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prometheus/promql/test.go

840 lines
22 KiB

// Copyright 2015 The Prometheus Authors
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
package promql
import (
"context"
"embed"
"errors"
"fmt"
"io/fs"
"math"
"strconv"
"strings"
"testing"
"time"
"github.com/grafana/regexp"
"github.com/prometheus/common/model"
"github.com/stretchr/testify/require"
"github.com/prometheus/prometheus/model/exemplar"
"github.com/prometheus/prometheus/model/histogram"
"github.com/prometheus/prometheus/model/labels"
"github.com/prometheus/prometheus/model/timestamp"
"github.com/prometheus/prometheus/promql/parser"
"github.com/prometheus/prometheus/promql/parser/posrange"
"github.com/prometheus/prometheus/storage"
"github.com/prometheus/prometheus/util/teststorage"
"github.com/prometheus/prometheus/util/testutil"
)
var (
minNormal = math.Float64frombits(0x0010000000000000) // The smallest positive normal value of type float64.
patSpace = regexp.MustCompile("[\t ]+")
patLoad = regexp.MustCompile(`^load\s+(.+?)$`)
patEvalInstant = regexp.MustCompile(`^eval(?:_(fail|ordered))?\s+instant\s+(?:at\s+(.+?))?\s+(.+)$`)
)
const (
epsilon = 0.000001 // Relative error allowed for sample values.
)
var testStartTime = time.Unix(0, 0).UTC()
// LoadedStorage returns storage with generated data using the provided load statements.
// Non-load statements will cause test errors.
func LoadedStorage(t testutil.T, input string) *teststorage.TestStorage {
test, err := newTest(t, input)
require.NoError(t, err)
for _, cmd := range test.cmds {
switch cmd.(type) {
case *loadCmd:
require.NoError(t, test.exec(cmd, nil))
default:
t.Errorf("only 'load' commands accepted, got '%s'", cmd)
}
}
return test.storage
}
// RunBuiltinTests runs an acceptance test suite against the provided engine.
func RunBuiltinTests(t *testing.T, engine engineQuerier) {
files, err := fs.Glob(testsFs, "*/*.test")
require.NoError(t, err)
for _, fn := range files {
t.Run(fn, func(t *testing.T) {
content, err := fs.ReadFile(testsFs, fn)
require.NoError(t, err)
RunTest(t, string(content), engine)
})
}
}
// RunTest parses and runs the test against the provided engine.
func RunTest(t testutil.T, input string, engine engineQuerier) {
test, err := newTest(t, input)
require.NoError(t, err)
defer func() {
if test.storage != nil {
test.storage.Close()
}
if test.cancelCtx != nil {
test.cancelCtx()
}
}()
for _, cmd := range test.cmds {
// TODO(fabxc): aggregate command errors, yield diffs for result
// comparison errors.
require.NoError(t, test.exec(cmd, engine))
}
}
// test is a sequence of read and write commands that are run
// against a test storage.
type test struct {
testutil.T
cmds []testCommand
storage *teststorage.TestStorage
context context.Context
cancelCtx context.CancelFunc
}
// newTest returns an initialized empty Test.
func newTest(t testutil.T, input string) (*test, error) {
test := &test{
T: t,
cmds: []testCommand{},
}
err := test.parse(input)
test.clear()
return test, err
}
//go:embed testdata
var testsFs embed.FS
type engineQuerier interface {
NewRangeQuery(ctx context.Context, q storage.Queryable, opts QueryOpts, qs string, start, end time.Time, interval time.Duration) (Query, error)
NewInstantQuery(ctx context.Context, q storage.Queryable, opts QueryOpts, qs string, ts time.Time) (Query, error)
}
func raise(line int, format string, v ...interface{}) error {
return &parser.ParseErr{
LineOffset: line,
Err: fmt.Errorf(format, v...),
}
}
func parseLoad(lines []string, i int) (int, *loadCmd, error) {
if !patLoad.MatchString(lines[i]) {
return i, nil, raise(i, "invalid load command. (load <step:duration>)")
}
parts := patLoad.FindStringSubmatch(lines[i])
gap, err := model.ParseDuration(parts[1])
if err != nil {
return i, nil, raise(i, "invalid step definition %q: %s", parts[1], err)
}
cmd := newLoadCmd(time.Duration(gap))
for i+1 < len(lines) {
i++
defLine := lines[i]
if len(defLine) == 0 {
i--
break
}
metric, vals, err := parseSeries(defLine, i)
if err != nil {
return i, nil, err
}
cmd.set(metric, vals...)
}
return i, cmd, nil
}
func parseSeries(defLine string, line int) (labels.Labels, []parser.SequenceValue, error) {
metric, vals, err := parser.ParseSeriesDesc(defLine)
if err != nil {
parser.EnrichParseError(err, func(parseErr *parser.ParseErr) {
parseErr.LineOffset = line
})
return labels.Labels{}, nil, err
}
return metric, vals, nil
}
func (t *test) parseEval(lines []string, i int) (int, *evalCmd, error) {
if !patEvalInstant.MatchString(lines[i]) {
return i, nil, raise(i, "invalid evaluation command. (eval[_fail|_ordered] instant [at <offset:duration>] <query>")
}
parts := patEvalInstant.FindStringSubmatch(lines[i])
var (
mod = parts[1]
at = parts[2]
expr = parts[3]
)
_, err := parser.ParseExpr(expr)
if err != nil {
parser.EnrichParseError(err, func(parseErr *parser.ParseErr) {
parseErr.LineOffset = i
posOffset := posrange.Pos(strings.Index(lines[i], expr))
parseErr.PositionRange.Start += posOffset
parseErr.PositionRange.End += posOffset
parseErr.Query = lines[i]
})
return i, nil, err
}
offset, err := model.ParseDuration(at)
if err != nil {
return i, nil, raise(i, "invalid step definition %q: %s", parts[1], err)
}
ts := testStartTime.Add(time.Duration(offset))
cmd := newEvalCmd(expr, ts, i+1)
switch mod {
case "ordered":
cmd.ordered = true
case "fail":
cmd.fail = true
}
for j := 1; i+1 < len(lines); j++ {
i++
defLine := lines[i]
if len(defLine) == 0 {
i--
break
}
if f, err := parseNumber(defLine); err == nil {
cmd.expect(0, parser.SequenceValue{Value: f})
break
}
metric, vals, err := parseSeries(defLine, i)
if err != nil {
return i, nil, err
}
// Currently, we are not expecting any matrices.
if len(vals) > 1 {
return i, nil, raise(i, "expecting multiple values in instant evaluation not allowed")
}
cmd.expectMetric(j, metric, vals...)
}
return i, cmd, nil
}
// getLines returns trimmed lines after removing the comments.
func getLines(input string) []string {
lines := strings.Split(input, "\n")
for i, l := range lines {
l = strings.TrimSpace(l)
if strings.HasPrefix(l, "#") {
l = ""
}
lines[i] = l
}
return lines
}
// parse the given command sequence and appends it to the test.
func (t *test) parse(input string) error {
lines := getLines(input)
var err error
// Scan for steps line by line.
for i := 0; i < len(lines); i++ {
l := lines[i]
if len(l) == 0 {
continue
}
var cmd testCommand
switch c := strings.ToLower(patSpace.Split(l, 2)[0]); {
case c == "clear":
cmd = &clearCmd{}
case c == "load":
i, cmd, err = parseLoad(lines, i)
case strings.HasPrefix(c, "eval"):
i, cmd, err = t.parseEval(lines, i)
default:
return raise(i, "invalid command %q", l)
}
if err != nil {
return err
}
t.cmds = append(t.cmds, cmd)
}
return nil
}
// testCommand is an interface that ensures that only the package internal
// types can be a valid command for a test.
type testCommand interface {
testCmd()
}
func (*clearCmd) testCmd() {}
func (*loadCmd) testCmd() {}
func (*evalCmd) testCmd() {}
// loadCmd is a command that loads sequences of sample values for specific
// metrics into the storage.
