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error-classes.json
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error-classes.json
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{
"AMBIGUOUS_COLUMN_OR_FIELD" : {
"message" : [
"Column or field <name> is ambiguous and has <n> matches."
],
"sqlState" : "42702"
},
"AMBIGUOUS_LATERAL_COLUMN_ALIAS" : {
"message" : [
"Lateral column alias <name> is ambiguous and has <n> matches."
],
"sqlState" : "42702"
},
"AMBIGUOUS_REFERENCE" : {
"message" : [
"Reference <name> is ambiguous, could be: <referenceNames>."
],
"sqlState" : "42704"
},
"ARITHMETIC_OVERFLOW" : {
"message" : [
"<message>.<alternative> If necessary set <config> to \"false\" to bypass this error."
],
"sqlState" : "22003"
},
"CANNOT_CAST_DATATYPE" : {
"message" : [
"Cannot cast <sourceType> to <targetType>."
],
"sqlState" : "42846"
},
"CANNOT_CONSTRUCT_PROTOBUF_DESCRIPTOR" : {
"message" : [
"Error constructing FileDescriptor for <descFilePath>."
]
},
"CANNOT_CONVERT_PROTOBUF_FIELD_TYPE_TO_SQL_TYPE" : {
"message" : [
"Cannot convert Protobuf <protobufColumn> to SQL <sqlColumn> because schema is incompatible (protobufType = <protobufType>, sqlType = <sqlType>)."
]
},
"CANNOT_CONVERT_PROTOBUF_MESSAGE_TYPE_TO_SQL_TYPE" : {
"message" : [
"Unable to convert <protobufType> of Protobuf to SQL type <toType>."
]
},
"CANNOT_CONVERT_SQL_TYPE_TO_PROTOBUF_ENUM_TYPE" : {
"message" : [
"Cannot convert SQL <sqlColumn> to Protobuf <protobufColumn> because <data> cannot be written since it's not defined in ENUM <enumString>."
]
},
"CANNOT_CONVERT_SQL_TYPE_TO_PROTOBUF_FIELD_TYPE" : {
"message" : [
"Cannot convert SQL <sqlColumn> to Protobuf <protobufColumn> because schema is incompatible (protobufType = <protobufType>, sqlType = <sqlType>)."
]
},
"CANNOT_DECODE_URL" : {
"message" : [
"Cannot decode url : <url>."
],
"sqlState" : "22546"
},
"CANNOT_LOAD_FUNCTION_CLASS" : {
"message" : [
"Cannot load class <className> when registering the function <functionName>, please make sure it is on the classpath."
]
},
"CANNOT_LOAD_PROTOBUF_CLASS" : {
"message" : [
"Could not load Protobuf class with name <protobufClassName>. <explanation>."
]
},
"CANNOT_PARSE_DECIMAL" : {
"message" : [
"Cannot parse decimal."
],
"sqlState" : "22018"
},
"CANNOT_PARSE_JSON_FIELD" : {
"message" : [
"Cannot parse the field name <fieldName> and the value <fieldValue> of the JSON token type <jsonType> to target Spark data type <dataType>."
],
"sqlState" : "2203G"
},
"CANNOT_PARSE_PROTOBUF_DESCRIPTOR" : {
"message" : [
"Error parsing file <descFilePath> descriptor byte[] into Descriptor object."
]
},
"CANNOT_PARSE_TIMESTAMP" : {
"message" : [
"<message>. If necessary set <ansiConfig> to \"false\" to bypass this error."
],
"sqlState" : "22007"
},
"CANNOT_RESTORE_PERMISSIONS_FOR_PATH" : {
"message" : [
"Failed to set permissions on created path <path> back to <permission>."
]
},
"CANNOT_UP_CAST_DATATYPE" : {
"message" : [
"Cannot up cast <expression> from <sourceType> to <targetType>.",
"<details>"
]
},
"CAST_INVALID_INPUT" : {
"message" : [
"The value <expression> of the type <sourceType> cannot be cast to <targetType> because it is malformed. Correct the value as per the syntax, or change its target type. Use `try_cast` to tolerate malformed input and return NULL instead. If necessary set <ansiConfig> to \"false\" to bypass this error."
