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aggregators_test.go
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// Copyright 2018 The Cockroach Authors.
//
// Use of this software is governed by the Business Source License
// included in the file licenses/BSL.txt.
//
// As of the Change Date specified in that file, in accordance with
// the Business Source License, use of this software will be governed
// by the Apache License, Version 2.0, included in the file
// licenses/APL.txt.
package colexec
import (
"context"
"fmt"
"strings"
"testing"
"github.com/cockroachdb/apd/v2"
"github.com/cockroachdb/cockroach/pkg/col/coldata"
"github.com/cockroachdb/cockroach/pkg/col/coldatatestutils"
"github.com/cockroachdb/cockroach/pkg/settings/cluster"
"github.com/cockroachdb/cockroach/pkg/sql/colexec/colexecagg"
"github.com/cockroachdb/cockroach/pkg/sql/colexecbase"
"github.com/cockroachdb/cockroach/pkg/sql/colmem"
"github.com/cockroachdb/cockroach/pkg/sql/execinfrapb"
"github.com/cockroachdb/cockroach/pkg/sql/sem/tree"
"github.com/cockroachdb/cockroach/pkg/sql/types"
"github.com/cockroachdb/cockroach/pkg/testutils/skip"
"github.com/cockroachdb/cockroach/pkg/util/duration"
"github.com/cockroachdb/cockroach/pkg/util/leaktest"
"github.com/cockroachdb/cockroach/pkg/util/log"
"github.com/cockroachdb/cockroach/pkg/util/mon"
"github.com/cockroachdb/cockroach/pkg/util/randutil"
"github.com/cockroachdb/cockroach/pkg/util/timeofday"
"github.com/stretchr/testify/require"
)
var (
defaultGroupCols = []uint32{0}
defaultAggCols = [][]uint32{{1}}
defaultAggFns = []execinfrapb.AggregatorSpec_Func{execinfrapb.AggregatorSpec_SUM_INT}
defaultTyps = []*types.T{types.Int, types.Int}
)
type aggregatorTestCase struct {
// typs, aggFns, groupCols, and aggCols will be set to their default
// values before running a test if nil.
typs []*types.T
aggFns []execinfrapb.AggregatorSpec_Func
groupCols []uint32
aggCols [][]uint32
constArguments [][]execinfrapb.Expression
// spec will be populated during init().
spec *execinfrapb.AggregatorSpec
aggDistinct []bool
aggFilter []int
input tuples
unorderedInput bool
expected tuples
name string
// convToDecimal will convert any float64s to apd.Decimals. If a string is
// encountered, a best effort is made to convert that string to an
// apd.Decimal.
convToDecimal bool
}
// aggType is a helper struct that allows tests to test both the ordered and
// hash aggregators at the same time.
type aggType struct {
new func(
allocator *colmem.Allocator,
memAccount *mon.BoundAccount,
input colexecbase.Operator,
inputTypes []*types.T,
spec *execinfrapb.AggregatorSpec,
evalCtx *tree.EvalContext,
constructors []execinfrapb.AggregateConstructor,
constArguments []tree.Datums,
outputTypes []*types.T,
isScalar bool,
) (colexecbase.Operator, error)
name string
}
var aggTypes = []aggType{
{
// This is a wrapper around NewHashAggregator so its signature is compatible
// with orderedAggregator.
new: func(
allocator *colmem.Allocator,
memAccount *mon.BoundAccount,
input colexecbase.Operator,
inputTypes []*types.T,
spec *execinfrapb.AggregatorSpec,
evalCtx *tree.EvalContext,
constructors []execinfrapb.AggregateConstructor,
constArguments []tree.Datums,
outputTypes []*types.T,
_ bool,
) (colexecbase.Operator, error) {
return NewHashAggregator(allocator, memAccount, input, inputTypes, spec, evalCtx, constructors, constArguments, outputTypes)
},
name: "hash",
},
{
new: NewOrderedAggregator,
name: "ordered",
},
}
func (tc *aggregatorTestCase) init() error {
if tc.convToDecimal {
for _, tuples := range []tuples{tc.input, tc.expected} {
for _, tuple := range tuples {
for i, e := range tuple {
switch v := e.(type) {
case float64:
d := &apd.Decimal{}
d, err := d.SetFloat64(v)
if err != nil {
return err
}
tuple[i] = *d
case string:
d := &apd.Decimal{}
d, _, err := d.SetString(v)
if err != nil {
// If there was an error converting the string to decimal, just
// leave the datum as is.
