ueshin opened a new pull request, #42617:
URL: https://github.com/apache/spark/pull/42617
### What changes were proposed in this pull request?
Supports named arguments in scalar Python/Pandas UDF.
For example:
```py
>>> @udf("int")
... def test_udf(a, b):
... return a + 10 * b
...
>>> spark.udf.register("test_udf", test_udf)
>>> spark.range(2).select(test_udf(b=col("id") * 10, a=col("id"))).show()
+---------------------------------+
|test_udf(b => (id * 10), a => id)|
+---------------------------------+
| 0|
| 101|
+---------------------------------+
>>> spark.sql("SELECT test_udf(b => id * 10, a => id) FROM range(2)").show()
+---------------------------------+
|test_udf(b => (id * 10), a => id)|
+---------------------------------+
| 0|
| 101|
+---------------------------------+
```
or:
```py
>>> @pandas_udf("int")
... def test_udf(a, b):
... return a + 10 * b
...
>>> spark.udf.register("test_udf", test_udf)
>>> spark.range(2).select(test_udf(b=col("id") * 10, a=col("id"))).show()
+---------------------------------+
|test_udf(b => (id * 10), a => id)|
+---------------------------------+
| 0|
| 101|
+---------------------------------+
>>> spark.sql("SELECT test_udf(b => id * 10, a => id) FROM range(2)").show()
+---------------------------------+
|test_udf(b => (id * 10), a => id)|
+---------------------------------+
| 0|
| 101|
+---------------------------------+
```
### Why are the changes needed?
Now that named arguments support was added
(https://github.com/apache/spark/pull/41796,
https://github.com/apache/spark/pull/42020).
Scalar Python/Pandas UDFs can support it.
### Does this PR introduce _any_ user-facing change?
Yes, named arguments will be available for scalar Python/Pandas UDFs.
### How was this patch tested?
Added related tests.
### Was this patch authored or co-authored using generative AI tooling?
No.
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