zhongyujiang commented on code in PR #2429:
URL: https://github.com/apache/iceberg-python/pull/2429#discussion_r2375526651
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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:
If I understand this correctly, this is creating a predicate to test whether
a row might exist in the `pyarrow_table` (matching on `join_cols`).
And since `Null == Any` should always return unknown in SQL, can we just
filter out any rows from the `pyarrow_table` where the `join_cols` fields
contain `None`(we treat `None` as SQL `Null`), and then build the match filter
based on the filtered pyarrow table (using the existing logic for building the
match filter)? This would be much simpler.
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