type loadCmd struct {
Add Exemplar Remote Write support (#8296) * Write exemplars to the WAL and send them over remote write. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Update example for exemplars, print data in a more obvious format. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Add metrics for remote write of exemplars. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Fix incorrect slices passed to send in remote write. Signed-off-by: Callum Styan <callumstyan@gmail.com> * We need to unregister the new metrics. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Address review comments Signed-off-by: Callum Styan <callumstyan@gmail.com> * Order of exemplar append vs write exemplar to WAL needs to change. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Several fixes to prevent sending uninitialized or incorrect samples with an exemplar. Fix dropping exemplar for missing series. Add tests for queue_manager sending exemplars Signed-off-by: Martin Disibio <mdisibio@gmail.com> * Store both samples and exemplars in the same timeseries buffer to remove the alloc when building final request, keep sub-slices in separate buffers for re-use Signed-off-by: Martin Disibio <mdisibio@gmail.com> * Condense sample/exemplar delivery tests to parameterized sub-tests Signed-off-by: Martin Disibio <mdisibio@gmail.com> * Rename test methods for clarity now that they also handle exemplars Signed-off-by: Martin Disibio <mdisibio@gmail.com> * Rename counter variable. Fix instances where metrics were not updated correctly Signed-off-by: Martin Disibio <mdisibio@gmail.com> * Add exemplars to LoadWAL benchmark Signed-off-by: Callum Styan <callumstyan@gmail.com> * last exemplars timestamp metric needs to convert value to seconds with ms precision Signed-off-by: Callum Styan <callumstyan@gmail.com> * Process exemplar records in a separate go routine when loading the WAL. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Address review comments related to clarifying comments and variable names. Also refactor sample/exemplar to enqueue prompb types. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Regenerate types proto with comments, update protoc version again. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Put remote write of exemplars behind a feature flag. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Address some of Ganesh's review comments. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Move exemplar remote write feature flag to a config file field. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Address Bartek's review comments. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Don't allocate exemplar buffers in queue_manager if we're not going to send exemplars over remote write. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Add ValidateExemplar function, validate exemplars when appending to head and log them all to WAL before adding them to exemplar storage. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Address more reivew comments from Ganesh. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Add exemplar total label length check. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Address a few last review comments Signed-off-by: Callum Styan <callumstyan@gmail.com> Co-authored-by: Martin Disibio <mdisibio@gmail.com>
4 years ago
gap time.Duration
metrics map[uint64]labels.Labels
defs map[uint64][]Sample
Add Exemplar Remote Write support (#8296) * Write exemplars to the WAL and send them over remote write. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Update example for exemplars, print data in a more obvious format. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Add metrics for remote write of exemplars. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Fix incorrect slices passed to send in remote write. Signed-off-by: Callum Styan <callumstyan@gmail.com> * We need to unregister the new metrics. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Address review comments Signed-off-by: Callum Styan <callumstyan@gmail.com> * Order of exemplar append vs write exemplar to WAL needs to change. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Several fixes to prevent sending uninitialized or incorrect samples with an exemplar. Fix dropping exemplar for missing series. Add tests for queue_manager sending exemplars Signed-off-by: Martin Disibio <mdisibio@gmail.com> * Store both samples and exemplars in the same timeseries buffer to remove the alloc when building final request, keep sub-slices in separate buffers for re-use Signed-off-by: Martin Disibio <mdisibio@gmail.com> * Condense sample/exemplar delivery tests to parameterized sub-tests Signed-off-by: Martin Disibio <mdisibio@gmail.com> * Rename test methods for clarity now that they also handle exemplars Signed-off-by: Martin Disibio <mdisibio@gmail.com> * Rename counter variable. Fix instances where metrics were not updated correctly Signed-off-by: Martin Disibio <mdisibio@gmail.com> * Add exemplars to LoadWAL benchmark Signed-off-by: Callum Styan <callumstyan@gmail.com> * last exemplars timestamp metric needs to convert value to seconds with ms precision Signed-off-by: Callum Styan <callumstyan@gmail.com> * Process exemplar records in a separate go routine when loading the WAL. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Address review comments related to clarifying comments and variable names. Also refactor sample/exemplar to enqueue prompb types. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Regenerate types proto with comments, update protoc version again. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Put remote write of exemplars behind a feature flag. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Address some of Ganesh's review comments. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Move exemplar remote write feature flag to a config file field. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Address Bartek's review comments. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Don't allocate exemplar buffers in queue_manager if we're not going to send exemplars over remote write. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Add ValidateExemplar function, validate exemplars when appending to head and log them all to WAL before adding them to exemplar storage. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Address more reivew comments from Ganesh. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Add exemplar total label length check. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Address a few last review comments Signed-off-by: Callum Styan <callumstyan@gmail.com> Co-authored-by: Martin Disibio <mdisibio@gmail.com>
4 years ago
exemplars map[uint64][]exemplar.Exemplar
}
func newLoadCmd(gap time.Duration) *loadCmd {
return &loadCmd{
Add Exemplar Remote Write support (#8296) * Write exemplars to the WAL and send them over remote write. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Update example for exemplars, print data in a more obvious format. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Add metrics for remote write of exemplars. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Fix incorrect slices passed to send in remote write. Signed-off-by: Callum Styan <callumstyan@gmail.com> * We need to unregister the new metrics. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Address review comments Signed-off-by: Callum Styan <callumstyan@gmail.com> * Order of exemplar append vs write exemplar to WAL needs to change. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Several fixes to prevent sending uninitialized or incorrect samples with an exemplar. Fix dropping exemplar for missing series. Add tests for queue_manager sending exemplars Signed-off-by: Martin Disibio <mdisibio@gmail.com> * Store both samples and exemplars in the same timeseries buffer to remove the alloc when building final request, keep sub-slices in separate buffers for re-use Signed-off-by: Martin Disibio <mdisibio@gmail.com> * Condense sample/exemplar delivery tests to parameterized sub-tests Signed-off-by: Martin Disibio <mdisibio@gmail.com> * Rename test methods for clarity now that they also handle exemplars Signed-off-by: Martin Disibio <mdisibio@gmail.com> * Rename counter variable. Fix instances where metrics were not updated correctly Signed-off-by: Martin Disibio <mdisibio@gmail.com> * Add exemplars to LoadWAL benchmark Signed-off-by: Callum Styan <callumstyan@gmail.com> * last exemplars timestamp metric needs to convert value to seconds with ms precision Signed-off-by: Callum Styan <callumstyan@gmail.com> * Process exemplar records in a separate go routine when loading the WAL. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Address review comments related to clarifying comments and variable names. Also refactor sample/exemplar to enqueue prompb types. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Regenerate types proto with comments, update protoc version again. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Put remote write of exemplars behind a feature flag. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Address some of Ganesh's review comments. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Move exemplar remote write feature flag to a config file field. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Address Bartek's review comments. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Don't allocate exemplar buffers in queue_manager if we're not going to send exemplars over remote write. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Add ValidateExemplar function, validate exemplars when appending to head and log them all to WAL before adding them to exemplar storage. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Address more reivew comments from Ganesh. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Add exemplar total label length check. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Address a few last review comments Signed-off-by: Callum Styan <callumstyan@gmail.com> Co-authored-by: Martin Disibio <mdisibio@gmail.com>
4 years ago
gap: gap,
metrics: map[uint64]labels.Labels{},
defs: map[uint64][]Sample{},
Add Exemplar Remote Write support (#8296) * Write exemplars to the WAL and send them over remote write. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Update example for exemplars, print data in a more obvious format. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Add metrics for remote write of exemplars. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Fix incorrect slices passed to send in remote write. Signed-off-by: Callum Styan <callumstyan@gmail.com> * We need to unregister the new metrics. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Address review comments Signed-off-by: Callum Styan <callumstyan@gmail.com> * Order of exemplar append vs write exemplar to WAL needs to change. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Several fixes to prevent sending uninitialized or incorrect samples with an exemplar. Fix dropping exemplar for missing series. Add tests for queue_manager sending exemplars Signed-off-by: Martin Disibio <mdisibio@gmail.com> * Store both samples and exemplars in the same timeseries buffer to remove the alloc when building final request, keep sub-slices in separate buffers for re-use Signed-off-by: Martin Disibio <mdisibio@gmail.com> * Condense sample/exemplar delivery tests to parameterized sub-tests Signed-off-by: Martin Disibio <mdisibio@gmail.com> * Rename test methods for clarity now that they also handle exemplars Signed-off-by: Martin Disibio <mdisibio@gmail.com> * Rename counter variable. Fix instances where metrics were not updated correctly Signed-off-by: Martin Disibio <mdisibio@gmail.com> * Add exemplars to LoadWAL benchmark Signed-off-by: Callum Styan <callumstyan@gmail.com> * last exemplars timestamp metric needs to convert value to seconds with ms precision Signed-off-by: Callum Styan <callumstyan@gmail.com> * Process exemplar records in a separate go routine when loading the WAL. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Address review comments related to clarifying comments and variable names. Also refactor sample/exemplar to enqueue prompb types. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Regenerate types proto with comments, update protoc version again. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Put remote write of exemplars behind a feature flag. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Address some of Ganesh's review comments. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Move exemplar remote write feature flag to a config file field. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Address Bartek's review comments. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Don't allocate exemplar buffers in queue_manager if we're not going to send exemplars over remote write. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Add ValidateExemplar function, validate exemplars when appending to head and log them all to WAL before adding them to exemplar storage. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Address more reivew comments from Ganesh. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Add exemplar total label length check. Signed-off-by: Callum Styan <callumstyan@gmail.com> * Address a few last review comments Signed-off-by: Callum Styan <callumstyan@gmail.com> Co-authored-by: Martin Disibio <mdisibio@gmail.com>
4 years ago
exemplars: map[uint64][]exemplar.Exemplar{},
}
}
func (cmd loadCmd) String() string {
return "load"
}
// set a sequence of sample values for the given metric.
