2016-04-13 07:08:22 -07:00
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// Copyright 2016 The Prometheus Authors
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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2015-03-30 10:13:36 -07:00
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package promql
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import (
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2017-10-04 12:04:15 -07:00
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"context"
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2019-10-09 17:06:53 -07:00
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"errors"
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2021-11-02 08:01:32 -07:00
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"fmt"
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2021-12-06 05:47:22 -08:00
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"math"
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2023-12-30 11:13:59 -08:00
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"os"
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2020-01-17 03:21:44 -08:00
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"sort"
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2015-03-30 10:13:36 -07:00
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"testing"
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"time"
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2021-06-11 09:17:59 -07:00
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"github.com/go-kit/log"
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2022-02-01 18:07:23 -08:00
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2020-10-29 02:43:23 -07:00
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"github.com/stretchr/testify/require"
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2020-10-22 02:00:08 -07:00
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"go.uber.org/goleak"
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2021-12-06 04:49:18 -08:00
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"github.com/prometheus/prometheus/model/histogram"
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2021-11-08 06:23:17 -08:00
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"github.com/prometheus/prometheus/model/labels"
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"github.com/prometheus/prometheus/model/timestamp"
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2020-02-03 08:32:23 -08:00
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"github.com/prometheus/prometheus/promql/parser"
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2023-09-14 09:57:31 -07:00
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"github.com/prometheus/prometheus/promql/parser/posrange"
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2018-02-15 04:08:00 -08:00
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"github.com/prometheus/prometheus/storage"
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2023-08-18 11:48:59 -07:00
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"github.com/prometheus/prometheus/tsdb/tsdbutil"
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2023-09-14 09:57:31 -07:00
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"github.com/prometheus/prometheus/util/annotations"
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2023-02-10 03:39:33 -08:00
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"github.com/prometheus/prometheus/util/stats"
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2023-08-18 11:48:59 -07:00
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"github.com/prometheus/prometheus/util/teststorage"
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2023-04-16 05:13:31 -07:00
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"github.com/prometheus/prometheus/util/testutil"
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2015-03-30 10:13:36 -07:00
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)
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2020-07-24 02:10:20 -07:00
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func TestMain(m *testing.M) {
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goleak.VerifyTestMain(m)
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}
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2015-09-23 02:06:51 -07:00
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func TestQueryConcurrency(t *testing.T) {
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2020-01-27 14:29:44 -08:00
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maxConcurrency := 10
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2023-12-30 11:13:59 -08:00
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dir, err := os.MkdirTemp("", "test_concurrency")
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require.NoError(t, err)
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defer os.RemoveAll(dir)
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2020-01-27 14:29:44 -08:00
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queryTracker := NewActiveQueryTracker(dir, maxConcurrency, nil)
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2022-02-27 04:49:33 -08:00
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t.Cleanup(queryTracker.Close)
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2020-01-27 14:29:44 -08:00
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2018-10-02 04:59:19 -07:00
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opts := EngineOpts{
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2020-01-27 14:29:44 -08:00
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Logger: nil,
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Reg: nil,
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MaxSamples: 10,
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Timeout: 100 * time.Second,
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ActiveQueryTracker: queryTracker,
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2018-10-02 04:59:19 -07:00
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}
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2018-01-09 08:44:23 -08:00
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2018-10-02 04:59:19 -07:00
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engine := NewEngine(opts)
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2016-09-15 15:58:06 -07:00
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ctx, cancelCtx := context.WithCancel(context.Background())
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defer cancelCtx()
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2015-04-30 15:49:19 -07:00
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block := make(chan struct{})
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processing := make(chan struct{})
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2021-11-27 04:45:06 -08:00
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done := make(chan int)
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defer close(done)
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2015-08-10 05:21:24 -07:00
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f := func(context.Context) error {
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2021-11-27 04:45:06 -08:00
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select {
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case processing <- struct{}{}:
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case <-done:
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}
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select {
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case <-block:
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case <-done:
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}
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2015-04-30 15:49:19 -07:00
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return nil
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2015-08-10 05:21:24 -07:00
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}
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2015-04-30 15:49:19 -07:00
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2020-01-28 12:38:49 -08:00
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for i := 0; i < maxConcurrency; i++ {
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2015-08-10 05:21:24 -07:00
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q := engine.newTestQuery(f)
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promql: Allow per-query contexts.
