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Applies to: 
Databricks SQL Databricks Runtime 12.2 and above
Error conditions are descriptive, human-readable strings that unique to the error they describe.
You can use error conditions to programmatically handle errors in your application without the need to parse the error message.
This is a list of common, named error conditions returned by Azure Databricks.
Databricks Runtime and Databricks SQL
ABAC_BUILTIN_FUNCTION_NOT_SUPPORTED
Built in functions are not supported as ABAC policy function
ABAC_FUNCTION_ONLY_ALLOWED_IN_POLICY
The function <funcName> can only be used in the context of CREATE POLICY statements.
ABAC_POLICY_MULTIPLE_BODIES_NOT_SUPPORTED
Multiple policy effects (for example, Row Filter, Column Mask) in a single CREATE POLICY statement are not supported. Create one policy per policy effect.
ADD_DEFAULT_UNSUPPORTED
Failed to execute <statementType> command because DEFAULT values are not supported when adding new columns to previously existing target data source with table provider: "<dataSource>".
AGGREGATE_FUNCTION_IN_NESTED_REFERENCES_NOT_SUPPORTED
Detected aggregate functions in outer scope references <expression> which is not supported.
AGGREGATE_FUNCTION_WITH_NONDETERMINISTIC_EXPRESSION
Non-deterministic expression <sqlExpr> should not appear in the arguments of an aggregate function.
AI_COMPARE_JOBS_ERROR
Failed to compare the two job runs.
For more details see AI_COMPARE_JOBS_ERROR
AI_DIAGNOSE_ERROR
Failed to diagnose the issue.
For more details see AI_DIAGNOSE_ERROR
AI_DIAGNOSE_NO_LOG_DELIVERY
Cluster <clusterId> does not have cluster log delivery configured. AI_DIAGNOSE requires cluster log delivery to access driver logs, event logs, and executor logs for diagnosis.
AI_FUNCTIONS_DISABLED
AI functions are disabled for this workspace. Your account or workspace administrator can enable AI functions.
AI_FUNCTION_COMPILATION_ERROR
An error occurred while compiling the AI function: <statusCode>: <errorMessage>
AI_FUNCTION_HTTP_PARSE_CAST_ERROR
Failed to parse model output when casting to the specified returnType: "<dataType>", response JSON was: "<responseString>". Please update the returnType to match the contents of the type represented by the response JSON and then retry the query again. Exception: <errorMessage>
AI_FUNCTION_HTTP_PARSE_COLUMNS_ERROR
The actual model output has more than one column "<responseString>". However, the specified return type["<dataType>"] has only one column. Please update the returnType to contain the same number of columns as the model output and then retry the query again.
AI_FUNCTION_HTTP_REQUEST_ERROR
Error occurred while making an HTTP request for function <funcName>: <errorMessage>
AI_FUNCTION_INVALID_HTTP_RESPONSE
Invalid HTTP response for function <funcName>: <errorMessage>
AI_FUNCTION_INVALID_MAX_WORDS
The maximum number of words must be a non-negative integer, but got <maxWords>.
AI_FUNCTION_INVALID_MODEL_PARAMETERS
The provided model parameters (<modelParameters>) are invalid in the AI_QUERY function for serving endpoint "<endpointName>".
For more details see AI_FUNCTION_INVALID_MODEL_PARAMETERS
AI_FUNCTION_INVALID_RESPONSE_FORMAT_TYPE
AI function: "<functionName>" requires valid <format> string for responseFormat parameter, but found the following response format: "<invalidResponseFormat>". Exception: <errorMessage>
AI_FUNCTION_JSON_PARSE_ERROR
Error occurred while parsing the JSON response for function <funcName>: <errorMessage>
AI_FUNCTION_MODEL_SCHEMA_PARSE_ERROR
Failed to parse the schema for the serving endpoint "<endpointName>": <errorMessage>, response JSON was: "<responseJson>".
Set the returnType parameter manually in the AI_QUERY function to override schema resolution.
AI_FUNCTION_REGISTRY_LOOKUP_FAILURE
Failed to dynamically resolve function "<functionName>" from AI Function Registry.
Error: <errorMessage>
AI_FUNCTION_SESSION_PERMISSION_DENIED
AI function <funcName> session creation failed. Permission denied: <errorMessage>.
AI_FUNCTION_UNSUPPORTED_DATA_TYPE
Failed to evaluate the SQL function "<functionName>" because the provided argument has "<invalidDataType>" data type, but <reason>. Please update the function call and retry the query again.
AI_FUNCTION_UNSUPPORTED_ERROR
The function <funcName> is not supported in the current environment. It is only available in Azure Databricks SQL Pro and Serverless.
AI_FUNCTION_UNSUPPORTED_REQUEST
Failed to evaluate the SQL function "<functionName>" because the provided argument of <invalidValue> has "<invalidDataType>", but only the following types are supported: <supportedDataTypes>. Please update the function call to provide an argument of string type and retry the query again.
AI_FUNCTION_UNSUPPORTED_RESPONSE_FORMAT
Found unsupported response format.
For more details see AI_FUNCTION_UNSUPPORTED_RESPONSE_FORMAT
AI_FUNCTION_UNSUPPORTED_RETURN_TYPE
AI function: "<functionName>" does not support the following type as return type: "<typeName>". Return type must be a valid SQL type understood by Catalyst and supported by AI function. Current supported types includes: <supportedValues>
AI_GENERATE_DESC_FAILURE
Failed to generate table description: <errorMessage>
AI_INVALID_ARGUMENT_VALUE_ERROR
Provided value "<argValue>" is not supported by argument "<argName>". Supported values are: <supportedValues>
AI_PREDICT_INSUFFICIENT_TRAINING_DATA
The function <funcName> does not have adequate training data to fit a model.
For more details see AI_PREDICT_INSUFFICIENT_TRAINING_DATA
AI_PREDICT_INVALID_PARAMETER
Invalid argument for the function <funcName>.
For more details see AI_PREDICT_INVALID_PARAMETER
AI_QUERY_ENDPOINT_NOT_SUPPORT_STRUCTURED_OUTPUT
Expected the serving endpoint task type to be "Chat" for structured output support, but found "<taskType>" for the endpoint "<endpointName>".
AI_QUERY_RETURN_TYPE_COLUMN_TYPE_MISMATCH
Provided "<sqlExpr>" is not supported by the argument returnType.
