Spenserrrr commented on code in PR #58900: URL: https://github.com/apache/spark/pull/58900#discussion_r4065994502
########## python/pyspark/eval_handlers/__init__.py: ########## @@ -22,7 +22,18 @@ Each eval type handled here is an ``EvalTypeHandler`` subclass (in ``_base``) that declares its ``eval_type`` and self-registers at class definition, which ``read_udfs`` looks up via ``get_eval_type_handler``. Importing this package -imports the concrete handler submodules (``_arrow``) so they register. +imports the concrete handler submodules so they register. + +``_arrow`` requires pyarrow and imports it at module top, so it is only imported +when pyarrow is available; the Arrow eval types it serves cannot run without it. """ -from pyspark.eval_handlers import _arrow # noqa: F401 # registers handlers on import +try: + from pyspark.sql.pandas.utils import require_minimum_pyarrow_version + + require_minimum_pyarrow_version() +except Exception: Review Comment: Thanks, the current change looks good to me. For the longer-term idea, I think your intuition makes sense. We may need a richer registry entry that stores both the handler (or a lazy loader) and its dependency check, instead of dropping the entry when PyArrow is unavailable. That would let lookup distinguish an unknown eval type from a known Arrow handler that cannot run, and report the PyArrow error. What do you think? If this direction makes sense, I’m happy to try it in a separate PR. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
