Details
-
Improvement
-
Status: Open
-
Major
-
Resolution: Unresolved
-
None
Description
The business logic of the python implementation of reading partitioned parquet datasets in pyarrow.parquet.ParquetDataset has been ported to C++ (ARROW-3764), and has also been optionally enabled in ParquetDataset(..) by using use_legacy_dataset=False (ARROW-8039).
But the question still is: what do we do with this class long term?
So for users who now do:
dataset = pq.ParquetDataset(...) dataset.metadata table = dataset.read()
what should they do in the future?
Do we keep a class like this (but backed by the pyarrow.dataset implementation), or do we deprecate the class entirely, pointing users to `dataset = ds.dataset(..., format="parquet")` ?
In any case, we should strive to entirely delete the current custom python implementation, but we could keep a ParquetDataset class that wraps or inherits pyarrow.dataset.FileSystemDataset and adds some parquet specifics to it (eg access to the parquet schema, the common metadata, exposing the parquet-specific constructor keywords more easily, ..).
Features the ParquetDataset currently has that are not exactly covered by pyarrow.dataset:
- Partitioning information (the .partitions attribute
- Access to common metadata (.metadata_path, .common_metadata_path and .metadata attributes)
- ParquetSchema of the dataset
Attachments
Issue Links
- is a parent of
-
ARROW-16119 [Python] Deprecate the legacy ParquetDataset custom python-based implementation
- Reopened
- relates to
-
ARROW-15725 [Python] Legacy dataset can't roundtrip Int64 with nulls if partitioned
- Open
-
ARROW-8047 [Python][Documentation] Document migration from ParquetDataset to pyarrow.datasets
- Open
-
ARROW-15868 [Python] Remove the legacy ParquetDataset custom python-based implementation
- Open