Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/22184#discussion_r212662840
--- Diff: docs/sql-programming-guide.md ---
@@ -1895,6 +1895,10 @@ working with timestamps in `pandas_udf`s to get the
best performance, see
- Since Spark 2.4, File listing for compute statistics is done in
parallel by default. This can be disabled by setting
`spark.sql.parallelFileListingInStatsComputation.enabled` to `False`.
- Since Spark 2.4, Metadata files (e.g. Parquet summary files) and
temporary files are not counted as data files when calculating table size
during Statistics computation.
+## Upgrading From Spark SQL 2.3.1 to 2.3.2 and above
+
+ - In version 2.3.1 and earlier, when reading from a Parquet table, Spark
always returns null for any column whose column names in Hive metastore schema
and Parquet schema are in different letter cases, no matter whether
`spark.sql.caseSensitive` is set to true or false. Since 2.3.2, when
`spark.sql.caseSensitive` is set to false, Spark does case insensitive column
name resolution between Hive metastore schema and Parquet schema, so even
column names are in different letter cases, Spark returns corresponding column
values. An exception is thrown if there is ambiguity, i.e. more than one
Parquet column is matched.
--- End diff --
First, we should not change the behavior of hive tables. It inherits many
behaviors from Hive and let's keep it as it was.
Second, why we treat it as a behavior change? I think it's a bug that we
don't respect `spark.sql.caseSensitive` in field resolution. In general we
should not add a config to restore a bug.
I don't think this document is helpful. It explains a subtle and
unreasonable behavior to users, which IMO just make them confused.
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