mdwint commented on code in PR #2429:
URL: https://github.com/apache/iceberg-python/pull/2429#discussion_r2375784099
##########
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:
Review Comment:
Would that mean it's impossible to upsert rows with null in the join
columns, since they are filtered out?
If so, that's not what I was going for. I'd like the solution to pass this
test:
https://github.com/mdwint/iceberg-python/blob/f818016e5c198581b7d7b11dba2b9ebd414e19bc/tests/table/test_upsert.py#L784-L831
This would be equivalent to the following Spark SQL (using the [null-safe
equality operator
`<=>`](https://spark.apache.org/docs/latest/sql-ref-null-semantics.html)):
```sql
MERGE INTO target_table AS t
USING source_table AS s
ON (t.foo <=> s.foo AND t.bar <=> s.bar)
WHEN MATCHED THEN UPDATE SET *
WHEN NOT MATCHED THEN INSERT *
```
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