AI_SEARCH_CONFLICTING_QUERY_PARAM_SUPPLY_ERROR
Conflicting parameters detected for vector_search SQL function: <conflictParamNames>.<hint>
AI_SEARCH_EMBEDDING_COLUMN_TYPE_UNSUPPORTED_ERROR
vector_search SQL function with embedding column type <embeddingColumnType> is not supported.
AI_SEARCH_EMPTY_QUERY_PARAM_ERROR
vector_search SQL function is missing query input parameter, please specify at least one from: <parameterNames>.
AI_SEARCH_HYBRID_QUERY_PARAM_DEPRECATION_ERROR
The parameter query to vector_search SQL function is not supported for hybrid vector search. Please use query_text instead.
AI_SEARCH_HYBRID_TEXT_NOT_FOUND_ERROR
Query text not found in the vector_search SQL function for hybrid vector search. Please provide query_text.
AI_SEARCH_INDEX_TYPE_UNSUPPORTED_ERROR
vector_search SQL function with index type <indexType> is not supported.
AI_SEARCH_MISSING_EMBEDDING_INPUT_ERROR
query_vector must be specified for index <indexName> because it is not associated with an embedding model endpoint.
AI_SEARCH_QUERY_TYPE_CONVERT_ENCODE_ERROR
Failure to materialize vector_search SQL function query from spark type <dataType> to scala-native objects during request-encoding with error: <errorMessage>.
AI_SEARCH_QUERY_TYPE_UNSUPPORTED_ERROR
vector_search SQL function with query type <unexpectedQueryType> is not supported. Please specify one from: <supportedQueryTypes>.
AI_SEARCH_UNSUPPORTED_NUM_RESULTS_ERROR
vector_search SQL function with num_results larger than <maxLimit> is not supported. The limit specified was <requestedLimit>. Please try again with num_results <= <maxLimit>
AI_TOP_DRIVERS_PARAM_OUT_OF_RANGE
The ai_top_drivers parameter <param> must be between <lo> and <hi>.
AI_TOP_DRIVERS_UNSUPPORTED_AGGREGATION_TYPE
ai_top_drivers does not support the <aggName> aggregate. Choose one of the following supported aggregates: <allowed>.
AI_TOP_DRIVERS_UNSUPPORTED_DIMENSION_TYPE
ai_top_drivers does not support numeric, map, or struct dimension columns. The column <colName> has type <dataType>. Remove this dimension or cast it to a supported type.
AI_TOP_DRIVERS_UNSUPPORTED_LABEL_TYPE
ai_top_drivers requires the label column type to be boolean. The column <colName> has type <dataType>. Change the label column or cast it to a supported type.
AI_TOP_DRIVERS_UNSUPPORTED_METRIC_TYPE
ai_top_drivers requires the metric column type to be numeric. The column <colName> has type <dataType>. Change the metric column or cast it to a supported type.
ALL_PARAMETERS_MUST_BE_NAMED
Using name parameterized queries requires all parameters to be named. Parameters missing names: <exprs>.
ALL_PARTITION_COLUMNS_NOT_ALLOWED
Cannot use all columns for partition columns.
ALTER_SCHEDULE_DOES_NOT_EXIST
Cannot alter <scheduleType> on a table without an existing schedule or trigger. Please add a schedule or trigger to the table before attempting to alter it.
ALTER_SCHEDULE_INVALID_ENTITY
Cannot alter schedule for <tableName>. Alter schedule is only supported for streaming tables, materialized views, and Managed Tables associated with a Flow.
ALTER_TABLE_COLUMN_DESCRIPTOR_DUPLICATE
ALTER TABLE <type> column <columnName> specifies descriptor "<optionName>" more than once, which is invalid.
AMBIGUOUS_ALIAS_IN_NESTED_CTE
Name <name> is ambiguous in nested CTE.
Please set <config> to "CORRECTED" so that name defined in inner CTE takes precedence. If set it to "LEGACY", outer CTE definitions will take precedence.
See 'https://spark.apache.org/docs/latest/sql-migration-guide.html#query-engine'.
AMBIGUOUS_COLUMN_OR_FIELD
Column or field <name> is ambiguous and has <n> matches.
AMBIGUOUS_COLUMN_REFERENCE
Column <name> is ambiguous. It's because you joined several DataFrame together, and some of these DataFrames are the same.
This column points to one of the DataFrames but Spark is unable to figure out which one.
Please alias the DataFrames with different names via DataFrame.alias before joining them,
and specify the column using qualified name, for example, df.alias("a").join(df.alias("b"), col("a.id") > col("b.id")).
AMBIGUOUS_CONSTRAINT
Ambiguous reference to constraint <constraint>.
AMBIGUOUS_LATERAL_COLUMN_ALIAS
Lateral column alias <name> is ambiguous and has <n> matches.
AMBIGUOUS_REFERENCE
Reference <name> is ambiguous, could be: <referenceNames>.
AMBIGUOUS_REFERENCE_TO_FIELDS
Ambiguous reference to the field <field>. It appears <count> times in the schema.
ANALYZE_CONSTRAINTS_NOT_SUPPORTED
ANALYZE CONSTRAINTS is not supported.
ANALYZE_TABLE_COMPUTE_STORAGE_METRICS_INVENTORY_ACCESS_ERROR
Inventory list at <path> is not accessible. Make sure you have permissions on the external location corresponding to the inventory location.
ANALYZE_TABLE_COMPUTE_STORAGE_METRICS_INVENTORY_CONTENTS_NOT_VALID
The inventory list is not in the expected format. <detail>
ANALYZE_TABLE_COMPUTE_STORAGE_METRICS_NOT_SUPPORTED
ANALYZE TABLE COMPUTE STORAGE METRICS is not supported:
For more details see ANALYZE_TABLE_COMPUTE_STORAGE_METRICS_NOT_SUPPORTED
ANSI_CONFIG_CANNOT_BE_DISABLED
The ANSI SQL configuration <config> cannot be disabled in this product.
APPEND_ONCE_FROM_BATCH_QUERY
Please use the stream() operator.
Creating a streaming table from a batch query prevents incremental loading of new data from source. Offending table: '<table>'.
Example usage:
CREATE STREAMING TABLE <target table name> ... AS SELECT ... FROM stream(<source table name>) ...
