Github user yhuai commented on the issue:
https://github.com/apache/spark/pull/13818
I have a few questions.
1. Is it a regression from 1.6? Looks like not?
2. Is it a correctness issue or a performance issue? Seems it is a
performance issue?
3. If it is a performance issue. What is the impact? For a hive parquet/orc
table, after we convert them to Spark's native code path, there is no
partitioning discovery. So, I guess the performance is mainly coming from
querying metastore? If so, what will be the perf difference after
`spark.sql.hive.metastorePartitionPruning` (only querying needed partition info
from Hive metastore) is enabled?
My feeling is that if it is a perf issue and it is not a regression from
1.6, merging to master should be good enough.
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