[query] Upgrade spark to 3.3.0 and dataproc to 2.1 #12701
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danking
previously requested changes
Feb 16, 2023
| @@ -20,25 +20,22 @@ def init_parser(parser): | |||
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why make these changes? Do we report the username in the underlying exception?
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The linter complained about using too generic an error class, it wants us to subclass Exception. I removed the try except because I felt like it wasn't actually any more helpful than whatever stack trace was underneath, but I didn't think about the username not being in there. Can add that back in if you want.
hail/python/requirements.txt
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| @@ -15,6 +15,6 @@ parsimonious<0.9 | |||
| plotly>=5.5.0,<5.11 | |||
| protobuf==3.20.2 | |||
| PyJWT | |||
| pyspark>=3.1.1,<3.2.0 | |||
| pyspark==3.3.0 | |||
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Seems reasonable to do pyspark>=3.3,<3.4?
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I guess I was distrusting of pyspark and wanted the exact same version that there is in dataproc, but maybe that is unfounded.
danking
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Feb 16, 2023
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CHANGELOG: Query on Spark now officially supports Spark 3.3.0 and Dataproc 2.1.x
Tested on dataproc via
make -C hail test-dataproc-37. Updating the dependencies introduced a few new linting checks that I fixed here. Updating pyspark necessitated a couple of changes, namely a different py4j jar and they removedSparkSession._wrapped(but maybe we didn't need that anyway? not sure). Most importantly, the newer spark version brings with it a newer jackson version which is sufficient for the azure-storage-blob dependency, meaning we don't need to build against two different spark versions for spark and batch.