Github user gatorsmile commented on a diff in the pull request:
https://github.com/apache/spark/pull/22184#discussion_r212006137
--- 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 --
This is a behavior change. I am not sure whether we should backport it to
2.3.2. How about sending a note to the dev mailing list?
BTW, this only affects data source table. How about hive serde table? Are
they consistent?
Could you add a test case? Create a table by the syntax like `CREATE TABLE
... STORED AS PARQUET`. You also need to turn off
`spark.sql.hive.convertMetastoreParquet` in the test case.
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