Reading Equality deletes

pyiceberg/io/pyarrow.py

@@ -1136,6 +1136,13 @@ def _read_deletes(io: FileIO, data_file: DataFile) -> dict[str, pa.ChunkedArray]
         raise ValueError(f"Delete file format not supported: {data_file.file_format}")
 
 
+def _read_equality_deletes(io: FileIO, delete_file: DataFile) -> pa.Table:
+    arrow_format = _get_file_format(delete_file.file_format, pre_buffer=True, buffer_size=ONE_MEGABYTE)
+    with io.new_input(delete_file.file_path).open() as fi:
+        fragment = arrow_format.make_fragment(fi)
+        return ds.Scanner.from_fragment(fragment=fragment).to_table()
+
+
 def _combine_positional_deletes(positional_deletes: list[pa.ChunkedArray], start_index: int, end_index: int) -> pa.Array:
     if len(positional_deletes) == 1:
         all_chunks = positional_deletes[0]
@@ -1609,6 +1616,7 @@ def _task_to_record_batches(
     table_schema: Schema,
     projected_field_ids: set[int],
     positional_deletes: list[ChunkedArray] | None,
+    equality_deletes: list[tuple[set[int], pa.Table]] | None,
     case_sensitive: bool,
     name_mapping: NameMapping | None = None,
     partition_spec: PartitionSpec | None = None,
@@ -1643,14 +1651,20 @@ def _task_to_record_batches(
             bound_file_filter = bind(file_schema, translated_row_filter, case_sensitive=case_sensitive)
             pyarrow_filter = expression_to_pyarrow(bound_file_filter, file_schema)
 
-        file_project_schema = prune_columns(file_schema, projected_field_ids, select_full_types=False)
+        # Ensure equality delete columns are also projected
+        all_projected_field_ids = projected_field_ids.copy()
+        if equality_deletes:
+            for eq_ids, _ in equality_deletes:
+                all_projected_field_ids.update(eq_ids)
+
+        file_project_schema = prune_columns(file_schema, all_projected_field_ids, select_full_types=False)
 
         fragment_scanner = ds.Scanner.from_fragment(
             fragment=fragment,
             schema=physical_schema,
             # This will push down the query to Arrow.
-            # But in case there are positional deletes, we have to apply them first
-            filter=pyarrow_filter if not positional_deletes else None,
+            # But in case there are positional or equality deletes, we have to apply them first
+            filter=pyarrow_filter if not positional_deletes and not equality_deletes else None,
             columns=[col.name for col in file_project_schema.columns],
         )
 
@@ -1666,6 +1680,38 @@ def _task_to_record_batches(
                 indices = _combine_positional_deletes(positional_deletes, current_index, current_index + len(batch))
                 current_batch = current_batch.take(indices)
 
+            if current_batch.num_rows > 0 and equality_deletes:
+                for eq_ids, eq_delete_table in equality_deletes:
+                    try:
+                        eq_file_schema = pyarrow_to_schema(
+                            eq_delete_table.schema,
+                            name_mapping=name_mapping,
+                            format_version=format_version,
+                        )
+
+                        rename_map = {}
+                        for field_id in eq_ids:
+                            file_name = eq_file_schema.find_column_name(field_id)
+                            current_name = table_schema.find_column_name(field_id)
+                            if file_name != current_name:
+                                rename_map[file_name] = current_name
+
+                        if rename_map:
+                            eq_delete_table = eq_delete_table.rename_columns(
+                                [rename_map.get(name, name) for name in eq_delete_table.column_names]
+                            )
+
+                        join_keys = [table_schema.find_column_name(field_id) for field_id in eq_ids]
+                        current_table = pa.Table.from_batches([current_batch])
+                        current_table = current_table.join(eq_delete_table, keys=join_keys, join_type="left anti")
+
+                        if current_table.num_rows == 0:
+                            current_batch = current_table.to_batches()[0]
+                            break
+                        current_batch = current_table.to_batches()[0]
+                    except (ValueError, ResolveError):
+                        continue
+
             # skip empty batches
             if current_batch.num_rows == 0:
                 continue
@@ -1691,23 +1737,57 @@ def _task_to_record_batches(
             )
 
