Github user liancheng commented on the issue:
https://github.com/apache/spark/pull/13989
One concern of mine is that, analyzed plan, optimized plan, and executed
(physical) plan stored in `QueryExecution` are all lazy vals, which means that
they won't be re-optimized/planned accordingly after refreshing metadata of the
corresponding logical plan.
Say we constructed a DataFrame `df` to join a small table `A` and a large
table `B`. After calling `df.write.parquet(...)`, analyzed, optimized, and
executed plans of `df` are all computed. Since `A` is small, the planner may
decide to broadcast it, and this decision is reflected in the physical plan.
Next, we add a bunch of files into the directory where table `A` lives to
make it super large, then call `df.refresh()` to refresh the logical plan. Now,
if we try to call `df.write.parquet(...)` again, the query may probably crash
since the physical plan is not refreshed and still thinks that `A` should be
broadcasted.
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