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Support executing collect_list on GPU with windowing. #1548

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merged 15 commits into from
Feb 5, 2021
Merged

Support executing collect_list on GPU with windowing. #1548

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838344d
Support collect_list on GPU for windowing.
firestarman Jan 19, 2021
54a9ed5
Let collect_list supports array of struct.
firestarman Jan 27, 2021
371b83f
Only allow array type for collect_list
firestarman Jan 27, 2021
42a5408
Restore the cast for non nested type.
firestarman Jan 27, 2021
63aa7a0
Updated the type check
firestarman Jan 28, 2021
bfef795
Add integration tests for collect_list.
firestarman Jan 28, 2021
127ff2e
Doc updates
firestarman Jan 28, 2021
971fcaa
Address some comments
firestarman Jan 29, 2021
171c495
Comment update
firestarman Jan 29, 2021
4592a81
Addressed some new comments
firestarman Feb 1, 2021
d8c2121
Add an empty line at the end
firestarman Feb 1, 2021
549f11d
Merge branch 'branch-0.4' into collect_list
firestarman Feb 2, 2021
24a864b
Merge branch 'branch-0.4' into collect_list
firestarman Feb 4, 2021
929602b
Merge branch 'branch-0.4' into collect_list
firestarman Feb 5, 2021
3a5f94e
Addressed some new comments again
firestarman Feb 5, 2021
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1 change: 1 addition & 0 deletions docs/configs.md
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Expand Up @@ -249,6 +249,7 @@ Name | SQL Function(s) | Description | Default Value | Notes
<a name="sql.expression.Year"></a>spark.rapids.sql.expression.Year|`year`|Returns the year from a date or timestamp|true|None|
<a name="sql.expression.AggregateExpression"></a>spark.rapids.sql.expression.AggregateExpression| |Aggregate expression|true|None|
<a name="sql.expression.Average"></a>spark.rapids.sql.expression.Average|`avg`, `mean`|Average aggregate operator|true|None|
<a name="sql.expression.CollectList"></a>spark.rapids.sql.expression.CollectList|`collect_list`|Collect a list of elements, now only supported by windowing.|false|This is disabled by default because for now the GPU collects null values to a list, but Spark does not. This will be fixed in future releases.|
<a name="sql.expression.Count"></a>spark.rapids.sql.expression.Count|`count`|Count aggregate operator|true|None|
<a name="sql.expression.First"></a>spark.rapids.sql.expression.First|`first_value`, `first`|first aggregate operator|true|None|
<a name="sql.expression.Last"></a>spark.rapids.sql.expression.Last|`last`, `last_value`|last aggregate operator|true|None|
Expand Down
153 changes: 143 additions & 10 deletions docs/supported_ops.md
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Expand Up @@ -745,9 +745,9 @@ Accelerator supports are described below.
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><em>PS* (missing nested NULL, BINARY, CALENDAR, ARRAY, MAP, UDT)</em></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><em>PS* (missing nested NULL, BINARY, CALENDAR, ARRAY, MAP, UDT)</em></td>
<td><b>NS</b></td>
</tr>
</table>
Expand Down Expand Up @@ -15449,7 +15449,7 @@ Accelerator support is described below.
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><em>PS* (missing nested NULL, BINARY, CALENDAR, ARRAY, MAP, STRUCT, UDT)</em></td>
<td><em>PS* (missing nested NULL, BINARY, CALENDAR, ARRAY, MAP, UDT)</em></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
Expand Down Expand Up @@ -15491,7 +15491,7 @@ Accelerator support is described below.
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><em>PS* (missing nested NULL, BINARY, CALENDAR, ARRAY, MAP, STRUCT, UDT)</em></td>
<td><em>PS* (missing nested NULL, BINARY, CALENDAR, ARRAY, MAP, UDT)</em></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
Expand Down Expand Up @@ -15675,7 +15675,7 @@ Accelerator support is described below.
<td>S</td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><em>PS* (missing nested NULL, BINARY, CALENDAR, ARRAY, MAP, UDT)</em></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
Expand Down Expand Up @@ -15717,7 +15717,7 @@ Accelerator support is described below.
<td>S</td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><em>PS* (missing nested NULL, BINARY, CALENDAR, ARRAY, MAP, UDT)</em></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
Expand All @@ -15739,7 +15739,7 @@ Accelerator support is described below.
<td>S</td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><em>PS* (missing nested NULL, BINARY, CALENDAR, ARRAY, MAP, UDT)</em></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
Expand Down Expand Up @@ -15781,7 +15781,7 @@ Accelerator support is described below.
<td>S</td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><em>PS* (missing nested NULL, BINARY, CALENDAR, ARRAY, MAP, UDT)</em></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
Expand All @@ -15803,7 +15803,7 @@ Accelerator support is described below.
<td>S</td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><em>PS* (missing nested NULL, BINARY, CALENDAR, ARRAY, MAP, UDT)</em></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
Expand Down Expand Up @@ -15845,7 +15845,7 @@ Accelerator support is described below.
<td>S</td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><em>PS* (missing nested NULL, BINARY, CALENDAR, ARRAY, MAP, UDT)</em></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
Expand Down Expand Up @@ -15984,6 +15984,139 @@ Accelerator support is described below.
<td> </td>
</tr>
<tr>
<td rowSpan="6">CollectList</td>
<td rowSpan="6">`collect_list`</td>
<td rowSpan="6">Collect a list of elements, now only supported by windowing.</td>
<td rowSpan="6">This is disabled by default because for now the GPU collects null values to a list, but Spark does not. This will be fixed in future releases.</td>
<td rowSpan="2">aggregation</td>
<td>input</td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
</tr>
<tr>
<td>result</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td><b>NS</b></td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr>
<td rowSpan="2">reduction</td>
<td>input</td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
</tr>
<tr>
<td>result</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td><b>NS</b></td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr>
<td rowSpan="2">window</td>
<td>input</td>
<td>S</td>
<td>S</td>
<td>S</td>
<td>S</td>
<td>S</td>
<td>S</td>
<td>S</td>
<td>S</td>
<td>S*</td>
<td>S</td>
<td>S*</td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><em>PS* (missing nested NULL, BINARY, CALENDAR, ARRAY, MAP, STRUCT, UDT)</em></td>
<td><b>NS</b></td>
</tr>
<tr>
<td>result</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td><em>PS* (missing nested NULL, BINARY, CALENDAR, ARRAY, MAP, UDT)</em></td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr>
<td rowSpan="6">Count</td>
<td rowSpan="6">`count`</td>
<td rowSpan="6">Count aggregate operator</td>
Expand Down
63 changes: 63 additions & 0 deletions integration_tests/src/main/python/window_function_test.py
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Expand Up @@ -223,3 +223,66 @@ def test_window_aggs_for_ranges_of_dates(data_gen):
' range between 1 preceding and 1 following) as sum_c_asc '
'from window_agg_table'
)