],
"sqlState" : "22018"
},
"CAST_OVERFLOW" : {
"message" : [
"The value <value> of the type <sourceType> cannot be cast to <targetType> due to an overflow. Use `try_cast` to tolerate overflow and return NULL instead. If necessary set <ansiConfig> to \"false\" to bypass this error."
],
"sqlState" : "22003"
},
"CAST_OVERFLOW_IN_TABLE_INSERT" : {
"message" : [
"Fail to insert a value of <sourceType> type into the <targetType> type column <columnName> due to an overflow. Use `try_cast` on the input value to tolerate overflow and return NULL instead."
],
"sqlState" : "22003"
},
"COLUMN_ALREADY_EXISTS" : {
"message" : [
"The column <columnName> already exists. Consider to choose another name or rename the existing column."
],
"sqlState" : "42711"
},
"COLUMN_NOT_FOUND" : {
"message" : [
"The column <colName> cannot be found. Verify the spelling and correctness of the column name according to the SQL config <caseSensitiveConfig>."
],
"sqlState" : "42703"
},
"COMPARATOR_RETURNS_NULL" : {
"message" : [
"The comparator has returned a NULL for a comparison between <firstValue> and <secondValue>. It should return a positive integer for \"greater than\", 0 for \"equal\" and a negative integer for \"less than\". To revert to deprecated behavior where NULL is treated as 0 (equal), you must set \"spark.sql.legacy.allowNullComparisonResultInArraySort\" to \"true\"."
]
},
"CONCURRENT_QUERY" : {
"message" : [
"Another instance of this query was just started by a concurrent session."
]
},
"CONNECT" : {
"message" : [
"Generic Spark Connect error."
],
"subClass" : {
"INTERCEPTOR_CTOR_MISSING" : {
"message" : [
"Cannot instantiate GRPC interceptor because <cls> is missing a default constructor without arguments."
]
},
"INTERCEPTOR_RUNTIME_ERROR" : {
"message" : [
"Error instantiating GRPC interceptor: <msg>"
]
},
"PLUGIN_CTOR_MISSING" : {
"message" : [
"Cannot instantiate Spark Connect plugin because <cls> is missing a default constructor without arguments."
]
},
"PLUGIN_RUNTIME_ERROR" : {
"message" : [
"Error instantiating Spark Connect plugin: <msg>"
]
}
}
},
"CONVERSION_INVALID_INPUT" : {
"message" : [
"The value <str> (<fmt>) cannot be converted to <targetType> because it is malformed. Correct the value as per the syntax, or change its format. Use <suggestion> to tolerate malformed input and return NULL instead."
],
"sqlState" : "22018"
},
"CREATE_TABLE_COLUMN_OPTION_DUPLICATE" : {
"message" : [
"CREATE TABLE column <columnName> specifies option \"<optionName>\" more than once, which is invalid."
],
"sqlState" : "42710"
},
"DATATYPE_MISMATCH" : {
"message" : [
"Cannot resolve <sqlExpr> due to data type mismatch:"
],
"subClass" : {
"ARRAY_FUNCTION_DIFF_TYPES" : {
"message" : [
"Input to <functionName> should have been <dataType> followed by a value with same element type, but it's [<leftType>, <rightType>]."
]
},
"BINARY_ARRAY_DIFF_TYPES" : {
"message" : [
"Input to function <functionName> should have been two <arrayType> with same element type, but it's [<leftType>, <rightType>]."
]
},
"BINARY_OP_DIFF_TYPES" : {
"message" : [
"the left and right operands of the binary operator have incompatible types (<left> and <right>)."
]
},
"BINARY_OP_WRONG_TYPE" : {
"message" : [
"the binary operator requires the input type <inputType>, not <actualDataType>."
]
},
"BLOOM_FILTER_BINARY_OP_WRONG_TYPE" : {
"message" : [
"The Bloom filter binary input to <functionName> should be either a constant value or a scalar subquery expression, but it's <actual>."
