davlee1972 commented on issue #44765:
URL: https://github.com/apache/arrow/issues/44765#issuecomment-2827489274
Yup I started using partition_base_dir which fixes my problem.. I took it
one step further to auto populate partition_base_dir if just a list of files is
used to initialize pyarrow.dataset.dataset(). I'm also tracking this base_dir
separately since there is the possibility I may want to WRITE new files to the
dataset and will need to refresh the dataset using the base_dir instead of the
original list of files.
```
# if there is no base_dir then set base_dir based on files common
path.
if files and not base_dir:
# calculate base_dir if not set
if self.partitioning_flavor and self.partitioning_flavor in [
"directory",
"hive",
]:
# get number of partition columns
partition_col_count = len(self.partitioning["schema"])
# build dict with filename: number of directory parts
file_paths_dict = {
file_path: len(file_path.split("/"))
for file_path in files
if self.filesystem.get_file_info(file_path).type
== fs.FileType["File"]
}
# find the most deeply nested file
if file_paths_dict:
max_dir_len = max(file_paths_dict.values())
for k, v in file_paths_dict.items():
if v == max_dir_len:
file_name = k
break
# construct base_dir minus partition columns
base_dir = "/".join(
file_name.split("/")[: -partition_col_count - 1]
)
else:
# find common directory across files
common_path = os.path.commonpath(files)
if common_path:
base_dir = common_path
```
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