For Weaveworks' Frankenstein, we need to support multitenancy. In
Frankenstein, we initially solved this without modifying the promql
package at all: we constructed a new promql.Engine for every
query and injected a storage implementation into that engine which would
be primed to only collect data for a given user.
This is problematic to upstream, however. Prometheus assumes that there
is only one engine: the query concurrency gate is part of the engine,
and the engine contains one central cancellable context to shut down all
queries. Also, creating a new engine for every query seems like overkill.
Thus, we want to be able to pass per-query contexts into a single engine.
This change gets rid of the promql.Engine's built-in base context and
allows passing in a per-query context instead. Central cancellation of
all queries is still possible by deriving all passed-in contexts from
one central one, but this is now the responsibility of the caller. The
central query context is now created in main() and passed into the
relevant components (web handler / API, rule manager).
In a next step, the per-query context would have to be passed to the
storage implementation, so that the storage can implement multi-tenancy
or other features based on the contextual information.
2016-09-15 04:52:50 -07:00
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go q.Exec(ctx)
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2015-04-30 15:49:19 -07:00
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select {
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case <-processing:
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// Expected.
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2015-09-23 02:06:51 -07:00
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case <-time.After(20 * time.Millisecond):
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2021-09-13 12:19:20 -07:00
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require.Fail(t, "Query within concurrency threshold not being executed")
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2015-04-30 15:49:19 -07:00
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}
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}
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2015-08-10 05:21:24 -07:00
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q := engine.newTestQuery(f)
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promql: Allow per-query contexts.
For Weaveworks' Frankenstein, we need to support multitenancy. In
Frankenstein, we initially solved this without modifying the promql
package at all: we constructed a new promql.Engine for every
query and injected a storage implementation into that engine which would
be primed to only collect data for a given user.
This is problematic to upstream, however. Prometheus assumes that there
is only one engine: the query concurrency gate is part of the engine,
and the engine contains one central cancellable context to shut down all
queries. Also, creating a new engine for every query seems like overkill.
Thus, we want to be able to pass per-query contexts into a single engine.
This change gets rid of the promql.Engine's built-in base context and
allows passing in a per-query context instead. Central cancellation of
all queries is still possible by deriving all passed-in contexts from
one central one, but this is now the responsibility of the caller. The
central query context is now created in main() and passed into the
relevant components (web handler / API, rule manager).
In a next step, the per-query context would have to be passed to the
storage implementation, so that the storage can implement multi-tenancy
or other features based on the contextual information.
2016-09-15 04:52:50 -07:00
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go q.Exec(ctx)
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2015-04-30 15:49:19 -07:00
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select {
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case <-processing:
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2021-09-13 12:19:20 -07:00
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require.Fail(t, "Query above concurrency threshold being executed")
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2015-09-23 02:06:51 -07:00
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case <-time.After(20 * time.Millisecond):
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2015-04-30 15:49:19 -07:00
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// Expected.
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}
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// Terminate a running query.
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block <- struct{}{}
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select {
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case <-processing:
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// Expected.
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2015-09-23 02:06:51 -07:00
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case <-time.After(20 * time.Millisecond):
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2021-09-13 12:19:20 -07:00
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require.Fail(t, "Query within concurrency threshold not being executed")
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2015-04-30 15:49:19 -07:00
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}
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// Terminate remaining queries.
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2020-01-28 12:38:49 -08:00
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for i := 0; i < maxConcurrency; i++ {
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2015-04-30 15:49:19 -07:00
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block <- struct{}{}
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}
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}
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2015-03-30 10:13:36 -07:00
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func TestQueryTimeout(t *testing.T) {
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2018-10-02 04:59:19 -07:00
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opts := EngineOpts{
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2020-01-28 12:38:49 -08:00
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Logger: nil,
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Reg: nil,
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MaxSamples: 10,
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Timeout: 5 * time.Millisecond,
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2018-10-02 04:59:19 -07:00
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}
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engine := NewEngine(opts)
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2016-09-15 15:58:06 -07:00
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ctx, cancelCtx := context.WithCancel(context.Background())
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defer cancelCtx()
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2015-03-30 10:13:36 -07:00
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2015-08-10 05:21:24 -07:00
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query := engine.newTestQuery(func(ctx context.Context) error {
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2020-01-22 02:09:31 -08:00
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time.Sleep(100 * time.Millisecond)
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2015-08-10 05:21:24 -07:00
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return contextDone(ctx, "test statement execution")
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2015-03-30 10:13:36 -07:00
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})
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promql: Allow per-query contexts.