func (cmd *loadCmd) set(m labels.Labels, vals ...parser.SequenceValue) {
h := m.Hash()
samples := make([]Sample, 0, len(vals))
ts := testStartTime
for _, v := range vals {
if !v.Omitted {
samples = append(samples, Sample{
T: ts.UnixNano() / int64(time.Millisecond/time.Nanosecond),
promql: Separate `Point` into `FPoint` and `HPoint` In other words: Instead of having a “polymorphous” `Point` that can either contain a float value or a histogram value, use an `FPoint` for floats and an `HPoint` for histograms. This seemingly small change has a _lot_ of repercussions throughout the codebase. The idea here is to avoid the increase in size of `Point` arrays that happened after native histograms had been added. The higher-level data structures (`Sample`, `Series`, etc.) are still “polymorphous”. The same idea could be applied to them, but at each step the trade-offs needed to be evaluated. The idea with this change is to do the minimum necessary to get back to pre-histogram performance for functions that do not touch histograms. Here are comparisons for the `changes` function. The test data doesn't include histograms yet. Ideally, there would be no change in the benchmark result at all. First runtime v2.39 compared to directly prior to this commit: ``` name old time/op new time/op delta RangeQuery/expr=changes(a_one[1d]),steps=1-16 391µs ± 2% 542µs ± 1% +38.58% (p=0.000 n=9+8) RangeQuery/expr=changes(a_one[1d]),steps=10-16 452µs ± 2% 617µs ± 2% +36.48% (p=0.000 n=10+10) RangeQuery/expr=changes(a_one[1d]),steps=100-16 1.12ms ± 1% 1.36ms ± 2% +21.58% (p=0.000 n=8+10) RangeQuery/expr=changes(a_one[1d]),steps=1000-16 7.83ms ± 1% 8.94ms ± 1% +14.21% (p=0.000 n=10+10) RangeQuery/expr=changes(a_ten[1d]),steps=1-16 2.98ms ± 0% 3.30ms ± 1% +10.67% (p=0.000 n=9+10) RangeQuery/expr=changes(a_ten[1d]),steps=10-16 3.66ms ± 1% 4.10ms ± 1% +11.82% (p=0.000 n=10+10) RangeQuery/expr=changes(a_ten[1d]),steps=100-16 10.5ms ± 0% 11.8ms ± 1% +12.50% (p=0.000 n=8+10) RangeQuery/expr=changes(a_ten[1d]),steps=1000-16 77.6ms ± 1% 87.4ms ± 1% +12.63% (p=0.000 n=9+9) RangeQuery/expr=changes(a_hundred[1d]),steps=1-16 30.4ms ± 2% 32.8ms ± 1% +8.01% (p=0.000 n=10+10) RangeQuery/expr=changes(a_hundred[1d]),steps=10-16 37.1ms ± 2% 40.6ms ± 2% +9.64% (p=0.000 n=10+10) RangeQuery/expr=changes(a_hundred[1d]),steps=100-16 105ms ± 1% 117ms ± 1% +11.69% (p=0.000 n=10+10) RangeQuery/expr=changes(a_hundred[1d]),steps=1000-16 783ms ± 3% 876ms ± 1% +11.83% (p=0.000 n=9+10) ``` And then runtime v2.39 compared to after this commit: ``` name old time/op new time/op delta RangeQuery/expr=changes(a_one[1d]),steps=1-16 391µs ± 2% 547µs ± 1% +39.84% (p=0.000 n=9+8) RangeQuery/expr=changes(a_one[1d]),steps=10-16 452µs ± 2% 616µs ± 2% +36.15% (p=0.000 n=10+10) RangeQuery/expr=changes(a_one[1d]),steps=100-16 1.12ms ± 1% 1.26ms ± 1% +12.20% (p=0.000 n=8+10) RangeQuery/expr=changes(a_one[1d]),steps=1000-16 7.83ms ± 1% 7.95ms ± 1% +1.59% (p=0.000 n=10+8) RangeQuery/expr=changes(a_ten[1d]),steps=1-16 2.98ms ± 0% 3.38ms ± 2% +13.49% (p=0.000 n=9+10) RangeQuery/expr=changes(a_ten[1d]),steps=10-16 3.66ms ± 1% 4.02ms ± 1% +9.80% (p=0.000 n=10+9) RangeQuery/expr=changes(a_ten[1d]),steps=100-16 10.5ms ± 0% 10.8ms ± 1% +3.08% (p=0.000 n=8+10) RangeQuery/expr=changes(a_ten[1d]),steps=1000-16 77.6ms ± 1% 78.1ms ± 1% +0.58% (p=0.035 n=9+10) RangeQuery/expr=changes(a_hundred[1d]),steps=1-16 30.4ms ± 2% 33.5ms ± 4% +10.18% (p=0.000 n=10+10) RangeQuery/expr=changes(a_hundred[1d]),steps=10-16 37.1ms ± 2% 40.0ms ± 1% +7.98% (p=0.000 n=10+10) RangeQuery/expr=changes(a_hundred[1d]),steps=100-16 105ms ± 1% 107ms ± 1% +1.92% (p=0.000 n=10+10) RangeQuery/expr=changes(a_hundred[1d]),steps=1000-16 783ms ± 3% 775ms ± 1% -1.02% (p=0.019 n=9+9) ``` In summary, the runtime doesn't really improve with this change for queries with just a few steps. For queries with many steps, this commit essentially reinstates the old performance. This is good because the many-step queries are the one that matter most (longest absolute runtime). In terms of allocations, though, this commit doesn't make a dent at all (numbers not shown). The reason is that most of the allocations happen in the sampleRingIterator (in the storage package), which has to be addressed in a separate commit. Signed-off-by: beorn7 <beorn@grafana.com>
2 years ago
F: v.Value,
H: v.Histogram,
})
}
ts = ts.Add(cmd.gap)
}
cmd.defs[h] = samples
cmd.metrics[h] = m
}
// append the defined time series to the storage.
func (cmd *loadCmd) append(a storage.Appender) error {
for h, smpls := range cmd.defs {
m := cmd.metrics[h]
for _, s := range smpls {
if err := appendSample(a, s, m); err != nil {
return err
}
}
}
return nil
}
func appendSample(a storage.Appender, s Sample, m labels.Labels) error {
if s.H != nil {
if _, err := a.AppendHistogram(0, m, s.T, nil, s.H); err != nil {
return err
}
} else {
if _, err := a.Append(0, m, s.T, s.F); err != nil {
return err
}
}
return nil
}
// evalCmd is a command that evaluates an expression for the given time (range)
// and expects a specific result.
type evalCmd struct {
Optimise PromQL (#3966) * Move range logic to 'eval' Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make aggregegate range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * PromQL is statically typed, so don't eval to find the type. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Extend rangewrapper to multiple exprs Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Start making function evaluation ranged Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make instant queries a special case of range queries Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Eliminate evalString Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Evaluate range vector functions one series at a time Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make unary operators range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make binops range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Pass time to range-aware functions. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make simple _over_time functions range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Reduce allocs when working with matrix selectors Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Add basic benchmark for range evaluation Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Reuse objects for function arguments Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Do dropmetricname and allocating output vector only once. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Add range-aware support for range vector functions with params Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Optimise holt_winters, cut cpu and allocs by ~25% Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make rate&friends range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make more functions range aware. Document calling convention. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make date functions range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make simple math functions range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Convert more functions to be range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make more functions range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Specialcase timestamp() with vector selector arg for range awareness Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Remove transition code for functions Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Remove the rest of the engine transition code Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Remove more obselete code Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Remove the last uses of the eval* functions Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Remove engine finalizers to prevent corruption The finalizers set by matrixSelector were being called just before the value they were retruning to the pool was then being provided to the caller. Thus a concurrent query could corrupt the data that the user has just been returned. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Add new benchmark suite for range functinos Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Migrate existing benchmarks to new system Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Expand promql benchmarks Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Simply test by removing unused range code Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * When testing instant queries, check range queries too. To protect against subsequent steps in a range query being affected by the previous steps, add a test that evaluates an instant query that we know works again as a range query with the tiimestamp we care about not being the first step. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Reuse ring for matrix iters. Put query results back in pool. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Reuse buffer when iterating over matrix selectors Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Unary minus should remove metric name Cut down benchmarks for faster runs. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Reduce repetition in benchmark test cases Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Work series by series when doing normal vectorSelectors Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Optimise benchmark setup, cuts time by 60% Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Have rangeWrapper use an evalNodeHelper to cache across steps Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Use evalNodeHelper with functions Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Cache dropMetricName within a node evaluation. This saves both the calculations and allocs done by dropMetricName across steps. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Reuse input vectors in rangewrapper Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Reuse the point slices in the matrixes input/output by rangeWrapper Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make benchmark setup faster using AddFast Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Simplify benchmark code. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Add caching in VectorBinop Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Use xor to have one-level resultMetric hash key Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Add more benchmarks Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Call Query.Close in apiv1 This allows point slices allocated for the response data to be reused by later queries, saving allocations. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Optimise histogram_quantile It's now 5-10% faster with 97% less garbage generated for 1k steps Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make the input collection in rangeVector linear rather than quadratic Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Optimise label_replace, for 1k steps 15x fewer allocs and 3x faster Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Optimise label_join, 1.8x faster and 11x less memory for 1k steps Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Expand benchmarks, cleanup comments, simplify numSteps logic. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Address Fabian's comments Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Comments from Alin. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Address jrv's comments Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Remove dead code Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Address Simon's comments. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Rename populateIterators, pre-init some sizes Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Handle case where function has non-matrix args first Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Split rangeWrapper out to rangeEval function, improve comments Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Cleanup and make things more consistent Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make EvalNodeHelper public Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Fabian's comments. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
7 years ago
expr string
start time.Time
line int
fail, ordered bool
metrics map[uint64]labels.Labels
expected map[uint64]entry
}
type entry struct {
pos int
vals []parser.SequenceValue
}
func (e entry) String() string {
return fmt.Sprintf("%d: %s", e.pos, e.vals)
}
func newEvalCmd(expr string, start time.Time, line int) *evalCmd {
return &evalCmd{
