HyukjinKwon commented on code in PR #42938:
URL: https://github.com/apache/spark/pull/42938#discussion_r1326888870
##########
python/pyspark/sql/functions.py:
##########
@@ -13041,6 +13041,120 @@ def json_object_keys(col: "ColumnOrName") -> Column:
return _invoke_function_over_columns("json_object_keys", col)
+@_try_remote_functions
+def from_xml(
+ col: "ColumnOrName",
+ schema: Union[StructType, Column, str],
+ options: Optional[Dict[str, str]] = None,
+) -> Column:
+ """
+ Parses a column containing a XML string to a row with
+ the specified schema. Returns `null`, in the case of an unparseable string.
+
+ .. versionadded:: 4.0.0
+
+ Parameters
+ ----------
+ col : :class:`~pyspark.sql.Column` or str
+ a column or column name in XML format
+ schema : :class:`StructType` or str
+ a StructType or Python string literal with a DDL-formatted string
+ to use when parsing the Xml column
+ options : dict, optional
+ options to control parsing. accepts the same options as the Xml
datasource.
+ See `Data Source Option
<https://spark.apache.org/docs/latest/sql-data-sources-xml.html#data-source-option>`_
+ for the version you use.
+
+ .. # noqa
+
+ Returns
+ -------
+ :class:`~pyspark.sql.Column`
+ a new column of complex type from given XML object.
+
+ Examples
+ --------
+ >>> from pyspark.sql.types import *
+ >>> from pyspark.sql.functions import from_xml, schema_of_xml, lit
+ >>> data = [(1, '''<p><a>1</a></p>''')]
+ >>> schema = StructType([StructField("a", IntegerType())])
+ >>> df = spark.createDataFrame(data, ("key", "value"))
+ >>> df.select(from_xml(df.value, schema).alias("xml")).collect()
+ [Row(xml=Row(a=1))]
+ >>> df.select(from_xml(df.value, "a INT").alias("xml")).collect()
+ [Row(xml=Row(a=1))]
+ >>> data = [(1, '<p><a>1</a><a>2</a></p>')]
+ >>> df = spark.createDataFrame(data, ("key", "value"))
+ >>> schema = StructType([StructField("a", ArrayType(IntegerType()))])
+ >>> df.select(from_xml(df.value, schema).alias("xml")).collect()
+ [Row(xml=Row(a=[1, 2]))]
+ >>> schema = schema_of_xml(lit(data[0][1]))
+ >>> df.select(from_xml(df.value, schema).alias("xml")).collect()
+ [Row(xml=Row(a=[1, 2]))]
+ """
+
+ if isinstance(schema, StructType):
+ schema = schema.json()
+ elif isinstance(schema, Column):
+ schema = _to_java_column(schema)
+ elif not isinstance(schema, str):
+ raise PySparkTypeError(
+ error_class="NOT_COLUMN_OR_STR_OR_STRUCT",
+ message_parameters={"arg_name": "schema", "arg_type":
type(schema).__name__},
+ )
+ return _invoke_function("from_xml", _to_java_column(col), schema,
_options_to_str(options))
+
+
+@_try_remote_functions
+def schema_of_xml(xml: "ColumnOrName", options: Optional[Dict[str, str]] =
None) -> Column:
+ """
+ Parses a XML string and infers its schema in DDL format.
+
+ .. versionadded:: 4.0.0
+
+ Parameters
+ ----------
+ xml : :class:`~pyspark.sql.Column` or str
+ a XML string or a foldable string column containing a XML string.
+ options : dict, optional
+ options to control parsing. accepts the same options as the XML
datasource.
+ See `Data Source Option
<https://spark.apache.org/docs/latest/sql-data-sources-xml.html#data-source-option>`_
+ for the version you use.
+
+ .. # noqa
+
+ .. versionchanged:: 4.0.0
Review Comment:
You can remove this since this is a new feature, we won't need to annotate
them.
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