APPEND_PARTITIONING_COLUMNS_MISMATCH
The provided partitioning or clustering columns do not match the existing table's.
provided:
<provided>table:
<tablePartitioning>
APPROX_TOP_K_INVALID_NUM_OF_ARGUMENTS
approx_top_k expects 1 to 3 arguments, but got <argNum>.
APPROX_TOP_K_MAX_ITEMS_TRACKED_EXCEEDS_LIMIT
The max items tracked maxItemsTracked(<maxItemsTracked>) of approx_top_k should be less than or equal to <limit>.
APPROX_TOP_K_MAX_ITEMS_TRACKED_LESS_THAN_K
The max items tracked maxItemsTracked(<maxItemsTracked>) of approx_top_k should be greater than or equal to k(<k>).
APPROX_TOP_K_NON_POSITIVE_ARG
The value of <argName> in approx_top_k must be a positive integer, but got <argValue>.
APPROX_TOP_K_NULL_ARG
The value of <argName> in approx_top_k cannot be NULL.
APPROX_TOP_K_SKETCH_SIZE_NOT_MATCH
Combining approx_top_k sketches of different sizes is not allowed. Found sketches of size <size1> and <size2>.
APPROX_TOP_K_SKETCH_TYPE_NOT_MATCH
Combining approx_top_k sketches of different types is not allowed. Found sketches of type <type1> and <type2>.
AQE_THREAD_INTERRUPTED
AQE thread is interrupted, probably due to query cancellation by user.
ARGUMENT_NOT_CONSTANT
The function <functionName> includes a parameter <parameterName> at position <pos> that requires a constant argument. Please compute the argument <sqlExpr> separately and pass the result as a constant.
ARITHMETIC_OVERFLOW
<message>.<alternative> If necessary set <config> to "false" to bypass this error.
For more details see ARITHMETIC_OVERFLOW
ARRAY_AGG_WITH_LIMIT_NOT_SUPPORTED_OUTSIDE_ROLLING_WINDOW
The <funcName> function with LIMIT is only supported within RollingWindow.
Normal aggregation (GROUP BY) and Window expressions are not supported.
ARROW_SCHEMA_FIELD_COUNT_MISMATCH
Arrow schema mismatch: encoder has <encoderFieldCount> fields but Arrow data only has <arrowColumnCount> columns.
ARROW_SCHEMA_FIELD_NAME_MISMATCH
Arrow schema mismatch: encoder field '<encoderFieldName>' does not match Arrow column '<arrowColumnName>' at the same position.
ARROW_TYPE_MISMATCH
Invalid schema from <operation>: expected <outputTypes>, got <actualDataTypes>.
ARTIFACT_ALREADY_EXISTS
The artifact <normalizedRemoteRelativePath> already exists. Please choose a different name for the new artifact because it cannot be overwritten.
ARTIFACT_NOT_FOUND
The artifact '<artifactName>' was not found in the session. Expected path: <expectedPath>. Please ensure the artifact was uploaded via spark.addArtifact() before referencing it in dependencies.
ASOF_JOIN_MATCH_CONDITION_INVALID_EXPRESSION
The MATCH_CONDITION operand contains an invalid expression: <expr>. Rewrite the query to avoid subqueries, aggregate functions, window functions, generator functions, or non-deterministic functions in MATCH_CONDITION operands.
ASOF_JOIN_MATCH_CONDITION_INVALID_OPERATOR
The MATCH_CONDITION operator must be one of >=, >, <=, <. Got: <operator>.
ASOF_JOIN_MATCH_CONDITION_INVALID_TYPE
The MATCH_CONDITION operands must be of an orderable, mutually comparable type. Got: <type1> and <type2>.
ASOF_JOIN_MATCH_CONDITION_TABLE_REFERENCE
Each operand of the MATCH_CONDITION comparison must reference only columns of one join input. Got: <refs1> and <refs2>.
ASSIGNMENT_ARITY_MISMATCH
The number of columns or variables assigned or aliased: <numTarget> does not match the number of source expressions: <numExpr>.
AS_OF_JOIN
Invalid as-of join.
For more details see AS_OF_JOIN
ATTEMPT_ANALYSIS_IN_PIPELINE_QUERY_FUNCTION
Operations that trigger DataFrame analysis or execution are not allowed in pipeline query functions. Move code outside of the pipeline query function.
AUTOCDC_BOTH_COLUMN_LIST_AND_EXCEPT_COLUMN_LIST
AutoCDC flow specifies both column_list and except_column_list; at most one may be provided.
AUTOCDC_BOTH_TRACK_HISTORY_COLUMN_LIST_AND_EXCEPT_COLUMN_LIST
AutoCDC flow specifies both track_history_column_list and track_history_except_column_list; at most one may be provided.
AUTOCDC_COLUMNS_NOT_FOUND_IN_SCHEMA
Using <caseSensitivity> column name comparison, the following columns are not present in the <schemaName> schema: <missingColumns>. Available columns: <availableColumns>.
AUTOCDC_EMPTY_KEYS
AutoCDC requires at least one key column to identify rows, but received an empty key set.
AUTOCDC_INVALID_STATE
Detected an invalid AutoCDC state for target table <tableName>:
For more details see AUTOCDC_INVALID_STATE
AUTOCDC_KEY_NOT_IN_SELECTED_SCHEMA
Using <caseSensitivity> column name comparison, the AutoCDC key column <keyColumnName> is not present in the flow's selected source schema. AutoCDC requires every key column to be present in the source change-data feed and retained by any configured column selection.
AUTOCDC_MICROBATCH_VALIDATION
AutoCDC flow on table <tableName> in batch <batchId> failed microbatch validation.
For more details see AUTOCDC_MICROBATCH_VALIDATION
AUTOCDC_MISSING_SEQUENCE_BY
AutoCDC flow is missing a required sequence_by expression. Specify a sequence_by column or expression that orders incoming change events.
AUTOCDC_MISSING_SOURCE
AutoCDC flow is missing a required source table name. Specify the name of the streaming source table the flow should read from.
AUTOCDC_MULTIPART_COLUMN_IDENTIFIER
Expected a single column identifier; got the multi-part identifier <columnName> (parts: <nameParts>).
AUTOCDC_MULTIPLE_FLOWS_TO_TARGET
Invalid AutoCDC destination <tableName> with multiple flows: <flows>. An AutoCDC target table must have exactly one flow writing to it.
