "message" : [ "Field name <fieldName> is invalid: <path> is not a struct."
],
+ "message" : [
+ "Field name <fieldName> is invalid: <path> is not a struct."
+ ],
"sqlState" : "42000"
},
"INVALID_FRACTION_OF_SECOND" : {
- "message" : [ "The fraction of sec must be zero. Valid range is [0, 60].
If necessary set <config> to \"false\" to bypass this error. " ],
+ "message" : [
+ "The fraction of sec must be zero. Valid range is [0, 60]. If necessary
set <config> to \"false\" to bypass this error."
+ ],
"sqlState" : "22023"
},
"INVALID_JSON_SCHEMA_MAP_TYPE" : {
- "message" : [ "Input schema <jsonSchema> can only contain STRING as a key
type for a MAP." ]
+ "message" : [
+ "Input schema <jsonSchema> can only contain STRING as a key type for a
MAP."
+ ]
},
"INVALID_PANDAS_UDF_PLACEMENT" : {
- "message" : [ "The group aggregate pandas UDF <functionName> cannot be
invoked together with as other, non-pandas aggregate functions." ]
+ "message" : [
+ "The group aggregate pandas UDF <functionName> cannot be invoked
together with as other, non-pandas aggregate functions."
+ ]
},
"INVALID_PARAMETER_VALUE" : {
- "message" : [ "The value of parameter(s) '<parameter>' in <functionName>
is invalid: <expected>" ],
+ "message" : [
+ "The value of parameter(s) '<parameter>' in <functionName> is invalid:
<expected>"
+ ],
"sqlState" : "22023"
},
"INVALID_PROPERTY_KEY" : {
- "message" : [ "<key> is an invalid property key, please use quotes, e.g.
SET <key>=<value>" ]
+ "message" : [
+ "<key> is an invalid property key, please use quotes, e.g. SET
<key>=<value>"
+ ]
},
"INVALID_PROPERTY_VALUE" : {
- "message" : [ "<value> is an invalid property value, please use quotes,
e.g. SET <key>=<value>" ]
+ "message" : [
+ "<value> is an invalid property value, please use quotes, e.g. SET
<key>=<value>"
+ ]
},
"INVALID_SQL_SYNTAX" : {
- "message" : [ "Invalid SQL syntax: <inputString>" ],
+ "message" : [
+ "Invalid SQL syntax: <inputString>"
+ ],
"sqlState" : "42000"
},
"MAP_KEY_DOES_NOT_EXIST" : {
- "message" : [ "Key <keyValue> does not exist. To return NULL instead, use
`try_element_at`. If necessary set <config> to \"false\" to bypass this error."
]
+ "message" : [
+ "Key <keyValue> does not exist. To return NULL instead, use
`try_element_at`. If necessary set <config> to \"false\" to bypass this error."
+ ]
},
"MISSING_COLUMN" : {
- "message" : [ "Column '<columnName>' does not exist. Did you mean one of
the following? [<proposal>]" ],
+ "message" : [
+ "Column '<columnName>' does not exist. Did you mean one of the
following? [<proposal>]"
+ ],
"sqlState" : "42000"
},
"MISSING_STATIC_PARTITION_COLUMN" : {
- "message" : [ "Unknown static partition column: <columnName>" ],
+ "message" : [
+ "Unknown static partition column: <columnName>"
+ ],
"sqlState" : "42000"
},
"MULTI_UDF_INTERFACE_ERROR" : {
- "message" : [ "Not allowed to implement multiple UDF interfaces, UDF class
<class>" ]
+ "message" : [
+ "Not allowed to implement multiple UDF interfaces, UDF class <class>"
+ ]
},
"MULTI_VALUE_SUBQUERY_ERROR" : {
- "message" : [ "more than one row returned by a subquery used as an
expression: <plan>" ]
+ "message" : [
+ "more than one row returned by a subquery used as an expression: <plan>"
+ ]
},
"NON_LITERAL_PIVOT_VALUES" : {
- "message" : [ "Literal expressions required for pivot values, found
'<expression>'" ],
+ "message" : [
+ "Literal expressions required for pivot values, found '<expression>'"
+ ],
"sqlState" : "42000"
},
"NON_PARTITION_COLUMN" : {
- "message" : [ "PARTITION clause cannot contain a non-partition column
name: <columnName>" ],
+ "message" : [
+ "PARTITION clause cannot contain a non-partition column name:
<columnName>"
+ ],
"sqlState" : "42000"
},
"NO_HANDLER_FOR_UDAF" : {
- "message" : [ "No handler for UDAF '<functionName>'. Use
sparkSession.udf.register(...) instead." ]
+ "message" : [
+ "No handler for UDAF '<functionName>'. Use
sparkSession.udf.register(...) instead."
