support time travel

pyiceberg/table/__init__.py

@@ -3423,7 +3423,7 @@ def _readable_metrics_struct(bound_type: PrimitiveType) -> pa.StructType:
             schema=entries_schema,
         )
 
-    def partitions(self) -> "pa.Table":
+    def partitions(self, snapshot_id: Optional[int] = None) -> "pa.Table":
         import pyarrow as pa
 
         from pyiceberg.io.pyarrow import schema_to_pyarrow
@@ -3495,16 +3495,16 @@ def update_partitions_map(
                 raise ValueError(f"Unknown DataFileContent ({file.content})")
 
         partitions_map: Dict[Tuple[str, Any], Any] = {}
-        if snapshot := self.tbl.metadata.current_snapshot():
-            for manifest in snapshot.manifests(self.tbl.io):
-                for entry in manifest.fetch_manifest_entry(io=self.tbl.io):
-                    partition = entry.data_file.partition
-                    partition_record_dict = {
-                        field.name: partition[pos]
-                        for pos, field in enumerate(self.tbl.metadata.specs()[manifest.partition_spec_id].fields)
-                    }
-                    entry_snapshot = self.tbl.snapshot_by_id(entry.snapshot_id) if entry.snapshot_id is not None else None
-                    update_partitions_map(partitions_map, entry.data_file, partition_record_dict, entry_snapshot)
+        snapshot = self._get_snapshot(snapshot_id)
+        for manifest in snapshot.manifests(self.tbl.io):
+            for entry in manifest.fetch_manifest_entry(io=self.tbl.io):
+                partition = entry.data_file.partition
+                partition_record_dict = {
+                    field.name: partition[pos]
+                    for pos, field in enumerate(self.tbl.metadata.specs()[manifest.partition_spec_id].fields)
+                }
+                entry_snapshot = self.tbl.snapshot_by_id(entry.snapshot_id) if entry.snapshot_id is not None else None
+                update_partitions_map(partitions_map, entry.data_file, partition_record_dict, entry_snapshot)
 
         return pa.Table.from_pylist(
             partitions_map.values(),

tests/integration/test_inspect_table.py

@@ -376,15 +376,17 @@ def test_inspect_partitions_partitioned(spark: SparkSession, session_catalog: Ca
     """
     )
 
-    df = session_catalog.load_table(identifier).inspect.partitions()
-
-    lhs = df.to_pandas()
-    rhs = spark.table(f"{identifier}.partitions").toPandas()
+    def check_pyiceberg_df_equals_spark_df(df: pa.Table, spark_df: DataFrame) -> None:
+        lhs = df.to_pandas().sort_values('spec_id')
+        rhs = spark_df.toPandas().sort_values('spec_id')
+        for column in df.column_names:
+            for left, right in zip(lhs[column].to_list(), rhs[column].to_list()):
+                if column == "partition":
+                    right = right.asDict()
+                assert left == right, f"Difference in column {column}: {left} != {right}"
 
-    lhs.sort_values('spec_id', inplace=True)
-    rhs.sort_values('spec_id', inplace=True)
-    for column in df.column_names:
-        for left, right in zip(lhs[column].to_list(), rhs[column].to_list()):
-            if column == "partition":
-                right = right.asDict()
-            assert left == right, f"Difference in column {column}: {left} != {right}"
+    tbl = session_catalog.load_table(identifier)
+    for snapshot in tbl.metadata.snapshots:
+        df = tbl.inspect.partitions(snapshot_id=snapshot.snapshot_id)
+        spark_df = spark.sql(f"SELECT * FROM {identifier}.partitions VERSION AS OF {snapshot.snapshot_id}")
+        check_pyiceberg_df_equals_spark_df(df, spark_df)