Muokkaa

Jaa


Metafunctions in mapping data flow

APPLIES TO: Azure Data Factory Azure Synapse Analytics

Tip

Try out Data Factory in Microsoft Fabric, an all-in-one analytics solution for enterprises. Microsoft Fabric covers everything from data movement to data science, real-time analytics, business intelligence, and reporting. Learn how to start a new trial for free!

Data flows are available both in Azure Data Factory and Azure Synapse Pipelines. This article applies to mapping data flows. If you are new to transformations, please refer to the introductory article Transform data using a mapping data flow.

The following articles provide details about metafunctions supported by Azure Data Factory and Azure Synapse Analytics in mapping data flows.

Metafunction list

Metafunctions primarily function on metadata in your data flow

Metafunction Task
byItem Find a sub item within a structure or array of structure. If there are multiple matches, the first match is returned. If no match it returns a NULL value. The returned value has to be type converted by one of the type conversion actions(? date, ? string ...). Column names known at design time should be addressed just by their name. Computed inputs aren't supported but you can use parameter substitutions
byOrigin Selects a column value by name in the origin stream. The second argument is the origin stream name. If there are multiple matches, the first match is returned. If no match it returns a NULL value. The returned value has to be type converted by one of the type conversion functions(TO_DATE, TO_STRING ...). Column names known at design time should be addressed just by their name. Computed inputs aren't supported but you can use parameter substitutions.
byOrigins Selects an array of columns by name in the stream. The second argument is the stream where it originated from. If there are multiple matches, the first match is returned. If no match it returns a NULL value. The returned value has to be type converted by one of the type conversion functions(TO_DATE, TO_STRING ...) Column names known at design time should be addressed just by their name. Computed inputs aren't supported but you can use parameter substitutions.
byName Selects a column value by name in the stream. You can pass an optional stream name as the second argument. If there are multiple matches, the first match is returned. If no match it returns a NULL value. The returned value has to be type converted by one of the type conversion functions(TO_DATE, TO_STRING ...). Column names known at design time should be addressed just by their name. Computed inputs aren't supported but you can use parameter substitutions.
byNames Select an array of columns by name in the stream. You can pass an optional stream name as the second argument. If there are multiple matches, the first match is returned. If there are no matches for a column, the entire output is a NULL value. The returned value requires a type conversion function (toDate, toString, ...). Column names known at design time should be addressed just by their name. Computed inputs aren't supported but you can use parameter substitutions.
byPath Finds a hierarchical path by name in the stream. You can pass an optional stream name as the second argument. If no such path is found, it returns null. Column names/paths known at design time should be addressed just by their name or dot notation path. Computed inputs aren't supported but you can use parameter substitutions.
byPosition Selects a column value by its relative position(1 based) in the stream. If the position is out of bounds, it returns a NULL value. The returned value has to be type converted by one of the type conversion functions(TO_DATE, TO_STRING ...) Computed inputs aren't supported but you can use parameter substitutions.
hasPath Checks if a certain hierarchical path exists by name in the stream. You can pass an optional stream name as the second argument. Column names/paths known at design time should be addressed just by their name or dot notation path. Computed inputs aren't supported but you can use parameter substitutions.
originColumns Gets all output columns for an origin stream where columns were created. Must be enclosed in another function.
hex Returns a hex string representation of a binary value
unhex Unhexes a binary value from its string representation. This can be used with sha2, md5 to convert from string to binary representation