gengliangwang opened a new pull request #35478:
URL: https://github.com/apache/spark/pull/35478
<!--
Thanks for sending a pull request! Here are some tips for you:
1. If this is your first time, please read our contributor guidelines:
https://spark.apache.org/contributing.html
2. Ensure you have added or run the appropriate tests for your PR:
https://spark.apache.org/developer-tools.html
3. If the PR is unfinished, add '[WIP]' in your PR title, e.g.,
'[WIP][SPARK-XXXX] Your PR title ...'.
4. Be sure to keep the PR description updated to reflect all changes.
5. Please write your PR title to summarize what this PR proposes.
6. If possible, provide a concise example to reproduce the issue for a
faster review.
7. If you want to add a new configuration, please read the guideline first
for naming configurations in
'core/src/main/scala/org/apache/spark/internal/config/ConfigEntry.scala'.
8. If you want to add or modify an error type or message, please read the
guideline first in
'core/src/main/resources/error/README.md'.
-->
### What changes were proposed in this pull request?
<!--
Please clarify what changes you are proposing. The purpose of this section
is to outline the changes and how this PR fixes the issue.
If possible, please consider writing useful notes for better and faster
reviews in your PR. See the examples below.
1. If you refactor some codes with changing classes, showing the class
hierarchy will help reviewers.
2. If you fix some SQL features, you can provide some references of other
DBMSes.
3. If there is design documentation, please add the link.
4. If there is a discussion in the mailing list, please add the link.
-->
Compared to the default behavior, the current ANSI type coercion rules don't
allow the following cases:
- comparing String with other simple types, e.g. date/timestamp/int ...
- arithmetic operation containing String and other simple types
- Union/Intersect/Except containing String and other simple types
- SQL function expects non-string types but got string input
- other SQL operators..
This PR is to remove the limitation. After changes, the String type can be
implicit cast as Long/Double/Date/Timestamp/Boolean/Binary/Interval.
Note that Byte/Short/Int is not on the precedent list of String: `str_col >
1` will become `cast(str_col as long) > 1L`. So that we can avoid string
parsing error if the string is out of the range of Byte/Short/Int in
comparison/arithmetic/union operations.
The design applies to Float/Decimal (especially Decimal), for SQL operators
containing Float/Decimal and String, the type coercion system will convert both
as Double.
### Why are the changes needed?
<!--
Please clarify why the changes are needed. For instance,
1. If you propose a new API, clarify the use case for a new API.
2. If you fix a bug, you can clarify why it is a bug.
-->
The purpose of the current limitation is to prevent potential String parsing
errors under ANSI mode. However, after doing research among real-world Spark
SQL queries, I find that many users are actually using String as
Date/Timestamp/Numeric in their queries. For example, the purpose of query
`where date_col > '2022-01-01'` is quite obvious, but users have to rewrite it
as `where date_col > date'2022-01-01'` under ANSI mode.
To make the migration to ANSI mode easier, I suggest removing this
limitation. Let's treat it as an extension in our SQL dialect.
### Does this PR introduce _any_ user-facing change?
<!--
Note that it means *any* user-facing change including all aspects such as
the documentation fix.
If yes, please clarify the previous behavior and the change this PR proposes
- provide the console output, description and/or an example to show the
behavior difference if possible.
If possible, please also clarify if this is a user-facing change compared to
the released Spark versions or within the unreleased branches such as master.
If no, write 'No'.
-->
Yes, allow implicitly casting String to other simple types under ANSI mode
### How was this patch tested?
<!--
If tests were added, say they were added here. Please make sure to add some
test cases that check the changes thoroughly including negative and positive
cases if possible.
If it was tested in a way different from regular unit tests, please clarify
how you tested step by step, ideally copy and paste-able, so that other
reviewers can test and check, and descendants can verify in the future.
If tests were not added, please describe why they were not added and/or why
it was difficult to add.
If benchmark tests were added, please run the benchmarks in GitHub Actions
for the consistent environment, and the instructions could accord to:
https://spark.apache.org/developer-tools.html#github-workflow-benchmarks.
-->
Unit tests
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: [email protected]
For queries about this service, please contact Infrastructure at:
[email protected]
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