Optimise PromQL (#3966) * Move range logic to 'eval' Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make aggregegate range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * PromQL is statically typed, so don't eval to find the type. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Extend rangewrapper to multiple exprs Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Start making function evaluation ranged Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make instant queries a special case of range queries Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Eliminate evalString Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Evaluate range vector functions one series at a time Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make unary operators range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make binops range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Pass time to range-aware functions. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make simple _over_time functions range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Reduce allocs when working with matrix selectors Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Add basic benchmark for range evaluation Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Reuse objects for function arguments Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Do dropmetricname and allocating output vector only once. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Add range-aware support for range vector functions with params Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Optimise holt_winters, cut cpu and allocs by ~25% Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make rate&friends range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make more functions range aware. Document calling convention. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make date functions range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make simple math functions range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Convert more functions to be range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make more functions range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Specialcase timestamp() with vector selector arg for range awareness Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Remove transition code for functions Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Remove the rest of the engine transition code Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Remove more obselete code Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Remove the last uses of the eval* functions Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Remove engine finalizers to prevent corruption The finalizers set by matrixSelector were being called just before the value they were retruning to the pool was then being provided to the caller. Thus a concurrent query could corrupt the data that the user has just been returned. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Add new benchmark suite for range functinos Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Migrate existing benchmarks to new system Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Expand promql benchmarks Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Simply test by removing unused range code Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * When testing instant queries, check range queries too. To protect against subsequent steps in a range query being affected by the previous steps, add a test that evaluates an instant query that we know works again as a range query with the tiimestamp we care about not being the first step. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Reuse ring for matrix iters. Put query results back in pool. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Reuse buffer when iterating over matrix selectors Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Unary minus should remove metric name Cut down benchmarks for faster runs. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Reduce repetition in benchmark test cases Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Work series by series when doing normal vectorSelectors Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Optimise benchmark setup, cuts time by 60% Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Have rangeWrapper use an evalNodeHelper to cache across steps Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Use evalNodeHelper with functions Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Cache dropMetricName within a node evaluation. This saves both the calculations and allocs done by dropMetricName across steps. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Reuse input vectors in rangewrapper Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Reuse the point slices in the matrixes input/output by rangeWrapper Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make benchmark setup faster using AddFast Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Simplify benchmark code. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Add caching in VectorBinop Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Use xor to have one-level resultMetric hash key Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Add more benchmarks Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Call Query.Close in apiv1 This allows point slices allocated for the response data to be reused by later queries, saving allocations. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Optimise histogram_quantile It's now 5-10% faster with 97% less garbage generated for 1k steps Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make the input collection in rangeVector linear rather than quadratic Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Optimise label_replace, for 1k steps 15x fewer allocs and 3x faster Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Optimise label_join, 1.8x faster and 11x less memory for 1k steps Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Expand benchmarks, cleanup comments, simplify numSteps logic. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Address Fabian's comments Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Comments from Alin. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Address jrv's comments Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Remove dead code Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Address Simon's comments. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Rename populateIterators, pre-init some sizes Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Handle case where function has non-matrix args first Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Split rangeWrapper out to rangeEval function, improve comments Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Cleanup and make things more consistent Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make EvalNodeHelper public Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Fabian's comments. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
7 years ago
expr: expr,
start: start,
line: line,
metrics: map[uint64]labels.Labels{},
expected: map[uint64]entry{},
}
}
func (ev *evalCmd) String() string {
return "eval"
}
// expect adds a sequence of values to the set of expected
// results for the query.
func (ev *evalCmd) expect(pos int, vals ...parser.SequenceValue) {
ev.expected[0] = entry{pos: pos, vals: vals}
}
// expectMetric adds a new metric with a sequence of values to the set of expected
// results for the query.
func (ev *evalCmd) expectMetric(pos int, m labels.Labels, vals ...parser.SequenceValue) {
h := m.Hash()
ev.metrics[h] = m
ev.expected[h] = entry{pos: pos, vals: vals}
}
// compareResult compares the result value with the defined expectation.
func (ev *evalCmd) compareResult(result parser.Value) error {
switch val := result.(type) {
case Matrix:
return errors.New("received range result on instant evaluation")
case Vector:
seen := map[uint64]bool{}
for pos, v := range val {
fp := v.Metric.Hash()
if _, ok := ev.metrics[fp]; !ok {
return fmt.Errorf("unexpected metric %s in result", v.Metric)
}
exp := ev.expected[fp]
if ev.ordered && exp.pos != pos+1 {
return fmt.Errorf("expected metric %s with %v at position %d but was at %d", v.Metric, exp.vals, exp.pos, pos+1)
}
exp0 := exp.vals[0]
expH := exp0.Histogram
if (expH == nil) != (v.H == nil) || (expH != nil && !expH.Equals(v.H)) {
return fmt.Errorf("expected %v for %s but got %s", HistogramTestExpression(expH), v.Metric, HistogramTestExpression(v.H))
}
if !almostEqual(exp0.Value, v.F) {
return fmt.Errorf("expected %v for %s but got %v", exp0.Value, v.Metric, v.F)
}
seen[fp] = true
}
for fp, expVals := range ev.expected {
if !seen[fp] {
fmt.Println("vector result", len(val), ev.expr)
for _, ss := range val {
promql: Separate `Point` into `FPoint` and `HPoint` In other words: Instead of having a “polymorphous” `Point` that can either contain a float value or a histogram value, use an `FPoint` for floats and an `HPoint` for histograms. This seemingly small change has a _lot_ of repercussions throughout the codebase. The idea here is to avoid the increase in size of `Point` arrays that happened after native histograms had been added. The higher-level data structures (`Sample`, `Series`, etc.) are still “polymorphous”. The same idea could be applied to them, but at each step the trade-offs needed to be evaluated. The idea with this change is to do the minimum necessary to get back to pre-histogram performance for functions that do not touch histograms. Here are comparisons for the `changes` function. The test data doesn't include histograms yet. Ideally, there would be no change in the benchmark result at all. First runtime v2.39 compared to directly prior to this commit: ``` name old time/op new time/op delta RangeQuery/expr=changes(a_one[1d]),steps=1-16 391µs ± 2% 542µs ± 1% +38.58% (p=0.000 n=9+8) RangeQuery/expr=changes(a_one[1d]),steps=10-16 452µs ± 2% 617µs ± 2% +36.48% (p=0.000 n=10+10) RangeQuery/expr=changes(a_one[1d]),steps=100-16 1.12ms ± 1% 1.36ms ± 2% +21.58% (p=0.000 n=8+10) RangeQuery/expr=changes(a_one[1d]),steps=1000-16 7.83ms ± 1% 8.94ms ± 1% +14.21% (p=0.000 n=10+10) RangeQuery/expr=changes(a_ten[1d]),steps=1-16 2.98ms ± 0% 3.30ms ± 1% +10.67% (p=0.000 n=9+10) RangeQuery/expr=changes(a_ten[1d]),steps=10-16 3.66ms ± 1% 4.10ms ± 1% +11.82% (p=0.000 n=10+10) RangeQuery/expr=changes(a_ten[1d]),steps=100-16 10.5ms ± 0% 11.8ms ± 1% +12.50% (p=0.000 n=8+10) RangeQuery/expr=changes(a_ten[1d]),steps=1000-16 77.6ms ± 1% 87.4ms ± 1% +12.63% (p=0.000 n=9+9) RangeQuery/expr=changes(a_hundred[1d]),steps=1-16 30.4ms ± 2% 32.8ms ± 1% +8.01% (p=0.000 n=10+10) RangeQuery/expr=changes(a_hundred[1d]),steps=10-16 37.1ms ± 2% 40.6ms ± 2% +9.64% (p=0.000 n=10+10) RangeQuery/expr=changes(a_hundred[1d]),steps=100-16 105ms ± 1% 117ms ± 1% +11.69% (p=0.000 n=10+10) RangeQuery/expr=changes(a_hundred[1d]),steps=1000-16 783ms ± 3% 876ms ± 1% +11.83% (p=0.000 n=9+10) ``` And then runtime v2.39 compared to after this commit: ``` name old time/op new time/op delta RangeQuery/expr=changes(a_one[1d]),steps=1-16 391µs ± 2% 547µs ± 1% +39.84% (p=0.000 n=9+8) RangeQuery/expr=changes(a_one[1d]),steps=10-16 452µs ± 2% 616µs ± 2% +36.15% (p=0.000 n=10+10) RangeQuery/expr=changes(a_one[1d]),steps=100-16 1.12ms ± 1% 1.26ms ± 1% +12.20% (p=0.000 n=8+10) RangeQuery/expr=changes(a_one[1d]),steps=1000-16 7.83ms ± 1% 7.95ms ± 1% +1.59% (p=0.000 n=10+8) RangeQuery/expr=changes(a_ten[1d]),steps=1-16 2.98ms ± 0% 3.38ms ± 2% +13.49% (p=0.000 n=9+10) RangeQuery/expr=changes(a_ten[1d]),steps=10-16 3.66ms ± 1% 4.02ms ± 1% +9.80% (p=0.000 n=10+9) RangeQuery/expr=changes(a_ten[1d]),steps=100-16 10.5ms ± 0% 10.8ms ± 1% +3.08% (p=0.000 n=8+10) RangeQuery/expr=changes(a_ten[1d]),steps=1000-16 77.6ms ± 1% 78.1ms ± 1% +0.58% (p=0.035 n=9+10) RangeQuery/expr=changes(a_hundred[1d]),steps=1-16 30.4ms ± 2% 33.5ms ± 4% +10.18% (p=0.000 n=10+10) RangeQuery/expr=changes(a_hundred[1d]),steps=10-16 37.1ms ± 2% 40.0ms ± 1% +7.98% (p=0.000 n=10+10) RangeQuery/expr=changes(a_hundred[1d]),steps=100-16 105ms ± 1% 107ms ± 1% +1.92% (p=0.000 n=10+10) RangeQuery/expr=changes(a_hundred[1d]),steps=1000-16 783ms ± 3% 775ms ± 1% -1.02% (p=0.019 n=9+9) ``` In summary, the runtime doesn't really improve with this change for queries with just a few steps. For queries with many steps, this commit essentially reinstates the old performance. This is good because the many-step queries are the one that matter most (longest absolute runtime). In terms of allocations, though, this commit doesn't make a dent at all (numbers not shown). The reason is that most of the allocations happen in the sampleRingIterator (in the storage package), which has to be addressed in a separate commit. Signed-off-by: beorn7 <beorn@grafana.com>
2 years ago
fmt.Println(" ", ss.Metric, ss.T, ss.F)
}
return fmt.Errorf("expected metric %s with %v not found", ev.metrics[fp], expVals)
}
}
case Scalar:
if len(ev.expected) != 1 {
return fmt.Errorf("expected vector result, but got scalar %s", val.String())
}
exp0 := ev.expected[0].vals[0]
if exp0.Histogram != nil {
return fmt.Errorf("expected Histogram %v but got scalar %s", exp0.Histogram.TestExpression(), val.String())
}
if !almostEqual(exp0.Value, val.V) {
return fmt.Errorf("expected Scalar %v but got %v", val.V, exp0.Value)
}
default:
panic(fmt.Errorf("promql.Test.compareResult: unexpected result type %T", result))
}
return nil
}
// HistogramTestExpression returns TestExpression() for the given histogram or "" if the histogram is nil.
