Skip to content

Commit

Permalink
Merge branch 'branch-0.4' into reduce_metrics_option
Browse files Browse the repository at this point in the history
  • Loading branch information
@revans2
revans2 committed Feb 5, 2021
2 parents 76077f5 + 462b878 commit fc4a82e
Show file tree
Hide file tree
Showing 36 changed files with 804 additions and 839 deletions.
1 change: 1 addition & 0 deletions docs/configs.md
View file
Original file line number Diff line number Diff line change
Expand Up @@ -250,6 +250,7 @@ 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
View file
Original file line number Diff line number Diff line change
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
View file
Original file line number Diff line number Diff line change
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.xfail(reason="https://github.com/NVIDIA/spark-rapids/issues/1638")
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'})
40 changes: 5 additions & 35 deletions sql-plugin/src/main/format/ShuffleMetadataResponse.fbs
View file
Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
// Copyright (c) 2019-2020, NVIDIA CORPORATION.
// Copyright (c) 2019-2021, NVIDIA CORPORATION.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
Expand All @@ -16,46 +16,16 @@ include "ShuffleCommon.fbs";

namespace com.nvidia.spark.rapids.format;

/// Metadata about cuDF Columns
table ColumnMeta {
/// number of nulls in the column or -1 if unknown
null_count: long;

/// number of rows in the column
row_count: long;

/// offset of the column's data buffer
data_offset: long;

/// length of the column's data buffer
data_length: long;

/// offset of the column's validity buffer
validity_offset: long;

/// offset of the column's offsets buffer
offsets_offset: long;

/// child column metadata
children: [ColumnMeta];

/// ordinal of DType enum
dtype_id: int;

/// DType scale for decimal types
dtype_scale: int;
}

/// Metadata about cuDF tables
table TableMeta {
/// metadata about the table buffer
/// metadata about the data encoding
buffer_meta: BufferMeta;

/// metadata for each column in the table buffer
column_metas: [ColumnMeta];

/// number of rows in the table
row_count: long;

/// opaque metadata describing the packed table schema and data layout
packed_meta: [byte];
}

/// Flat buffer for Rapids UCX Shuffle Metadata Response
Expand Down
6 changes: 3 additions & 3 deletions sql-plugin/src/main/java/com/nvidia/spark/rapids/GpuColumnVector.java
View file
Original file line number Diff line number Diff line change
Expand Up @@ -702,9 +702,9 @@ public final int numNulls() {
public static long getTotalDeviceMemoryUsed(ColumnarBatch batch) {
long sum = 0;
if (batch.numCols() > 0) {
if (batch.column(0) instanceof GpuCompressedColumnVector) {
GpuCompressedColumnVector gccv = (GpuCompressedColumnVector) batch.column(0);
sum += gccv.getBuffer().getLength();
if (batch.column(0) instanceof WithTableBuffer) {
WithTableBuffer wtb = (WithTableBuffer) batch.column(0);
sum += wtb.getTableBuffer().getLength();
} else {
for (int i = 0; i < batch.numCols(); i++) {
sum += ((GpuColumnVector) batch.column(i)).getBase().getDeviceMemorySize();
Expand Down
Loading

0 comments on commit fc4a82e

Please sign in to comment.