jorisvandenbossche commented on code in PR #6775:
URL: https://github.com/apache/iceberg/pull/6775#discussion_r1211778965
##########
python/pyiceberg/io/pyarrow.py:
##########
@@ -498,6 +504,49 @@ def expression_to_pyarrow(expr: BooleanExpression) ->
pc.Expression:
return boolean_expression_visit(expr, _ConvertToArrowExpression())
+@lru_cache
+def _get_file_format(file_format: FileFormat, **kwargs: Dict[str, Any]) ->
ds.FileFormat:
+ if file_format == FileFormat.PARQUET:
+ return ds.ParquetFileFormat(**kwargs)
+ else:
+ raise ValueError(f"Unsupported file format: {file_format}")
+
+
+def _construct_fragment(fs: FileSystem, data_file: DataFile,
file_format_kwargs: Dict[str, Any] = EMPTY_DICT) -> ds.Fragment:
+ _, path = PyArrowFileIO.parse_location(data_file.file_path)
+ return _get_file_format(data_file.file_format,
**file_format_kwargs).make_fragment(path, fs)
+
+
+def _read_deletes(fs: FileSystem, data_file: DataFile) -> Dict[str,
pa.ChunkedArray]:
+ delete_fragment = _construct_fragment(
+ fs, data_file, file_format_kwargs={"dictionary_columns":
("file_path",), "pre_buffer": True, "buffer_size": ONE_MEGABYTE}
+ )
+ table = ds.Scanner.from_fragment(fragment=delete_fragment).to_table()
+ table.unify_dictionaries()
+ return {
+ file.as_py(): table.filter(pc.field("file_path") == file).column("pos")
+ for file in table.column("file_path").chunks[0].dictionary
+ }
+
+
+def _create_positional_deletes_indices(positional_deletes:
List[pa.ChunkedArray], fn_rows: Callable[[], int]) -> pa.Array:
+ sorted_deleted = merge(*positional_deletes)
+
+ def generator() -> Generator[int, None, None]:
+ deleted_pos = next(sorted_deleted).as_py() # type: ignore
+ for pos in range(fn_rows()):
+ if deleted_pos == pos:
+ try:
+ deleted_pos = next(sorted_deleted).as_py() # type: ignore
+ except StopIteration:
+ deleted_pos = -1
+ else:
+ yield pos
+
+ # Filter on the positions
+ return pa.array(generator(), type=pa.int64())
Review Comment:
To be honest, I think you could just already use the numpy variant that I
mentioned above (it's shorter/simpler than the current code IMO, conversion to
numpy is zero-copy for integers, and numpy should already releas ethe GIL as
well)
(I think we are certainly open to have this in pyarrow, but given you can
easily do it in numpy, it's probably not a high priority issue. But will answer
on the issue you opened on the arrow side. Thanks for that!)
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