func HistogramTestExpression(h *histogram.FloatHistogram) string {
if h != nil {
return h.TestExpression()
}
return ""
}
// clearCmd is a command that wipes the test's storage state.
type clearCmd struct{}
func (cmd clearCmd) String() string {
return "clear"
}
type atModifierTestCase struct {
expr string
evalTime time.Time
}
func atModifierTestCases(exprStr string, evalTime time.Time) ([]atModifierTestCase, error) {
expr, err := parser.ParseExpr(exprStr)
if err != nil {
return nil, err
}
ts := timestamp.FromTime(evalTime)
containsNonStepInvariant := false
// Setting the @ timestamp for all selectors to be evalTime.
// If there is a subquery, then the selectors inside it don't get the @ timestamp.
// If any selector already has the @ timestamp set, then it is untouched.
parser.Inspect(expr, func(node parser.Node, path []parser.Node) error {
_, _, subqTs := subqueryTimes(path)
if subqTs != nil {
// There is a subquery with timestamp in the path,
// hence don't change any timestamps further.
return nil
}
switch n := node.(type) {
case *parser.VectorSelector:
if n.Timestamp == nil {
n.Timestamp = makeInt64Pointer(ts)
}
case *parser.MatrixSelector:
if vs := n.VectorSelector.(*parser.VectorSelector); vs.Timestamp == nil {
vs.Timestamp = makeInt64Pointer(ts)
}
case *parser.SubqueryExpr:
if n.Timestamp == nil {
n.Timestamp = makeInt64Pointer(ts)
}
case *parser.Call:
_, ok := AtModifierUnsafeFunctions[n.Func.Name]
containsNonStepInvariant = containsNonStepInvariant || ok
}
return nil
})
if containsNonStepInvariant {
// Expression contains a function whose result can vary with evaluation
// time, even though its arguments are step invariant: skip it.
return nil, nil
}
newExpr := expr.String() // With all the @ evalTime set.
additionalEvalTimes := []int64{-10 * ts, 0, ts / 5, ts, 10 * ts}
if ts == 0 {
additionalEvalTimes = []int64{-1000, -ts, 1000}
}
testCases := make([]atModifierTestCase, 0, len(additionalEvalTimes))
for _, et := range additionalEvalTimes {
testCases = append(testCases, atModifierTestCase{
expr: newExpr,
evalTime: timestamp.Time(et),
})
}
return testCases, nil
}
// exec processes a single step of the test.
func (t *test) exec(tc testCommand, engine engineQuerier) error {
switch cmd := tc.(type) {
case *clearCmd:
t.clear()
case *loadCmd:
app := t.storage.Appender(t.context)
if err := cmd.append(app); err != nil {
app.Rollback()
return err
}
if err := app.Commit(); err != nil {
return err
}
case *evalCmd:
queries, err := atModifierTestCases(cmd.expr, cmd.start)
if err != nil {
return err
}
queries = append([]atModifierTestCase{{expr: cmd.expr, evalTime: cmd.start}}, queries...)
for _, iq := range queries {
q, err := engine.NewInstantQuery(t.context, t.storage, nil, iq.expr, iq.evalTime)
if err != nil {
return err
}
defer q.Close()
res := q.Exec(t.context)
if res.Err != nil {
if cmd.fail {
continue
}
return fmt.Errorf("error evaluating query %q (line %d): %w", iq.expr, cmd.line, res.Err)
}
if res.Err == nil && cmd.fail {
return fmt.Errorf("expected error evaluating query %q (line %d) but got none", iq.expr, cmd.line)
}
err = cmd.compareResult(res.Value)
if err != nil {
return fmt.Errorf("error in %s %s (line %d): %w", cmd, iq.expr, cmd.line, err)
}
// Check query returns same result in range mode,
// by checking against the middle step.
q, err = engine.NewRangeQuery(t.context, t.storage, nil, iq.expr, iq.evalTime.Add(-time.Minute), iq.evalTime.Add(time.Minute), time.Minute)
if err != nil {
return err
}
rangeRes := q.Exec(t.context)
if rangeRes.Err != nil {
return fmt.Errorf("error evaluating query %q (line %d) in range mode: %w", iq.expr, cmd.line, rangeRes.Err)
}
defer q.Close()
if cmd.ordered {
// Ordering isn't defined for range queries.
continue
}
mat := rangeRes.Value.(Matrix)
vec := make(Vector, 0, len(mat))
for _, series := range mat {
// We expect either Floats or Histograms.
promql: Separate `Point` into `FPoint` and `HPoint` In other words: Instead of having a “polymorphous” `Point` that can either contain a float value or a histogram value, use an `FPoint` for floats and an `HPoint` for histograms. This seemingly small change has a _lot_ of repercussions throughout the codebase. The idea here is to avoid the increase in size of `Point` arrays that happened after native histograms had been added. The higher-level data structures (`Sample`, `Series`, etc.) are still “polymorphous”. The same idea could be applied to them, but at each step the trade-offs needed to be evaluated. The idea with this change is to do the minimum necessary to get back to pre-histogram performance for functions that do not touch histograms. Here are comparisons for the `changes` function. The test data doesn't include histograms yet. Ideally, there would be no change in the benchmark result at all. First runtime v2.39 compared to directly prior to this commit: ``` name old time/op new time/op delta RangeQuery/expr=changes(a_one[1d]),steps=1-16 391µs ± 2% 542µs ± 1% +38.58% (p=0.000 n=9+8) RangeQuery/expr=changes(a_one[1d]),steps=10-16 452µs ± 2% 617µs ± 2% +36.48% (p=0.000 n=10+10) RangeQuery/expr=changes(a_one[1d]),steps=100-16 1.12ms ± 1% 1.36ms ± 2% +21.58% (p=0.000 n=8+10) RangeQuery/expr=changes(a_one[1d]),steps=1000-16 7.83ms ± 1% 8.94ms ± 1% +14.21% (p=0.000 n=10+10) RangeQuery/expr=changes(a_ten[1d]),steps=1-16 2.98ms ± 0% 3.30ms ± 1% +10.67% (p=0.000 n=9+10) RangeQuery/expr=changes(a_ten[1d]),steps=10-16 3.66ms ± 1% 4.10ms ± 1% +11.82% (p=0.000 n=10+10) RangeQuery/expr=changes(a_ten[1d]),steps=100-16 10.5ms ± 0% 11.8ms ± 1% +12.50% (p=0.000 n=8+10) RangeQuery/expr=changes(a_ten[1d]),steps=1000-16 77.6ms ± 1% 87.4ms ± 1% +12.63% (p=0.000 n=9+9) RangeQuery/expr=changes(a_hundred[1d]),steps=1-16 30.4ms ± 2% 32.8ms ± 1% +8.01% (p=0.000 n=10+10) RangeQuery/expr=changes(a_hundred[1d]),steps=10-16 37.1ms ± 2% 40.6ms ± 2% +9.64% (p=0.000 n=10+10) RangeQuery/expr=changes(a_hundred[1d]),steps=100-16 105ms ± 1% 117ms ± 1% +11.69% (p=0.000 n=10+10) RangeQuery/expr=changes(a_hundred[1d]),steps=1000-16 783ms ± 3% 876ms ± 1% +11.83% (p=0.000 n=9+10) ``` And then runtime v2.39 compared to after this commit: ``` name old time/op new time/op delta RangeQuery/expr=changes(a_one[1d]),steps=1-16 391µs ± 2% 547µs ± 1% +39.84% (p=0.000 n=9+8) RangeQuery/expr=changes(a_one[1d]),steps=10-16 452µs ± 2% 616µs ± 2% +36.15% (p=0.000 n=10+10) RangeQuery/expr=changes(a_one[1d]),steps=100-16 1.12ms ± 1% 1.26ms ± 1% +12.20% (p=0.000 n=8+10) RangeQuery/expr=changes(a_one[1d]),steps=1000-16 7.83ms ± 1% 7.95ms ± 1% +1.59% (p=0.000 n=10+8) RangeQuery/expr=changes(a_ten[1d]),steps=1-16 2.98ms ± 0% 3.38ms ± 2% +13.49% (p=0.000 n=9+10) RangeQuery/expr=changes(a_ten[1d]),steps=10-16 3.66ms ± 1% 4.02ms ± 1% +9.80% (p=0.000 n=10+9) RangeQuery/expr=changes(a_ten[1d]),steps=100-16 10.5ms ± 0% 10.8ms ± 1% +3.08% (p=0.000 n=8+10) RangeQuery/expr=changes(a_ten[1d]),steps=1000-16 77.6ms ± 1% 78.1ms ± 1% +0.58% (p=0.035 n=9+10) RangeQuery/expr=changes(a_hundred[1d]),steps=1-16 30.4ms ± 2% 33.5ms ± 4% +10.18% (p=0.000 n=10+10) RangeQuery/expr=changes(a_hundred[1d]),steps=10-16 37.1ms ± 2% 40.0ms ± 1% +7.98% (p=0.000 n=10+10) RangeQuery/expr=changes(a_hundred[1d]),steps=100-16 105ms ± 1% 107ms ± 1% +1.92% (p=0.000 n=10+10) RangeQuery/expr=changes(a_hundred[1d]),steps=1000-16 783ms ± 3% 775ms ± 1% -1.02% (p=0.019 n=9+9) ``` In summary, the runtime doesn't really improve with this change for queries with just a few steps. For queries with many steps, this commit essentially reinstates the old performance. This is good because the many-step queries are the one that matter most (longest absolute runtime). In terms of allocations, though, this commit doesn't make a dent at all (numbers not shown). The reason is that most of the allocations happen in the sampleRingIterator (in the storage package), which has to be addressed in a separate commit. Signed-off-by: beorn7 <beorn@grafana.com>
2 years ago
for _, point := range series.Floats {
if point.T == timeMilliseconds(iq.evalTime) {
promql: Separate `Point` into `FPoint` and `HPoint` In other words: Instead of having a “polymorphous” `Point` that can either contain a float value or a histogram value, use an `FPoint` for floats and an `HPoint` for histograms. This seemingly small change has a _lot_ of repercussions throughout the codebase. The idea here is to avoid the increase in size of `Point` arrays that happened after native histograms had been added. The higher-level data structures (`Sample`, `Series`, etc.) are still “polymorphous”. The same idea could be applied to them, but at each step the trade-offs needed to be evaluated. The idea with this change is to do the minimum necessary to get back to pre-histogram performance for functions that do not touch histograms. Here are comparisons for the `changes` function. The test data doesn't include histograms yet. Ideally, there would be no change in the benchmark result at all. First runtime v2.39 compared to directly prior to this commit: ``` name old time/op new time/op delta RangeQuery/expr=changes(a_one[1d]),steps=1-16 391µs ± 2% 542µs ± 1% +38.58% (p=0.000 n=9+8) RangeQuery/expr=changes(a_one[1d]),steps=10-16 452µs ± 2% 617µs ± 2% +36.48% (p=0.000 n=10+10) RangeQuery/expr=changes(a_one[1d]),steps=100-16 1.12ms ± 1% 1.36ms ± 2% +21.58% (p=0.000 n=8+10) RangeQuery/expr=changes(a_one[1d]),steps=1000-16 7.83ms ± 1% 8.94ms ± 1% +14.21% (p=0.000 n=10+10) RangeQuery/expr=changes(a_ten[1d]),steps=1-16 2.98ms ± 0% 3.30ms ± 1% +10.67% (p=0.000 n=9+10) RangeQuery/expr=changes(a_ten[1d]),steps=10-16 3.66ms ± 1% 4.10ms ± 1% +11.82% (p=0.000 n=10+10) RangeQuery/expr=changes(a_ten[1d]),steps=100-16 10.5ms ± 0% 11.8ms ± 1% +12.50% (p=0.000 n=8+10) RangeQuery/expr=changes(a_ten[1d]),steps=1000-16 77.6ms ± 1% 87.4ms ± 1% +12.63% (p=0.000 n=9+9) RangeQuery/expr=changes(a_hundred[1d]),steps=1-16 30.4ms ± 2% 32.8ms ± 1% +8.01% (p=0.000 n=10+10) RangeQuery/expr=changes(a_hundred[1d]),steps=10-16 37.1ms ± 2% 40.6ms ± 2% +9.64% (p=0.000 n=10+10) RangeQuery/expr=changes(a_hundred[1d]),steps=100-16 105ms ± 1% 117ms ± 1% +11.69% (p=0.000 n=10+10) RangeQuery/expr=changes(a_hundred[1d]),steps=1000-16 783ms ± 3% 876ms ± 1% +11.83% (p=0.000 n=9+10) ``` And then runtime v2.39 compared to after this commit: ``` name old time/op new time/op delta RangeQuery/expr=changes(a_one[1d]),steps=1-16 391µs ± 2% 547µs ± 1% +39.84% (p=0.000 n=9+8) RangeQuery/expr=changes(a_one[1d]),steps=10-16 452µs ± 2% 616µs ± 2% +36.15% (p=0.000 n=10+10) RangeQuery/expr=changes(a_one[1d]),steps=100-16 1.12ms ± 1% 1.26ms ± 1% +12.20% (p=0.000 n=8+10) RangeQuery/expr=changes(a_one[1d]),steps=1000-16 7.83ms ± 1% 7.95ms ± 1% +1.59% (p=0.000 n=10+8) RangeQuery/expr=changes(a_ten[1d]),steps=1-16 2.98ms ± 0% 3.38ms ± 2% +13.49% (p=0.000 n=9+10) RangeQuery/expr=changes(a_ten[1d]),steps=10-16 3.66ms ± 1% 4.02ms ± 1% +9.80% (p=0.000 n=10+9) RangeQuery/expr=changes(a_ten[1d]),steps=100-16 10.5ms ± 0% 10.8ms ± 1% +3.08% (p=0.000 n=8+10) RangeQuery/expr=changes(a_ten[1d]),steps=1000-16 77.6ms ± 1% 78.1ms ± 1% +0.58% (p=0.035 n=9+10) RangeQuery/expr=changes(a_hundred[1d]),steps=1-16 30.4ms ± 2% 33.5ms ± 4% +10.18% (p=0.000 n=10+10) RangeQuery/expr=changes(a_hundred[1d]),steps=10-16 37.1ms ± 2% 40.0ms ± 1% +7.98% (p=0.000 n=10+10) RangeQuery/expr=changes(a_hundred[1d]),steps=100-16 105ms ± 1% 107ms ± 1% +1.92% (p=0.000 n=10+10) RangeQuery/expr=changes(a_hundred[1d]),steps=1000-16 783ms ± 3% 775ms ± 1% -1.02% (p=0.019 n=9+9) ``` In summary, the runtime doesn't really improve with this change for queries with just a few steps. For queries with many steps, this commit essentially reinstates the old performance. This is good because the many-step queries are the one that matter most (longest absolute runtime). In terms of allocations, though, this commit doesn't make a dent at all (numbers not shown). The reason is that most of the allocations happen in the sampleRingIterator (in the storage package), which has to be addressed in a separate commit. Signed-off-by: beorn7 <beorn@grafana.com>