+ ]
},
"NO_UDF_INTERFACE_ERROR" : {
- "message" : [ "UDF class <class> doesn't implement any UDF interface" ]
+ "message" : [
+ "UDF class <class> doesn't implement any UDF interface"
+ ]
},
"PARSE_CHAR_MISSING_LENGTH" : {
- "message" : [ "DataType <type> requires a length parameter, for example
<type>(10). Please specify the length." ],
+ "message" : [
+ "DataType <type> requires a length parameter, for example <type>(10).
Please specify the length."
+ ],
"sqlState" : "42000"
},
"PARSE_EMPTY_STATEMENT" : {
- "message" : [ "Syntax error, unexpected empty statement" ],
+ "message" : [
+ "Syntax error, unexpected empty statement"
+ ],
"sqlState" : "42000"
},
"PARSE_SYNTAX_ERROR" : {
- "message" : [ "Syntax error at or near <error><hint>" ],
+ "message" : [
+ "Syntax error at or near <error><hint>"
+ ],
"sqlState" : "42000"
},
"PIVOT_VALUE_DATA_TYPE_MISMATCH" : {
- "message" : [ "Invalid pivot value '<value>': value data type <valueType>
does not match pivot column data type <pivotType>" ],
+ "message" : [
+ "Invalid pivot value '<value>': value data type <valueType> does not
match pivot column data type <pivotType>"
+ ],
"sqlState" : "42000"
},
"RENAME_SRC_PATH_NOT_FOUND" : {
- "message" : [ "Failed to rename as <sourcePath> was not found" ],
+ "message" : [
+ "Failed to rename as <sourcePath> was not found"
+ ],
"sqlState" : "22023"
},
"SECOND_FUNCTION_ARGUMENT_NOT_INTEGER" : {
- "message" : [ "The second argument of '<functionName>' function needs to
be an integer." ],
+ "message" : [
+ "The second argument of '<functionName>' function needs to be an
integer."
+ ],
"sqlState" : "22023"
},
"UNABLE_TO_ACQUIRE_MEMORY" : {
- "message" : [ "Unable to acquire <requestedBytes> bytes of memory, got
<receivedBytes>" ]
+ "message" : [
+ "Unable to acquire <requestedBytes> bytes of memory, got <receivedBytes>"
+ ]
},
"UNRECOGNIZED_SQL_TYPE" : {
- "message" : [ "Unrecognized SQL type <typeName>" ],
+ "message" : [
+ "Unrecognized SQL type <typeName>"
+ ],
"sqlState" : "42000"
},
"UNSUPPORTED_DATATYPE" : {
- "message" : [ "Unsupported data type <typeName>" ],
+ "message" : [
+ "Unsupported data type <typeName>"
+ ],
"sqlState" : "0A000"
},
"UNSUPPORTED_DESERIALIZER" : {
- "message" : [ "The deserializer is not supported: " ],
+ "message" : [
+ "The deserializer is not supported: "
+ ],
"subClass" : {
"DATA_TYPE_MISMATCH" : {
- "message" : [ "need <quantifier> <desiredType> field but got
<dataType>." ]
+ "message" : [
+ "need <quantifier> <desiredType> field but got <dataType>."
