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Allow structs and arrays to pass through for Shuffle and Sort #1477

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Jan 12, 2021
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Allow structs and arrays to pass through for Shuffle and Sort #1477

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f0bcbb9
whitelist structsiand arrays for shuffle
kuhushukla Jan 7, 2021
d690b3b
Merge remote-tracking branch 'refs/remotes/origin-github/branch-0.4' …
kuhushukla Jan 8, 2021
efce100
Add more tests
kuhushukla Jan 12, 2021
b655ebe
Merge remote-tracking branch 'refs/remotes/origin-github/branch-0.4' …
kuhushukla Jan 12, 2021
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kuhushukla Jan 12, 2021
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8 changes: 4 additions & 4 deletions docs/supported_ops.md
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Original file line number Diff line number Diff line change
Expand Up @@ -352,9 +352,9 @@ Accelerator supports are described below.
<td>S</td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><em>PS* (missing nested BINARY, CALENDAR, MAP, UDT)</em></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><em>PS* (missing nested BINARY, CALENDAR, MAP, UDT)</em></td>
<td><b>NS</b></td>
</tr>
<tr>
Expand Down Expand Up @@ -536,9 +536,9 @@ Accelerator supports are described below.
<td>S</td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><em>PS* (missing nested BINARY, CALENDAR, MAP, UDT)</em></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><em>PS* (missing nested BINARY, CALENDAR, MAP, UDT)</em></td>
<td><b>NS</b></td>
</tr>
<tr>
Expand Down
11 changes: 8 additions & 3 deletions integration_tests/src/main/python/array_test.py
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Expand Up @@ -14,11 +14,9 @@

import pytest

from asserts import assert_gpu_and_cpu_are_equal_collect
from asserts import assert_gpu_and_cpu_are_equal_collect, assert_gpu_and_cpu_are_equal_sql
from data_gen import *
from marks import incompat
from pyspark.sql.types import *
import pyspark.sql.functions as f

# Once we support arrays as literals then we can support a[null] and
# negative indexes for all array gens. When that happens
Expand Down Expand Up @@ -46,3 +44,10 @@ def test_nested_array_index(data_gen):
'a[1]',
'a[3]',
'a[50]'))

@pytest.mark.parametrize('data_gen', single_level_array_gens_non_decimal, ids=idfn)
def test_orderby_array(data_gen):
assert_gpu_and_cpu_are_equal_sql(
lambda spark : unary_op_df(spark, data_gen),
'array_table',
'select array_table.a, array_table.a[0] as first_val from array_table order by first_val')
1 change: 1 addition & 0 deletions integration_tests/src/main/python/data_gen.py
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Expand Up @@ -772,6 +772,7 @@ def gen_scalars_for_sql(data_gen, count, seed=0, force_no_nulls=False):
boolean_gens = [boolean_gen]

single_level_array_gens = [ArrayGen(sub_gen) for sub_gen in all_basic_gens + decimal_gens + [null_gen]]
single_level_array_gens_non_decimal = [ArrayGen(sub_gen) for sub_gen in all_basic_gens + [null_gen]]
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Why not include decimal?

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I saw some analysis exception with legacy setting for negative scale when trying to sort arrays with decimal type which I am not fully aware on where we stand in terms of support so I excluded those in that particular test.

I will check it out further


# Be careful to not make these too large of data generation takes for ever
# This is only a few nested array gens, because nesting can be very deep
Expand Down
15 changes: 12 additions & 3 deletions integration_tests/src/main/python/struct_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,11 +14,9 @@

import pytest

from asserts import assert_gpu_and_cpu_are_equal_collect
from asserts import assert_gpu_and_cpu_are_equal_collect, assert_gpu_and_cpu_are_equal_sql
from data_gen import *
from marks import incompat
from pyspark.sql.types import *
import pyspark.sql.functions as f

@pytest.mark.parametrize('data_gen', [StructGen([["first", boolean_gen], ["second", byte_gen], ["third", float_gen]]),
StructGen([["first", short_gen], ["second", int_gen], ["third", long_gen]]),
Expand All @@ -32,10 +30,21 @@ def test_struct_get_item(data_gen):
'a.second',
'a.third'))


@pytest.mark.parametrize('data_gen', all_basic_gens + [decimal_gen_default, decimal_gen_scale_precision], ids=idfn)
def test_make_struct(data_gen):
assert_gpu_and_cpu_are_equal_collect(
lambda spark : binary_op_df(spark, data_gen).selectExpr(
'struct(a, b)',
'named_struct("foo", b, "bar", 5, "end", a)'))


@pytest.mark.parametrize('data_gen', [StructGen([["first", boolean_gen], ["second", byte_gen], ["third", float_gen]]),
StructGen([["first", short_gen], ["second", int_gen], ["third", long_gen]]),
StructGen([["first", long_gen], ["second", long_gen], ["third", long_gen]]),
StructGen([["first", string_gen], ["second", ArrayGen(string_gen)], ["third", ArrayGen(string_gen)]])], ids=idfn)
def test_orderby_struct(data_gen):
assert_gpu_and_cpu_are_equal_sql(
lambda spark : unary_op_df(spark, data_gen),
'struct_table',
'select struct_table.a, struct_table.a.first as first_val from struct_table order by first_val')
6 changes: 4 additions & 2 deletions sql-plugin/src/main/scala/com/nvidia/spark/rapids/GpuOverrides.scala
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Expand Up @@ -2321,7 +2321,8 @@ object GpuOverrides {
}),
exec[ShuffleExchangeExec](
"The backend for most data being exchanged between processes",
ExecChecks(TypeSig.commonCudfTypes + TypeSig.NULL + TypeSig.DECIMAL, TypeSig.all),
ExecChecks((TypeSig.commonCudfTypes + TypeSig.NULL + TypeSig.DECIMAL + TypeSig.ARRAY +
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TypeSig.STRUCT).nested(), TypeSig.all),
(shuffle, conf, p, r) => new GpuShuffleMeta(shuffle, conf, p, r)),
exec[UnionExec](
"The backend for the union operator",
Expand Down Expand Up @@ -2372,7 +2373,8 @@ object GpuOverrides {
(agg, conf, p, r) => new GpuSortAggregateMeta(agg, conf, p, r)),
exec[SortExec](
"The backend for the sort operator",
ExecChecks(TypeSig.commonCudfTypes + TypeSig.NULL + TypeSig.DECIMAL, TypeSig.all),
ExecChecks((TypeSig.commonCudfTypes + TypeSig.NULL + TypeSig.DECIMAL + TypeSig.ARRAY +
TypeSig.STRUCT).nested(), TypeSig.all),
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(sort, conf, p, r) => new GpuSortMeta(sort, conf, p, r)),
exec[ExpandExec](
"The backend for the expand operator",
Expand Down