to_arrow_batches

pyiceberg/io/pyarrow.py

@@ -1002,24 +1002,39 @@ def _task_to_record_batches(
         for batch in batches:
             if positional_deletes:
                 # Create the mask of indices that we're interested in
-                indices = _combine_positional_deletes(positional_deletes, current_index, len(batch))
-
+                indices = _combine_positional_deletes(positional_deletes, current_index, current_index + len(batch))
+                print(f"DEBUG: {indices=} {current_index=} {len(batch)=}")
+                print(f"{batch=}")
                 batch = batch.take(indices)
+                print(f"{batch=}")
                 # Apply the user filter
                 if pyarrow_filter is not None:
                     # we need to switch back and forth between RecordBatch and Table
                     # as Expression filter isn't yet supported in RecordBatch
                     # https://github.com/apache/arrow/issues/39220
                     arrow_table = pa.Table.from_batches([batch])
                     arrow_table = arrow_table.filter(pyarrow_filter)
-                    arrow_batches = arrow_table.to_batches()
-                    for arrow_batch in arrow_batches:
-                        yield to_requested_schema(projected_schema, file_project_schema, arrow_table)
-            else:
-                yield to_requested_schema(projected_schema, file_project_schema, arrow_table)
+                    batch = arrow_table.to_batches()[0]
+            yield to_requested_schema(projected_schema, file_project_schema, batch)
             current_index += len(batch)
 
 
+def _task_to_table(
+    fs: FileSystem,
+    task: FileScanTask,
+    bound_row_filter: BooleanExpression,
+    projected_schema: Schema,
+    projected_field_ids: Set[int],
+    positional_deletes: Optional[List[ChunkedArray]],
+    case_sensitive: bool,
+    name_mapping: Optional[NameMapping] = None,
+) -> pa.Table:
+    batches = _task_to_record_batches(
+        fs, task, bound_row_filter, projected_schema, projected_field_ids, positional_deletes, case_sensitive, name_mapping
+    )
+    return pa.Table.from_batches(batches, schema=schema_to_pyarrow(projected_schema))
+
+
 def _read_all_delete_files(fs: FileSystem, tasks: Iterable[FileScanTask]) -> Dict[str, List[ChunkedArray]]:
     deletes_per_file: Dict[str, List[ChunkedArray]] = {}
     unique_deletes = set(itertools.chain.from_iterable([task.delete_files for task in tasks]))
@@ -1096,7 +1111,6 @@ def project_table(
             projected_field_ids,
             deletes_per_file.get(task.file.file_path),
             case_sensitive,
-            limit,
             table_metadata.name_mapping(),
         )
         for task in tasks
@@ -1130,8 +1144,78 @@ def project_table(
     return result
 