 
-def _read_all_delete_files(io: FileIO, tasks: Iterable[FileScanTask]) -> dict[str, list[ChunkedArray]]:
-    deletes_per_file: dict[str, list[ChunkedArray]] = {}
+def _read_all_delete_files(
+    io: FileIO, tasks: Iterable[FileScanTask]
+) -> tuple[dict[str, list[ChunkedArray]], dict[str, list[tuple[set[int], pa.Table]]]]:
+    pos_deletes_per_file: dict[str, list[ChunkedArray]] = {}
+    eq_deletes_per_file: dict[str, list[tuple[set[int], pa.Table]]] = {}
+
     unique_deletes = set(itertools.chain.from_iterable([task.delete_files for task in tasks]))
     if len(unique_deletes) > 0:
+        unique_pos_deletes = {d for d in unique_deletes if d.content == DataFileContent.POSITION_DELETES}
+        unique_eq_deletes = {d for d in unique_deletes if d.content == DataFileContent.EQUALITY_DELETES}
+
         executor = ExecutorFactory.get_or_create()
-        deletes_per_files: Iterator[dict[str, ChunkedArray]] = executor.map(
-            lambda args: _read_deletes(*args),
-            [(io, delete_file) for delete_file in unique_deletes],
-        )
-        for delete in deletes_per_files:
-            for file, arr in delete.items():
-                if file in deletes_per_file:
-                    deletes_per_file[file].append(arr)
-                else:
-                    deletes_per_file[file] = [arr]
 
-    return deletes_per_file
+        if len(unique_pos_deletes) > 0:
+            pos_deletes: Iterator[dict[str, ChunkedArray]] = executor.map(
+                lambda args: _read_deletes(*args),
+                [(io, delete_file) for delete_file in unique_pos_deletes],
+            )
+            for delete in pos_deletes:
+                for file, arr in delete.items():
+                    if file in pos_deletes_per_file:
+                        pos_deletes_per_file[file].append(arr)
+                    else:
+                        pos_deletes_per_file[file] = [arr]
+
+        if len(unique_eq_deletes) > 0:
+            # We map each unique eq delete file location to its loaded table and its equality IDs
+            eq_deletes_tables: dict[str, tuple[set[int], pa.Table]] = dict(
+                zip(
+                    [d.file_path for d in unique_eq_deletes],
+                    zip(
+                        [set(d.equality_ids) if d.equality_ids else set() for d in unique_eq_deletes],
+                        executor.map(
+                            lambda args: _read_equality_deletes(*args),
+                            [(io, d) for d in unique_eq_deletes],
+                        ),
+                        strict=True,
+                    ),
+                    strict=True,
+                )
+            )
+
+            # Map eq deletes to each task's data file path
+            for task in tasks:
+                eq_deletes_for_task = [
+                    eq_deletes_tables[d.file_path] for d in task.delete_files if d.content == DataFileContent.EQUALITY_DELETES
+                ]
+                if eq_deletes_for_task:
+                    eq_deletes_per_file[task.file.file_path] = eq_deletes_for_task
+
+    return pos_deletes_per_file, eq_deletes_per_file
 
 
 class ArrowScan:
@@ -1807,7 +1887,7 @@ def to_record_batches(self, tasks: Iterable[FileScanTask]) -> Iterator[pa.Record
             ResolveError: When a required field cannot be found in the file
             ValueError: When a field type in the file cannot be projected to the schema type
         """
-        deletes_per_file = _read_all_delete_files(self._io, tasks)
+        pos_deletes_per_file, eq_deletes_per_file = _read_all_delete_files(self._io, tasks)
 
