HyukjinKwon commented on code in PR #47907:
URL: https://github.com/apache/spark/pull/47907#discussion_r1735727659
##########
python/pyspark/sql/functions/builtin.py:
##########
@@ -16308,6 +16308,54 @@ def try_parse_json(
return _invoke_function("try_parse_json", _to_java_column(col))
+@_try_remote_functions
+def to_variant_object(
+ col: "ColumnOrName",
+) -> Column:
+ """
+ Converts a column containing nested inputs (array/map/struct) into a
variants where maps and
+ structs are converted to variant objects which are unordered unlike SQL
structs. Input maps can
+ only have string keys.
+
+ .. versionadded:: 4.0.0
+
+ Parameters
+ ----------
+ col : :class:`~pyspark.sql.Column` or str
+ a column with a nested schema or column name
+
+ Returns
+ -------
+ :class:`~pyspark.sql.Column`
+ a new column of VariantType.
+
+ Examples
+ --------
+ >>> from pyspark.sql.types import ArrayType, StructType, StructField,
StringType, IntegerType, \
+ ... MapType
+ >>> from pyspark.sql.functions.builtin import to_variant_object
+ >>> schema = StructType([ \
+ ... StructField("i", IntegerType(), True), \
+ ... StructField("v", ArrayType(StructType([ \
+ ... StructField("a", MapType(StringType(), IntegerType()), True) \
+ ... ]), True)) \
+ ... ])
+ >>> data = [(1, [{"a": {"b": 2}}])]
+ >>> df = spark.createDataFrame(data, schema)
+ >>> df.select(to_variant_object(df.v))
+ DataFrame[to_variant_object(v): variant]
+ >>> df.select(to_variant_object(df.v)).show(truncate=False)
Review Comment:
Let's add a test in real unittests, e.g., test_functions.py so the test can
run in both Spark Connect and Spark Classic.
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