Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/22379#discussion_r224985980
--- Diff: sql/core/src/main/scala/org/apache/spark/sql/functions.scala ---
@@ -3854,6 +3854,38 @@ object functions {
@scala.annotation.varargs
def map_concat(cols: Column*): Column = withExpr {
MapConcat(cols.map(_.expr)) }
+ /**
+ * Parses a column containing a CSV string into a `StructType` with the
specified schema.
+ * Returns `null`, in the case of an unparseable string.
+ *
+ * @param e a string column containing CSV data.
+ * @param schema the schema to use when parsing the CSV string
+ * @param options options to control how the CSV is parsed. accepts the
same options and the
+ * CSV data source.
+ *
+ * @group collection_funcs
+ * @since 3.0.0
+ */
+ def from_csv(e: Column, schema: StructType, options: Map[String,
String]): Column = withExpr {
+ CsvToStructs(schema, options, e.expr)
+ }
+
+ /**
+ * (Java-specific) Parses a column containing a CSV string into a
`StructType`
+ * with the specified schema. Returns `null`, in the case of an
unparseable string.
+ *
+ * @param e a string column containing CSV data.
+ * @param schema the schema to use when parsing the CSV string
+ * @param options options to control how the CSV is parsed. accepts the
same options and the
+ * CSV data source.
+ *
+ * @group collection_funcs
+ * @since 3.0.0
+ */
+ def from_csv(e: Column, schema: String, options: java.util.Map[String,
String]): Column = {
--- End diff --
Eh, I wasn't following too. Is the problem related to its parameters for
the function? we can just define `characterOrColumn` and use it. We can do
something like:
```
if # is character
schema <- lit(schema)
else if # is column
schema <- schema
else
stop("it should be column or characters")
```
like you did in Python side.
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