Fokko commented on code in PR #6775:
URL: https://github.com/apache/iceberg/pull/6775#discussion_r1183775135
##########
python/pyiceberg/io/pyarrow.py:
##########
@@ -498,6 +503,96 @@ 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
+ }
+
+
+class _OrderedChunkedArrayConsumer:
+ """
+ A wrapper to consume multiple individually ordered chunked-arrays
+ simultaneously as an ordered iterator
+ """
+
+ arrays: Tuple[pa.ChunkedArray, ...]
+ arrays_len: Tuple[int, ...]
+ arrays_pos: List[int]
+
+ def _reset(self) -> None:
+ self.arrays_pos = [0] * len(self.arrays)
+ self.arrays_len = tuple(len(array) for array in self.arrays)
+
+ def __init__(self, *arrays: pa.ChunkedArray) -> None:
+ self.arrays = arrays
+ self._reset()
+
+ def __iter__(self) -> _OrderedChunkedArrayConsumer:
+ self._reset()
+ return self
+
+ def __next__(self) -> int:
Review Comment:
Good one. I replaced the whole `_OrderedChunkedArrayConsumer` with
[`heapq.merge` from Python
itself](https://docs.python.org/3/library/heapq.html#heapq.merge). Time
complexity is `O(NlogK)` where `N` is the total number of elements whereas `K`
is the number of iterables for comparison.
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