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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