Bug: dynamic_partition_overwrite silently skips spec-0 manifests after partition spec evolution
Nadie ha tomado este issue todavía.
Evaluación
- Dificultad
- 4/5
- Tiempo estimado
- 3-5 días
- Aptitud para principiantes
- 70/100
Línea de trabajo
Comienza en table/init.py, en Table.dynamic_partition_overwrite, y sigue después _build_partition_projection y el cambio de manifest-pruning de #3011. Reproduce el snapshot mixto de spec-0/spec-1 descrito en el issue y compáralo con el fix relacionado anterior de #1108. Se considera terminado cuando una prueba de regresión confirma que la sobrescritura elimina las filas coincidentes de ambos specs históricos sin borrar datos no relacionados.
Escrito por el modelo de indexación a partir del texto del issue.
Descripción
Summary
dynamic_partition_overwrite produces incorrect results when a table has undergone
partition spec evolution. Manifests written under older specs are silently skipped
by the manifest pruning logic introduced in #3011, leaving stale data files that
should have been deleted.
Root cause
In Table.dynamic_partition_overwrite (table/__init__.py), the delete predicate
is built using only the current partition spec:
delete_filter = self._build_partition_predicate(
partition_records=partitions_to_overwrite,
spec=self.table_metadata.spec(), # always current spec
schema=self.table_metadata.schema()
)
A snapshot with mixed partition_spec_ids (spec-0 and spec-1 manifests) passes
this single predicate to _DeleteFiles. The manifest evaluator in _build_partition_projection
uses inclusive_projection(schema, spec) — when projecting a spec-1 predicate
(e.g. category=A AND region=us) through spec-0 (which only has category), the
region reference has no corresponding partition field, causing the evaluator to
incorrectly skip spec-0 manifests entirely.
Reproduction
import tempfile, pyarrow as pa
from pyiceberg.catalog import load_catalog
from pyiceberg.schema import Schema
from pyiceberg.types import NestedField, StringType, LongType
from pyiceberg.partitioning import PartitionSpec, PartitionField
from pyiceberg.transforms import IdentityTransform
schema = Schema(
NestedField(1, "category", StringType(), required=False),
NestedField(2, "region", StringType(), required=False),
NestedField(3, "value", LongType(), required=False),
)
spec_v0 = PartitionSpec(
PartitionField(source_id=1, field_id=1000, transform=IdentityTransform(), name="category")
)
with tempfile.TemporaryDirectory() as warehouse:
catalog = load_catalog("test", **{"type": "sql", "uri": f"sqlite:///{warehouse}/catalog.db", "warehouse": f"file://{warehouse}"})
catalog.create_namespace("default")
table = catalog.create_table("default.test", schema=schema, partition_spec=spec_v0)
# Write under spec 0
table.append(pa.table({"category": ["A","A","B"], "region": [None,None,None], "value": [1,2,10]}))
# Evolve spec
with table.update_spec() as u:
u.add_field("region", IdentityTransform(), "region")
table = catalog.load_table("default.test")
# Write under spec 1
table.append(pa.table({"category": ["A","B"], "region": ["us","us"], "value": [100,200]}))
# Overwrite category=A — should delete ALL A rows (both specs)
table.dynamic_partition_overwrite(
pa.table({"category": ["A"], "region": ["us"], "value": [999]})
)
result = table.scan().to_arrow().to_pydict()
a_values = [v for c,v in zip(result["category"], result["value"]) if c == "A"]
print(a_values) # BUG: prints [1, 2, 100, 999] — stale rows from spec-0 not deleted
# EXPECTED: [999]
Fix
Build the delete predicate per historical spec present in the snapshot, projecting
the new data files' partition values into each spec's coordinate space before evaluating.
PR with fix and regression tests to follow.
Related
- #3011 (introduced the manifest pruning optimization)
- #1108 (prior related fix by @Fokko for spec evolution in manifest rewriting)
- Lenguaje dominante
- Python
- Estrellas
- 1.1k
- Forks
- 589
- Merge medio
- 2 d 2 h
- PR fusionados (30 d)
- 70
Guía de contribución
No hay ninguna guía de contribución indexada para este repositorio
Primeros pasos
- Lee el issue completo y luego la guía de contribución del proyecto.
- Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
- Haz un fork del repositorio y trabaja en una rama.
- Abre un pull request que haga referencia al número del issue.
Más de apache/iceberg-python
-
kind:bug
Dificultad 1/5 Menos de una hora Aptitud para principiantes 92/100
apache/iceberg-python#4006 ·
-
Dificultad 2/5 1-3 horas Aptitud para principiantes 78/100
apache/iceberg-python#3996 ·
-
Deletion vector bitmap count is read from the blob and used as a loop bound without validation Abiertobug
Dificultad 2/5 1-3 horas Aptitud para principiantes 72/100
apache/iceberg-python#3979 ·
-
Dificultad 2/5 1-3 horas Aptitud para principiantes 78/100
apache/iceberg-python#3885 ·
-
[Bug] PyArrowFileIO fails to propagate s3.ssl.ca-cert to pyarrow.fs.S3FileSystem tls_ca_file_path Abierto
Dificultad 2/5 1-3 horas Aptitud para principiantes 76/100
apache/iceberg-python#3866 · 1 comentario ·
Todos los issues de apache/iceberg-python
Issues similares
-
enhancement
Dificultad 2/5 1-3 horas Aptitud para principiantes 70/100
canonical/paas-charm#368 · 1 comentario ·
-
Dificultad 2/5 1-3 horas Aptitud para principiantes 75/100
-
tech debt
Dificultad 2/5 1-3 horas Aptitud para principiantes 75/100
-
addition to tracking list Abierto
Dificultad 1/5 Menos de una hora Aptitud para principiantes 90/100
StevenBlack/hosts#3256 ·
-
Dificultad 1/5 Menos de una hora Aptitud para principiantes 90/100
qualcomm/qai-appbuilder#275 ·