def _gen_data_for_collect(nullable=True):
return [
('a', RepeatSeqGen(LongGen(), length=20)),
('b', IntegerGen()),
('c_int', IntegerGen(nullable=nullable)),
('c_long', LongGen(nullable=nullable)),
('c_time', DateGen(nullable=nullable)),
('c_string', StringGen(nullable=nullable)),
('c_float', FloatGen(nullable=nullable)),
('c_decimal', DecimalGen(nullable=nullable, precision=8, scale=3)),
('c_struct', StructGen(nullable=nullable, children=[
['child_int', IntegerGen()],
['child_time', DateGen()],
['child_string', StringGen()],
['child_decimal', DecimalGen(precision=8, scale=3)]]))]


_collect_sql_string =\
'''
select
collect_list(c_int) over
(partition by a order by b,c_int rows between UNBOUNDED preceding and CURRENT ROW) as collect_int,
collect_list(c_long) over
(partition by a order by b,c_int rows between UNBOUNDED preceding and CURRENT ROW) as collect_long,
collect_list(c_time) over
(partition by a order by b,c_int rows between UNBOUNDED preceding and CURRENT ROW) as collect_time,
collect_list(c_string) over
(partition by a order by b,c_int rows between UNBOUNDED preceding and CURRENT ROW) as collect_string,
collect_list(c_float) over
(partition by a order by b,c_int rows between UNBOUNDED preceding and CURRENT ROW) as collect_float,
collect_list(c_decimal) over
(partition by a order by b,c_int rows between UNBOUNDED preceding and CURRENT ROW) as collect_decimal,
collect_list(c_struct) over
(partition by a order by b,c_int rows between UNBOUNDED preceding and CURRENT ROW) as collect_struct
from window_collect_table
'''

# SortExec does not support array type, so sort the result locally.
@ignore_order(local=True)
@pytest.mark.skip("https://github.com/NVIDIA/spark-rapids/issues/1638")
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def test_window_aggs_for_rows_collect_list():
assert_gpu_and_cpu_are_equal_sql(
lambda spark : gen_df(spark, _gen_data_for_collect(), length=2048),
"window_collect_table",
_collect_sql_string,
{'spark.rapids.sql.expression.CollectList': 'true'})