 
-def to_requested_schema(requested_schema: Schema, file_schema: Schema, table: pa.RecordBatch) -> pa.RecordBatch:
-    struct_array = visit_with_partner(requested_schema, table, ArrowProjectionVisitor(file_schema), ArrowAccessor(file_schema))
+def project_batches(
+    tasks: Iterable[FileScanTask],
+    table_metadata: TableMetadata,
+    io: FileIO,
+    row_filter: BooleanExpression,
+    projected_schema: Schema,
+    case_sensitive: bool = True,
+    limit: Optional[int] = None,
+) -> Iterator[pa.ReordBatch]:
+    """Resolve the right columns based on the identifier.
+
+    Args:
+        tasks (Iterable[FileScanTask]): A URI or a path to a local file.
+        table_metadata (TableMetadata): The table metadata of the table that's being queried
+        io (FileIO): A FileIO to open streams to the object store
+        row_filter (BooleanExpression): The expression for filtering rows.
+        projected_schema (Schema): The output schema.
+        case_sensitive (bool): Case sensitivity when looking up column names.
+        limit (Optional[int]): Limit the number of records.
+
+    Raises:
+        ResolveError: When an incompatible query is done.
+    """
+    scheme, netloc, _ = PyArrowFileIO.parse_location(table_metadata.location)
+    if isinstance(io, PyArrowFileIO):
+        fs = io.fs_by_scheme(scheme, netloc)
+    else:
+        try:
+            from pyiceberg.io.fsspec import FsspecFileIO
+
+            if isinstance(io, FsspecFileIO):
+                from pyarrow.fs import PyFileSystem
+
+                fs = PyFileSystem(FSSpecHandler(io.get_fs(scheme)))
+            else:
+                raise ValueError(f"Expected PyArrowFileIO or FsspecFileIO, got: {io}")
+        except ModuleNotFoundError as e:
+            # When FsSpec is not installed
+            raise ValueError(f"Expected PyArrowFileIO or FsspecFileIO, got: {io}") from e
+
+    bound_row_filter = bind(table_metadata.schema(), row_filter, case_sensitive=case_sensitive)
+
+    projected_field_ids = {
+        id for id in projected_schema.field_ids if not isinstance(projected_schema.find_type(id), (MapType, ListType))
+    }.union(extract_field_ids(bound_row_filter))
+
+    deletes_per_file = _read_all_delete_files(fs, tasks)
+
+    total_row_count = 0
+
+    for task in tasks:
+        batches = _task_to_record_batches(
+            fs,
+            task,
+            bound_row_filter,
+            projected_schema,
+            projected_field_ids,
+            deletes_per_file.get(task.file.file_path),
+            case_sensitive,
+            table_metadata.name_mapping(),
+        )
+        for batch in batches:
+            if limit is not None:
+                if total_row_count + len(batch) >= limit:
+                    yield batch.take(limit - total_row_count)
+                    break
+            yield batch
+            total_row_count += len(batch)
+
+
+def to_requested_schema(requested_schema: Schema, file_schema: Schema, batch: pa.RecordBatch) -> pa.RecordBatch:
+    struct_array = visit_with_partner(requested_schema, batch, ArrowProjectionVisitor(file_schema), ArrowAccessor(file_schema))
 
     arrays = []
     fields = []
@@ -1240,8 +1324,8 @@ def field_partner(self, partner_struct: Optional[pa.Array], field_id: int, _: st
 
             if isinstance(partner_struct, pa.StructArray):
                 return partner_struct.field(name)
-            elif isinstance(partner_struct, pa.Table):
-                return partner_struct.column(name).combine_chunks()
+            elif isinstance(partner_struct, pa.RecordBatch):
+                return partner_struct.column(name)
 
         return None
 
@@ -1778,15 +1862,19 @@ def write_file(io: FileIO, table_metadata: TableMetadata, tasks: Iterator[WriteT
 
     def write_parquet(task: WriteTask) -> DataFile:
         table_schema = task.schema
-        arrow_table = pa.Table.from_batches(task.record_batches)
+
         # if schema needs to be transformed, use the transformed schema and adjust the arrow table accordingly
         # otherwise use the original schema
         if (sanitized_schema := sanitize_column_names(table_schema)) != table_schema:
             file_schema = sanitized_schema
-            arrow_table = to_requested_schema(requested_schema=file_schema, file_schema=table_schema, table=arrow_table)
+            batches = [
+                to_requested_schema(requested_schema=file_schema, file_schema=table_schema, batch=batch)
+                for batch in task.record_batches
+            ]
         else:
             file_schema = table_schema
-
+            batches = task.record_batches
+        arrow_table = pa.Table.from_batches(batches)
         file_path = f'{table_metadata.location}/data/{task.generate_data_file_path("parquet")}'
         fo = io.new_output(file_path)
         with fo.create(overwrite=True) as fos:

pyiceberg/table/__init__.py

@@ -1763,6 +1763,19 @@ def to_arrow(self) -> pa.Table:
             limit=self.limit,
         )
 
+    def to_arrow_batches(self) -> pa.Table:
+        from pyiceberg.io.pyarrow import project_batches
+
+        return project_batches(
+            self.plan_files(),
+            self.table_metadata,
+            self.io,
+            self.row_filter,
+            self.projection(),
+            case_sensitive=self.case_sensitive,
+            limit=self.limit,
+        )
+
     def to_pandas(self, **kwargs: Any) -> pd.DataFrame:
         return self.to_arrow().to_pandas(**kwargs)