AUTOCDC_NON_COLUMN_IDENTIFIER
Expected a column identifier; got the non-attribute expression <expression>. AutoCDC keys, sequence_by, column_list, and except_column_list must reference unqualified column names.
AUTOCDC_RESERVED_COLUMN_NAME_CONFLICT
The column <columnName> in the <schemaName> schema collides with a reserved AutoCDC <scdType> column name (using <caseSensitivity> column name comparison). The following column names are reserved by AutoCDC and cannot appear in the source: <reservedColumnNames>. Rename or remove the column.
AUTOCDC_RESERVED_COLUMN_NAME_PREFIX_CONFLICT
The column <columnName> in the <schemaName> schema collides with the reserved AutoCDC column name prefix <reservedColumnNamePrefix> (using <caseSensitivity> column name comparison). Rename or remove the column.
AUTOCDC_TARGET_DOES_NOT_SUPPORT_MERGE
Cannot start AutoCDC flow: the target table <tableName> (format: <format>) does not support row-level operations. AutoCDC requires a target backed by a connector that supports MERGE.
AUTOCDC_TRACK_HISTORY_REQUIRES_SCD2
AutoCDC flow specifies track_history_column_list or track_history_except_column_list but is not stored as SCD Type 2; history-tracking columns are only supported for SCD Type 2 target tables.
AUTOCOMMIT_BEGIN_TRANSACTION_DURING_AUTOCOMMIT_TRANSACTION
Cannot start a new transaction while an implicit transaction started by SET AUTOCOMMIT=FALSE is active. COMMIT or ROLLBACK the current transaction first and SET AUTOCOMMIT=TRUE before starting a new transaction.
AUTOCOMMIT_NOT_SUPPORTED
AUTOCOMMIT is not enabled in this Azure Databricks environment. AUTOCOMMIT is only supported on Azure Databricks SQL warehouses.
AUTOCOMMIT_OLD_SQL_EDITOR_NOT_SUPPORTED
AUTOCOMMIT cannot be accessed or modified in the legacy SQL Editor. Switch to the new SQL Editor (<docLink>) or use a notebook connected with a Azure Databricks SQL warehouse.
AUTOCOMMIT_ONLY_SUPPORTED_IN_DBSQL
AUTOCOMMIT can only be accessed or modified in Azure Databricks SQL warehouses.
AUTOCOMMIT_SET_DURING_ACTIVE_TRANSACTION
Changing AUTOCOMMIT is not allowed during an active interactive transaction. COMMIT or ROLLBACK the current transaction first.
AUTOCOMMIT_SET_FALSE_ALREADY_DISABLED
SET AUTOCOMMIT=FALSE is not allowed when AUTOCOMMIT is already disabled. Run SET AUTOCOMMIT to check the current state.
AUTOCOMMIT_SET_TRUE_DURING_AUTOCOMMIT_TRANSACTION
SET AUTOCOMMIT=TRUE is not allowed after statements have executed in an implicit transaction started by SET AUTOCOMMIT=FALSE. It is only allowed immediately after SET AUTOCOMMIT=FALSE (before any statements), or after COMMIT or ROLLBACK.
AUTO_CDC_BITEMPORAL_REQUIRES_SYSTEM_SEQUENCE_BY
STORED AS BITEMPORAL requires a SYSTEM SEQUENCE BY clause.
AUTO_CDC_SYSTEM_SEQUENCE_BY_REQUIRES_BITEMPORAL
SYSTEM SEQUENCE BY can only be used with STORED AS BITEMPORAL.
AUTO_INDEX_FEATURE_NOT_ENABLED
Auto index feature is not enabled for your account. To enable it, contact your Azure Databricks representative.
AUTO_TTL_COLUMN_NOT_FOUND
Invalid table schema for Auto-TTL. Auto-TTL column <column> cannot be found.
AUTO_TTL_COLUMN_TYPE_NOT_SUPPORTED
Auto-TTL column <column> has type <type> which is invalid. Column type has to be one of TIMESTAMP, TIMESTAMP_NTZ, or DATE.
AUTO_TTL_FEATURE_NOT_ENABLED
Please contact your Azure Databricks representative to enable the Auto-TTL feature.
AUTO_TTL_INCOMPLETE_SETTING
To set Auto-TTL on a table, both table properties autottl.timestampColumn and autottl.expireInDays are required, but <missing> is missing.
AUTO_TTL_INVALID_COLUMN_CHANGE
Column <column> cannot be <operation> since it's used in the definition of Auto-TTL.
AUTO_TTL_NOT_SUPPORT_SHALLOW_CLONES
Auto-TTL is not supported on shallow clone tables.
AUTO_TTL_NOT_SUPPORT_TABLES_HAVING_SHALLOW_CLONES
Auto-TTL is not supported on tables that have shallow clones.
AUTO_TTL_REQUIRES_PREDICTIVE_OPTIMIZATION
Auto-TTL requires Predictive Optimization to be enabled.
AUTO_TTL_REQUIRES_UC_MANAGED_TABLE
Auto-TTL is only supported on UC Managed tables.
AVRO_CANNOT_WRITE_NULL_FIELD
Cannot write null value for field <name> defined as non-null Avro data type <dataType>.
To allow null value for this field, specify its avro schema as a union type with "null" using avroSchema option.
AVRO_DATA_SOURCE_NOT_ENABLED
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.
AVRO_DEFAULT_VALUES_UNSUPPORTED
The use of default values is not supported whenrescuedDataColumn is enabled. You may be able to remove this check by setting spark.databricks.sql.avro.rescuedDataBlockUserDefinedSchemaDefaultValue to false, but the default values will not apply and null values will still be used.
AVRO_ENCODE_FAILED
Failed to encode value to Avro. Reason: <reason>.
AVRO_INCOMPATIBLE_READ_TYPE
Cannot convert Avro <avroPath> to SQL <sqlPath> because the original encoded data type is <avroType>, however you're trying to read the field as <sqlType>, which would lead to an incorrect answer.
To allow reading this field, enable the SQL configuration: "spark.sql.legacy.avro.allowIncompatibleSchema".
AVRO_INCOMPATIBLE_SCHEMA
Cannot <direction> Avro type <avroType> as SQL type <sqlType>. Reason: <reason>.
AVRO_NOT_LOADED_SQL_FUNCTIONS_UNUSABLE
Cannot call the <functionName> SQL function because the Avro data source is not loaded.