+ ]
},
"FIELD_NUMBER_MISMATCH" : {
- "message" : [ "try to map <schema> to Tuple<ordinal>, but failed as
the number of fields does not line up." ]
+ "message" : [
+ "try to map <schema> to Tuple<ordinal>, but failed as the number of
fields does not line up."
+ ]
}
}
},
"UNSUPPORTED_FEATURE" : {
- "message" : [ "The feature is not supported: " ],
+ "message" : [
+ "The feature is not supported: "
+ ],
"subClass" : {
"AES_MODE" : {
- "message" : [ "AES-<mode> with the padding <padding> by the
<functionName> function." ]
+ "message" : [
+ "AES-<mode> with the padding <padding> by the <functionName>
function."
+ ]
},
"DESC_TABLE_COLUMN_PARTITION" : {
- "message" : [ "DESC TABLE COLUMN for a specific partition." ]
+ "message" : [
+ "DESC TABLE COLUMN for a specific partition."
+ ]
},
"DISTRIBUTE_BY" : {
- "message" : [ "DISTRIBUTE BY clause." ]
+ "message" : [
+ "DISTRIBUTE BY clause."
+ ]
},
"INSERT_PARTITION_SPEC_IF_NOT_EXISTS" : {
- "message" : [ "INSERT INTO <tableName> IF NOT EXISTS in the PARTITION
spec." ]
+ "message" : [
+ "INSERT INTO <tableName> IF NOT EXISTS in the PARTITION spec."
+ ]
},
"JDBC_TRANSACTION" : {
- "message" : [ "The target JDBC server does not support transactions
and can only support ALTER TABLE with a single action." ]
+ "message" : [
+ "The target JDBC server does not support transactions and can only
support ALTER TABLE with a single action."
+ ]
},
"LATERAL_JOIN_OF_TYPE" : {
- "message" : [ "<joinType> JOIN with LATERAL correlation." ]
+ "message" : [
+ "<joinType> JOIN with LATERAL correlation."
+ ]
},
"LATERAL_JOIN_USING" : {
- "message" : [ "JOIN USING with LATERAL correlation." ]
+ "message" : [
+ "JOIN USING with LATERAL correlation."
+ ]
},
"LATERAL_NATURAL_JOIN" : {
- "message" : [ "NATURAL join with LATERAL correlation." ]
+ "message" : [
+ "NATURAL join with LATERAL correlation."
+ ]
},
"LITERAL_TYPE" : {
- "message" : [ "Literal for '<value>' of <type>." ]
+ "message" : [
+ "Literal for '<value>' of <type>."
+ ]
},
"NATURAL_CROSS_JOIN" : {
- "message" : [ "NATURAL CROSS JOIN." ]
+ "message" : [
+ "NATURAL CROSS JOIN."
+ ]
},
"ORC_TYPE_CAST" : {
- "message" : [ "Unable to convert <orcType> of Orc to data type
<toType>." ]
+ "message" : [
+ "Unable to convert <orcType> of Orc to data type <toType>."
+ ]
},
"PANDAS_UDAF_IN_PIVOT" : {
- "message" : [ "Pandas user defined aggregate function in the PIVOT
clause." ]
+ "message" : [
+ "Pandas user defined aggregate function in the PIVOT clause."
+ ]
},
"PIVOT_AFTER_GROUP_BY" : {
- "message" : [ "PIVOT clause following a GROUP BY clause." ]
+ "message" : [
+ "PIVOT clause following a GROUP BY clause."
+ ]
},
"PIVOT_TYPE" : {
- "message" : [ "Pivoting by the value '<value>' of the column data type
<type>." ]
+ "message" : [
+ "Pivoting by the value '<value>' of the column data type <type>."
+ ]
},
"PYTHON_UDF_IN_ON_CLAUSE" : {
- "message" : [ "Python UDF in the ON clause of a <joinType> JOIN." ]
+ "message" : [
+ "Python UDF in the ON clause of a <joinType> JOIN."
+ ]
},
"REPEATED_PIVOT" : {
- "message" : [ "Repeated PIVOT operation." ]
+ "message" : [
+ "Repeated PIVOT operation."