         total_row_count = 0
         executor = ExecutorFactory.get_or_create()
@@ -1816,7 +1896,7 @@ def batches_for_task(task: FileScanTask) -> list[pa.RecordBatch]:
             # Materialize the iterator here to ensure execution happens within the executor.
             # Otherwise, the iterator would be lazily consumed later (in the main thread),
             # defeating the purpose of using executor.map.
-            return list(self._record_batches_from_scan_tasks_and_deletes([task], deletes_per_file))
+            return list(self._record_batches_from_scan_tasks_and_deletes([task], pos_deletes_per_file, eq_deletes_per_file))
 
         limit_reached = False
         for batches in executor.map(batches_for_task, tasks):
@@ -1836,7 +1916,10 @@ def batches_for_task(task: FileScanTask) -> list[pa.RecordBatch]:
                 break
 
     def _record_batches_from_scan_tasks_and_deletes(
-        self, tasks: Iterable[FileScanTask], deletes_per_file: dict[str, list[ChunkedArray]]
+        self,
+        tasks: Iterable[FileScanTask],
+        pos_deletes_per_file: dict[str, list[ChunkedArray]],
+        eq_deletes_per_file: dict[str, list[pa.Table]],
     ) -> Iterator[pa.RecordBatch]:
         total_row_count = 0
         for task in tasks:
@@ -1849,7 +1932,8 @@ def _record_batches_from_scan_tasks_and_deletes(
                 self._projected_schema,
                 self._table_metadata.schema(),
                 self._projected_field_ids,
-                deletes_per_file.get(task.file.file_path),
+                pos_deletes_per_file.get(task.file.file_path),
+                eq_deletes_per_file.get(task.file.file_path),
                 self._case_sensitive,
                 self._table_metadata.name_mapping(),
                 self._table_metadata.specs().get(task.file.spec_id),

pyiceberg/table/__init__.py

@@ -1829,20 +1829,13 @@ def from_rest_response(
 
         Returns:
             A FileScanTask with the converted data and delete files.
-
-        Raises:
-            NotImplementedError: If equality delete files are encountered.
         """
-        from pyiceberg.catalog.rest.scan_planning import RESTEqualityDeleteFile
-
         data_file = _rest_file_to_data_file(rest_task.data_file)
 
         resolved_deletes: set[DataFile] = set()
         if rest_task.delete_file_references:
             for idx in rest_task.delete_file_references:
                 delete_file = delete_files[idx]
-                if isinstance(delete_file, RESTEqualityDeleteFile):
-                    raise NotImplementedError(f"PyIceberg does not yet support equality deletes: {delete_file.file_path}")
                 resolved_deletes.add(_rest_file_to_data_file(delete_file))
 
         return FileScanTask(
@@ -1854,18 +1847,28 @@ def from_rest_response(
 
 def _rest_file_to_data_file(rest_file: RESTContentFile) -> DataFile:
     """Convert a REST content file to a manifest DataFile."""
-    from pyiceberg.catalog.rest.scan_planning import RESTDataFile
+    from pyiceberg.catalog.rest.scan_planning import RESTDataFile, RESTEqualityDeleteFile, RESTPositionDeleteFile
+
+    column_sizes = None
+    value_counts = None
+    null_value_counts = None
+    nan_value_counts = None
+    equality_ids = None
+    referenced_data_file = None
+    content_offset = None
+    content_size_in_bytes = None
 
     if isinstance(rest_file, RESTDataFile):
         column_sizes = rest_file.column_sizes.to_dict() if rest_file.column_sizes else None
         value_counts = rest_file.value_counts.to_dict() if rest_file.value_counts else None
         null_value_counts = rest_file.null_value_counts.to_dict() if rest_file.null_value_counts else None
         nan_value_counts = rest_file.nan_value_counts.to_dict() if rest_file.nan_value_counts else None
-    else:
-        column_sizes = None
-        value_counts = None
-        null_value_counts = None
-        nan_value_counts = None
+    elif isinstance(rest_file, RESTEqualityDeleteFile):
+        equality_ids = rest_file.equality_ids
+    elif isinstance(rest_file, RESTPositionDeleteFile):
+        referenced_data_file = rest_file.referenced_data_file
+        content_offset = rest_file.content_offset
+        content_size_in_bytes = rest_file.content_size_in_bytes
 