Please restart your job or session with the 'spark-avro' package loaded, such as by using the --packages argument on the command line, and then retry your query or command again.
AVRO_POSITIONAL_FIELD_MATCHING_UNSUPPORTED
The use of positional field matching is not supported when either rescuedDataColumn or failOnUnknownFields is enabled. Remove these options to proceed.
AZURE_INVALID_CREDENTIALS_CONFIGURATION
Azure key provider or credentials are configured incorrectly. Reason: <causedBy>.
BATCH_METADATA_NOT_FOUND
Unable to find batch <batchMetadataFile>.
BEAN_ENCODER_INTERFACE_NOT_SUPPORTED
Bean encoder does not support interface type <className>.
BIGQUERY_DATA_SOURCE_OPTIONS_VALIDATION_FAILED
The BigQuery data source option(s) validation failed.
For more details see BIGQUERY_DATA_SOURCE_OPTIONS_VALIDATION_FAILED
BIGQUERY_OPTIONS_ARE_MUTUALLY_EXCLUSIVE
BigQuery connection credentials must be specified with either the 'GoogleServiceAccountKeyJson' parameter or all of 'projectId', 'OAuthServiceAcctEmail', 'OAuthPvtKey'
BINARY_ARITHMETIC_OVERFLOW
<value1> <symbol> <value2> caused overflow. Use <functionName> to ignore overflow problem and return NULL.
BINARY_FILE_DATA_SOURCE_SCHEMA_MISMATCH
The schema provided to the binary file data source does not match the expected schema.
Expected schema: <expectedSchema>
Provided schema: <providedSchema>
BIN_BY_ALIGN_TO_TYPE_MISMATCH
The ALIGN TO expression type <originType> must match the range column type <rangeType>.
BIN_BY_DUPLICATE_DISTRIBUTE_COLUMN
Column <columnName> appears multiple times in the BIN BY DISTRIBUTE UNIFORM clause.
BIN_BY_INVALID_ALIGN_TO
The ALIGN TO expression could not be evaluated to a constant value. Got: <expr>.
BIN_BY_INVALID_BIN_WIDTH
The BIN WIDTH expression could not be evaluated to a constant value. Got: <expr>.
BIN_BY_INVALID_BIN_WIDTH_TYPE
The BIN WIDTH expression must be of type "INTERVAL DAY TO SECOND", but got <inputType>.
BIN_BY_INVALID_DISTRIBUTE_COLUMN_TYPE
The DISTRIBUTE UNIFORM columns in BIN BY must be FLOAT or DOUBLE. Column <columnName> has type <columnType>.
BIN_BY_INVALID_RANGE
The BIN BY range is invalid: the range start (<rangeStart>) must be less than or equal to the range end (<rangeEnd>).
BIN_BY_MISSING_DISTRIBUTE
BIN BY requires at least one column in DISTRIBUTE UNIFORM (...).
BIN_BY_NON_POSITIVE_BIN_WIDTH
The BIN WIDTH expression must be positive. Got: <expr>.
BIN_BY_NULL_ARGUMENT
The <clause> expression in BIN BY must not be NULL.
BIN_BY_RANGE_TYPE_MISMATCH
The range columns in BIN BY must be TIMESTAMP or TIMESTAMP_NTZ. Column <columnName> has type <columnType>. Both range columns must have the same type.
BIN_BY_REQUIRES_TOP_LEVEL_COLUMN
BIN BY requires a top-level column, but <columnName> is a nested or computed field. Alias it to a top-level column in a select before BIN BY.
BUILTIN_TVF_CREDENTIAL_RETRIEVAL_FAILED
SQLSTATE: none assigned
Failed to retrieve Azure Databricks credentials for built-in Python TVF '<procedureName>': <message>
BUILT_IN_CATALOG
<operation> doesn't support built-in catalogs.
CACHE_ACTIVE_LIMIT_EXCEEDED
Cannot cache the DataFrame: the session has reached the maximum of <maxActiveEntries> cached DataFrames. To make room, call df.unpersist() on a cached DataFrame you no longer need. Note that any DataFrame derived from that one also becomes unusable.
CACHE_DATAFRAME_ACCESS_AFTER_UNCACHE
This DataFrame, or a DataFrame it was derived from, has been uncached and can no longer be accessed. Create a new DataFrame from the original data source to continue working with the data.
CACHE_ENTRY_RESOLUTION_FAILED
Failed to resolve cached data. This can happen when the session has been active for a long time and the underlying cached data has been cleaned up. Re-run df.cache() to re-materialize the DataFrame.
Details: planId=<planId>, reason=<reason>.
For more details see CACHE_ENTRY_RESOLUTION_FAILED
CACHE_TOTAL_LIMIT_EXCEEDED
Cannot cache the DataFrame: the session has reached the total limit of <maxTotalEntries> cached and uncached DataFrames. To cache a new DataFrame, start a new session.
CALL_IN_EXECUTE_IMMEDIATE
CALL statements in EXECUTE IMMEDIATE commands are not allowed. Ensure that the SQL query provided (<sqlString>) is not a CALL statement.
CALL_ON_STREAMING_DATASET_UNSUPPORTED
The method <methodName> can not be called on streaming Dataset/DataFrame.
CANNOT_ALTER_COLLATION_BUCKET_COLUMN
ALTER TABLE (ALTER|CHANGE) COLUMN cannot change collation of type/subtypes of bucket columns, but found the bucket column <columnName> in the table <tableName>.
CANNOT_ALTER_PARTITION_COLUMN
ALTER TABLE (ALTER|CHANGE) COLUMN is not supported for partition columns, but found the partition column <columnName> in the table <tableName>.
CANNOT_ASSIGN_EVENT_TIME_COLUMN_WITHOUT_WATERMARK
Watermark needs to be defined to reassign event time column. Failed to find watermark definition in the streaming query.
CANNOT_CAST_DATATYPE
Cannot cast <sourceType> to <targetType>.
CANNOT_CONVERT_PROTOBUF_FIELD_TYPE_TO_SQL_TYPE
Cannot convert Protobuf <protobufColumn> to SQL <sqlColumn> because schema is incompatible (protobufType = <protobufType>, sqlType = <sqlType>).
CANNOT_CONVERT_PROTOBUF_MESSAGE_TYPE_TO_SQL_TYPE
Unable to convert <protobufType> of Protobuf to SQL type <toType>.
