Ngone51 opened a new pull request #28572:
URL: https://github.com/apache/spark/pull/28572


   <!--
   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'.
   -->
   
   ### 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.
   -->
   
   Eliminate the `UpCast` if it's child data type is already decimal type.
   
   ### 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.
   -->
   
   While deserializing internal `Decimal` value to external 
`BigDecimal`(Java/Scala) value, Spark should also respect `Decimal`'s precision 
and scale, otherwise it will cause precision lost and look weird in some cases, 
e.g.:
    
   ```
   sql("select cast(11111111111111111111111111111111111111 as decimal(38, 0)) 
as d")
     .write.mode("overwrite")
     .parquet(f.getAbsolutePath)
   
   // can fail
   spark.read.parquet(f.getAbsolutePath).as[BigDecimal]
   ```
   ```
   [info]   org.apache.spark.sql.AnalysisException: Cannot up cast `d` from 
decimal(38,0) to decimal(38,18).
   [info] The type path of the target object is:
   [info] - root class: "scala.math.BigDecimal"
   [info] You can either add an explicit cast to the input data or choose a 
higher precision type of the field in the target object;
   [info]   at 
org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveUpCast$.org$apache$spark$sql$catalyst$analysis$Analyzer$ResolveUpCast$$fail(Analyzer.scala:3060)
   [info]   at 
org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveUpCast$$anonfun$apply$33$$anonfun$applyOrElse$174.applyOrElse(Analyzer.scala:3087)
   [info]   at 
org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveUpCast$$anonfun$apply$33$$anonfun$applyOrElse$174.applyOrElse(Analyzer.scala:3071)
   [info]   at 
org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDown$1(TreeNode.scala:309)
   [info]   at 
org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:72)
   [info]   at 
org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:309)
   [info]   at 
org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDown$3(TreeNode.scala:314)
   ```
   
   
   ### 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, for cases(cause precision lost) mentioned above will fail before this 
change but run successfully after this change.
   
   
   
   ### 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.
   -->
   
   Added 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.

For queries about this service, please contact Infrastructure at:
[email protected]



---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]

Reply via email to