'''
Spark will drop nulls when collecting, but seems GPU does not yet, so exceptions come up.
Now set nullable to false to verify the current functionality without null values.
Once native supports dropping nulls, will enable the tests above and remove this one.
'''
# SortExec does not support array type, so sort the result locally.
@ignore_order(local=True)
def test_window_aggs_for_rows_collect_list_no_nulls():
assert_gpu_and_cpu_are_equal_sql(
lambda spark : gen_df(spark, _gen_data_for_collect(False), length=2048),
"window_collect_table",
_collect_sql_string,
{'spark.rapids.sql.expression.CollectList': 'true'})
32 changes: 25 additions & 7 deletions sql-plugin/src/main/scala/com/nvidia/spark/rapids/GpuOverrides.scala
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Expand Up @@ -774,12 +774,11 @@ object GpuOverrides {
"Calculates a return value for every input row of a table based on a group (or " +
"\"window\") of rows",
ExprChecks.windowOnly(
TypeSig.commonCudfTypes + TypeSig.DECIMAL +
TypeSig.ARRAY.nested(TypeSig.commonCudfTypes + TypeSig.DECIMAL),
(TypeSig.commonCudfTypes + TypeSig.DECIMAL).nested + TypeSig.ARRAY.nested(TypeSig.STRUCT),
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TypeSig.all,
Seq(ParamCheck("windowFunction",
TypeSig.commonCudfTypes + TypeSig.DECIMAL +
TypeSig.ARRAY.nested(TypeSig.commonCudfTypes + TypeSig.DECIMAL),
(TypeSig.commonCudfTypes + TypeSig.DECIMAL).nested +
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TypeSig.ARRAY.nested(TypeSig.STRUCT),
TypeSig.all),
ParamCheck("windowSpec",
TypeSig.CALENDAR + TypeSig.NULL + TypeSig.integral + TypeSig.DECIMAL,
Expand Down Expand Up @@ -1684,11 +1683,13 @@ object GpuOverrides {
expr[AggregateExpression](
"Aggregate expression",
ExprChecks.fullAgg(
TypeSig.commonCudfTypes + TypeSig.NULL + TypeSig.DECIMAL,
(TypeSig.commonCudfTypes + TypeSig.DECIMAL).nested() + TypeSig.NULL +
TypeSig.ARRAY.nested(TypeSig.STRUCT),
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TypeSig.all,
Seq(ParamCheck(
"aggFunc",
TypeSig.commonCudfTypes + TypeSig.NULL + TypeSig.DECIMAL,
(TypeSig.commonCudfTypes + TypeSig.DECIMAL).nested() + TypeSig.NULL +
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TypeSig.ARRAY.nested(TypeSig.STRUCT),
TypeSig.all)),
Some(RepeatingParamCheck("filter", TypeSig.BOOLEAN, TypeSig.BOOLEAN))),
(a, conf, p, r) => new ExprMeta[AggregateExpression](a, conf, p, r) {
Expand Down Expand Up @@ -2264,6 +2265,21 @@ object GpuOverrides {
override def convertToGpu(child: Expression): GpuExpression =
GpuMakeDecimal(child, a.precision, a.scale, a.nullOnOverflow)
}),
expr[CollectList](
"Collect a list of elements, now only supported by windowing.",
/* It should be 'fullAgg' eventually but now only support windowing,
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so 'aggNotGroupByOrReduction' */
ExprChecks.aggNotGroupByOrReduction(
TypeSig.ARRAY.nested(TypeSig.commonCudfTypes + TypeSig.DECIMAL + TypeSig.STRUCT),
TypeSig.ARRAY.nested(TypeSig.all),
Seq(ParamCheck("input",
(TypeSig.commonCudfTypes + TypeSig.DECIMAL).nested() + TypeSig.STRUCT,
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again I think longer is clearer, but in this case because there is only one nested type I think it is okay. So we don't support doing a collect list on a struct that also contains a struct?

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This should be supported. If it's not working, it might be a bug in libcudf. I'd be happy to investigate.

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@firestarman firestarman Feb 5, 2021 •
edited
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Updated it, and the struct of struct is not covered in integration tests, so do not add it here. Shall we need it ?

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if yes, i will add it in a following PR

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Sounds good to me

TypeSig.all))),
(c, conf, p, r) => new ExprMeta[CollectList](c, conf, p, r) {
override def convertToGpu(): GpuExpression = GpuCollectList(
childExprs.head.convertToGpu(), c.mutableAggBufferOffset, c.inputAggBufferOffset)
}).disabledByDefault("for now the GPU collects null values to a list, but Spark does not." +
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" This will be fixed in future releases."),
expr[ScalarSubquery](
"Subquery that will return only one row and one column",
ExprChecks.projectOnly(
Expand Down Expand Up @@ -2604,7 +2620,9 @@ object GpuOverrides {
(expand, conf, p, r) => new GpuExpandExecMeta(expand, conf, p, r)),
exec[WindowExec](
"Window-operator backend",
ExecChecks(TypeSig.commonCudfTypes + TypeSig.DECIMAL, TypeSig.all),
ExecChecks(
(TypeSig.commonCudfTypes + TypeSig.DECIMAL + TypeSig.STRUCT).nested() + TypeSig.ARRAY,
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TypeSig.all),
(windowOp, conf, p, r) =>
new GpuWindowExecMeta(windowOp, conf, p, r)
),
Expand Down
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