hcrosse opened a new issue, #4276:
URL: https://github.com/apache/iceberg-python/issues/4276
### Apache Iceberg version
0.12.0 (latest release)
### Please describe the bug 🐞
For a table identity-partitioned on a nullable string column `p`, filtering
with `p != 'a'` gives `scan.count() == 9` but `len(scan.to_arrow()) == 4`. The
table has 3 rows with `p = 'a'`, 4 with `p = 'b'`, and 5 with `p = NULL`.
`count()` includes the NULL rows, while `to_arrow()` excludes them.
`to_arrow_batch_reader()` also returns 4 rows.
I reproduced this on PyIceberg 0.12.0 and main at
`e3564a54c10960307147377c578680276865ed18`, with PyArrow 26.0.1 on Linux/amd64.
Install `pyiceberg[pyarrow,sql-sqlite]==0.12.0` and `pyarrow==26.0.1`. The
reproduction uses an in-process SQLite catalog and a temporary warehouse:
```python
import tempfile
import pyarrow as pa
from pyiceberg.catalog.sql import SqlCatalog
from pyiceberg.expressions import NotEqualTo
from pyiceberg.partitioning import PartitionField, PartitionSpec
from pyiceberg.schema import Schema
from pyiceberg.transforms import IdentityTransform
from pyiceberg.types import LongType, NestedField, StringType
warehouse = tempfile.mkdtemp()
catalog = SqlCatalog(
"default", uri=f"sqlite:///{warehouse}/catalog.db",
warehouse=f"file://{warehouse}"
)
catalog.create_namespace("ns")
schema = Schema(
NestedField(1, "id", LongType(), required=False),
NestedField(2, "p", StringType(), required=False),
NestedField(3, "v", LongType(), required=False),
)
spec = PartitionSpec(
PartitionField(source_id=2, field_id=1000,
transform=IdentityTransform(), name="p")
)
table = catalog.create_table("ns.t", schema=schema, partition_spec=spec)
table.append(
pa.table(
{
"id": list(range(1, 13)),
"p": ["a"] * 3 + ["b"] * 4 + [None] * 5,
"v": [1, 2, None, 1, 2, None, 3, 1, None, 2, 3, None],
},
schema=pa.schema([("id", pa.int64()), ("p", pa.string()), ("v",
pa.int64())]),
)
)
for field, value in [("p", "a"), ("v", 1)]:
scan = table.scan(row_filter=NotEqualTo(field, value))
print(
f"{field}: count()={scan.count()} to_arrow()={len(scan.to_arrow())}
batch_reader={len(scan.to_arrow_batch_reader().read_all())}"
)
```
Output on both 0.12.0 and main at `e3564a54`:
```text
p: count()=9 to_arrow()=4 batch_reader=4
v: count()=5 to_arrow()=5 batch_reader=5
```
The NULL-partition task has `residual=AlwaysTrue()` because the [residual
evaluator compares `None !=
'a'`](https://github.com/apache/iceberg-python/blob/e3564a54c10960307147377c578680276865ed18/pyiceberg/expressions/visitors.py#L1927-L1931),
so [`count()` adds the file's `record_count` of 5 without reading the
file](https://github.com/apache/iceberg-python/blob/e3564a54c10960307147377c578680276865ed18/pyiceberg/table/__init__.py#L2553-L2578).
`to_arrow()` does not use the residual: it [passes the scan's full row filter
to
`ArrowScan`](https://github.com/apache/iceberg-python/blob/e3564a54c10960307147377c578680276865ed18/pyiceberg/table/__init__.py#L2378-L2392)
and [re-applies that filter to each
file](https://github.com/apache/iceberg-python/blob/e3564a54c10960307147377c578680276865ed18/pyiceberg/io/pyarrow.py#L1738-L1755).
Arrow evaluates `NULL != 'a'` to null, and the scanner drops those rows.
Two null conventions are involved. [PyIceberg's row
evaluator](https://github.com/apache/iceberg-python/blob/e3564a54c10960307147377c578680276865ed18/pyiceberg/expressions/visitors.py#L501-L502),
Java's
[`Evaluator.notEq`](https://github.com/apache/iceberg/blob/ae869756da0f30a4bf67723351c8de2a2a8b924b/api/src/main/java/org/apache/iceberg/expressions/Evaluator.java#L128-L138),
and Java's
[`ResidualEvaluator.notEq`](https://github.com/apache/iceberg/blob/ae869756da0f30a4bf67723351c8de2a2a8b924b/api/src/main/java/org/apache/iceberg/expressions/ResidualEvaluator.java#L195-L198)
treat NULL as matching `!=`, agreeing with `count()` here. Java uses a
[nulls-first string
comparator](https://github.com/apache/iceberg/blob/ae869756da0f30a4bf67723351c8de2a2a8b924b/api/src/main/java/org/apache/iceberg/expressions/Literals.java#L540-L542).
Arrow treats NULL as not matching, agreeing with `to_arrow()`. When [`count()`
must read a file](https://github.com/apache/iceberg-python/blob/e3564a54c1096
0307147377c578680276865ed18/pyiceberg/table/__init__.py#L2569-L2577), it
follows Arrow too: for the non-partition column `v`, both methods return 5 for
`NotEqualTo("v", 1)`, while the row evaluator matches 9.
`Not(EqualTo("p", "a"))` and `NotStartsWith("p", "a")` also give 9 and 4. A
two-value `NotIn("p", {"a", "c"})` returns 9 from both methods, because Arrow's
`~isin` keeps NULL rows. The table in
https://github.com/apache/iceberg-python/pull/3918 shows the same difference.
Expected: `count()` returns `len(to_arrow())` for the same scan. Whether
NULL rows match `!=` is the choice left open in
https://github.com/apache/iceberg-python/pull/3918. That draft PR described the
two conventions and their implications for `delete()` and `overwrite()`, and it
was closed as stale.
Related:
- https://github.com/apache/iceberg-python/issues/3498 covers other residual
and evaluator null cases, rather than this `count()` versus `to_arrow()`
mismatch.
- The open https://github.com/apache/iceberg-python/pull/4219 addresses
https://github.com/apache/iceberg-python/issues/4184 by making `delete()`
remove exactly the rows returned by `scan(row_filter=f)`, including `!=` on a
column containing NULL. That ties `delete()` to Arrow's convention.
- https://github.com/apache/iceberg-python/issues/4197 compares the same
read paths for an expired snapshot ID, rather than a nullable partition filter.
The merged https://github.com/apache/iceberg-python/pull/4218 made `count()`
raise like `to_arrow()` for that case.
### Willingness to contribute
- [ ] I can contribute a fix for this bug independently
- [x] I would be willing to contribute a fix for this bug with guidance from
the Iceberg community
- [ ] I cannot contribute a fix for this bug at this time
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