]
},
"BLOOM_FILTER_WRONG_TYPE" : {
"message" : [
"Input to function <functionName> should have been <expectedLeft> followed by value with <expectedRight>, but it's [<actual>]."
]
},
"CANNOT_CONVERT_TO_JSON" : {
"message" : [
"Unable to convert column <name> of type <type> to JSON."
]
},
"CANNOT_DROP_ALL_FIELDS" : {
"message" : [
"Cannot drop all fields in struct."
]
},
"CAST_WITHOUT_SUGGESTION" : {
"message" : [
"cannot cast <srcType> to <targetType>."
]
},
"CAST_WITH_CONF_SUGGESTION" : {
"message" : [
"cannot cast <srcType> to <targetType> with ANSI mode on.",
"If you have to cast <srcType> to <targetType>, you can set <config> as <configVal>."
]
},
"CAST_WITH_FUNC_SUGGESTION" : {
"message" : [
"cannot cast <srcType> to <targetType>.",
"To convert values from <srcType> to <targetType>, you can use the functions <functionNames> instead."
]
},
"CREATE_MAP_KEY_DIFF_TYPES" : {
"message" : [
"The given keys of function <functionName> should all be the same type, but they are <dataType>."
]
},
"CREATE_MAP_VALUE_DIFF_TYPES" : {
"message" : [
"The given values of function <functionName> should all be the same type, but they are <dataType>."
]
},
"CREATE_NAMED_STRUCT_WITHOUT_FOLDABLE_STRING" : {
"message" : [
"Only foldable `STRING` expressions are allowed to appear at odd position, but they are <inputExprs>."
]
},
"DATA_DIFF_TYPES" : {
"message" : [
"Input to <functionName> should all be the same type, but it's <dataType>."
]
},
"HASH_MAP_TYPE" : {
"message" : [
"Input to the function <functionName> cannot contain elements of the \"MAP\" type. In Spark, same maps may have different hashcode, thus hash expressions are prohibited on \"MAP\" elements. To restore previous behavior set \"spark.sql.legacy.allowHashOnMapType\" to \"true\"."
]
},
"INPUT_SIZE_NOT_ONE" : {
"message" : [
"Length of <exprName> should be 1."
]
},
"INVALID_ARG_VALUE" : {
"message" : [
"The <inputName> value must to be a <requireType> literal of <validValues>, but got <inputValue>."
]
},
"INVALID_JSON_MAP_KEY_TYPE" : {
"message" : [
"Input schema <schema> can only contain STRING as a key type for a MAP."
]
},
"INVALID_JSON_SCHEMA" : {
"message" : [
"Input schema <schema> must be a struct, an array or a map."
]
},
"INVALID_MAP_KEY_TYPE" : {
"message" : [
"The key of map cannot be/contain <keyType>."
]
},
"INVALID_ORDERING_TYPE" : {
"message" : [
"The <functionName> does not support ordering on type <dataType>."
]
},
"IN_SUBQUERY_DATA_TYPE_MISMATCH" : {
"message" : [
"The data type of one or more elements in the left hand side of an IN subquery is not compatible with the data type of the output of the subquery. Mismatched columns: [<mismatchedColumns>], left side: [<leftType>], right side: [<rightType>]."
]
},
"IN_SUBQUERY_LENGTH_MISMATCH" : {
"message" : [
"The number of columns in the left hand side of an IN subquery does not match the number of columns in the output of subquery. Left hand side columns(length: <leftLength>): [<leftColumns>], right hand side columns(length: <rightLength>): [<rightColumns>]."
]
},
"MAP_CONCAT_DIFF_TYPES" : {
"message" : [
"The <functionName> should all be of type map, but it's <dataType>."
]
},
"MAP_FUNCTION_DIFF_TYPES" : {
"message" : [
"Input to <functionName> should have been <dataType> followed by a value with same key type, but it's [<leftType>, <rightType>]."
]
},
"MAP_ZIP_WITH_DIFF_TYPES" : {
"message" : [
"Input to the <functionName> should have been two maps with compatible key types, but it's [<leftType>, <rightType>]."
