# UDTF > The functions for table processors. ## Function of content - [UNROLL](#unroll) - [JSON_UNROLL](#json_unroll) ## Description The UDTFs(user-defined table functions) are used to process the data from source to sink. It is a part of the processing pipeline. It can be used in the pre-processing, processing, and post-processing pipeline. Each processor can assemble UDTFs into a pipeline. Within the pipeline, events are processed by each Function in order, top‑>down. Unlike scalar functions, which return a single value, UDTFs are particularly useful when you need to explode or unroll data, transforming a single input row into multiple output rows. ## UDTF Definition The UDTFs and UDFs share similar input and context structures, please refer to [UDF](udf.md). ## Functions ### UNROLL The Unroll Function handles an array field—or an expression evaluating to an array—and unrolls it into individual events. ```UNROLL(filter, lookup_fields, output_fields[, parameters])``` - filter: optional - lookup_fields: required - output_fields: required - parameters: optional - regex: `` optional. If lookup_fields is a string, the regex parameter is used to split the string into an array. The default value is a comma. #### Example ```yaml functions: - function: UNROLL lookup_fields: [ monitor_rule_list ] output_fields: [ monitor_rule ] ``` ### JSON_UNROLL The JSON Unroll Function handles a JSON object, unrolls/explodes an array of objects therein into individual events, while also inheriting top level fields. ```JSON_UNROLL(filter, lookup_fields, output_fields[, parameters])``` - filter: optional - lookup_fields: required - output_fields: required - parameters: optional - path: `` optional. Path to array to unroll, default is the root of the JSON object. - new_path: `` optional. Rename path to new_path, default is the same as path. #### Example ```yaml functions: - function: JSON_UNROLL lookup_fields: [ device_tag ] output_fields: [ device_tag ] parameters: - path: tags - new_path: tag ```