improve error messages in func when complex column passed

Author: shcheklein

src/datachain/func/func.py

@@ -7,6 +7,7 @@
 
 from datachain.lib.convert.python_to_sql import python_to_sql
 from datachain.lib.convert.sql_to_python import sql_to_python
+from datachain.lib.model_store import ModelStore
 from datachain.lib.utils import DataChainColumnError, DataChainParamsError
 from datachain.query.schema import Column, ColumnMeta
 from datachain.sql.functions import numeric
@@ -415,6 +416,20 @@ def get_column(
         label: str | None = None,
         table: "TableClause | None" = None,
     ) -> Column:
+        # Guard against using complex (pydantic) object columns in SQL funcs
+        if signals_schema and self._db_cols:
+            for arg in self._db_cols:
+                # _db_cols normalizes known columns to strings; skip non-string args
+                if not isinstance(arg, str):
+                    continue
+                t_with_sub = signals_schema.get_column_type(arg, with_subtree=True)
+                if ModelStore.is_pydantic(t_with_sub):
+                    raise DataChainParamsError(
+                        f"Function {self.name} doesn't support complex object "
+                        f"columns like '{arg}'. Use a leaf field (e.g., "
+                        f"'{arg}.path') or use UDFs to operate on complex objects."
+                    )
+
         col_type = self.get_result_type(signals_schema)
         sql_type = python_to_sql(col_type)
 
@@ -434,6 +449,7 @@ def get_col(col: ColT, string_as_literal=False) -> ColT:
             return col
 
         cols = [get_col(col) for col in self._db_cols]
+
         kwargs = {k: get_col(v, string_as_literal=True) for k, v in self.kwargs.items()}
         func_col = self.inner(*cols, *self.args, **kwargs)
 
@@ -470,9 +486,8 @@ def get_db_col_type(signals_schema: "SignalSchema", col: ColT) -> "DataType":
     if isinstance(col, ColumnElement) and not hasattr(col, "name"):
         return sql_to_python(col)
 
-    return signals_schema.get_column_type(
-        col.name if isinstance(col, ColumnElement) else col  # type: ignore[arg-type]
-    )
+    name = col.name if isinstance(col, ColumnElement) else col  # type: ignore[assignment]
+    return signals_schema.get_column_type(name)  # type: ignore[arg-type]
 
 
 def _truediv(a, b):

tests/func/functions/test_aggregate.py

@@ -1,5 +1,8 @@
+import pytest
+
 import datachain as dc
 from datachain import func
+from datachain.lib.utils import DataChainParamsError
 
 
 def test_aggregate_avg(test_session):
@@ -169,3 +172,31 @@ class Data(dc.DataModel):
     assert all(isinstance(x[3], int) and x[3] in (20, 40, 60) for x in ds)
     assert all(isinstance(x[4], float) and x[4] in (2.0, 4.0, 6.0) for x in ds)
     assert all(isinstance(x[5], str) and x[5] in ("x", "y", "z") for x in ds)
+
+
+def test_funcs_disallow_complex_object_collect(test_session):
+    class Rec(dc.DataModel):
+        i: int
+
+    ds = dc.read_values(
+        id=(1, 2),
+        rec=(Rec(i=1), Rec(i=2)),
+        session=test_session,
+    )
+
+    with pytest.raises(DataChainParamsError, match="doesn't support complex object"):
+        ds.group_by(res=func.collect("rec"), partition_by="id")
+
+
+def test_funcs_disallow_complex_object_min(test_session):
+    class Rec(dc.DataModel):
+        i: int
+
+    ds = dc.read_values(
+        id=(1, 2),
+        rec=(Rec(i=1), Rec(i=2)),
+        session=test_session,
+    )
+
+    with pytest.raises(DataChainParamsError, match="doesn't support complex object"):
+        ds.group_by(res=func.min("rec"), partition_by="id")

tests/func/functions/test_conditional.py

@@ -1,5 +1,8 @@
+import pytest
+
 import datachain as dc
 from datachain import func
+from datachain.lib.utils import DataChainParamsError
 from tests.utils import skip_if_not_sqlite
 
 
@@ -174,3 +177,13 @@ class Data(dc.DataModel):
         (20, 2.0, 20, 2.0, 40, 4.0, 40, 3.5, 40, 1.5),
         (30, 3.0, 25, 2.5, 60, 6.0, 50, 3.5, 60, 1.5),
     ]
+
+
+def test_conditional_funcs_disallow_complex_object_greatest(test_session):
+    class Rec(dc.DataModel):
+        i: int
+
+    ds = dc.read_values(id=[1, 2], rec=[Rec(i=1), Rec(i=2)], session=test_session)
+
+    with pytest.raises(DataChainParamsError, match="doesn't support complex object"):
+        ds.mutate(t=func.greatest("rec", 1))

tests/unit/test_datachain_hash.py

@@ -187,15 +187,15 @@ def agg_persons(persons):
         .sample(10)
         .offset(2)
         .limit(5)
-        .group_by(age_avg=func.avg("persons.age"), partition_by="persons.name")
+        .group_by(age_avg=func.avg("persons.ages"), partition_by="persons.name")
         .select("persons.name", "age_avg")
         .subtract(
             players_chain,
             on=["persons.name"],
             right_on=["player.name"],
         )
         .hash()
-    ) == "2c8d3fffade5574a418c45545f4c821cbe734f648cfcbfa55843673796bc35eb"
+    ) == "ff0ab3df5e69f5e4f14ee7ddbeeddecfa1f237540b83ba5166ca3671625c6d4d"
 
 
 def test_diff(test_session):

tests/unit/test_func.py

@@ -2,6 +2,7 @@
 from sqlalchemy import Label
 
 import datachain as dc
+from datachain import func
 from datachain.func import (
     and_,
     bit_hamming_distance,
@@ -18,6 +19,7 @@
 from datachain.func.random import rand
 from datachain.func.string import length as strlen
 from datachain.lib.signal_schema import SignalSchema
+from datachain.lib.utils import DataChainParamsError
 from datachain.sql.sqlite.base import (
     sqlite_bit_hamming_distance,
     sqlite_byte_hamming_distance,
@@ -69,6 +71,19 @@ def test_get_column():
     assert col.name == "rand"
 
 
+def test_get_column_disallow_complex_object_in_sql_funcs():
+    class Rec(dc.DataModel):
+        i: int
+
+    schema = SignalSchema({"rec": Rec})
+
+    with pytest.raises(DataChainParamsError, match="doesn't support complex object"):
+        func.min("rec").get_column(schema)
+
+    with pytest.raises(DataChainParamsError, match="doesn't support complex object"):
+        func.collect("rec").get_column(schema)
+
+
 def test_add():
     rnd1, rnd2 = rand(), rand()