Partial support for time windows #3074
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Signed-off-by: Robert (Bobby) Evans <bobby@apache.org>
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jlowe
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Jul 28, 2021
sql-plugin/src/main/scala/com/nvidia/spark/rapids/GpuOverrides.scala
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@jlowe I think I have addressed all of your review comments. |
jlowe
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Jul 29, 2021
revans2
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Jul 29, 2021
| @@ -868,9 +868,9 @@ def gen_scalars_for_sql(data_gen, count, seed=0, force_no_nulls=False): | |||
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| boolean_gens = [boolean_gen] | |||
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| single_level_array_gens = [ArrayGen(sub_gen) for sub_gen in all_basic_gens + decimal_gens + [null_gen]] | |||
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FYI null_gen was already a part of all_basic_gens so we would double up a few tests.
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build got stuck and I aborted it. Not sure what happened. The logs didn't show any errors beyond |
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This fixes #2943
Spark supports time windows, and technically this lets us also support them, but without support for grouping by a struct of (timestamp, timestamp) or partitioning by this on window operations it is not going to do a lot for actual customers.
Time windows are supported by the
windowfunction. This essentially does a bunch of math operations along with an expand to produce a struct column with the timestamp bucketed into a number of different time window buckets. You can then group by these buckets or do window operations on them to get either tumbling or sliding window aggregations. This adds some tests that are in the context of those more complete operations, because just creating the bucket, though interesting, is not that useful on its own. When we do support groupby and window operations on structs then this can be extended.