+ ]
},
"SET_NAMESPACE_PROPERTY" : {
- "message" : [ "<property> is a reserved namespace property, <msg>." ]
+ "message" : [
+ "<property> is a reserved namespace property, <msg>."
+ ]
},
"SET_PROPERTIES_AND_DBPROPERTIES" : {
- "message" : [ "set PROPERTIES and DBPROPERTIES at the same time." ]
+ "message" : [
+ "set PROPERTIES and DBPROPERTIES at the same time."
+ ]
},
"SET_TABLE_PROPERTY" : {
- "message" : [ "<property> is a reserved table property, <msg>." ]
+ "message" : [
+ "<property> is a reserved table property, <msg>."
+ ]
},
"TOO_MANY_TYPE_ARGUMENTS_FOR_UDF_CLASS" : {
- "message" : [ "UDF class with <n> type arguments." ]
+ "message" : [
+ "UDF class with <n> type arguments."
+ ]
},
"TRANSFORM_DISTINCT_ALL" : {
- "message" : [ "TRANSFORM with the DISTINCT/ALL clause." ]
+ "message" : [
+ "TRANSFORM with the DISTINCT/ALL clause."
+ ]
},
"TRANSFORM_NON_HIVE" : {
- "message" : [ "TRANSFORM with SERDE is only supported in hive mode." ]
+ "message" : [
+ "TRANSFORM with SERDE is only supported in hive mode."
+ ]
}
},
"sqlState" : "0A000"
},
"UNSUPPORTED_GROUPING_EXPRESSION" : {
- "message" : [ "grouping()/grouping_id() can only be used with
GroupingSets/Cube/Rollup" ]
+ "message" : [
+ "grouping()/grouping_id() can only be used with GroupingSets/Cube/Rollup"
+ ]
},
"UNSUPPORTED_SAVE_MODE" : {
- "message" : [ "The save mode <saveMode> is not supported for: " ],
+ "message" : [
+ "The save mode <saveMode> is not supported for: "
+ ],
"subClass" : {
"EXISTENT_PATH" : {
- "message" : [ "an existent path." ]
+ "message" : [
+ "an existent path."
+ ]
},
"NON_EXISTENT_PATH" : {
- "message" : [ "a not existent path." ]
+ "message" : [
+ "a not existent path."
+ ]
}
}
},
"UNTYPED_SCALA_UDF" : {
- "message" : [ "You're using untyped Scala UDF, which does not have the
input type information. Spark may blindly pass null to the Scala closure with
primitive-type argument, and the closure will see the default value of the Java
type for the null argument, e.g. `udf((x: Int) => x, IntegerType)`, the result
is 0 for null input. To get rid of this error, you could:\n1. use typed Scala
UDF APIs(without return type parameter), e.g. `udf((x: Int) => x)`\n2. use Java
UDF APIs, e.g. `udf(new UDF1[String, Integer] { override def call(s: String):
Integer = s.length() }, IntegerType)`, if input types are all non primitive\n3.
set \"spark.sql.legacy.allowUntypedScalaUDF\" to \"true\" and use this API with
caution" ]
+ "message" : [
+ "You're using untyped Scala UDF, which does not have the input type
information. Spark may blindly pass null to the Scala closure with
primitive-type argument, and the closure will see the default value of the Java
type for the null argument, e.g. `udf((x: Int) => x, IntegerType)`, the result
is 0 for null input. ",
+ "To get rid of this error, you could: ",
+ "1. use typed Scala UDF APIs(without return type parameter), e.g.
`udf((x: Int) => x)` ",
+ "2. use Java UDF APIs, e.g. `udf(new UDF1[String, Integer] { override
def call(s: String): Integer = s.length() }, IntegerType)`, if input types are
all non primitive ",
+ "3. set \"spark.sql.legacy.allowUntypedScalaUDF\" to \"true\" and use
this API with caution"
Review Comment:
not related to this PR, but I think we should not hardcode the config name
in the error message template, we should pass it in as a parameter, in case we
renamed the config name in the future. cc @MaxGekk
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