CANNOT_CONVERT_SQL_TYPE_TO_PROTOBUF_FIELD_TYPE
Cannot convert SQL <sqlColumn> to Protobuf <protobufColumn> because schema is incompatible (protobufType = <protobufType>, sqlType = <sqlType>).
CANNOT_CONVERT_SQL_VALUE_TO_PROTOBUF_ENUM_TYPE
Cannot convert SQL <sqlColumn> to Protobuf <protobufColumn> because <data> is not in defined values for enum: <enumString>.
CANNOT_COPY_STATE
Cannot copy catalog state like current database and temporary views from Unity Catalog to a legacy catalog.
CANNOT_CREATE_DATA_SOURCE_TABLE
Failed to create data source table <tableName>:
For more details see CANNOT_CREATE_DATA_SOURCE_TABLE
CANNOT_DECODE_URL
The provided URL cannot be decoded: <url>. Please ensure that the URL is properly formatted and try again.
CANNOT_DELETE_SYSTEM_OWNED
System owned <resourceType> cannot be deleted.
CANNOT_DROP_AMBIGUOUS_CONSTRAINT
Cannot drop the constraint with the name <constraintName> shared by a CHECK constraint
and a PRIMARY KEY or FOREIGN KEY constraint. You can drop the PRIMARY KEY or
FOREIGN KEY constraint by queries:
ALTER TABLE .. DROP PRIMARY KEY or
ALTER TABLE .. DROP FOREIGN KEY ..
CANNOT_DROP_BASE_TABLE_REFERENCED_BY_SHALLOW_CLONE
Cannot drop table <baseTable> because it is referenced by one or more shallow clone(s): <cloneList>. DROP all shallow clones before dropping the base table. If this is not possible, consider to use 'DROP TABLE ... FORCE' to drop the table forcefully. WARNING: This will immediately break all existing shallow clone references and make them unusable.
CANNOT_ESTABLISH_CONNECTION
Cannot establish connection to remote <jdbcDialectName> database. Please check connection information and credentials for example, host, port, user, password and database options. ** If you believe the information is correct, please check your workspace's network setup and ensure it does not have outbound restrictions to the host. Please also check that the host does not block inbound connections from the network where the workspace's Spark clusters are deployed. ** Detailed error message: <causeErrorMessage>.
CANNOT_ESTABLISH_CONNECTION_SERVERLESS
Cannot establish connection to remote <jdbcDialectName> database. Please check connection information and credentials for example, host, port, user, password and database options. ** If you believe the information is correct, please allow inbound traffic from the Internet to your host, as you are using Serverless Compute. If your network policies do not allow inbound Internet traffic, please use non Serverless Compute, or you may reach out to your Azure Databricks representative to learn about Serverless Private Networking. ** Detailed error message: <causeErrorMessage>.
CANNOT_ESTABLISH_CONNECTION_TO_HOST
Cannot establish <protocol> connection to <host>. Please check your workspace's network setup and ensure it does not have outbound restrictions to the host. Please also check that the host does not block inbound connections from the network where the workspace's clusters are deployed.
CANNOT_INSTANTIATE_HIVE_FUNCTION
Cannot instantiate the Hive UDF/UDAF/UDTF wrapper class <clazz>. Check that the function arguments and their types are supported.
CANNOT_INVOKE_IN_TRANSFORMATIONS
Dataset transformations and actions can only be invoked by the driver, not inside of other Dataset transformations; for example, dataset1.map(x => dataset2.values.count() * x) is invalid because the values transformation and count action cannot be performed inside of the dataset1.map transformation. For more information, see SPARK-28702.
CANNOT_LOAD_CATALOG
Cannot load catalog '<name>' with the plugin class '<pluginClassName>':
For more details see CANNOT_LOAD_CATALOG
CANNOT_LOAD_CHECKPOINT_FILE_MANAGER
Error loading streaming checkpoint file manager for path=<path>.
For more details see CANNOT_LOAD_CHECKPOINT_FILE_MANAGER
CANNOT_LOAD_FUNCTION_CLASS
Cannot load class <className> when registering the function <functionName>, please make sure it is on the classpath.
CANNOT_LOAD_PROTOBUF_CLASS
Could not load Protobuf class with name <protobufClassName>. <explanation>.
CANNOT_LOAD_STATE_STORE
An error occurred during loading state.
For more details see CANNOT_LOAD_STATE_STORE
CANNOT_MERGE_INCOMPATIBLE_DATA_TYPE
Failed to merge incompatible data types <left> and <right>. Please check the data types of the columns being merged and ensure that they are compatible. If necessary, consider casting the columns to compatible data types before attempting the merge.
CANNOT_MERGE_SCHEMAS
Failed merging schemas:
Initial schema:
<left>
Schema that cannot be merged with the initial schema:
<right>.
CANNOT_MODIFY_CONFIG
Cannot modify the value of the Spark config: <key>.
See also 'https://spark.apache.org/docs/latest/sql-migration-guide.html#ddl-statements'.
CANNOT_MODIFY_STATIC_CONFIG
Cannot modify the value of the static Spark config: <key>.
CANNOT_PARSE_DECIMAL
Cannot parse decimal. Please ensure that the input is a valid number with optional decimal point or comma separators.
CANNOT_PARSE_INTERVAL
Unable to parse <intervalString>. Please ensure that the value provided is in a valid format for defining an interval. You can reference the documentation for the correct format. If the issue persists, please double check that the input value is not null or empty and try again.
CANNOT_PARSE_JSON_FIELD
Cannot parse the field name <fieldName> and the value <fieldValue> of the JSON token type <jsonType> to target Spark data type <dataType>.
CANNOT_PARSE_PROTOBUF_DESCRIPTOR
Error parsing descriptor bytes into Protobuf FileDescriptorSet.
CANNOT_PARSE_TIME
The input string <input> cannot be parsed to a TIME value because it does not match to the datetime format <format>.
CANNOT_PARSE_TIMESTAMP
<message>. Use <func> to tolerate invalid input string and return NULL instead.
CANNOT_QUERY_TABLE_DURING_INITIALIZATION
Cannot query MV/ST during initialization.
For more details see CANNOT_QUERY_TABLE_DURING_INITIALIZATION
CANNOT_READ_ARCHIVED_FILE
Cannot read file at path <path> because it has been archived. Please adjust your query filters to exclude archived files.