continue
}
tuple[i] = *d
}
}
}
}
}
if tc.groupCols == nil {
tc.groupCols = defaultGroupCols
}
if tc.aggFns == nil {
tc.aggFns = defaultAggFns
}
if tc.aggCols == nil {
tc.aggCols = defaultAggCols
}
if tc.typs == nil {
tc.typs = defaultTyps
}
aggregations := make([]execinfrapb.AggregatorSpec_Aggregation, len(tc.aggFns))
for i, aggFn := range tc.aggFns {
aggregations[i].Func = aggFn
aggregations[i].ColIdx = tc.aggCols[i]
if tc.constArguments != nil {
aggregations[i].Arguments = tc.constArguments[i]
}
if tc.aggDistinct != nil {
aggregations[i].Distinct = tc.aggDistinct[i]
}
if tc.aggFilter != nil && tc.aggFilter[i] != tree.NoColumnIdx {
filterColIdx := uint32(tc.aggFilter[i])
aggregations[i].FilterColIdx = &filterColIdx
}
}
tc.spec = &execinfrapb.AggregatorSpec{
GroupCols: tc.groupCols,
Aggregations: aggregations,
}
return nil
}
func TestAggregatorOneFunc(t *testing.T) {
defer leaktest.AfterTest(t)()
defer log.Scope(t).Close(t)
testCases := []aggregatorTestCase{
{
input: tuples{
{0, 1},
},
expected: tuples{
{1},
},
name: "OneTuple",
},
{
input: tuples{
{0, 1},
{0, 1},
},
expected: tuples{
{2},
},
name: "OneGroup",
},
{
input: tuples{
{0, 1},
{0, 0},
{0, 1},
{1, 4},
{2, 5},
},
expected: tuples{
{2},
{4},
{5},
},
name: "MultiGroup",
},
{
input: tuples{
{0, 1},
{0, 2},
{0, 3},
{1, 4},
{1, 5},
},
expected: tuples{
{6},
{9},
},
name: "CarryBetweenInputBatches",
},
{
input: tuples{
{0, 1},
{0, 2},
{0, 3},
{0, 4},
{1, 5},
{2, 6},
},
expected: tuples{
{10},
{5},
{6},
},
name: "CarryBetweenOutputBatches",
},
{
input: tuples{
{0, 1},
{0, 1},
{1, 2},
{2, 3},
{2, 3},
{3, 4},
{3, 4},
{4, 5},
{5, 6},
{6, 7},
{7, 8},
},
expected: tuples{
{2},
{2},
{6},
{8},
{5},
{6},
{7},
{8},
},
name: "CarryBetweenInputAndOutputBatches",
},
{
input: tuples{
{0, 1},
{0, 2},
{0, 3},
{0, 4},
},
expected: tuples{
{10},
},
name: "NoGroupingCols",
groupCols: []uint32{},
},
{
input: tuples{
{1, 0, 0},
{2, 0, 0},
{3, 0, 0},
{4, 0, 0},
},
expected: tuples{
{10},
},
name: "UnusedInputColumns",
typs: []*types.T{types.Int, types.Int, types.Int},
groupCols: []uint32{1, 2},
aggCols: [][]uint32{{0}},
},
{
input: tuples{
{nil, 1},
{4, 42},
{nil, 2},
},
expected: tuples{
{3},
{42},
},
name: "UnorderedWithNullsInGroupingCol",
unorderedInput: true,
},
{
aggFns: []execinfrapb.AggregatorSpec_Func{
execinfrapb.AggregatorSpec_ANY_NOT_NULL,
execinfrapb.AggregatorSpec_COUNT_ROWS,
},
aggCols: [][]uint32{{0}, {}},
typs: []*types.T{types.Int},
unorderedInput: true,
input: tuples{
{1},
{2},
{1},
{nil},
{3},
{1},
{3},
{4},
{1},
{nil},
{2},
{4},
{2},
},
expected: tuples{
{nil, 2},
{1, 4},
{2, 3},
{3, 2},
{4, 2},
},
},
}
evalCtx := tree.MakeTestingEvalContext(cluster.MakeTestingClusterSettings())
defer evalCtx.Stop(context.Background())
// Run tests with deliberate batch sizes and no selection vectors.
