feat(pyspark): add support for pyarrow and python UDFs

ibis/backends/pyspark/__init__.py

@@ -46,7 +46,7 @@
     from ibis.expr.api import Watermark
 
 PYSPARK_LT_34 = vparse(pyspark.__version__) < vparse("3.4")
-
+PYSPARK_LT_35 = vparse(pyspark.__version__) < vparse("3.5")
 ConnectionMode = Literal["streaming", "batch"]
 
 
@@ -359,18 +359,26 @@ def wrapper(*args):
     def _register_udfs(self, expr: ir.Expr) -> None:
         node = expr.op()
         for udf in node.find(ops.ScalarUDF):
-            if udf.__input_type__ not in (InputType.PANDAS, InputType.BUILTIN):
-                raise NotImplementedError(
-                    "Only Builtin UDFs and Pandas UDFs are supported in the PySpark backend"
-                )
-            # register pandas UDFs
+            udf_name = self.compiler.__sql_name__(udf)
+            udf_return = PySparkType.from_ibis(udf.dtype)
             if udf.__input_type__ == InputType.PANDAS:
-                udf_name = self.compiler.__sql_name__(udf)
                 udf_func = self._wrap_udf_to_return_pandas(udf.__func__, udf.dtype)
-                udf_return = PySparkType.from_ibis(udf.dtype)
                 spark_udf = F.pandas_udf(udf_func, udf_return, F.PandasUDFType.SCALAR)
-                self._session.udf.register(udf_name, spark_udf)
-
+            elif udf.__input_type__ == InputType.PYTHON:
+                udf_func = udf.__func__
+                spark_udf = F.udf(udf_func, udf_return)
+            elif udf.__input_type__ == InputType.PYARROW:
+                # raise not implemented error if running on pyspark < 3.5
+                if PYSPARK_LT_35:
+                    raise NotImplementedError(
+                        "pyarrow UDFs are only supported in pyspark >= 3.5"
+                    )
+                udf_func = udf.__func__
+                spark_udf = F.udf(udf_func, udf_return, useArrow=True)
+            else:
+                # Builtin functions don't need to be registered
+                continue
+            self._session.udf.register(udf_name, spark_udf)
         for udf in node.find(ops.ElementWiseVectorizedUDF):
             udf_name = self.compiler.__sql_name__(udf)
             udf_func = self._wrap_udf_to_return_pandas(udf.func, udf.return_type)

ibis/backends/pyspark/tests/test_udf.py

@@ -4,6 +4,7 @@
 import pytest
 
 import ibis
+from ibis.backends.pyspark import PYSPARK_LT_35
 
 pytest.importorskip("pyspark")
 
@@ -22,12 +23,36 @@
 def repeat(x, n) -> str: ...
 
 
+@ibis.udf.scalar.python
+def py_repeat(x: str, n: int) -> str:
+    return x * n
+
+
+@ibis.udf.scalar.pyarrow
+def pyarrow_repeat(x: str, n: int) -> str:
+    return x * n
+
+
 def test_builtin_udf(t, df):
     result = t.mutate(repeated=repeat(t.str_col, 2)).execute()
     expected = df.assign(repeated=df.str_col * 2)
     tm.assert_frame_equal(result, expected)
 
 
+def test_python_udf(t, df):
+    result = t.mutate(repeated=py_repeat(t.str_col, 2)).execute()
+    expected = df.assign(repeated=df.str_col * 2)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.xfail(PYSPARK_LT_35, reason="pyarrow UDFs require PySpark 3.5+")
+def test_pyarrow_udf(t, df):
+    result = t.mutate(repeated=pyarrow_repeat(t.str_col, 2)).execute()
+    expected = df.assign(repeated=df.str_col * 2)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.xfail(not PYSPARK_LT_35, reason="pyarrow UDFs require PySpark 3.5+")
 def test_illegal_udf_type(t):
     @ibis.udf.scalar.pyarrow
     def my_add_one(x) -> str:
@@ -39,6 +64,6 @@
 
     with pytest.raises(
         NotImplementedError,
-        match="Only Builtin UDFs and Pandas UDFs are supported in the PySpark backend",
+        match="pyarrow UDFs are only supported in pyspark >= 3.5",
     ):
         expr.execute()

ibis/backends/tests/test_udf.py

@@ -34,7 +34,6 @@
 
 @no_python_udfs
 @cloudpickle_version_mismatch
-@mark.notimpl(["pyspark"])
 @mark.notyet(["datafusion"], raises=NotImplementedError)
 def test_udf(batting):
     @udf.scalar.python
@@ -59,7 +58,6 @@ def num_vowels(s: str, include_y: bool = False) -> int:
 
 @no_python_udfs
 @cloudpickle_version_mismatch
-@mark.notimpl(["pyspark"])
 @mark.notyet(
     ["postgres"], raises=TypeError, reason="postgres only supports map<string, string>"
 )
@@ -89,7 +87,6 @@ def num_vowels_map(s: str, include_y: bool = False) -> dict[str, int]:
 
 @no_python_udfs
 @cloudpickle_version_mismatch
-@mark.notimpl(["pyspark"])
 @mark.notyet(
     ["postgres"], raises=TypeError, reason="postgres only supports map<string, string>"
 )
@@ -174,10 +171,11 @@ def add_one_pyarrow(s: int) -> int:  # s is series, int is the element type
             add_one_pyarrow,
             marks=[
                 mark.notyet(
-                    ["snowflake", "sqlite", "pyspark", "flink"],
+                    ["snowflake", "sqlite", "flink"],
                     raises=NotImplementedError,
                     reason="backend doesn't support pyarrow UDFs",
-                )
+                ),
+                mark.xfail_version(pyspark=["pyspark<3.5"]),
             ],
         ),
     ],