Skip to main content
JSON functions provide tools for querying, analyzing, and manipulating JSON data. All functions are available via fc.json.*.

​
jq

Applies a JQ query to a column containing JSON-formatted strings. 
fc.json.jq(column: ColumnOrName, query: str) -> Column
​
column
ColumnOrName
required
Input column of type JsonType.
​
query
str
required
A JQ expression used to extract or transform values.
​
return
Column
A column containing the result of applying the JQ query to each row’s JSON input.
The input column must be of type JsonType. Use cast(JsonType) if needed to ensure correct typing.

​
Examples

df.select(fc.json.jq(fc.col("json_col"), ".user.name"))

​
get_type

Get the JSON type of each value. 
fc.json.get_type(column: ColumnOrName) -> Column
​
column
ColumnOrName
required
Input column of type JsonType.
​
return
Column
A column of strings indicating the JSON type: "string", "number", "boolean", "array", "object", or "null".

​
Examples

df.select(fc.json.get_type(fc.col("json_data")))

​
contains

Check if a JSON value contains the specified value using recursive deep search. 
fc.json.contains(column: ColumnOrName, value: str) -> Column
​
column
ColumnOrName
required
Input column of type JsonType.
​
value
str
required
Valid JSON string to search for.
​
return
Column
A column of booleans indicating whether the JSON contains the value.

​
Matching Rules

  • Objects: Uses partial matching - {"role": "admin"} matches {"role": "admin", "level": 5}
  • Arrays: Uses exact matching - [1, 2] only matches exactly [1, 2], not [1, 2, 3]
  • Primitives: Uses exact matching - 42 matches 42 but not "42"
  • Search is recursive: Searches at all nesting levels throughout the JSON structure
  • Type-aware: Distinguishes between 42 (number) and "42" (string)

​
Examples

df.select(fc.json.contains(fc.col("json_data"), '{"name": "Alice"}'))
# Matches: {"name": "Alice", "age": 30} and {"user": {"name": "Alice"}}

Build docs developers (and LLMs) love

Get started for freeTalk to us