ctx := context.Background()
for _, tc := range testCases {
if err := tc.init(); err != nil {
t.Fatal(err)
}
constructors, constArguments, outputTypes, err := colexecagg.ProcessAggregations(
&evalCtx, nil /* semaCtx */, tc.spec.Aggregations, tc.typs,
)
require.NoError(t, err)
for _, agg := range aggTypes {
if tc.unorderedInput && agg.name == "ordered" {
// This test case has unordered input, so we skip ordered
// aggregator.
continue
}
log.Infof(ctx, "%s/%s", tc.name, agg.name)
runTestsWithTyps(t, []tuples{tc.input}, [][]*types.T{tc.typs}, tc.expected, unorderedVerifier,
func(input []colexecbase.Operator) (colexecbase.Operator, error) {
return agg.new(
testAllocator, testMemAcc, input[0], tc.typs, tc.spec, &evalCtx,
constructors, constArguments, outputTypes, false, /* isScalar */
)
})
}
}
}
func TestAggregatorMultiFunc(t *testing.T) {
defer leaktest.AfterTest(t)()
defer log.Scope(t).Close(t)
// TODO(yuzefovich): introduce nicer aliases for the protobuf generated
// ones and use those throughout the codebase.
avgFn := execinfrapb.AggregatorSpec_AVG
testCases := []aggregatorTestCase{
{
aggFns: []execinfrapb.AggregatorSpec_Func{execinfrapb.AggregatorSpec_SUM_INT, execinfrapb.AggregatorSpec_SUM_INT},
aggCols: [][]uint32{
{2}, {1},
},
input: tuples{
{0, 1, 2},
{0, 1, 2},
},
typs: []*types.T{types.Int, types.Int, types.Int},
expected: tuples{
{4, 2},
},
name: "OutputOrder",
},
{
aggFns: []execinfrapb.AggregatorSpec_Func{execinfrapb.AggregatorSpec_SUM, execinfrapb.AggregatorSpec_SUM_INT},
aggCols: [][]uint32{
{2}, {1},
},
input: tuples{
{0, 1, 1.3},
{0, 1, 1.6},
{0, 1, 0.5},
{1, 1, 1.2},
},
typs: []*types.T{types.Int, types.Int, types.Decimal},
expected: tuples{
{3.4, 3},
{1.2, 1},
},
name: "SumMultiType",
convToDecimal: true,
},
{
aggFns: []execinfrapb.AggregatorSpec_Func{execinfrapb.AggregatorSpec_AVG, execinfrapb.AggregatorSpec_SUM},
aggCols: [][]uint32{
{1}, {1},
},
input: tuples{
{0, 1.1},
{0, 1.2},
{0, 2.3},
{1, 6.21},
{1, 2.43},
},
typs: []*types.T{types.Int, types.Decimal},
expected: tuples{
{"1.5333333333333333333", 4.6},
{4.32, 8.64},
},
name: "AvgSumSingleInputBatch",
convToDecimal: true,
},
{
aggFns: []execinfrapb.AggregatorSpec_Func{
execinfrapb.AggregatorSpec_BOOL_AND,
execinfrapb.AggregatorSpec_BOOL_OR,
},
aggCols: [][]uint32{
{1}, {1},
},
input: tuples{
{0, true},
{1, false},
{2, true},
{2, false},
{3, true},
{3, true},
{4, false},
{4, false},
{5, false},
{5, nil},
{6, nil},
{6, true},
{7, nil},
{7, false},
{7, true},
{8, nil},
{8, nil},
},
typs: []*types.T{types.Int, types.Bool},
expected: tuples{
{true, true},
{false, false},
{false, true},
{true, true},
{false, false},
{false, false},
{true, true},
{false, true},
{nil, nil},
},
name: "BoolAndOrBatch",
},
{
aggFns: []execinfrapb.AggregatorSpec_Func{
execinfrapb.AggregatorSpec_ANY_NOT_NULL,
execinfrapb.AggregatorSpec_ANY_NOT_NULL,
