VictoriaMetrics/app/vmselect/promql/aggr_incremental_test.go
Aliaksandr Valialkin 6875fb411a app/vmselect/promql: parallelize incremental aggregation to multiple CPU cores
This may reduce response times for aggregation over big number of time series
with small step between output data points.
2019-07-12 15:53:12 +03:00

187 lines
4.9 KiB
Go

package promql
import (
"fmt"
"math"
"reflect"
"runtime"
"sync"
"testing"
)
func TestIncrementalAggr(t *testing.T) {
defaultTimestamps := []int64{100e3, 200e3, 300e3, 400e3}
values := [][]float64{
{1, nan, 2, nan},
{3, nan, nan, 4},
{nan, nan, 5, 6},
{7, nan, 8, 9},
{4, nan, nan, nan},
{2, nan, 3, 2},
{0, nan, 1, 1},
}
tssSrc := make([]*timeseries, len(values))
for i, vs := range values {
ts := &timeseries{
Timestamps: defaultTimestamps,
Values: vs,
}
tssSrc[i] = ts
}
copyTimeseries := func(tssSrc []*timeseries) []*timeseries {
tssDst := make([]*timeseries, len(tssSrc))
for i, tsSrc := range tssSrc {
var tsDst timeseries
tsDst.CopyFromShallowTimestamps(tsSrc)
tssDst[i] = &tsDst
}
return tssDst
}
f := func(name string, valuesExpected []float64) {
t.Helper()
callbacks := getIncrementalAggrFuncCallbacks(name)
ae := &aggrFuncExpr{
Name: name,
}
tssExpected := []*timeseries{{
Timestamps: defaultTimestamps,
Values: valuesExpected,
}}
// run the test multiple times to make sure there are no side effects on concurrency
for i := 0; i < 10; i++ {
iafc := newIncrementalAggrFuncContext(ae, callbacks)
tssSrcCopy := copyTimeseries(tssSrc)
if err := testIncrementalParallelAggr(iafc, tssSrcCopy, tssExpected); err != nil {
t.Fatalf("unexpected error on iteration %d: %s", i, err)
}
}
}
t.Run("sum", func(t *testing.T) {
t.Parallel()
valuesExpected := []float64{17, nan, 19, 22}
f("sum", valuesExpected)
})
t.Run("min", func(t *testing.T) {
t.Parallel()
valuesExpected := []float64{0, nan, 1, 1}
f("min", valuesExpected)
})
t.Run("max", func(t *testing.T) {
t.Parallel()
valuesExpected := []float64{7, nan, 8, 9}
f("max", valuesExpected)
})
t.Run("avg", func(t *testing.T) {
t.Parallel()
valuesExpected := []float64{2.8333333333333335, nan, 3.8, 4.4}
f("avg", valuesExpected)
})
t.Run("count", func(t *testing.T) {
t.Parallel()
valuesExpected := []float64{6, 0, 5, 5}
f("count", valuesExpected)
})
t.Run("sum2", func(t *testing.T) {
t.Parallel()
valuesExpected := []float64{79, nan, 103, 138}
f("sum2", valuesExpected)
})
t.Run("geomean", func(t *testing.T) {
t.Parallel()
valuesExpected := []float64{0, nan, 2.9925557394776896, 3.365865436338599}
f("geomean", valuesExpected)
})
}
func testIncrementalParallelAggr(iafc *incrementalAggrFuncContext, tssSrc, tssExpected []*timeseries) error {
const workersCount = 3
tsCh := make(chan *timeseries)
var wg sync.WaitGroup
wg.Add(workersCount)
for i := 0; i < workersCount; i++ {
go func(workerID uint) {
defer wg.Done()
for ts := range tsCh {
runtime.Gosched() // allow other goroutines performing the work
iafc.updateTimeseries(ts, workerID)
}
}(uint(i))
}
for _, ts := range tssSrc {
tsCh <- ts
}
close(tsCh)
wg.Wait()
tssActual := iafc.finalizeTimeseries()
if err := expectTimeseriesEqual(tssActual, tssExpected); err != nil {
return fmt.Errorf("%s; tssActual=%v, tssExpected=%v", err, tssActual, tssExpected)
}
return nil
}
func expectTimeseriesEqual(actual, expected []*timeseries) error {
if len(actual) != len(expected) {
return fmt.Errorf("unexpected number of time series; got %d; want %d", len(actual), len(expected))
}
mActual := timeseriesToMap(actual)
mExpected := timeseriesToMap(expected)
if len(mActual) != len(mExpected) {
return fmt.Errorf("unexpected number of time series after converting to map; got %d; want %d", len(mActual), len(mExpected))
}
for k, tsExpected := range mExpected {
tsActual := mActual[k]
if tsActual == nil {
return fmt.Errorf("missing time series for key=%q", k)
}
if err := expectTsEqual(tsActual, tsExpected); err != nil {
return err
}
}
return nil
}
func timeseriesToMap(tss []*timeseries) map[string]*timeseries {
m := make(map[string]*timeseries, len(tss))
for _, ts := range tss {
k := ts.MetricName.Marshal(nil)
m[string(k)] = ts
}
return m
}
func expectTsEqual(actual, expected *timeseries) error {
mnActual := actual.MetricName.Marshal(nil)
mnExpected := expected.MetricName.Marshal(nil)
if string(mnActual) != string(mnExpected) {
return fmt.Errorf("unexpected metric name; got %q; want %q", mnActual, mnExpected)
}
if !reflect.DeepEqual(actual.Timestamps, expected.Timestamps) {
return fmt.Errorf("unexpected timestamps; got %v; want %v", actual.Timestamps, expected.Timestamps)
}
if err := compareValues(actual.Values, expected.Values); err != nil {
return fmt.Errorf("%s; actual %v; expected %v", err, actual.Values, expected.Values)
}
return nil
}
func compareValues(vs1, vs2 []float64) error {
if len(vs1) != len(vs2) {
return fmt.Errorf("unexpected number of values; got %d; want %d", len(vs1), len(vs2))
}
for i, v1 := range vs1 {
v2 := vs2[i]
if math.IsNaN(v1) {
if !math.IsNaN(v2) {
return fmt.Errorf("unexpected value; got %v; want %v", v1, v2)
}
continue
}
if v1 != v2 {
return fmt.Errorf("unexpected value; got %v; want %v", v1, v2)
}
}
return nil
}