     data_file = DataFile.from_args(
         content=DataFileContent.from_rest_type(rest_file.content),
@@ -1879,6 +1882,10 @@ def _rest_file_to_data_file(rest_file: RESTContentFile) -> DataFile:
         null_value_counts=null_value_counts,
         nan_value_counts=nan_value_counts,
         split_offsets=rest_file.split_offsets,
+        equality_ids=equality_ids,
+        referenced_data_file=referenced_data_file,
+        content_offset=content_offset,
+        content_size_in_bytes=content_size_in_bytes,
         sort_order_id=rest_file.sort_order_id,
     )
     data_file.spec_id = rest_file.spec_id
@@ -2067,10 +2074,8 @@ def _plan_files_local(self) -> Iterable[FileScanTask]:
             data_file = manifest_entry.data_file
             if data_file.content == DataFileContent.DATA:
                 data_entries.append(manifest_entry)
-            elif data_file.content == DataFileContent.POSITION_DELETES:
+            elif data_file.content in {DataFileContent.POSITION_DELETES, DataFileContent.EQUALITY_DELETES}:
                 delete_index.add_delete_file(manifest_entry, partition_key=data_file.partition)
-            elif data_file.content == DataFileContent.EQUALITY_DELETES:
-                raise ValueError("PyIceberg does not yet support equality deletes: https://github.com/apache/iceberg/issues/6568")
             else:
                 raise ValueError(f"Unknown DataFileContent ({data_file.content}): {manifest_entry}")
         return [

pyiceberg/table/delete_file_index.py

@@ -20,7 +20,7 @@
 
 from pyiceberg.expressions import EqualTo
 from pyiceberg.expressions.visitors import _InclusiveMetricsEvaluator
-from pyiceberg.manifest import INITIAL_SEQUENCE_NUMBER, POSITIONAL_DELETE_SCHEMA, DataFile, ManifestEntry
+from pyiceberg.manifest import INITIAL_SEQUENCE_NUMBER, POSITIONAL_DELETE_SCHEMA, DataFile, DataFileContent, ManifestEntry
 from pyiceberg.typedef import Record
 
 PATH_FIELD_ID = 2147483546
@@ -59,6 +59,15 @@ def referenced_delete_files(self) -> list[DataFile]:
         return [data_file for data_file, _ in self._files]
 
 
+class EqualityDeletes(PositionDeletes):
+    """Collects equality delete files and indexes them by sequence number."""
+
+    def add(self, delete_file: DataFile, seq_num: int) -> None:
+        # Equality deletes are indexed by sequence number - 1 to ensure they only
+        # apply to data files added in strictly earlier snapshots.
+        super().add(delete_file, seq_num - 1)
+
+
 def _has_path_bounds(delete_file: DataFile) -> bool:
     lower = delete_file.lower_bounds
     upper = delete_file.upper_bounds
@@ -103,26 +112,32 @@ def _partition_key(spec_id: int, partition: Record | None) -> tuple[int, Record]
 
 
 class DeleteFileIndex:
-    """Indexes position delete files by partition and by exact data file path."""
+    """Indexes position and equality delete files by partition and by exact data file path."""
 
