yaooqinn opened a new pull request, #49933:
URL: https://github.com/apache/spark/pull/49933

   ### What changes were proposed in this pull request?
   
   The `Dataset.hint` method takes `Any*` as parameters for both the partition 
number and other columns, and the `Partitioning Hints` resolution process 
treats anything that CAN NOT be resolved to a partition number as a column 
candidate. For example, cases like below will report an ambagious error message 
for users.
   
   ```scala
   val n: Short = 123
   spark.range(10000).hint("rebalance", n).show
   ```
   
   ```
   REBALANCE Hint parameter should include columns, but 123 found.
   ```
   
   This PR makes Partitioning Hints resolution accept byte and short values and 
improves the debugging error message.
   
   ### Why are the changes needed?
   
   - A byte or short value can possibly exist in a user's spark pipeline and 
takes as a partition number, w/ this PR, runtime errors can be reduced
   - improve error message for debugging
   - For developers that build Spark Connect clients in other languages, there 
are some cases in which they can not handle or distinguish integrals. It's good 
for the server side to handle these valid cases.
   
   
   ### Does this PR introduce _any_ user-facing change?
   <!--
   Note that it means *any* user-facing change including all aspects such as 
new features, bug fixes, or other behavior changes. Documentation-only updates 
are not considered user-facing changes.
   
   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, `Dataset.hint("coalesce", 3.toByte)` and `/*+ COALESCE(3S) */` now are 
valid
   
   ### 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.
   -->
   new tests
   
   ### Was this patch authored or co-authored using generative AI tooling?
   <!--
   If generative AI tooling has been used in the process of authoring this 
patch, please include the
   phrase: 'Generated-by: ' followed by the name of the tool and its version.
   If no, write 'No'.
   Please refer to the [ASF Generative Tooling 
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   -->
   no


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