2 years ago
vec = append(vec, Sample{Metric: series.Metric, T: point.T, F: point.F})
break
}
Optimise PromQL (#3966) * Move range logic to 'eval' Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make aggregegate range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * PromQL is statically typed, so don't eval to find the type. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Extend rangewrapper to multiple exprs Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Start making function evaluation ranged Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make instant queries a special case of range queries Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Eliminate evalString Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Evaluate range vector functions one series at a time Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make unary operators range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make binops range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Pass time to range-aware functions. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make simple _over_time functions range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Reduce allocs when working with matrix selectors Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Add basic benchmark for range evaluation Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Reuse objects for function arguments Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Do dropmetricname and allocating output vector only once. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Add range-aware support for range vector functions with params Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Optimise holt_winters, cut cpu and allocs by ~25% Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make rate&friends range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make more functions range aware. Document calling convention. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make date functions range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make simple math functions range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Convert more functions to be range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make more functions range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Specialcase timestamp() with vector selector arg for range awareness Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Remove transition code for functions Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Remove the rest of the engine transition code Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Remove more obselete code Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Remove the last uses of the eval* functions Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Remove engine finalizers to prevent corruption The finalizers set by matrixSelector were being called just before the value they were retruning to the pool was then being provided to the caller. Thus a concurrent query could corrupt the data that the user has just been returned. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Add new benchmark suite for range functinos Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Migrate existing benchmarks to new system Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Expand promql benchmarks Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Simply test by removing unused range code Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * When testing instant queries, check range queries too. To protect against subsequent steps in a range query being affected by the previous steps, add a test that evaluates an instant query that we know works again as a range query with the tiimestamp we care about not being the first step. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Reuse ring for matrix iters. Put query results back in pool. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Reuse buffer when iterating over matrix selectors Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Unary minus should remove metric name Cut down benchmarks for faster runs. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Reduce repetition in benchmark test cases Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Work series by series when doing normal vectorSelectors Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Optimise benchmark setup, cuts time by 60% Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Have rangeWrapper use an evalNodeHelper to cache across steps Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Use evalNodeHelper with functions Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Cache dropMetricName within a node evaluation. This saves both the calculations and allocs done by dropMetricName across steps. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Reuse input vectors in rangewrapper Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Reuse the point slices in the matrixes input/output by rangeWrapper Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make benchmark setup faster using AddFast Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Simplify benchmark code. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Add caching in VectorBinop Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Use xor to have one-level resultMetric hash key Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Add more benchmarks Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Call Query.Close in apiv1 This allows point slices allocated for the response data to be reused by later queries, saving allocations. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Optimise histogram_quantile It's now 5-10% faster with 97% less garbage generated for 1k steps Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make the input collection in rangeVector linear rather than quadratic Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Optimise label_replace, for 1k steps 15x fewer allocs and 3x faster Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Optimise label_join, 1.8x faster and 11x less memory for 1k steps Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Expand benchmarks, cleanup comments, simplify numSteps logic. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Address Fabian's comments Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Comments from Alin. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Address jrv's comments Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Remove dead code Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Address Simon's comments. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Rename populateIterators, pre-init some sizes Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Handle case where function has non-matrix args first Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Split rangeWrapper out to rangeEval function, improve comments Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Cleanup and make things more consistent Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make EvalNodeHelper public Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Fabian's comments. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
7 years ago
}
for _, point := range series.Histograms {
if point.T == timeMilliseconds(iq.evalTime) {
vec = append(vec, Sample{Metric: series.Metric, T: point.T, H: point.H})
break
}
}
Optimise PromQL (#3966) * Move range logic to 'eval' Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make aggregegate range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * PromQL is statically typed, so don't eval to find the type. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Extend rangewrapper to multiple exprs Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Start making function evaluation ranged Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make instant queries a special case of range queries Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Eliminate evalString Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Evaluate range vector functions one series at a time Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make unary operators range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make binops range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Pass time to range-aware functions. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make simple _over_time functions range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Reduce allocs when working with matrix selectors Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Add basic benchmark for range evaluation Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Reuse objects for function arguments Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Do dropmetricname and allocating output vector only once. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Add range-aware support for range vector functions with params Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Optimise holt_winters, cut cpu and allocs by ~25% Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make rate&friends range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make more functions range aware. Document calling convention. