Bug: dynamic_partition_overwrite silently skips spec-0 manifests after partition spec evolution
还没有人认领这个 Issue。
评估
调研方向
从 table/init.py 中的 Table.dynamic_partition_overwrite 开始,然后跟踪 _build_partition_projection 以及 #3011 中的 manifest-pruning 更改。复现 issue 中描述的混合 spec-0/spec-1 snapshot,并与 #1108 中之前的相关 fix 进行比较。完成的标准是回归测试确认覆盖写会从两个历史 spec 中移除匹配的行,同时不会删除无关数据。
由索引模型根据 Issue 内容生成。
描述
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)
- 主要语言
- Python
- 星标
- 1.1k
- 派生
- 589
- 平均合并
- 2 天 2 小时
- 30 天内合并 PR
- 70
贡献指南
这个仓库没有索引到贡献指南
从这里开始
- 先读完整个 Issue,再读项目的贡献指南。
- 在 Issue 下留言说明你要接手 —— 这能避免两个人做同样的事。
- Fork 仓库,在一个分支上完成修改。
- 提交 Pull Request,并在描述里引用这个 Issue 编号。
apache/iceberg-python 的其他 Issue
-
kind:bug
难度 1/5 1 小时以内 新手友好度 92/100
apache/iceberg-python#4006 ·
-
难度 2/5 1-3 小时 新手友好度 78/100
apache/iceberg-python#3996 ·
-
bug
难度 2/5 1-3 小时 新手友好度 72/100
apache/iceberg-python#3979 ·
-
难度 2/5 1-3 小时 新手友好度 78/100
apache/iceberg-python#3885 ·
-
[Bug] PyArrowFileIO fails to propagate s3.ssl.ca-cert to pyarrow.fs.S3FileSystem tls_ca_file_path 未关闭
难度 2/5 1-3 小时 新手友好度 76/100
apache/iceberg-python#3866 · 1 条评论 ·
查看 apache/iceberg-python 的全部 Issue
相似的 Issue
-
难度 2/5 1-3 小时 新手友好度 75/100
anthropics/skills#1811 · 1 条评论 ·
-
难度 2/5 1-3 小时 新手友好度 75/100
speaches-ai/speaches#678 ·
-
bug
难度 2/5 1-3 小时 新手友好度 75/100
datalayer/mcp-compose#42 ·
-
难度 2/5 1-3 小时 新手友好度 75/100
conda-forge/spacy-feedstock#177 ·
-
难度 2/5 1-3 小时 新手友好度 70/100
UKGovernmentBEIS/inspect_evals#2523 ·