CANNOT_READ_FILE
Cannot read <format> file at path: <path>.
For more details see CANNOT_READ_FILE
CANNOT_READ_SENSITIVE_KEY_FROM_SECURE_PROVIDER
Cannot read sensitive key '<key>' from secure provider.
CANNOT_READ_ZIP_ENTRY
Cannot read zip entry <entry> in archive <path>: encrypted or an unsupported compression method.
CANNOT_RECOGNIZE_HIVE_TYPE
Cannot recognize hive type string: <fieldType>, column: <fieldName>. The specified data type for the field cannot be recognized by Spark SQL. Please check the data type of the specified field and ensure that it is a valid Spark SQL data type. Refer to the Spark SQL documentation for a list of valid data types and their format. If the data type is correct, please ensure that you are using a supported version of Spark SQL.
CANNOT_REFERENCE_UC_IN_HMS
Cannot reference a Unity Catalog <objType> in Hive Metastore objects.
CANNOT_REFRESH_NON_MATERIALIZED_VIEW
Cannot refresh a non-materialized view with REFRESH MATERIALIZED VIEW.
CANNOT_REMOVE_RESERVED_PROPERTY
Cannot remove reserved property: <property>.
CANNOT_RENAME_ACROSS_CATALOG
Renaming a <type> across catalogs is not allowed.
CANNOT_RENAME_ACROSS_SCHEMA
Renaming a <type> across schemas is not allowed.
CANNOT_RESOLVE_DATAFRAME_COLUMN
Cannot resolve dataframe column <name>. It's probably because of illegal references like df1.select(df2.col("a")).
CANNOT_RESOLVE_STAR_EXPAND
Cannot resolve <targetString>.* given input columns <columns>. Please check that the specified table or struct exists and is accessible in the input columns.
CANNOT_RESTORE_PERMISSIONS_FOR_PATH
Failed to set permissions on created path <path> back to <permission>.
CANNOT_SHALLOW_CLONE_ACROSS_UC_AND_HMS
Cannot shallow-clone tables across Unity Catalog and Hive Metastore.
CANNOT_SHALLOW_CLONE_AUTO_TTL
Cannot shallow-clone a table <table> that has Auto-TTL enabled.
CANNOT_SHALLOW_CLONE_NESTED
Cannot shallow-clone a table <table> that is already a shallow clone.
CANNOT_SHALLOW_CLONE_NON_UC_MANAGED_TABLE_AS_SOURCE_OR_TARGET
Shallow clone is only supported for the MANAGED table type. The table <table> is not MANAGED table.
CANNOT_TRUNCATE_EXTERNAL_TABLE
Operation not allowed: TRUNCATE TABLE on external tables: <tableName>.
CANNOT_UPDATE_FIELD
Cannot update <table> field <fieldName> type:
For more details see CANNOT_UPDATE_FIELD
CANNOT_UPDATE_PARTITION_COLUMNS
Declared partitioning <requestedPartitionColumns> conflicts with existing table partitioning <existingPartitionColumns>.
Please delete the table or change the declared partitioning to match its partitions.
CANNOT_UP_CAST_DATATYPE
Cannot up cast <expression> from <sourceType> to <targetType>.
<details>
CANNOT_USE_KRYO
Cannot load Kryo serialization codec. Kryo serialization cannot be used in the Spark Connect client. Use Java serialization, provide a custom Codec, or use Spark Classic instead.
CANNOT_USE_MULTI_ALIASES_IN_WATERMARK_CLAUSE
Multiple aliases are not supported in watermark clause.
CANNOT_USE_RESERVED_STREAMING_PROVIDER_NAME
Cannot use reserved streaming provider name: <providerName>.
CANNOT_VALIDATE_CONNECTION
Validation of <jdbcDialectName> connection is not supported. Please contact Azure Databricks support for alternative solutions, or set "spark.databricks.testConnectionBeforeCreation" to "false" to skip connection testing before creating a connection object.
CANNOT_WRITE_MANAGED_FILE_AS_EXTERNAL
Cannot write a MANAGED FILE reference to an EXTERNAL FILE column. Convert the MANAGED FILE into an EXTERNAL FILE with copy_file().
CANNOT_WRITE_STATE_STORE
Error writing state store files for provider <providerClass>.
For more details see CANNOT_WRITE_STATE_STORE
CAST_INVALID_INPUT
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.
For more details see CAST_INVALID_INPUT
CAST_OVERFLOW
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.
CAST_OVERFLOW_IN_TABLE_INSERT
Fail to assign a value of <sourceType> type to the <targetType> type column or variable <columnName> due to an overflow. Use try_cast on the input value to tolerate overflow and return NULL instead.
CAST_TO_FILE_TYPE
Cannot CAST <sourceType> to FILE.
For more details see CAST_TO_FILE_TYPE
CATALOG_ALREADY_EXISTS
Cannot create catalog <catalogName> because it already exists.
Choose a different name, drop or replace the existing catalog, or add the IF NOT EXISTS clause to tolerate a pre-existing catalog.
CATALOG_CONVERSION_FOREIGN_ENTITY_PRESENT
Catalog conversion from UC Foreign to UC Standard failed because catalog contains foreign entities (up to 10 are shown here): <entityNames>. To see the full list of foreign entities in this catalog, please refer to the scripts in 'ALTER CATALOG DROP CONNECTION' command documentation.
CATALOG_CONVERSION_UNSUPPORTED_CATALOG_TYPE
Catalog cannot be converted from UC Foreign to UC Standard. Only <convertableCatalogTypes> Foreign UC catalogs can be converted to UC Standard.
CATALOG_MANAGED_TABLE_ACCESS_NOT_SUPPORTED
Cannot access the streaming table or materialized view <tableName>: accessing a catalog-managed table is only supported on a shared or serverless cluster. If the issue persists after switching, please contact Azure Databricks support.
CATALOG_MANAGED_TABLE_CREATION_NOT_ALLOWED
Creating Delta tables with <feature> table feature is not allowed. Please contact Azure Databricks support.
CATALOG_MANAGED_TABLE_CREATION_ON_NON_UC_MANAGED_NOT_ALLOWED
Creating Delta tables with the catalog-managed table feature is only supported for Unity Catalog managed tables.
