cloud-fan commented on code in PR #42462:
URL: https://github.com/apache/spark/pull/42462#discussion_r1300806528
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connector/connect/client/jvm/src/main/scala/org/apache/spark/sql/functions.scala:
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@@ -7227,6 +7227,112 @@ object functions {
*/
def to_csv(e: Column): Column = to_csv(e, Collections.emptyMap())
+ // scalastyle:off line.size.limit
+ /**
+ * Parses a column containing a XML string into the data type corresponding
to the specified
+ * schema. Returns `null`, in the case of an unparseable string.
+ *
+ * @param e
+ * a string column containing XML data.
+ * @param schema
+ * the schema to use when parsing the XML string
+ * @param options
+ * options to control how the XML is parsed. accepts the same options and
the XML data source.
+ * See <a href=
+ *
"https://spark.apache.org/docs/latest/sql-data-sources-xml.html#data-source-option";>
Data
+ * Source Option</a> in the version you use.
+ * @group collection_funcs
+ *
+ * @since 4.0.0
+ */
+ // scalastyle:on line.size.limit
+ def from_xml(e: Column, schema: StructType, options: Map[String, String]):
Column =
Review Comment:
the schema parameter can be `StructType` or `Column`, the options parameter
can be scala or java map, or omitted. This means we need 6 overloads of
`from_xml`.
Does it really worth it? I know we did the same thing for `from_json`, but
this is really convoluted.
How about something like
```
TextParsingFunction.newBuilder()
.withSchema(...) // It has multiple overloads
.withOptions(...) // It has multiple overloads
.xml() // returns a Column
```
Anyway, it's unrelated to this PR. We can do it later. cc @HyukjinKwon
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