Fix test failure with Spark 3.3 by looking for less specific error message #5185
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andygrove
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Signed-off-by: Andy Grove <[email protected]>
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tgravescs
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Apr 8, 2022
jlowe
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| @@ -788,7 +788,7 @@ def test_div_overflow_exception_when_ansi(expr, ansi_enabled): | |||
| assert_gpu_and_cpu_error( | |||
| df_fun=lambda spark: _get_div_overflow_df(spark, expr).collect(), | |||
| conf=ansi_conf, | |||
| error_message='java.lang.ArithmeticException: Overflow in integral divide') | |||
| error_message='ArithmeticException: Overflow in integral divide') | |||
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Isn't the point to ensure we're throwing the same exception? As such I'd rather see this test check for the correct exception class based on the Spark version -- if Spark 3.3 throws a different exception for this, then our plugin should throw the same class for the same type of exception on Spark 3.3.
I'm OK if we want to do this as a followup to refine the test if we aren't already doing the correct behavior now. If we are, then this should choose a different message to check based on which Spark we're using as is done in other tests checking exception types.
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Yes, that's a good point. I filed #5196 as a follow-on.
jlowe
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Apr 11, 2022
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Closes #5182
Spark 3.3 changed which exception they throw for a divide that causes an overflow in apache/spark@c2ed15d, causing our tests to fail.
This PR changes the affected test to just look for
ArithmeticExceptionrather than specificallyjava.lang.ArithmeticException. This is the same approach used in the other tests.