execinfrapb.AggregatorSpec_ANY_NOT_NULL,
execinfrapb.AggregatorSpec_MIN,
execinfrapb.AggregatorSpec_SUM,
},
input: tuples{
{2, 1.0, "1.0", 2.0},
{2, 1.0, "1.0", 4.0},
{2, 2.0, "2.0", 6.0},
},
expected: tuples{
{2, 1.0, "1.0", 2.0, 6.0},
{2, 2.0, "2.0", 6.0, 6.0},
},
typs: []*types.T{types.Int, types.Decimal, types.Bytes, types.Decimal},
name: "MultiGroupColsWithPointerTypes",
groupCols: []uint32{0, 1, 2},
aggCols: [][]uint32{
{0}, {1}, {2}, {3}, {3},
},
},
{
aggFns: []execinfrapb.AggregatorSpec_Func{
execinfrapb.AggregatorSpec_ANY_NOT_NULL,
execinfrapb.AggregatorSpec_SUM_INT,
},
input: tuples{
{tree.NewDTimeTZFromOffset(timeofday.FromInt(0), 0), -1},
{tree.NewDTimeTZFromOffset(timeofday.FromInt(0), 1), 1},
{tree.NewDTimeTZFromOffset(timeofday.FromInt(0), 1), 2},
{tree.NewDTimeTZFromOffset(timeofday.FromInt(0), 2), 10},
{tree.NewDTimeTZFromOffset(timeofday.FromInt(0), 2), 11},
{tree.NewDTimeTZFromOffset(timeofday.FromInt(0), 3), 100},
{tree.NewDTimeTZFromOffset(timeofday.FromInt(0), 3), 101},
{tree.NewDTimeTZFromOffset(timeofday.FromInt(0), 4), 102},
},
expected: tuples{
{tree.NewDTimeTZFromOffset(timeofday.FromInt(0), 0), -1},
{tree.NewDTimeTZFromOffset(timeofday.FromInt(0), 1), 3},
{tree.NewDTimeTZFromOffset(timeofday.FromInt(0), 2), 21},
{tree.NewDTimeTZFromOffset(timeofday.FromInt(0), 3), 201},
{tree.NewDTimeTZFromOffset(timeofday.FromInt(0), 4), 102},
},
typs: []*types.T{types.TimeTZ, types.Int},
name: "GroupOnTimeTZColumns",
groupCols: []uint32{0},
aggCols: [][]uint32{
{0}, {1},
},
},
{
input: tuples{
{0, nil, 1, 1, 1.0, 1.0, duration.MakeDuration(1, 1, 1)},
{0, 1, nil, 2, 2.0, 2.0, duration.MakeDuration(2, 2, 2)},
{0, 2, 2, nil, 3.0, 3.0, duration.MakeDuration(3, 3, 3)},
{0, 3, 3, 3, nil, 4.0, duration.MakeDuration(4, 4, 4)},
{0, 4, 4, 4, 4.0, nil, duration.MakeDuration(5, 5, 5)},
{0, 5, 5, 5, 5.0, 5.0, nil},
},
expected: tuples{
{3.0, 3.0, 3.0, 3.0, 3.0, duration.MakeDuration(3, 3, 3)},
},
typs: []*types.T{types.Int, types.Int2, types.Int4, types.Int, types.Decimal, types.Float, types.Interval},
aggFns: []execinfrapb.AggregatorSpec_Func{avgFn, avgFn, avgFn, avgFn, avgFn, avgFn},
aggCols: [][]uint32{{1}, {2}, {3}, {4}, {5}, {6}},
name: "AVG on all types",
},
{
input: tuples{
{1, "1"},
{1, "2"},
{1, "3"},
{2, nil},
{2, "1"},
{2, "2"},
{3, "1"},
{3, nil},
{3, "2"},
{4, nil},
{4, nil},
},
expected: tuples{
{"123"},
{"12"},
{"12"},
{nil},
},
typs: []*types.T{types.Int, types.Bytes},
aggFns: []execinfrapb.AggregatorSpec_Func{execinfrapb.AggregatorSpec_CONCAT_AGG},
groupCols: []uint32{0},
aggCols: [][]uint32{{1}},
},
}
evalCtx := tree.MakeTestingEvalContext(cluster.MakeTestingClusterSettings())
defer evalCtx.Stop(context.Background())
for _, agg := range aggTypes {
for _, tc := range testCases {
log.Infof(context.Background(), "%s/%s/Randomized", agg.name, tc.name)
if err := tc.init(); err != nil {
