Github user gatorsmile commented on a diff in the pull request:
https://github.com/apache/spark/pull/22184#discussion_r213135626
--- 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 --
For Hive tables, column resolution is always case insensitive. However,
When `spark.sql.hive.convertMetastoreParquet` is true, users might face
inconsistent behaviors when they use native parquet reader to resolve the
columns in the case sensitive mode. We still introduce behavior changes. Better
error messages sounds good enough, instead of disabling
`spark.sql.hive.convertMetastoreParquet` when the mode is case sensitive. cc
@cloud-fan
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