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make date functions range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make simple math functions range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Convert more functions to be range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make more functions range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Specialcase timestamp() with vector selector arg for range awareness Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Remove transition code for functions Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Remove the rest of the engine transition code Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Remove more obselete code Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Remove the last uses of the eval* functions Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Remove engine finalizers to prevent corruption The finalizers set by matrixSelector were being called just before the value they were retruning to the pool was then being provided to the caller. Thus a concurrent query could corrupt the data that the user has just been returned. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Add new benchmark suite for range functinos Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Migrate existing benchmarks to new system Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Expand promql benchmarks Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Simply test by removing unused range code Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * When testing instant queries, check range queries too. To protect against subsequent steps in a range query being affected by the previous steps, add a test that evaluates an instant query that we know works again as a range query with the tiimestamp we care about not being the first step. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Reuse ring for matrix iters. Put query results back in pool. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Reuse buffer when iterating over matrix selectors Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Unary minus should remove metric name Cut down benchmarks for faster runs. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Reduce repetition in benchmark test cases Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Work series by series when doing normal vectorSelectors Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Optimise benchmark setup, cuts time by 60% Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Have rangeWrapper use an evalNodeHelper to cache across steps Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Use evalNodeHelper with functions Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Cache dropMetricName within a node evaluation. This saves both the calculations and allocs done by dropMetricName across steps. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Reuse input vectors in rangewrapper Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Reuse the point slices in the matrixes input/output by rangeWrapper Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make benchmark setup faster using AddFast Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Simplify benchmark code. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Add caching in VectorBinop Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Use xor to have one-level resultMetric hash key Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Add more benchmarks Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Call Query.Close in apiv1 This allows point slices allocated for the response data to be reused by later queries, saving allocations. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Optimise histogram_quantile It's now 5-10% faster with 97% less garbage generated for 1k steps Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make the input collection in rangeVector linear rather than quadratic Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Optimise label_replace, for 1k steps 15x fewer allocs and 3x faster Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Optimise label_join, 1.8x faster and 11x less memory for 1k steps Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Expand benchmarks, cleanup comments, simplify numSteps logic. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Address Fabian's comments Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Comments from Alin. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Address jrv's comments Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Remove dead code Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Address Simon's comments. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Rename populateIterators, pre-init some sizes Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Handle case where function has non-matrix args first Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Split rangeWrapper out to rangeEval function, improve comments Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Cleanup and make things more consistent Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make EvalNodeHelper public Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Fabian's comments. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
7 years ago
}
if _, ok := res.Value.(Scalar); ok {
promql: Separate `Point` into `FPoint` and `HPoint` In other words: Instead of having a “polymorphous” `Point` that can either contain a float value or a histogram value, use an `FPoint` for floats and an `HPoint` for histograms. This seemingly small change has a _lot_ of repercussions throughout the codebase. The idea here is to avoid the increase in size of `Point` arrays that happened after native histograms had been added. The higher-level data structures (`Sample`, `Series`, etc.) are still “polymorphous”. The same idea could be applied to them, but at each step the trade-offs needed to be evaluated. The idea with this change is to do the minimum necessary to get back to pre-histogram performance for functions that do not touch histograms. Here are comparisons for the `changes` function. The test data doesn't include histograms yet. Ideally, there would be no change in the benchmark result at all. First runtime v2.39 compared to directly prior to this commit: ``` name old time/op new time/op delta RangeQuery/expr=changes(a_one[1d]),steps=1-16 391µs ± 2% 542µs ± 1% +38.58% (p=0.000 n=9+8) RangeQuery/expr=changes(a_one[1d]),steps=10-16 452µs ± 2% 617µs ± 2% +36.48% (p=0.000 n=10+10) RangeQuery/expr=changes(a_one[1d]),steps=100-16 1.12ms ± 1% 1.36ms ± 2% +21.58% (p=0.000 n=8+10) RangeQuery/expr=changes(a_one[1d]),steps=1000-16 7.83ms ± 1% 8.94ms ± 1% +14.21% (p=0.000 n=10+10) RangeQuery/expr=changes(a_ten[1d]),steps=1-16 2.98ms ± 0% 3.30ms ± 1% +10.67% (p=0.000 n=9+10) RangeQuery/expr=changes(a_ten[1d]),steps=10-16 3.66ms ± 1% 4.10ms ± 1% +11.82% (p=0.000 n=10+10) RangeQuery/expr=changes(a_ten[1d]),steps=100-16 10.5ms ± 0% 11.8ms ± 1% +12.50% (p=0.000 n=8+10) RangeQuery/expr=changes(a_ten[1d]),steps=1000-16 77.6ms ± 1% 87.4ms ± 1% +12.63% (p=0.000 n=9+9) RangeQuery/expr=changes(a_hundred[1d]),steps=1-16 30.4ms ± 2% 32.8ms ± 1% +8.01% (p=0.000 n=10+10) RangeQuery/expr=changes(a_hundred[1d]),steps=10-16 37.1ms ± 2% 40.6ms ± 2% +9.64% (p=0.000 n=10+10) RangeQuery/expr=changes(a_hundred[1d]),steps=100-16 105ms ± 1% 117ms ± 1% +11.69% (p=0.000 n=10+10) RangeQuery/expr=changes(a_hundred[1d]),steps=1000-16 783ms ± 3% 876ms ± 1% +11.83% (p=0.000 n=9+10) ``` And then runtime v2.39 compared to after this commit: ``` name old time/op new time/op delta RangeQuery/expr=changes(a_one[1d]),steps=1-16 391µs ± 2% 547µs ± 1% +39.84% (p=0.000 n=9+8) RangeQuery/expr=changes(a_one[1d]),steps=10-16 452µs ± 2% 616µs ± 2% +36.15% (p=0.000 n=10+10) RangeQuery/expr=changes(a_one[1d]),steps=100-16 1.12ms ± 1% 1.26ms ± 1% +12.20% (p=0.000 n=8+10) RangeQuery/expr=changes(a_one[1d]),steps=1000-16 7.83ms ± 1% 7.95ms ± 1% +1.59% (p=0.000 n=10+8) RangeQuery/expr=changes(a_ten[1d]),steps=1-16 2.98ms ± 0% 3.38ms ± 2% +13.49% (p=0.000 n=9+10) RangeQuery/expr=changes(a_ten[1d]),steps=10-16 3.66ms ± 1% 4.02ms ± 1% +9.80% (p=0.000 n=10+9) RangeQuery/expr=changes(a_ten[1d]),steps=100-16 10.5ms ± 0% 10.8ms ± 1% +3.08% (p=0.000 n=8+10) RangeQuery/expr=changes(a_ten[1d]),steps=1000-16 77.6ms ± 1% 78.1ms ± 1% +0.58% (p=0.035 n=9+10) RangeQuery/expr=changes(a_hundred[1d]),steps=1-16 30.4ms ± 2% 33.5ms ± 4% +10.18% (p=0.000 n=10+10) RangeQuery/expr=changes(a_hundred[1d]),steps=10-16 37.1ms ± 2% 40.0ms ± 1% +7.98% (p=0.000 n=10+10) RangeQuery/expr=changes(a_hundred[1d]),steps=100-16 105ms ± 1% 107ms ± 1% +1.92% (p=0.000 n=10+10) RangeQuery/expr=changes(a_hundred[1d]),steps=1000-16 783ms ± 3% 775ms ± 1% -1.02% (p=0.019 n=9+9) ``` In summary, the runtime doesn't really improve with this change for queries with just a few steps. For queries with many steps, this commit essentially reinstates the old performance. This is good because the many-step queries are the one that matter most (longest absolute runtime). In terms of allocations, though, this commit doesn't make a dent at all (numbers not shown). The reason is that most of the allocations happen in the sampleRingIterator (in the storage package), which has to be addressed in a separate commit. Signed-off-by: beorn7 <beorn@grafana.com>
2 years ago
err = cmd.compareResult(Scalar{V: vec[0].F})
} else {
err = cmd.compareResult(vec)
}
if err != nil {
return fmt.Errorf("error in %s %s (line %d) range mode: %w", cmd, iq.expr, cmd.line, err)
}