t.Fatal(err)
}
constructors, constArguments, outputTypes, err := colexecagg.ProcessAggregations(
&evalCtx, nil /* semaCtx */, tc.spec.Aggregations, tc.typs,
)
require.NoError(t, err)
runTestsWithTyps(t, []tuples{tc.input}, [][]*types.T{tc.typs}, tc.expected, unorderedVerifier,
func(input []colexecbase.Operator) (colexecbase.Operator, error) {
return agg.new(
testAllocator, testMemAcc, input[0], tc.typs, tc.spec, &evalCtx,
constructors, constArguments, outputTypes, false, /* isScalar */
)
})
}
}
}
func TestAggregatorAllFunctions(t *testing.T) {
defer leaktest.AfterTest(t)()
defer log.Scope(t).Close(t)
testCases := []aggregatorTestCase{
{
aggFns: []execinfrapb.AggregatorSpec_Func{
execinfrapb.AggregatorSpec_ANY_NOT_NULL,
execinfrapb.AggregatorSpec_ANY_NOT_NULL,
execinfrapb.AggregatorSpec_AVG,
execinfrapb.AggregatorSpec_COUNT_ROWS,
execinfrapb.AggregatorSpec_COUNT,
execinfrapb.AggregatorSpec_SUM,
execinfrapb.AggregatorSpec_SUM_INT,
execinfrapb.AggregatorSpec_MIN,
execinfrapb.AggregatorSpec_MAX,
execinfrapb.AggregatorSpec_BOOL_AND,
execinfrapb.AggregatorSpec_BOOL_OR,
execinfrapb.AggregatorSpec_CONCAT_AGG,
},
aggCols: [][]uint32{{0}, {4}, {1}, {}, {1}, {1}, {2}, {2}, {2}, {3}, {3}, {4}},
typs: []*types.T{types.Int, types.Decimal, types.Int, types.Bool, types.Bytes},
input: tuples{
{0, 3.1, 2, true, "zero"},
{0, 1.1, 3, false, "zero"},
{1, 1.1, 1, false, "one"},
{1, 4.1, 0, false, "one"},
{2, 1.1, 1, true, "two"},
{3, 4.1, 0, false, "three"},
{3, 5.1, 0, true, "three"},
},
expected: tuples{
{0, "zero", 2.1, 2, 2, 4.2, 5, 2, 3, false, true, "zerozero"},
{1, "one", 2.6, 2, 2, 5.2, 1, 0, 1, false, false, "oneone"},
{2, "two", 1.1, 1, 1, 1.1, 1, 1, 1, true, true, "two"},
{3, "three", 4.6, 2, 2, 9.2, 0, 0, 0, false, true, "threethree"},
},
convToDecimal: true,
},
// Test case for null handling.
{
aggFns: []execinfrapb.AggregatorSpec_Func{
execinfrapb.AggregatorSpec_ANY_NOT_NULL,
execinfrapb.AggregatorSpec_ANY_NOT_NULL,
execinfrapb.AggregatorSpec_COUNT_ROWS,
execinfrapb.AggregatorSpec_COUNT,
execinfrapb.AggregatorSpec_SUM,
execinfrapb.AggregatorSpec_SUM_INT,
execinfrapb.AggregatorSpec_MIN,
execinfrapb.AggregatorSpec_MAX,
execinfrapb.AggregatorSpec_AVG,
execinfrapb.AggregatorSpec_BOOL_AND,
execinfrapb.AggregatorSpec_BOOL_OR,
execinfrapb.AggregatorSpec_CONCAT_AGG,
},
aggCols: [][]uint32{{0}, {1}, {}, {1}, {1}, {2}, {2}, {2}, {1}, {3}, {3}, {4}},
typs: []*types.T{types.Int, types.Decimal, types.Int, types.Bool, types.Bytes},
input: tuples{
{nil, 1.1, 4, true, "a"},
{0, nil, nil, nil, nil},
{0, 3.1, 5, nil, "b"},
{1, nil, nil, nil, nil},
{1, nil, nil, false, nil},
},
expected: tuples{
{nil, 1.1, 1, 1, 1.1, 4, 4, 4, 1.1, true, true, "a"},
{0, 3.1, 2, 1, 3.1, 5, 5, 5, 3.1, nil, nil, "b"},
{1, nil, 2, 0, nil, nil, nil, nil, nil, false, false, nil},
},
convToDecimal: true,
},
// Test DISTINCT aggregation.
