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Fix test failure with Spark 3.3 by looking for less specific error message #5185

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merged 2 commits into from
Apr 11, 2022
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Fix test failure with Spark 3.3 by looking for less specific error message #5185

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a6193b1
Temporarily ignore tests that fail with Spark 3.3
andygrove Apr 8, 2022
1ef2cff
fix
andygrove Apr 8, 2022
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2 changes: 1 addition & 1 deletion integration_tests/src/main/python/arithmetic_ops_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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.

else:
assert_gpu_and_cpu_are_equal_collect(
func=lambda spark: _get_div_overflow_df(spark, expr),
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