]
},
"NON_FOLDABLE_INPUT" : {
"message" : [
"the input <inputName> should be a foldable <inputType> expression; however, got <inputExpr>."
]
},
"NON_STRING_TYPE" : {
"message" : [
"all arguments must be strings."
]
},
"NULL_TYPE" : {
"message" : [
"Null typed values cannot be used as arguments of <functionName>."
]
},
"PARAMETER_CONSTRAINT_VIOLATION" : {
"message" : [
"The <leftExprName>(<leftExprValue>) must be <constraint> the <rightExprName>(<rightExprValue>)."
]
},
"RANGE_FRAME_INVALID_TYPE" : {
"message" : [
"The data type <orderSpecType> used in the order specification does not match the data type <valueBoundaryType> which is used in the range frame."
]
},
"RANGE_FRAME_MULTI_ORDER" : {
"message" : [
"A range window frame with value boundaries cannot be used in a window specification with multiple order by expressions: <orderSpec>."
]
},
"RANGE_FRAME_WITHOUT_ORDER" : {
"message" : [
"A range window frame cannot be used in an unordered window specification."
]
},
"SEQUENCE_WRONG_INPUT_TYPES" : {
"message" : [
"<functionName> uses the wrong parameter type. The parameter type must conform to:",
"1. The start and stop expressions must resolve to the same type.",
"2. If start and stop expressions resolve to the <startType> type, then the step expression must resolve to the <stepType> type.",
"3. Otherwise, if start and stop expressions resolve to the <otherStartType> type, then the step expression must resolve to the same type."
]
},
"SPECIFIED_WINDOW_FRAME_DIFF_TYPES" : {
"message" : [
"Window frame bounds <lower> and <upper> do not have the same type: <lowerType> <> <upperType>."
]
},
"SPECIFIED_WINDOW_FRAME_INVALID_BOUND" : {
"message" : [
"Window frame upper bound <upper> does not follow the lower bound <lower>."
]
},
"SPECIFIED_WINDOW_FRAME_UNACCEPTED_TYPE" : {
"message" : [
"The data type of the <location> bound <exprType> does not match the expected data type <expectedType>."
]
},
"SPECIFIED_WINDOW_FRAME_WITHOUT_FOLDABLE" : {
"message" : [
"Window frame <location> bound <expression> is not a literal."
]
},
"SPECIFIED_WINDOW_FRAME_WRONG_COMPARISON" : {
"message" : [
"The lower bound of a window frame must be <comparison> to the upper bound."
]
},
"STACK_COLUMN_DIFF_TYPES" : {
"message" : [
"The data type of the column (<columnIndex>) do not have the same type: <leftType> (<leftParamIndex>) <> <rightType> (<rightParamIndex>)."
]
},
"UNEXPECTED_CLASS_TYPE" : {
"message" : [
"class <className> not found."
]
},
"UNEXPECTED_INPUT_TYPE" : {
"message" : [
"Parameter <paramIndex> requires the <requiredType> type, however <inputSql> has the type <inputType>."
]
},
"UNEXPECTED_NULL" : {
"message" : [
"The <exprName> must not be null."
]
},
"UNEXPECTED_RETURN_TYPE" : {
"message" : [
"The <functionName> requires return <expectedType> type, but the actual is <actualType> type."
]
},
"UNEXPECTED_STATIC_METHOD" : {
"message" : [
"cannot find a static method <methodName> that matches the argument types in <className>."
]
},
"UNSUPPORTED_INPUT_TYPE" : {
"message" : [
"The input of <functionName> can't be <dataType> type data."
]
},
"VALUE_OUT_OF_RANGE" : {
"message" : [
"The <exprName> must be between <valueRange> (current value = <currentValue>)."
]
},
"WRONG_NUM_ENDPOINTS" : {
"message" : [
"The number of endpoints must be >= 2 to construct intervals but the actual number is <actualNumber>."
]
}
},
"sqlState" : "42K09"
},
"DATATYPE_MISSING_SIZE" : {
"message" : [
"DataType <type> requires a length parameter, for example <type>(10). Please specify the length."