CATALOG_NOT_FOUND
The catalog <catalogName> not found. Consider to set the SQL config <config> to a catalog plugin.
CHECKPOINT_DIR_NOT_SET
Checkpoint directory has not been set in the SparkContext.
Please set it with SparkContext.setCheckpointDir().
CHECKPOINT_RDD_BLOCK_ID_NOT_FOUND
Checkpoint block <rddBlockId> not found!
Either the executor that originally checkpointed this partition is no longer alive, or the original RDD is unpersisted.
If this problem persists, you may consider using rdd.checkpoint() instead, which is slower than local checkpointing but more fault-tolerant.
CHECK_CONSTRAINT_VIOLATION
CHECK constraint <constraintName> <expression> violated by row with values:
<values>
CIRCULAR_CLASS_REFERENCE
Cannot have circular references in class, but got the circular reference of class <t>.
CLASS_NOT_OVERRIDE_EXPECTED_METHOD
<className> must override either <method1> or <method2>.
CLASS_UNSUPPORTED_BY_MAP_OBJECTS
MapObjects does not support the class <cls> as resulting collection.
CLEANROOM_COMMANDS_NOT_SUPPORTED
Clean Room commands are not supported
CLEANROOM_INVALID_SHARED_DATA_OBJECT_NAME
Invalid name to reference a <type> inside a Clean Room. Use a <type>'s name inside the clean room following the format of [catalog].[schema].[<type>].
If you are unsure about what name to use, you can run "SHOW ALL IN CLEANROOM [clean_room]" and use the value in the "name" column.
CLONING_WITH_HISTORY_INVALID_OPTION
Cloning with history is specified with an invalid option: <invalidOption>.
Valid syntax: CREATE (OR REPLACE) TABLE ... DEEP CLONE ... WITH HISTORY.
CLONING_WITH_HISTORY_UNSUPPORTED
Cloning with history is not supported.
CLOUDFILES_SCHEMA_LOCATION_REQUIRED
Could not find required option: schemaLocation. Provide a schema location using cloudFiles.schemaLocation for storing the inferred schema and supporting schema evolution.
CLOUD_ACCESS_DENIED
Access denied to <path> cloud path for <operation> operation. Cloud provider error message: <original>
CLOUD_FILE_ALREADY_EXISTS
Cloud file already exists. Reason: <causedBy>.
CLOUD_FILE_NOT_FOUND
Cloud file is not found. Reason: <causedBy>.
CLOUD_FILE_SOURCE_FILE_NOT_FOUND
A file notification was received for file: <filePath> but it does not exist anymore. Please ensure that files are not deleted before they are processed. To continue your stream, you can set the Spark SQL configuration <config> to true.
CLOUD_INVALID_PATH
The specified path contains invalid characters and cannot be resolved. Path: <path>. Reason: <reason>
CLOUD_IO_INVALID_ARGUMENT
Cloud storage request rejected as invalid: <request_context_and_error_msg>
CLOUD_PROVIDER_ERROR
Cloud provider error: <message>
CLUSTERING_COLUMNS_MISMATCH
Specified clustering does not match that of the existing table <tableName>.
Specified clustering columns: [<specifiedClusteringString>].
Existing clustering columns: [<existingClusteringString>].
CLUSTERING_NOT_SUPPORTED
'<operation>' does not support clustering.
CLUSTER_BY_AUTO_FEATURE_NOT_ENABLED
Please contact your Azure Databricks representative to enable the cluster-by-auto feature.
CLUSTER_BY_AUTO_REQUIRED_FOR_OPERATION
Predictive optimization tried to automatically apply clustering keys to <tableName> but <tableName> no longer has CLUSTER BY AUTO enabled, therefore the operation failed.
Please re-enable CLUSTER BY AUTO if you want the clustering keys to be picked automatically.
CLUSTER_BY_AUTO_REQUIRES_CLUSTERING_FEATURE_ENABLED
Please enable clusteringTable.enableClusteringTableFeature to use CLUSTER BY AUTO.
CLUSTER_BY_AUTO_REQUIRES_PREDICTIVE_OPTIMIZATION
CLUSTER BY AUTO requires Predictive Optimization to be enabled.
CLUSTER_BY_AUTO_UNSUPPORTED_TABLE_TYPE_ERROR
CLUSTER BY AUTO is only supported on UC Managed tables.
CODEC_NOT_AVAILABLE
The codec <codecName> is not available.
For more details see CODEC_NOT_AVAILABLE
CODEC_SHORT_NAME_NOT_FOUND
Cannot find a short name for the codec <codecName>.
COLLATION_INVALID_NAME
The value <collationName> does not represent a correct collation name. Suggested valid collation names: [<proposals>].
COLLATION_INVALID_PROVIDER
The value <provider> does not represent a correct collation provider. Supported providers are: [<supportedProviders>].
COLLATION_MISMATCH
Could not determine which collation to use for string functions and operators.
For more details see COLLATION_MISMATCH
COLLECTION_SIZE_LIMIT_EXCEEDED
Can't create array with <numberOfElements> elements which exceeding the array size limit <maxRoundedArrayLength>,
For more details see COLLECTION_SIZE_LIMIT_EXCEEDED
COLUMN_ALIASES_NOT_ALLOWED
Column aliases are not allowed in <op>.
COLUMN_ALREADY_EXISTS
The column <columnName> already exists. Choose another name or rename the existing column.
COLUMN_ARRAY_ELEMENT_TYPE_MISMATCH
Some values in field <pos> are incompatible with the column array type. Expected type <type>.
COLUMN_IS_NOT_VARIANT_TYPE
The semi-structured extraction operator (:) can only be applied to a column of the VARIANT type.
COLUMN_MASKS_ABAC_MISMATCH
Column masks could not be resolved on <tableName> because the column mask inherited from ABAC policies does not apply the same function as the explicitly defined column mask on the table.
For resolution steps, contact the table owner or policy administrator, or see https://docs.databricks.com/aws/en/data-governance/unity-catalog/abac/policy-evaluation#troubleshooting-policy-conflicts
COLUMN_MASKS_CATALOG_SCHEMA_OUT_OF_SYNC
Cannot set column mask on column <columnName> for table <tableName> because the column exists in the Delta schema but not in the Unity Catalog schema. This means the table metadata is out of sync. Run MSCK REPAIR TABLE <tableName> SYNC METADATA or re-add the column with catalog sync enabled before setting a column mask.