Optimise PromQL (#3966) * Move range logic to 'eval' Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make aggregegate range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * PromQL is statically typed, so don't eval to find the type. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Extend rangewrapper to multiple exprs Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Start making function evaluation ranged Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make instant queries a special case of range queries Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Eliminate evalString Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Evaluate range vector functions one series at a time Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make unary operators range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make binops range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Pass time to range-aware functions. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make simple _over_time functions range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Reduce allocs when working with matrix selectors Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Add basic benchmark for range evaluation Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Reuse objects for function arguments Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Do dropmetricname and allocating output vector only once. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Add range-aware support for range vector functions with params Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Optimise holt_winters, cut cpu and allocs by ~25% Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make rate&friends range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make more functions range aware. Document calling convention. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make date functions range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make simple math functions range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Convert more functions to be range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make more functions range aware Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Specialcase timestamp() with vector selector arg for range awareness Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Remove transition code for functions Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Remove the rest of the engine transition code Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Remove more obselete code Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Remove the last uses of the eval* functions Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Remove engine finalizers to prevent corruption The finalizers set by matrixSelector were being called just before the value they were retruning to the pool was then being provided to the caller. Thus a concurrent query could corrupt the data that the user has just been returned. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Add new benchmark suite for range functinos Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Migrate existing benchmarks to new system Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Expand promql benchmarks Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Simply test by removing unused range code Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * When testing instant queries, check range queries too. To protect against subsequent steps in a range query being affected by the previous steps, add a test that evaluates an instant query that we know works again as a range query with the tiimestamp we care about not being the first step. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Reuse ring for matrix iters. Put query results back in pool. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Reuse buffer when iterating over matrix selectors Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Unary minus should remove metric name Cut down benchmarks for faster runs. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Reduce repetition in benchmark test cases Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Work series by series when doing normal vectorSelectors Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Optimise benchmark setup, cuts time by 60% Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Have rangeWrapper use an evalNodeHelper to cache across steps Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Use evalNodeHelper with functions Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Cache dropMetricName within a node evaluation. This saves both the calculations and allocs done by dropMetricName across steps. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Reuse input vectors in rangewrapper Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Reuse the point slices in the matrixes input/output by rangeWrapper Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make benchmark setup faster using AddFast Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Simplify benchmark code. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Add caching in VectorBinop Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Use xor to have one-level resultMetric hash key Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Add more benchmarks Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Call Query.Close in apiv1 This allows point slices allocated for the response data to be reused by later queries, saving allocations. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Optimise histogram_quantile It's now 5-10% faster with 97% less garbage generated for 1k steps Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make the input collection in rangeVector linear rather than quadratic Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Optimise label_replace, for 1k steps 15x fewer allocs and 3x faster Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Optimise label_join, 1.8x faster and 11x less memory for 1k steps Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Expand benchmarks, cleanup comments, simplify numSteps logic. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Address Fabian's comments Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Comments from Alin. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Address jrv's comments Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Remove dead code Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Address Simon's comments. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Rename populateIterators, pre-init some sizes Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Handle case where function has non-matrix args first Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Split rangeWrapper out to rangeEval function, improve comments Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Cleanup and make things more consistent Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Make EvalNodeHelper public Signed-off-by: Brian Brazil <brian.brazil@robustperception.io> * Fabian's comments. Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
7 years ago
}
default:
panic("promql.Test.exec: unknown test command type")
}
return nil
}
// clear the current test storage of all inserted samples.
func (t *test) clear() {
if t.storage != nil {
err := t.storage.Close()
require.NoError(t.T, err, "Unexpected error while closing test storage.")
}
if t.cancelCtx != nil {
t.cancelCtx()
}
t.storage = teststorage.New(t)
t.context, t.cancelCtx = context.WithCancel(context.Background())
}
// samplesAlmostEqual returns true if the two sample lines only differ by a
// small relative error in their sample value.
func almostEqual(a, b float64) bool {
// NaN has no equality but for testing we still want to know whether both values
// are NaN.
if math.IsNaN(a) && math.IsNaN(b) {
return true
}
// Cf. http://floating-point-gui.de/errors/comparison/
if a == b {
return true
}
diff := math.Abs(a - b)
if a == 0 || b == 0 || diff < minNormal {
return diff < epsilon*minNormal
}
return diff/(math.Abs(a)+math.Abs(b)) < epsilon
}
func parseNumber(s string) (float64, error) {
n, err := strconv.ParseInt(s, 0, 64)
f := float64(n)
if err != nil {
f, err = strconv.ParseFloat(s, 64)
}
if err != nil {
return 0, fmt.Errorf("error parsing number: %w", err)
}
return f, nil
}
// LazyLoader lazily loads samples into storage.
// This is specifically implemented for unit testing of rules.
type LazyLoader struct {
testutil.T
loadCmd *loadCmd
storage storage.Storage
SubqueryInterval time.Duration
queryEngine *Engine
context context.Context
cancelCtx context.CancelFunc
opts LazyLoaderOpts
}
// LazyLoaderOpts are options for the lazy loader.
type LazyLoaderOpts struct {
// Both of these must be set to true for regular PromQL (as of
// Prometheus v2.33). They can still be disabled here for legacy and
// other uses.
EnableAtModifier, EnableNegativeOffset bool
}
// NewLazyLoader returns an initialized empty LazyLoader.
func NewLazyLoader(t testutil.T, input string, opts LazyLoaderOpts) (*LazyLoader, error) {
ll := &LazyLoader{
T: t,
opts: opts,
}
err := ll.parse(input)
ll.clear()
return ll, err
}
// parse the given load command.
func (ll *LazyLoader) parse(input string) error {
lines := getLines(input)
// Accepts only 'load' command.
for i := 0; i < len(lines); i++ {
l := lines[i]
if len(l) == 0 {
continue
}
if strings.ToLower(patSpace.Split(l, 2)[0]) == "load" {
_, cmd, err := parseLoad(lines, i)
if err != nil {
return err
}
ll.loadCmd = cmd
return nil
}
return raise(i, "invalid command %q", l)
}
return errors.New("no \"load\" command found")
}
// clear the current test storage of all inserted samples.
func (ll *LazyLoader) clear() {
if ll.storage != nil {
err := ll.storage.Close()
require.NoError(ll.T, err, "Unexpected error while closing test storage.")
}
if ll.cancelCtx != nil {
ll.cancelCtx()
}
ll.storage = teststorage.New(ll)
opts := EngineOpts{
Logger: nil,
Reg: nil,
MaxSamples: 10000,
Timeout: 100 * time.Second,
NoStepSubqueryIntervalFn: func(int64) int64 { return durationMilliseconds(ll.SubqueryInterval) },
EnableAtModifier: ll.opts.EnableAtModifier,
EnableNegativeOffset: ll.opts.EnableNegativeOffset,
}
ll.queryEngine = NewEngine(opts)
ll.context, ll.cancelCtx = context.WithCancel(context.Background())
}
// appendTill appends the defined time series to the storage till the given timestamp (in milliseconds).
func (ll *LazyLoader) appendTill(ts int64) error {
app := ll.storage.Appender(ll.Context())
for h, smpls := range ll.loadCmd.defs {
m := ll.loadCmd.metrics[h]
for i, s := range smpls {
if s.T > ts {
// Removing the already added samples.
ll.loadCmd.defs[h] = smpls[i:]
break
}
if err := appendSample(app, s, m); err != nil {
return err
}
if i == len(smpls)-1 {
ll.loadCmd.defs[h] = nil
}
}
}
return app.Commit()
}
// WithSamplesTill loads the samples till given timestamp and executes the given function.
func (ll *LazyLoader) WithSamplesTill(ts time.Time, fn func(error)) {
tsMilli := ts.Sub(time.Unix(0, 0).UTC()) / time.Millisecond
fn(ll.appendTill(int64(tsMilli)))
}
// QueryEngine returns the LazyLoader's query engine.
func (ll *LazyLoader) QueryEngine() *Engine {
return ll.queryEngine
}
// Queryable allows querying the LazyLoader's data.
// Note: only the samples till the max timestamp used
// in `WithSamplesTill` can be queried.
func (ll *LazyLoader) Queryable() storage.Queryable {
return ll.storage
}
// Context returns the LazyLoader's context.
func (ll *LazyLoader) Context() context.Context {
return ll.context
}
// Storage returns the LazyLoader's storage.
func (ll *LazyLoader) Storage() storage.Storage {
return ll.storage
}
// Close closes resources associated with the LazyLoader.
func (ll *LazyLoader) Close() {
ll.cancelCtx()
err := ll.storage.Close()
require.NoError(ll.T, err, "Unexpected error while closing test storage.")
}