Github user gatorsmile commented on a diff in the pull request: https://github.com/apache/spark/pull/22184#discussion_r212834477 --- 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 -- @cloud-fan We need to keep the behaviors consistent no matter whether we use Hive serde reader or our native parquet reader. In the PR https://github.com/apache/spark/pull/22148, we already introduced a change for hive table, if `spark.sql.hive.convertMetastoreParquet` is set to true, right? For Spark native parquet tables that were created by us, this is a bug fix because the previous work does not respect `spark.sql.caseSensitive`; for the parquet tables created by Hive, the field resolution should be consistent no matter whether it is using our reader or Hive parquet reader. To most of end users, they do not know the difference between Hive serde reader and native parquet reader
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