fix(iceberg): Correct test setup to ensure delete files are created (#5864)

## Changes Made
The integration test
TestIcebergCountPushdown.test_count_pushdown_with_delete_files was
failing for the test_overlapping_deletes table because it incorrectly
enabled count pushdown.
The root cause was that the initial Spark write created multiple small
data files. Subsequent DELETE operations were optimized by Iceberg to
mark entire data files as removed instead of generating
position/equality delete files. As a result, Daft's _has_delete_files()
check did not find any delete files and incorrectly allowed the count
pushdown optimization.
This PR fixes the test by adding coalesce(1) to the Spark DataFrame
before writing the initial data for the test_overlapping_deletes table.
This ensures the data is written to a single Parquet file, forcing
subsequent DELETE operations to generate actual delete files. This
aligns the test's behavior with its intent, correctly disabling count
pushdown when delete files are present.

## Related Issues
#5863 5863
<!-- Link to related GitHub issues, e.g., "Closes #123" -->

---------

Co-authored-by: root <root@bytedance>
Co-authored-by: huleilei <huleilei@bytedance>

Author: 3 people

daft/io/iceberg/iceberg_scan.py

@@ -321,7 +321,7 @@ def can_absorb_select(self) -> bool:
         return True
 
     def supports_count_pushdown(self) -> bool:
-        return True and not self._has_delete_files()
+        return not self._has_delete_files()
 
     def supported_count_modes(self) -> list[CountMode]:
         return [CountMode.All]

tests/integration/iceberg/docker-compose/provision.py

@@ -452,27 +452,27 @@
 """
 )
 
-spark.sql(
-    """
-    INSERT INTO default.test_overlapping_deletes
-    VALUES
-        (1, 'Alice', 100.0, 'A'),
-        (2, 'Bob', 200.0, 'B'),
-        (3, 'Charlie', 300.0, 'A'),
-        (4, 'David', 400.0, 'B'),
-        (5, 'Eve', 500.0, 'A'),
-        (6, 'Frank', 600.0, 'B'),
-        (7, 'Grace', 700.0, 'A'),
-        (8, 'Henry', 800.0, 'B'),
-        (9, 'Ivy', 900.0, 'A'),
-        (10, 'Jack', 1000.0, 'B'),
-        (11, 'Kate', 1100.0, 'A'),
-        (12, 'Leo', 1200.0, 'B'),
-        (13, 'Mary', 1300.0, 'A'),
-        (14, 'Nick', 1400.0, 'B'),
-        (15, 'Olivia', 1500.0, 'A');
-"""
-)
+data = [
+    (1, "Alice", 100.0, "A"),
+    (2, "Bob", 200.0, "B"),
+    (3, "Charlie", 300.0, "A"),
+    (4, "David", 400.0, "B"),
+    (5, "Eve", 500.0, "A"),
+    (6, "Frank", 600.0, "B"),
+    (7, "Grace", 700.0, "A"),
+    (8, "Henry", 800.0, "B"),
+    (9, "Ivy", 900.0, "A"),
+    (10, "Jack", 1000.0, "B"),
+    (11, "Kate", 1100.0, "A"),
+    (12, "Leo", 1200.0, "B"),
+    (13, "Mary", 1300.0, "A"),
+    (14, "Nick", 1400.0, "B"),
+    (15, "Olivia", 1500.0, "A"),
+]
+columns = ["id", "name", "value", "category"]
+df = spark.createDataFrame(data, columns)
+df = df.coalesce(1)
+df.writeTo("default.test_overlapping_deletes").append()
 
 spark.sql(
     """