{
aggFns: []execinfrapb.AggregatorSpec_Func{
execinfrapb.AggregatorSpec_ANY_NOT_NULL,
execinfrapb.AggregatorSpec_COUNT,
execinfrapb.AggregatorSpec_COUNT,
execinfrapb.AggregatorSpec_SUM_INT,
execinfrapb.AggregatorSpec_SUM_INT,
},
aggCols: [][]uint32{{0}, {1}, {1}, {1}, {1}},
aggDistinct: []bool{false, false, true, false, true},
typs: []*types.T{types.Int, types.Int},
input: tuples{
{0, 1},
{0, 2},
{0, 2},
{0, nil},
{0, 1},
{0, nil},
{1, 1},
{1, 2},
{1, 2},
},
expected: tuples{
{0, 4, 2, 6, 3},
{1, 3, 2, 5, 3},
},
},
// Test aggregation with FILTERs.
{
aggFns: []execinfrapb.AggregatorSpec_Func{
execinfrapb.AggregatorSpec_ANY_NOT_NULL,
execinfrapb.AggregatorSpec_COUNT_ROWS,
execinfrapb.AggregatorSpec_SUM_INT,
},
aggCols: [][]uint32{{0}, {}, {1}},
aggFilter: []int{tree.NoColumnIdx, 2, 2},
typs: []*types.T{types.Int, types.Int, types.Bool},
input: tuples{
{0, 1, false},
{0, 2, true},
{0, 2, true},
{0, nil, nil},
{0, 1, nil},
{0, nil, true},
{1, 1, true},
{1, 2, nil},
{1, 2, true},
},
expected: tuples{
{0, 3, 4},
{1, 2, 3},
},
},
// Test aggregation with FILTERs when the whole groups are filtered out.
{
aggFns: []execinfrapb.AggregatorSpec_Func{
execinfrapb.AggregatorSpec_ANY_NOT_NULL,
execinfrapb.AggregatorSpec_COUNT_ROWS,
execinfrapb.AggregatorSpec_SUM_INT,
},
aggCols: [][]uint32{{0}, {}, {1}},
aggFilter: []int{tree.NoColumnIdx, 2, 2},
typs: []*types.T{types.Int, types.Int, types.Bool},
input: tuples{
{0, 1, false},
{0, nil, nil},
{0, 2, false},
{1, 1, true},
{1, 2, nil},
{1, 2, true},
{2, 1, false},
{2, nil, nil},
{2, 2, nil},
},
expected: tuples{
{0, 0, nil},
{1, 2, 3},
{2, 0, nil},
},
},
// Test aggregation with FILTERs and DISTINCTs intertwined.
{
aggFns: []execinfrapb.AggregatorSpec_Func{
execinfrapb.AggregatorSpec_ANY_NOT_NULL,
execinfrapb.AggregatorSpec_COUNT,
execinfrapb.AggregatorSpec_COUNT,
execinfrapb.AggregatorSpec_COUNT,
execinfrapb.AggregatorSpec_SUM_INT,
execinfrapb.AggregatorSpec_SUM_INT,
execinfrapb.AggregatorSpec_SUM_INT,
},
aggCols: [][]uint32{{0}, {1}, {1}, {1}, {1}, {1}, {1}},
aggDistinct: []bool{false, false, true, true, false, true, true},
aggFilter: []int{tree.NoColumnIdx, 2, tree.NoColumnIdx, 2, 2, tree.NoColumnIdx, 2},
typs: []*types.T{types.Int, types.Int, types.Bool},
input: tuples{
{0, 1, false},
{0, 2, true},
{0, 2, true},
{0, nil, nil},
{0, 1, nil},
{0, nil, true},
{1, 1, true},
{1, 2, nil},
{1, 2, true},
},
expected: tuples{
{0, 2, 2, 1, 4, 3, 2},
{1, 2, 2, 2, 3, 3, 3},
},
},
}
evalCtx := tree.MakeTestingEvalContext(cluster.MakeTestingClusterSettings())
defer evalCtx.Stop(context.Background())
for _, agg := range aggTypes {
for i, tc := range testCases {
if agg.name != "hash" && tc.aggFilter != nil {
// Filtering aggregation is only supported with hash aggregator.
