Github user viirya commented on the issue:
https://github.com/apache/spark/pull/9940
This is introduced for real use case and it is very useful. Actually this
patch is developed to fix the problem when I was in previous company which is a
heavy Spark user for machine learning and ETL. In practice, schema merging is
common because of schema expansion. Instead of reverting this, I would suggest
we can fix this issues (documenting and hide the API you just did). It surely
causes performance regression in such cases without this patch.
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