Yicong-Huang commented on code in PR #58900:
URL: https://github.com/apache/spark/pull/58900#discussion_r4064268774


##########
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 @Spenserrrr I had some debates in my mind and I think you are right. 
I was hoping to get rid of some type ignores and string based type annotations, 
and wished to have arrow imported once for all arrow eval types so that it can 
be bundled with registration: if we have arrow, arrow eval type handlers will 
be registered, if not, arrow eval types handlers will not exist. I do think 
that might be better in the long run.
   
   However, as you mentioned, currently when arrow is missing, we have a 
regression on its error message and "unknown eval type" would be more confusing 
than something like "arrow does not exist". As a result, I decided to revert 
the above changes and defer to later. 
   
   if you have better ways to support the long term goal without compromising 
on the error cases, please feel free to improve with new PR!



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