continue
}
log.Infof(context.Background(), "%s/%d", agg.name, i)
if err := tc.init(); err != nil {
t.Fatal(err)
}
constructors, constArguments, outputTypes, err := colexecagg.ProcessAggregations(
&evalCtx, nil /* semaCtx */, tc.spec.Aggregations, tc.typs,
)
require.NoError(t, err)
verifier := orderedVerifier
if strings.Contains(agg.name, "hash") {
verifier = unorderedVerifier
}
runTestsWithTyps(
t,
[]tuples{tc.input},
[][]*types.T{tc.typs},
tc.expected,
verifier,
func(input []colexecbase.Operator) (colexecbase.Operator, error) {
return agg.new(
testAllocator, testMemAcc, input[0], tc.typs, tc.spec, &evalCtx,
constructors, constArguments, outputTypes, false, /* isScalar */
)
})
}
}
}
func TestAggregatorRandom(t *testing.T) {
defer leaktest.AfterTest(t)()
defer log.Scope(t).Close(t)
evalCtx := tree.MakeTestingEvalContext(cluster.MakeTestingClusterSettings())
defer evalCtx.Stop(context.Background())
// This test aggregates random inputs, keeping track of the expected results
// to make sure the aggregations are correct.
rng, _ := randutil.NewPseudoRand()
for _, groupSize := range []int{1, 2, coldata.BatchSize() / 4, coldata.BatchSize() / 2} {
if groupSize == 0 {
// We might be varying coldata.BatchSize() so that when it is divided by
// 4, groupSize is 0. We want to skip such configuration.
continue
}
for _, numInputBatches := range []int{1, 2, 64} {
for _, hasNulls := range []bool{true, false} {
for _, agg := range aggTypes {
log.Infof(context.Background(), "%s/groupSize=%d/numInputBatches=%d/hasNulls=%t", agg.name, groupSize, numInputBatches, hasNulls)
nTuples := coldata.BatchSize() * numInputBatches
typs := []*types.T{types.Int, types.Float}
cols := []coldata.Vec{
testAllocator.NewMemColumn(typs[0], nTuples),
testAllocator.NewMemColumn(typs[1], nTuples),
}
groups, aggCol, aggColNulls := cols[0].Int64(), cols[1].Float64(), cols[1].Nulls()
expectedTuples := tuples{}
var expRowCounts, expCounts []int64
var expSums, expMins, expMaxs []float64
// SUM, MIN, MAX, and AVG aggregators can output null.
var expNulls []bool
curGroup := -1
for i := range groups {
if i%groupSize == 0 {
if curGroup != -1 {
if expNulls[curGroup] {
expectedTuples = append(expectedTuples, tuple{
expRowCounts[curGroup], expCounts[curGroup], nil, nil, nil, nil,
})
} else {
expectedTuples = append(expectedTuples, tuple{
expRowCounts[curGroup], expCounts[curGroup], expSums[curGroup], expMins[curGroup], expMaxs[curGroup], expSums[curGroup] / float64(expCounts[curGroup]),
})
}
}
expRowCounts = append(expRowCounts, 0)
expCounts = append(expCounts, 0)
expSums = append(expSums, 0)
expMins = append(expMins, 2048)
expMaxs = append(expMaxs, -2048)
expNulls = append(expNulls, true)
curGroup++
}
// Keep the inputs small so they are a realistic size. Using a
// large range is not realistic and makes decimal operations
// slower.
aggCol[i] = 2048 * (rng.Float64() - 0.5)
// NULL values contribute to the row count, so we're updating
// the row counts outside of the if block.