],
"sqlState" : "42K01"
},
"DATA_SOURCE_NOT_FOUND" : {
"message" : [
"Failed to find the data source: <provider>. Please find packages at `https://spark.apache.org/third-party-projects.html`."
],
"sqlState" : "42K02"
},
"DATETIME_OVERFLOW" : {
"message" : [
"Datetime operation overflow: <operation>."
],
"sqlState" : "22008"
},
"DECIMAL_PRECISION_EXCEEDS_MAX_PRECISION" : {
"message" : [
"Decimal precision <precision> exceeds max precision <maxPrecision>."
],
"sqlState" : "22003"
},
"DEFAULT_DATABASE_NOT_EXISTS" : {
"message" : [
"Default database <defaultDatabase> does not exist, please create it first or change default database to `<defaultDatabase>`."
],
"sqlState" : "42704"
},
"DIVIDE_BY_ZERO" : {
"message" : [
"Division by zero. Use `try_divide` to tolerate divisor being 0 and return NULL instead. If necessary set <config> to \"false\" to bypass this error."
],
"sqlState" : "22012"
},
"DUPLICATE_KEY" : {
"message" : [
"Found duplicate keys <keyColumn>."
],
"sqlState" : "23505"
},
"ELEMENT_AT_BY_INDEX_ZERO" : {
"message" : [
"The index 0 is invalid. An index shall be either < 0 or > 0 (the first element has index 1)."
],
"sqlState" : "22003"
},
"ENCODER_NOT_FOUND" : {
"message" : [
"Not found an encoder of the type <typeName> to Spark SQL internal representation. Consider to change the input type to one of supported at https://spark.apache.org/docs/latest/sql-ref-datatypes.html."
]
},
"FAILED_EXECUTE_UDF" : {
"message" : [
"Failed to execute user defined function (<functionName>: (<signature>) => <result>)."
],
"sqlState" : "39000"
},
"FAILED_FUNCTION_CALL" : {
"message" : [
"Failed preparing of the function <funcName> for call. Please, double check function's arguments."
],
"sqlState" : "38000"
},
"FAILED_RENAME_PATH" : {
"message" : [
"Failed to rename <sourcePath> to <targetPath> as destination already exists."
],
"sqlState" : "42K04"
},
"FIELD_NOT_FOUND" : {
"message" : [
"No such struct field <fieldName> in <fields>."
],
"sqlState" : "42704"
},
"FORBIDDEN_OPERATION" : {
"message" : [
"The operation <statement> is not allowed on the <objectType>: <objectName>."
],
"sqlState" : "42809"
},
"GRAPHITE_SINK_INVALID_PROTOCOL" : {
"message" : [
"Invalid Graphite protocol: <protocol>."
]
},
"GRAPHITE_SINK_PROPERTY_MISSING" : {
"message" : [
"Graphite sink requires '<property>' property."
]
},
"GROUPING_COLUMN_MISMATCH" : {
"message" : [
"Column of grouping (<grouping>) can't be found in grouping columns <groupingColumns>."
],
"sqlState" : "42803"
},
"GROUPING_ID_COLUMN_MISMATCH" : {
"message" : [
"Columns of grouping_id (<groupingIdColumn>) does not match grouping columns (<groupByColumns>)."
],
"sqlState" : "42803"
},
"GROUPING_SIZE_LIMIT_EXCEEDED" : {
"message" : [
"Grouping sets size cannot be greater than <maxSize>."
],
"sqlState" : "54000"
},
"GROUP_BY_AGGREGATE" : {
"message" : [
"Aggregate functions are not allowed in GROUP BY, but found <sqlExpr>."
],
"sqlState" : "42903"
},
"GROUP_BY_POS_AGGREGATE" : {
"message" : [
"GROUP BY <index> refers to an expression <aggExpr> that contains an aggregate function. Aggregate functions are not allowed in GROUP BY."
],
"sqlState" : "42903"
},
"GROUP_BY_POS_OUT_OF_RANGE" : {
"message" : [
"GROUP BY position <index> is not in select list (valid range is [1, <size>])."
