Fix TypeError in create_match_filter for Composite Keys (#1693)

**Old Code Behavior:**

Even if there's only one such condition, the code wraps it in an Or()
operator.
But Or() is meant to combine two or more conditions (like “condition A
OR condition B”).
If you give it only one condition, it complains because it expects a
second condition.

**New Code Behavior:**

The new change checks how many conditions you have.
If there's only one condition, it simply returns that condition.
If there are more than one, it uses Or() to combine them.

---------

Co-authored-by: Fokko Driesprong <fokko@apache.org>

Author: omkenge

pyiceberg/table/upsert_util.py

@@ -16,12 +16,14 @@
 # under the License.
 import functools
 import operator
+from typing import List, cast
 
 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,
@@ -36,7 +38,16 @@ def create_match_filter(df: pyarrow_table, join_cols: list[str]) -> BooleanExpre
     if len(join_cols) == 1:
         return In(join_cols[0], unique_keys[0].to_pylist())
     else:
-        return Or(*[And(*[EqualTo(col, row[col]) for col in join_cols]) for row in unique_keys.to_pylist()])
+        filters: List[BooleanExpression] = [
+            cast(BooleanExpression, And(*[EqualTo(col, row[col]) for col in join_cols])) for row in unique_keys.to_pylist()
+        ]
+
+        if len(filters) == 0:
+            return AlwaysFalse()
+        elif len(filters) == 1:
+            return filters[0]
+        else:
+            return functools.reduce(lambda a, b: Or(a, b), filters)
 
 
 def has_duplicate_rows(df: pyarrow_table, join_cols: list[str]) -> bool:
@@ -86,7 +97,7 @@ def get_rows_to_update(source_table: pa.Table, target_table: pa.Table, join_cols
     if rows_to_update:
         rows_to_update_table = pa.concat_tables(rows_to_update)
     else:
-        rows_to_update_table = pa.Table.from_arrays([], names=source_table.column_names)
+        rows_to_update_table = source_table.schema.empty_table()
 
     common_columns = set(source_table.column_names).intersection(set(target_table.column_names))
     rows_to_update_table = rows_to_update_table.select(list(common_columns))

tests/table/test_upsert.py

@@ -23,8 +23,11 @@
 
 from pyiceberg.catalog import Catalog
 from pyiceberg.exceptions import NoSuchTableError
+from pyiceberg.expressions import And, EqualTo, Reference
+from pyiceberg.expressions.literals import LongLiteral
 from pyiceberg.schema import Schema
 from pyiceberg.table import UpsertResult
+from pyiceberg.table.upsert_util import create_match_filter
 from pyiceberg.types import IntegerType, NestedField, StringType
 from tests.catalog.test_base import InMemoryCatalog, Table
 
@@ -366,3 +369,22 @@ def test_upsert_with_identifier_fields(catalog: Catalog) -> None:
 
     assert upd.rows_updated == 1
     assert upd.rows_inserted == 1
+
+
+def test_create_match_filter_single_condition() -> None:
+    """
+    Test create_match_filter with a composite key where the source yields exactly one unique key.
+    Expected: The function returns the single And condition directly.
+    """
+
+    data = [
+        {"order_id": 101, "order_line_id": 1, "extra": "x"},
+        {"order_id": 101, "order_line_id": 1, "extra": "x"},  # duplicate
+    ]
+    schema = pa.schema([pa.field("order_id", pa.int32()), pa.field("order_line_id", pa.int32()), pa.field("extra", pa.string())])
+    table = pa.Table.from_pylist(data, schema=schema)
+    expr = create_match_filter(table, ["order_id", "order_line_id"])
+    assert expr == And(
+        EqualTo(term=Reference(name="order_id"), literal=LongLiteral(101)),
+        EqualTo(term=Reference(name="order_line_id"), literal=LongLiteral(1)),
+    )