     def __init__(self) -> None:
-        self._by_partition: dict[tuple[int, Record], PositionDeletes] = {}
-        self._by_path: dict[str, PositionDeletes] = {}
+        self._pos_by_partition: dict[tuple[int, Record], PositionDeletes] = {}
+        self._pos_by_path: dict[str, PositionDeletes] = {}
+        self._eq_by_partition: dict[tuple[int, Record], EqualityDeletes] = {}
 
     def is_empty(self) -> bool:
-        return not self._by_partition and not self._by_path
+        return not self._pos_by_partition and not self._pos_by_path and not self._eq_by_partition
 
     def add_delete_file(self, manifest_entry: ManifestEntry, partition_key: Record | None = None) -> None:
         delete_file = manifest_entry.data_file
         seq = manifest_entry.sequence_number or INITIAL_SEQUENCE_NUMBER
-        target_path = _referenced_data_file_path(delete_file)
 
-        if target_path:
-            deletes = self._by_path.setdefault(target_path, PositionDeletes())
-            deletes.add(delete_file, seq)
-        else:
+        if delete_file.content == DataFileContent.POSITION_DELETES:
+            target_path = _referenced_data_file_path(delete_file)
+            if target_path:
+                deletes = self._pos_by_path.setdefault(target_path, PositionDeletes())
+                deletes.add(delete_file, seq)
+            else:
+                key = _partition_key(delete_file.spec_id or 0, partition_key)
+                deletes = self._pos_by_partition.setdefault(key, PositionDeletes())
+                deletes.add(delete_file, seq)
+        elif delete_file.content == DataFileContent.EQUALITY_DELETES:
             key = _partition_key(delete_file.spec_id or 0, partition_key)
-            deletes = self._by_partition.setdefault(key, PositionDeletes())
+            deletes = self._eq_by_partition.setdefault(key, EqualityDeletes())
             deletes.add(delete_file, seq)
 
     def for_data_file(self, seq_num: int, data_file: DataFile, partition_key: Record | None = None) -> set[DataFile]:
@@ -131,27 +146,36 @@ def for_data_file(self, seq_num: int, data_file: DataFile, partition_key: Record
 
         deletes: set[DataFile] = set()
         spec_id = data_file.spec_id or 0
-
         key = _partition_key(spec_id, partition_key)
-        partition_deletes = self._by_partition.get(key)
-        if partition_deletes:
-            for delete_file in partition_deletes.filter_by_seq(seq_num):
+
+        # Add position deletes
+        partition_pos_deletes = self._pos_by_partition.get(key)
+        if partition_pos_deletes:
+            for delete_file in partition_pos_deletes.filter_by_seq(seq_num):
                 if _applies_to_data_file(delete_file, data_file):
                     deletes.add(delete_file)
 
-        path_deletes = self._by_path.get(data_file.file_path)
-        if path_deletes:
-            deletes.update(path_deletes.filter_by_seq(seq_num))
+        path_pos_deletes = self._pos_by_path.get(data_file.file_path)
+        if path_pos_deletes:
+            deletes.update(path_pos_deletes.filter_by_seq(seq_num))
+
+        # Add equality deletes
+        partition_eq_deletes = self._eq_by_partition.get(key)
+        if partition_eq_deletes:
+            deletes.update(partition_eq_deletes.filter_by_seq(seq_num))
 
         return deletes
 
     def referenced_delete_files(self) -> list[DataFile]:
         data_files: list[DataFile] = []
 
-        for deletes in self._by_partition.values():
+        for deletes in self._pos_by_partition.values():
+            data_files.extend(deletes.referenced_delete_files())
+
+        for deletes in self._pos_by_path.values():
             data_files.extend(deletes.referenced_delete_files())
 
-        for deletes in self._by_path.values():
+        for deletes in self._eq_by_partition.values():
             data_files.extend(deletes.referenced_delete_files())
 
         return data_files

pyiceberg/table/update/snapshot.py

@@ -145,6 +145,8 @@ def _validate_target_branch(self, branch: str | None) -> str | None:
         return branch
 
     def append_data_file(self, data_file: DataFile) -> _SnapshotProducer[U]:
+        if data_file.content == DataFileContent.EQUALITY_DELETES:
+            raise NotImplementedError(f"PyIceberg does not support writing {data_file.content.name}")
         self._added_data_files.append(data_file)
         return self