],
"sqlState" : "42805"
},
"INCOMPARABLE_PIVOT_COLUMN" : {
"message" : [
"Invalid pivot column <columnName>. Pivot columns must be comparable."
],
"sqlState" : "42818"
},
"INCOMPATIBLE_DATASOURCE_REGISTER" : {
"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"
],
"subClass" : {
"DATETIME_PATTERN_RECOGNITION" : {
"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."
]
},
"PARSE_DATETIME_BY_NEW_PARSER" : {
"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:",
"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 or the datasource option to \"CORRECTED\"."
]
},
"WRITE_ANCIENT_DATETIME" : {
"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 the 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."
]
}
},
"sqlState" : "42K0B"
},
"INCORRECT_END_OFFSET" : {
"message" : [
"Max offset with <rowsPerSecond> rowsPerSecond is <maxSeconds>, but it's <endSeconds> now."
],
"sqlState" : "22003"
},
"INCORRECT_RAMP_UP_RATE" : {
"message" : [
"Max offset with <rowsPerSecond> rowsPerSecond is <maxSeconds>, but 'rampUpTimeSeconds' is <rampUpTimeSeconds>."
],
"sqlState" : "22003"
},
"INDEX_ALREADY_EXISTS" : {
"message" : [
"Cannot create the index <indexName> on table <tableName> because it already exists."
],
"sqlState" : "42710"
},
"INDEX_NOT_FOUND" : {
"message" : [
"Cannot find the index <indexName> on table <tableName>."
],
"sqlState" : "42704"
},
"INTERNAL_ERROR" : {
"message" : [
"<message>"
],
"sqlState" : "XX000"
},
"INTERVAL_ARITHMETIC_OVERFLOW" : {
"message" : [
"<message>.<alternative>"
],
"sqlState" : "22015"
},
"INTERVAL_DIVIDED_BY_ZERO" : {
"message" : [
"Division by zero. Use `try_divide` to tolerate divisor being 0 and return NULL instead."
],
"sqlState" : "22012"
},
"INVALID_ARRAY_INDEX" : {
"message" : [
"The index <indexValue> is out of bounds. The array has <arraySize> elements. Use the SQL function `get()` to tolerate accessing element at invalid index and return NULL instead. If necessary set <ansiConfig> to \"false\" to bypass this error."
],
"sqlState" : "22003"
},
"INVALID_ARRAY_INDEX_IN_ELEMENT_AT" : {
"message" : [
"The index <indexValue> is out of bounds. The array has <arraySize> elements. Use `try_element_at` to tolerate accessing element at invalid index and return NULL instead. If necessary set <ansiConfig> to \"false\" to bypass this error."
],
"sqlState" : "22003"
},
"INVALID_BUCKET_FILE" : {
"message" : [
"Invalid bucket file: <path>."
]
},
"INVALID_BYTE_STRING" : {
"message" : [
"The expected format is ByteString, but was <unsupported> (<class>)."
]
},
"INVALID_COLUMN_OR_FIELD_DATA_TYPE" : {
"message" : [
"Column or field <name> is of type <type> while it's required to be <expectedType>."
],
"sqlState" : "42000"
},
"INVALID_EMPTY_LOCATION" : {
"message" : [
"The location name cannot be empty string, but `<location>` was given."
],
"sqlState" : "42K05"
},
"INVALID_EXTRACT_FIELD" : {
"message" : [
"Cannot extract <field> from <expr>."
],
"sqlState" : "42601"
},
"INVALID_FIELD_NAME" : {
"message" : [
"Field name <fieldName> is invalid: <path> is not a struct."
],
"sqlState" : "42000"
},
"INVALID_FORMAT" : {
"message" : [
"The format is invalid: <format>."
],
"subClass" : {
"CONT_THOUSANDS_SEPS" : {
"message" : [
"Thousands separators (, or G) must have digits in between them in the number format."
]
},
"CUR_MUST_BEFORE_DEC" : {
"message" : [
"Currency characters must appear before any decimal point in the number format."
]
},
"CUR_MUST_BEFORE_DIGIT" : {
"message" : [
"Currency characters must appear before digits in the number format."
]
},
"EMPTY" : {
"message" : [
"The number format string cannot be empty."
