mdwint commented on code in PR #2429:
URL: https://github.com/apache/iceberg-python/pull/2429#discussion_r2375133403


##########
pyiceberg/table/upsert_util.py:
##########
@@ -14,38 +14,61 @@
 # KIND, either express or implied.  See the License for the
 # specific language governing permissions and limitations
 # under the License.
-import functools
-import operator
+from math import isnan
+from typing import Any
 
 import pyarrow as pa
 from pyarrow import Table as pyarrow_table
 from pyarrow import compute as pc
 
 from pyiceberg.expressions import (
     AlwaysFalse,
+    And,
     BooleanExpression,
     EqualTo,
     In,
+    IsNaN,
+    IsNull,
     Or,
 )
 
 
 def create_match_filter(df: pyarrow_table, join_cols: list[str]) -> 
BooleanExpression:
     unique_keys = df.select(join_cols).group_by(join_cols).aggregate([])
+    filters: list[BooleanExpression] = []
 
     if len(join_cols) == 1:
-        return In(join_cols[0], unique_keys[0].to_pylist())
+        column = join_cols[0]

Review Comment:
   Yes, the `In` operator cannot handle null by design, and this goes for SQL 
as well.
   
   The following SQL is invalid:
   ```sql
   WHERE x IN (1, 2, 3, NULL)
   ```
   Instead it should be this:
   ```sql
   WHERE x IN (1, 2, 3) OR x IS NULL
   ```
   
   Testing for null requires `IS NULL` (or `IS NOT NULL`), and it's impossible 
with `IN` or `=`.
   
   This is the reason for changing the `create_match_filter` function: we need 
to build more complex expressions if null is involved. Examples of such 
expressions are shown in the test cases.
   
   If there's a better way I'm open to changing it, but I believe the added 
complexity in building filter expressions with null is justified. When null is 
not involved we produce the same `In` expression as before.



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