expRowCounts[curGroup]++
if hasNulls && rng.Float64() < nullProbability {
aggColNulls.SetNull(i)
} else {
expNulls[curGroup] = false
expCounts[curGroup]++
expSums[curGroup] += aggCol[i]
expMins[curGroup] = min64(aggCol[i], expMins[curGroup])
expMaxs[curGroup] = max64(aggCol[i], expMaxs[curGroup])
}
groups[i] = int64(curGroup)
}
// Add result for last group.
if expNulls[curGroup] {
expectedTuples = append(expectedTuples, tuple{
expRowCounts[curGroup], expCounts[curGroup], nil, nil, nil, nil,
})
} else {
expectedTuples = append(expectedTuples, tuple{
expRowCounts[curGroup], expCounts[curGroup], expSums[curGroup], expMins[curGroup], expMaxs[curGroup], expSums[curGroup] / float64(expCounts[curGroup]),
})
}
source := newChunkingBatchSource(typs, cols, nTuples)
tc := aggregatorTestCase{
typs: typs,
aggFns: []execinfrapb.AggregatorSpec_Func{
execinfrapb.AggregatorSpec_COUNT_ROWS,
execinfrapb.AggregatorSpec_COUNT,
execinfrapb.AggregatorSpec_SUM,
execinfrapb.AggregatorSpec_MIN,
execinfrapb.AggregatorSpec_MAX,
execinfrapb.AggregatorSpec_AVG,
},
groupCols: []uint32{0},
aggCols: [][]uint32{{}, {1}, {1}, {1}, {1}, {1}},
}
require.NoError(t, tc.init())
constructors, constArguments, outputTypes, err := colexecagg.ProcessAggregations(
&evalCtx, nil /* semaCtx */, tc.spec.Aggregations, tc.typs,
)
require.NoError(t, err)
a, err := agg.new(
testAllocator, testMemAcc, source, tc.typs, tc.spec, &evalCtx,
constructors, constArguments, outputTypes, false, /* isScalar */
)
if err != nil {
t.Fatal(err)
}
a.Init()
testOutput := newOpTestOutput(a, expectedTuples)
if strings.Contains(agg.name, "hash") {
err = testOutput.VerifyAnyOrder()
} else {
err = testOutput.Verify()
}
if err != nil {
t.Fatal(err)
}
}
}
}
}
}
func benchmarkAggregateFunction(
b *testing.B,
agg aggType,
aggFn execinfrapb.AggregatorSpec_Func,
aggInputTypes []*types.T,
groupSize int,
distinctProb float64,
numInputRows int,
) {
if groupSize > numInputRows {
// In this case all tuples will be part of the same group, and we have
// likely already benchmarked such scenario with this value of
// numInputRows, so we short-circuit.
return
}
rng, _ := randutil.NewPseudoRand()
ctx := context.Background()
evalCtx := tree.MakeTestingEvalContext(cluster.MakeTestingClusterSettings())
defer evalCtx.Stop(ctx)
aggMemAcc := evalCtx.Mon.MakeBoundAccount()
defer aggMemAcc.Close(ctx)
evalCtx.SingleDatumAggMemAccount = &aggMemAcc
const bytesFixedLength = 8
typs := append([]*types.T{types.Int}, aggInputTypes...)
cols := make([]coldata.Vec, len(typs))
for i := range typs {
cols[i] = testAllocator.NewMemColumn(typs[i], numInputRows)
}
groups := cols[0].Int64()
if agg.name == "hash" {
numGroups := numInputRows / groupSize
for i := 0; i < numInputRows; i++ {
groups[i] = int64(rng.Intn(numGroups))
}
} else {
curGroup := -1
for i := 0; i < numInputRows; i++ {
if groupSize == 1 || i%groupSize == 0 {
curGroup++
}
groups[i] = int64(curGroup)
}
}
for _, col := range cols[1:] {
coldatatestutils.RandomVec(coldatatestutils.RandomVecArgs{
Rand: rng,
Vec: col,
N: numInputRows,
NullProbability: 0,
BytesFixedLength: bytesFixedLength,
})
}
if aggFn == execinfrapb.AggregatorSpec_SUM_INT {
// Integer summation of random Int64 values can lead