]
},
"ESC_AT_THE_END" : {
"message" : [
"The escape character is not allowed to end with."
]
},
"ESC_IN_THE_MIDDLE" : {
"message" : [
"The escape character is not allowed to precede <char>."
]
},
"THOUSANDS_SEPS_MUST_BEFORE_DEC" : {
"message" : [
"Thousands separators (, or G) may not appear after the decimal point in the number format."
]
},
"UNEXPECTED_TOKEN" : {
"message" : [
"Found the unexpected <token> in the format string; the structure of the format string must match: [MI|S] [$] [0|9|G|,]* [.|D] [0|9]* [$] [PR|MI|S]."
]
},
"WRONG_NUM_DIGIT" : {
"message" : [
"The format string requires at least one number digit."
]
},
"WRONG_NUM_TOKEN" : {
"message" : [
"At most one <token> is allowed in the number format."
]
}
},
"sqlState" : "42601"
},
"INVALID_FRACTION_OF_SECOND" : {
"message" : [
"The fraction of sec must be zero. Valid range is [0, 60]. If necessary set <ansiConfig> to \"false\" to bypass this error."
],
"sqlState" : "22023"
},
"INVALID_IDENTIFIER" : {
"message" : [
"The identifier <ident> is invalid. Please, consider quoting it with back-quotes as `<ident>`."
],
"sqlState" : "42602"
},
"INVALID_JSON_ROOT_FIELD" : {
"message" : [
"Cannot convert JSON root field to target Spark type."
],
"sqlState" : "22032"
},
"INVALID_JSON_SCHEMA_MAP_TYPE" : {
"message" : [
"Input schema <jsonSchema> can only contain STRING as a key type for a MAP."
],
"sqlState" : "22032"
},
"INVALID_LATERAL_JOIN_TYPE" : {
"message" : [
"The <joinType> JOIN with LATERAL correlation is not allowed because an OUTER subquery cannot correlate to its join partner. Remove the LATERAL correlation or use an INNER JOIN, or LEFT OUTER JOIN instead."
],
"sqlState" : "42613"
},
"INVALID_OPTIONS" : {
"message" : [
"Invalid options:"
],
"subClass" : {
"NON_MAP_FUNCTION" : {
"message" : [
"Must use the `map()` function for options."
]
},
"NON_STRING_TYPE" : {
"message" : [
"A type of keys and values in `map()` must be string, but got <mapType>."
]
}
},
"sqlState" : "42K06"
},
"INVALID_PANDAS_UDF_PLACEMENT" : {
"message" : [
"The group aggregate pandas UDF <functionList> cannot be invoked together with as other, non-pandas aggregate functions."
],
"sqlState" : "0A000"
},
"INVALID_PARAMETER_VALUE" : {
"message" : [
"The value of parameter(s) <parameter> in <functionName> is invalid:"
],
"subClass" : {
"AES_KEY" : {
"message" : [
"detail message: <detailMessage>"
]
},
"AES_KEY_LENGTH" : {
"message" : [
"expects a binary value with 16, 24 or 32 bytes, but got <actualLength> bytes."
]
},
"PATTERN" : {
"message" : [
"<value>."
]
},
"ZERO_INDEX" : {
"message" : [
"expects %1$, %2$ and so on, but got %0$."
]
}
},
"sqlState" : "22023"
},
"INVALID_PROPERTY_KEY" : {
"message" : [
"<key> is an invalid property key, please use quotes, e.g. SET <key>=<value>."
],
"sqlState" : "42602"
},
"INVALID_PROPERTY_VALUE" : {
"message" : [
"<value> is an invalid property value, please use quotes, e.g. SET <key>=<value>"
],
"sqlState" : "42602"
},
"INVALID_SCHEMA" : {
"message" : [
"The input schema <inputSchema> is not a valid schema string."
],
"subClass" : {
"NON_STRING_LITERAL" : {
"message" : [
"The input expression must be string literal and not null."
]
},
"NON_STRUCT_TYPE" : {
"message" : [
"The input expression should be evaluated to struct type, but got <dataType>."
