Github user mridulm commented on a diff in the pull request:
https://github.com/apache/spark/pull/21259#discussion_r186603250
--- Diff: docs/sql-programming-guide.md ---
@@ -1812,6 +1812,8 @@ working with timestamps in `pandas_udf`s to get the
best performance, see
- Since Spark 2.4, creating a managed table with nonempty location is
not allowed. An exception is thrown when attempting to create a managed table
with nonempty location. To set `true` to
`spark.sql.allowCreatingManagedTableUsingNonemptyLocation` restores the
previous behavior. This option will be removed in Spark 3.0.
- Since Spark 2.4, the type coercion rules can automatically promote the
argument types of the variadic SQL functions (e.g., IN/COALESCE) to the widest
common type, no matter how the input arguments order. In prior Spark versions,
the promotion could fail in some specific orders (e.g., TimestampType,
IntegerType and StringType) and throw an exception.
- In version 2.3 and earlier, `to_utc_timestamp` and
`from_utc_timestamp` respect the timezone in the input timestamp string, which
breaks the assumption that the input timestamp is in a specific timezone.
Therefore, these 2 functions can return unexpected results. In version 2.4 and
later, this problem has been fixed. `to_utc_timestamp` and `from_utc_timestamp`
will return null if the input timestamp string contains timezone. As an
example, `from_utc_timestamp('2000-10-10 00:00:00', 'GMT+1')` will return
`2000-10-10 01:00:00` in both Spark 2.3 and 2.4. However,
`from_utc_timestamp('2000-10-10 00:00:00+00:00', 'GMT+1')`, assuming a local
timezone of GMT+8, will return `2000-10-10 09:00:00` in Spark 2.3 but `null` in
2.4. For people who don't care about this problem and want to retain the
previous behaivor to keep their query unchanged, you can set
`spark.sql.function.rejectTimezoneInString` to false. This option will be
removed in Spark 3.0 and should only be used as a tempora
ry workaround.
+ - In version 2.3 and earlier, Spark converts Parquet Hive tables by
default but ignores table properties like `TBLPROPERTIES (parquet.compression
'NONE')`. This happens for ORC Hive table properties like `TBLPROPERTIES
(orc.compress 'NONE')` in case of `spark.sql.hive.convertMetastoreOrc=true`,
too. Since Spark 2.4, Spark supports Parquet/ORC specific table properties
while converting Parquet/ORC Hive tables. As an example, `CREATE TABLE t(id
int) STORED AS PARQUET TBLPROPERTIES (parquet.compression 'NONE')` would
generate Snappy parquet files during insertion in Spark 2.3, and in Spark 2.4,
the result would be uncompressed parquet files. To set `false` to
`spark.sql.hive.convertMetastoreTableProperty` restores the previous behavior.
--- End diff --
Setting a property and expecting spark to ignore it does not sound logical
(spark not honoring a property is a bug IMO - which, thankfully, has been fixed
in 2.4).
Having said that, I agree with you that mentioning this in migration guide
might be sufficient; we have behavior changes between versions all the time
and a conf is not necessary when the change is in the right direction.
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