Use the correct spec when rewiting existing manifests

pyiceberg/table/update/snapshot.py

@@ -545,7 +545,7 @@ def _existing_manifests(self) -> List[ManifestFile]:
                     if any(entry.data_file not in found_deleted_data_files for entry in entries):
                         with write_manifest(
                             format_version=self._transaction.table_metadata.format_version,
-                            spec=self._transaction.table_metadata.spec(),
+                            spec=self._transaction.table_metadata.specs()[manifest_file.partition_spec_id],
                             schema=self._transaction.table_metadata.schema(),
                             output_file=self.new_manifest_output(),
                             snapshot_id=self._snapshot_id,

tests/integration/test_writes/test_writes.py

@@ -18,14 +18,15 @@
 import math
 import os
 import time
-from datetime import date, datetime
+from datetime import date, datetime, timedelta
 from pathlib import Path
 from typing import Any, Dict
 from urllib.parse import urlparse
 
 import numpy as np
 import pandas as pd
 import pyarrow as pa
+import pyarrow.compute as pc
 import pyarrow.parquet as pq
 import pytest
 import pytz
@@ -39,12 +40,12 @@
 from pyiceberg.catalog.rest import RestCatalog
 from pyiceberg.catalog.sql import SqlCatalog
 from pyiceberg.exceptions import NoSuchTableError
-from pyiceberg.expressions import GreaterThanOrEqual, In, Not
+from pyiceberg.expressions import And, EqualTo, GreaterThanOrEqual, In, LessThan, Not
 from pyiceberg.io.pyarrow import _dataframe_to_data_files
 from pyiceberg.partitioning import PartitionField, PartitionSpec
 from pyiceberg.schema import Schema
 from pyiceberg.table import TableProperties
-from pyiceberg.transforms import DayTransform, IdentityTransform
+from pyiceberg.transforms import DayTransform, HourTransform, IdentityTransform
 from pyiceberg.types import (
     DateType,
     DoubleType,
@@ -1344,18 +1345,7 @@ def test_overwrite_all_data_with_filter(session_catalog: Catalog) -> None:
 
 
 @pytest.mark.integration
-def test_delete_threshold() -> None:
-    catalog = load_catalog(
-        "local",
-        **{
-            "type": "rest",
-            "uri": "http://localhost:8181",
-            "s3.endpoint": "http://localhost:9000",
-            "s3.access-key-id": "admin",
-            "s3.secret-access-key": "password",
-        },
-    )
-
+def test_delete_threshold(session_catalog: Catalog) -> None:
     schema = Schema(
         NestedField(field_id=101, name="id", field_type=LongType(), required=True),
         NestedField(field_id=103, name="created_at", field_type=DateType(), required=False),
@@ -1365,13 +1355,13 @@ def test_delete_threshold() -> None:
     partition_spec = PartitionSpec(PartitionField(source_id=103, field_id=2000, transform=DayTransform(), name="created_at_day"))
 
     try:
-        catalog.drop_table(
+        session_catalog.drop_table(
             identifier="default.scores",
         )
     except NoSuchTableError:
         pass
 
-    catalog.create_table(
+    session_catalog.create_table(
         identifier="default.scores",
         schema=schema,
         partition_spec=partition_spec,
@@ -1395,7 +1385,7 @@ def test_delete_threshold() -> None:
     # Create the dataframe
     df = pd.DataFrame({"id": id_column, "created_at": created_at_column, "relevancy_score": relevancy_score_column})
 
-    iceberg_table = catalog.load_table("default.scores")
+    iceberg_table = session_catalog.load_table("default.scores")
 
     # Convert the pandas DataFrame to a PyArrow Table with the Iceberg schema
     arrow_schema = iceberg_table.schema().as_arrow()
@@ -1409,3 +1399,52 @@ def test_delete_threshold() -> None:
     assert len(iceberg_table.scan(row_filter=Not(delete_condition)).to_arrow()) == lower_before
     iceberg_table.delete(delete_condition)
     assert len(iceberg_table.scan().to_arrow()) == lower_before
+
+
+@pytest.mark.integration
+def test_rewrite_manifest_after_partition_evolution(session_catalog: Catalog) -> None:
+    np.random.seed(876)
+    N = 1440
+    d = {
+        "timestamp": pa.array([datetime(2023, 1, 1, 0, 0, 0) + timedelta(minutes=i) for i in range(N)]),
+        "category": pa.array([np.random.choice(["A", "B", "C"]) for _ in range(N)]),
+        "value": pa.array(np.random.normal(size=N)),
+    }
+    data = pa.Table.from_pydict(d)
+
+    try:
+        session_catalog.drop_table(
+            identifier="default.test_error_table",
+        )
+    except NoSuchTableError:
+        pass
+
+    table = session_catalog.create_table(
+        "default.test_error_table",
+        schema=data.schema,
+    )
+
+    with table.update_spec() as update:
+        update.add_field("timestamp", transform=HourTransform())
+
+    table.append(data)
+
+    with table.update_spec() as update:
+        update.add_field("category", transform=IdentityTransform())
+
+    data_ = data.filter(
+        (pc.field("category") == "A")
+        & (pc.field("timestamp") >= datetime(2023, 1, 1, 0))
+        & (pc.field("timestamp") < datetime(2023, 1, 1, 1))
+    )
+
+    table.overwrite(
+        df=data_,
+        overwrite_filter=And(
+            And(
+                GreaterThanOrEqual("timestamp", datetime(2023, 1, 1, 0).isoformat()),
+                LessThan("timestamp", datetime(2023, 1, 1, 1).isoformat()),
+            ),
+            EqualTo("category", "A"),
+        ),
+    )