]
},
"PARSE_ERROR" : {
"message" : [
"Cannot parse the schema:",
"<reason>"
]
}
},
"sqlState" : "42K07"
},
"INVALID_SQL_ARG" : {
"message" : [
"The argument <name> of `sql()` is invalid. Consider to replace it by a SQL literal."
]
},
"INVALID_SQL_SYNTAX" : {
"message" : [
"Invalid SQL syntax: <inputString>."
],
"sqlState" : "42000"
},
"INVALID_SUBQUERY_EXPRESSION" : {
"message" : [
"Invalid subquery:"
],
"subClass" : {
"SCALAR_SUBQUERY_RETURN_MORE_THAN_ONE_OUTPUT_COLUMN" : {
"message" : [
"Scalar subquery must return only one column, but got <number>."
]
}
},
"sqlState" : "42823"
},
"INVALID_TYPED_LITERAL" : {
"message" : [
"The value of the typed literal <valueType> is invalid: <value>."
],
"sqlState" : "42604"
},
"INVALID_WHERE_CONDITION" : {
"message" : [
"The WHERE condition <condition> contains invalid expressions: <expressionList>.",
"Rewrite the query to avoid window functions, aggregate functions, and generator functions in the WHERE clause."
],
"sqlState" : "42903"
},
"LOCATION_ALREADY_EXISTS" : {
"message" : [
"Cannot name the managed table as <identifier>, as its associated location <location> already exists. Please pick a different table name, or remove the existing location first."
],
"sqlState" : "42710"
},
"MALFORMED_CSV_RECORD" : {
"message" : [
"Malformed CSV record: <badRecord>"
]
},
"MALFORMED_PROTOBUF_MESSAGE" : {
"message" : [
"Malformed Protobuf messages are detected in message deserialization. Parse Mode: <failFastMode>. To process malformed protobuf message as null result, try setting the option 'mode' as 'PERMISSIVE'."
]
},
"MISSING_AGGREGATION" : {
"message" : [
"The non-aggregating expression <expression> is based on columns which are not participating in the GROUP BY clause.",
"Add the columns or the expression to the GROUP BY, aggregate the expression, or use <expressionAnyValue> if you do not care which of the values within a group is returned."
],
"sqlState" : "42803"
},
"MISSING_GROUP_BY" : {
"message" : [
"The query does not include a GROUP BY clause. Add GROUP BY or turn it into the window functions using OVER clauses."
],
"sqlState" : "42803"
},
"MULTI_UDF_INTERFACE_ERROR" : {
"message" : [
"Not allowed to implement multiple UDF interfaces, UDF class <className>."
]
},
"NESTED_AGGREGATE_FUNCTION" : {
"message" : [
"It is not allowed to use an aggregate function in the argument of another aggregate function. Please use the inner aggregate function in a sub-query."
],
"sqlState" : "42607"
},
"NON_LAST_MATCHED_CLAUSE_OMIT_CONDITION" : {
"message" : [
"When there are more than one MATCHED clauses in a MERGE statement, only the last MATCHED clause can omit the condition."
],
"sqlState" : "42613"
},
"NON_LAST_NOT_MATCHED_BY_SOURCE_CLAUSE_OMIT_CONDITION" : {
"message" : [
"When there are more than one NOT MATCHED BY SOURCE clauses in a MERGE statement, only the last NOT MATCHED BY SOURCE clause can omit the condition."
],
"sqlState" : "42613"
},
"NON_LAST_NOT_MATCHED_BY_TARGET_CLAUSE_OMIT_CONDITION" : {
"message" : [
"When there are more than one NOT MATCHED [BY TARGET] clauses in a MERGE statement, only the last NOT MATCHED [BY TARGET] clause can omit the condition."
],
"sqlState" : "42613"
},
"NON_LITERAL_PIVOT_VALUES" : {
"message" : [
"Literal expressions required for pivot values, found <expression>."
],
"sqlState" : "42K08"
},
"NON_PARTITION_COLUMN" : {
"message" : [
"PARTITION clause cannot contain the non-partition column: <columnName>."
],
"sqlState" : "42000"