kevinjqliu commented on code in PR #1043:
URL: https://github.com/apache/iceberg-python/pull/1043#discussion_r1717681434
##########
pyiceberg/io/pyarrow.py:
##########
@@ -1304,6 +1305,195 @@ def _read_all_delete_files(fs: FileSystem, tasks:
Iterable[FileScanTask]) -> Dic
return deletes_per_file
+def _fs_from_file_path(file_path: str, io: FileIO) -> FileSystem:
+ scheme, netloc, _ = _parse_location(file_path)
+ if isinstance(io, PyArrowFileIO):
+ return io.fs_by_scheme(scheme, netloc)
+ else:
+ try:
+ from pyiceberg.io.fsspec import FsspecFileIO
+
+ if isinstance(io, FsspecFileIO):
+ from pyarrow.fs import PyFileSystem
+
+ return 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
+
+
+class PyArrowProjector:
+ _table_metadata: TableMetadata
+ _io: FileIO
+ _fs: FileSystem
+ _projected_schema: Schema
+ _bound_row_filter: BooleanExpression
+ _case_sensitive: bool
+ _limit: Optional[int]
+ """Projects an Iceberg Table to a PyArrow construct.
+
+ Attributes:
+ _table_metadata: Current table metadata of the Iceberg table
+ _io: PyIceberg FileIO implementation from which to fetch the io
properties
+ _fs: PyArrow FileSystem to use to read the files
+ _projected_schema: Iceberg Schema to project onto the data files
+ _bound_row_filter: Schema bound row expression to filter the data with
+ _case_sensitive: Case sensitivity when looking up column names
+ _limit: Limit the number of records.
+ """
+
+ def __init__(
+ self,
+ table_metadata: TableMetadata,
+ io: FileIO,
+ projected_schema: Schema,
+ row_filter: BooleanExpression,
+ case_sensitive: bool = True,
+ limit: Optional[int] = None,
+ ) -> None:
+ self._table_metadata = table_metadata
+ self._io = io
+ self._fs = _fs_from_file_path(table_metadata.location, io) # TODO:
use different FileSystem per file
+ self._projected_schema = projected_schema
+ self._bound_row_filter = bind(table_metadata.schema(), row_filter,
case_sensitive=case_sensitive)
+ self._case_sensitive = case_sensitive
+ self._limit = limit
+
+ @property
+ def _use_large_types(self) -> bool:
+ """Whether to represent data as large arrow types.
+
+ Defaults to True.
+ """
+ return property_as_bool(self._io.properties,
PYARROW_USE_LARGE_TYPES_ON_READ, True)
+
+ @property
+ def _projected_field_ids(self) -> Set[int]:
+ """Set of field IDs that should be projected from the data files."""
+ return {
+ id
+ for id in self._projected_schema.field_ids
+ if not isinstance(self._projected_schema.find_type(id), (MapType,
ListType))
+ }.union(extract_field_ids(self._bound_row_filter))
+
+ def project_table(self, tasks: Iterable[FileScanTask]) -> pa.Table:
+ """Project the Iceberg table to a pa.Table.
+
+ Returns a pa.Table with data from the Iceberg table by resolving the
+ right columns that match the current table schema. Only data that
+ matches the provided row_filter expression is returned.
+
+ Args:
+ tasks: FileScanTasks representing the data files and delete files
to read from.
+
+ Returns:
+ A PyArrow table. Result is capped at the limit, if specified.
+
+ Raises:
+ 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._fs, tasks)
+ executor = ExecutorFactory.get_or_create()
+
+ def _project_table_from_scan_task(task: FileScanTask) -> pa.Table:
+ batches =
list(self._project_batches_from_scan_tasks_and_deletes([task],
deletes_per_file))
+ if len(batches) > 0:
+ return pa.Table.from_batches(batches)
+ else:
+ return None
+
+ futures = [
+ executor.submit(
+ _project_table_from_scan_task,
+ task,
+ )
+ for task in tasks
+ ]
+ total_row_count = 0
+ # for consistent ordering, we need to maintain future order
+ futures_index = {f: i for i, f in enumerate(futures)}
+ completed_futures: SortedList[Future[pa.Table]] =
SortedList(iterable=[], key=lambda f: futures_index[f])
+ for future in concurrent.futures.as_completed(futures):
+ completed_futures.add(future)
+ if table_result := future.result():
+ total_row_count += len(table_result)
+ # stop early if limit is satisfied
+ if self._limit is not None and total_row_count >= self._limit:
+ break
+
+ # by now, we've either completed all tasks or satisfied the limit
+ if self._limit is not None:
+ _ = [f.cancel() for f in futures if not f.done()]
+
+ tables = [f.result() for f in completed_futures if f.result()]
+
+ if len(tables) < 1:
+ return pa.Table.from_batches([],
schema=schema_to_pyarrow(self._projected_schema, include_field_ids=False))
+
+ result = pa.concat_tables(tables, promote_options="permissive")
Review Comment:
this is something im curious about, and probably need to look into how
PyArrow handles its internal data structure.
Right now, `_project_table_from_scan_task` returns a `pa.Table` object by
transforming batches to table
```
batches =
list(self._project_batches_from_scan_tasks_and_deletes([task],
deletes_per_file))
if len(batches) > 0:
return pa.Table.from_batches(batches)
```
We then parallelize this and end up with multiple `pa.Table`s before joining
all the tables together
```
result = pa.concat_tables(tables, promote_options="permissive")
```
So two operations here
* record_batches to table
* joining tables into a single table
Is it more performant to return record batches from
`_project_table_from_scan_task` and then join the record batches to the table?
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