Fix collection_ops_tests for Spark 4.0 [databricks]
#11414
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Fixes NVIDIA#11011. This commit fixes the failures in `collection_ops_tests` on Spark 4.0. On all versions of Spark, when a Sequence is collected with rows that exceed MAX_INT, an exception is thrown indicating that the collected Sequence/array is larger than permissible. The different versions of Spark vary in the contents of the exception message. On Spark 4, one sees that the error message now contains more information than all prior versions, including: 1. The name of the op causing the error 2. The errant sequence size This commit introduces a shim to make this new information available in the exception. Note that this shim does not fit cleanly in RapidsErrorUtils, because there are differences within major Spark versions. For instance, Spark 3.4.0-1 have a different message as compared to 3.4.2 and 3.4.3. Likewise, the differences in 3.5.0, 3.5.1, 3.5.2. Signed-off-by: MithunR <mithunr@nvidia.com>
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This moves the construction of the long-sequence error strings into RapidsErrorUtils. The process involved introducing many new RapidsErrorUtils classes, and using mix-ins of concrete implementations for the error-string construction.
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Apologies for the noise. I had to rebase this to target @razajafri is already examining this change. The others can ignore this. |
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collection_ops_tests for Spark 4.0collection_ops_tests for Spark 4.0 [databricks]
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Fixes #11011.
This commit fixes the failures in
collection_ops_testson Spark 4.0.On all versions of Spark, when a Sequence is collected with rows that exceed MAX_INT,
an exception is thrown indicating that the collected Sequence/array is
larger than permissible. The different versions of Spark vary in the
contents of the exception message.
On Spark 4, one sees that the error message now contains more
information than all prior versions, including:
This commit introduces a shim to make this new information available in
the exception.
Note that this shim does not fit cleanly in RapidsErrorUtils, because
there are differences within major Spark versions. For instance, Spark
3.4.0-1 have a different message as compared to 3.4.2 and 3.4.3.
Likewise, the differences in 3.5.0, 3.5.1, 3.5.2.