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new 430a407c3963 [SPARK-47494][DOC] Add migration doc for the behavior
change of Parquet timestamp inference since Spark 3.3
430a407c3963 is described below
commit 430a407c39633637dba738482877edf806561ba7
Author: Gengliang Wang <gengli...@apache.org>
AuthorDate: Wed Mar 20 15:17:23 2024 -0700
[SPARK-47494][DOC] Add migration doc for the behavior change of Parquet
timestamp inference since Spark 3.3
### What changes were proposed in this pull request?
Add migration doc for the behavior change of Parquet timestamp inference
since Spark 3.3
### Why are the changes needed?
Show the behavior change to users.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
It's just doc change
### Was this patch authored or co-authored using generative AI tooling?
Yes, there are some doc suggestion from copilot in
docs/sql-migration-guide.md
Closes #45623 from gengliangwang/SPARK-47494.
Authored-by: Gengliang Wang <gengli...@apache.org>
Signed-off-by: Dongjoon Hyun <dh...@apple.com>
(cherry picked from commit 11247d804cd370aaeb88736a706c587e7f5c83b3)
Signed-off-by: Dongjoon Hyun <dh...@apple.com>
---
docs/sql-migration-guide.md | 2 ++
1 file changed, 2 insertions(+)
diff --git a/docs/sql-migration-guide.md b/docs/sql-migration-guide.md
index 0e54c33c6d12..f788d89c4999 100644
--- a/docs/sql-migration-guide.md
+++ b/docs/sql-migration-guide.md
@@ -99,6 +99,8 @@ license: |
- Since Spark 3.3, the `unbase64` function throws error for a malformed
`str` input. Use `try_to_binary(<str>, 'base64')` to tolerate malformed input
and return NULL instead. In Spark 3.2 and earlier, the `unbase64` function
returns a best-efforts result for a malformed `str` input.
+ - Since Spark 3.3, when reading Parquet files that were not produced by
Spark, Parquet timestamp columns with annotation `isAdjustedToUTC = false` are
inferred as TIMESTAMP_NTZ type during schema inference. In Spark 3.2 and
earlier, these columns are inferred as TIMESTAMP type. To restore the behavior
before Spark 3.3, you can set `spark.sql.parquet.inferTimestampNTZ.enabled` to
`false`.
+
- Since Spark 3.3.1 and 3.2.3, for `SELECT ... GROUP BY a GROUPING SETS
(b)`-style SQL statements, `grouping__id` returns different values from Apache
Spark 3.2.0, 3.2.1, 3.2.2, and 3.3.0. It computes based on user-given group-by
expressions plus grouping set columns. To restore the behavior before 3.3.1 and
3.2.3, you can set `spark.sql.legacy.groupingIdWithAppendedUserGroupBy`. For
details, see [SPARK-40218](https://issues.apache.org/jira/browse/SPARK-40218)
and [SPARK-40562](https:/ [...]
## Upgrading from Spark SQL 3.1 to 3.2
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