wengh opened a new pull request, #49535: URL: https://github.com/apache/spark/pull/49535
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If you want to add or modify an error type or message, please read the guideline first in 'common/utils/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. --> Add new configuration `spark.sql.execution.pyspark.udf.hideTraceback.enabled`. If set, when handling an exception from Python UDF, only the exception class and message are included. The configuration is turned off by default. ### 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. --> This allows library provided UDFs to show only the relevant message without unnecessary stack trace. ### 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'. --> If the configuration is turned off, no user change. Otherwise, the stack trace is not included in the error message when handling an exception from Python UDF. <details> <summary>Example that illustrates the difference</summary> ```py from pyspark.errors.exceptions.base import PySparkRuntimeError from pyspark.sql.types import IntegerType, StructField, StructType from pyspark.sql.udtf import AnalyzeArgument, AnalyzeResult from pyspark.sql.functions import udtf @udtf() class PythonUDTF: @staticmethod def analyze(x: AnalyzeArgument) -> AnalyzeResult: raise PySparkRuntimeError("[XXX] My PySpark runtime error.") def eval(self, x: int): yield (x,) spark.udtf.register("my_udtf", PythonUDTF) spark.sql("select * from my_udtf(1)").show() ``` With configuration turned off, the last line gives: ``` ... pyspark.errors.exceptions.captured.AnalysisException: [TABLE_VALUED_FUNCTION_FAILED_TO_ANALYZE_IN_PYTHON] Failed to analyze the Python user defined table function: Traceback (most recent call last): File "<stdin>", line 7, in analyze pyspark.errors.exceptions.base.PySparkRuntimeError: [XXX] My PySpark runtime error. SQLSTATE: 38000; line 1 pos 14 ``` With configuration turned on, the last line gives: ``` ... pyspark.errors.exceptions.captured.AnalysisException: [TABLE_VALUED_FUNCTION_FAILED_TO_ANALYZE_IN_PYTHON] Failed to analyze the Python user defined table function: pyspark.errors.exceptions.base.PySparkRuntimeError: [XXX] My PySpark runtime error. SQLSTATE: 38000; line 1 pos 14 ``` </details> ### 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. --> Added unit test in `python/pyspark/tests/test_util.py`, testing two cases with the configuration turned on and off respectively. ### 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 Guidance](https://www.apache.org/legal/generative-tooling.html) for details. --> No -- 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. 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