For Weaveworks' Frankenstein, we need to support multitenancy. In
Frankenstein, we initially solved this without modifying the promql
package at all: we constructed a new promql.Engine for every
query and injected a storage implementation into that engine which would
be primed to only collect data for a given user.
This is problematic to upstream, however. Prometheus assumes that there
is only one engine: the query concurrency gate is part of the engine,
and the engine contains one central cancellable context to shut down all
queries. Also, creating a new engine for every query seems like overkill.
Thus, we want to be able to pass per-query contexts into a single engine.
This change gets rid of the promql.Engine's built-in base context and
allows passing in a per-query context instead. Central cancellation of
all queries is still possible by deriving all passed-in contexts from
one central one, but this is now the responsibility of the caller. The
central query context is now created in main() and passed into the
relevant components (web handler / API, rule manager).
In a next step, the per-query context would have to be passed to the
storage implementation, so that the storage can implement multi-tenancy
or other features based on the contextual information.
2016-09-15 04:52:50 -07:00
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res := query.Exec(ctx)
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2020-10-29 02:43:23 -07:00
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require.Error(t, res.Err, "expected timeout error but got none")
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2019-10-09 17:06:53 -07:00
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var e ErrQueryTimeout
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2023-12-07 03:35:01 -08:00
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require.ErrorAs(t, res.Err, &e, "expected timeout error but got: %s", res.Err)
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2015-03-30 10:13:36 -07:00
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}
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2019-10-09 17:06:53 -07:00
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const errQueryCanceled = ErrQueryCanceled("test statement execution")
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2015-03-30 10:13:36 -07:00
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func TestQueryCancel(t *testing.T) {
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2018-10-02 04:59:19 -07:00
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opts := EngineOpts{
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2020-01-28 12:38:49 -08:00
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Logger: nil,
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Reg: nil,
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MaxSamples: 10,
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Timeout: 10 * time.Second,
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2018-10-02 04:59:19 -07:00
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}
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engine := NewEngine(opts)
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2016-09-15 15:58:06 -07:00
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ctx, cancelCtx := context.WithCancel(context.Background())
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defer cancelCtx()
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2015-03-30 10:13:36 -07:00
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2015-08-10 05:21:24 -07:00
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// Cancel a running query before it completes.
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block := make(chan struct{})
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processing := make(chan struct{})
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2015-03-30 10:13:36 -07:00
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2015-08-10 05:21:24 -07:00
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query1 := engine.newTestQuery(func(ctx context.Context) error {
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processing <- struct{}{}
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<-block
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return contextDone(ctx, "test statement execution")
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})
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2015-04-29 02:08:56 -07:00
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2015-03-30 10:13:36 -07:00
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var res *Result
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go func() {
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promql: Allow per-query contexts.
For Weaveworks' Frankenstein, we need to support multitenancy. In
Frankenstein, we initially solved this without modifying the promql
package at all: we constructed a new promql.Engine for every
query and injected a storage implementation into that engine which would
be primed to only collect data for a given user.
This is problematic to upstream, however. Prometheus assumes that there
is only one engine: the query concurrency gate is part of the engine,
and the engine contains one central cancellable context to shut down all
queries. Also, creating a new engine for every query seems like overkill.
Thus, we want to be able to pass per-query contexts into a single engine.
This change gets rid of the promql.Engine's built-in base context and
allows passing in a per-query context instead. Central cancellation of
all queries is still possible by deriving all passed-in contexts from
one central one, but this is now the responsibility of the caller. The
central query context is now created in main() and passed into the
relevant components (web handler / API, rule manager).
In a next step, the per-query context would have to be passed to the
storage implementation, so that the storage can implement multi-tenancy
or other features based on the contextual information.
2016-09-15 04:52:50 -07:00
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res = query1.Exec(ctx)
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2015-08-10 05:21:24 -07:00
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processing <- struct{}{}
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2015-03-30 10:13:36 -07:00
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}()
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2015-08-10 05:21:24 -07:00
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<-processing
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2015-03-30 10:13:36 -07:00
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query1.Cancel()
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2015-08-10 05:21:24 -07:00
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block <- struct{}{}
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<-processing
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2015-03-30 10:13:36 -07:00
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2020-10-29 02:43:23 -07:00
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require.Error(t, res.Err, "expected cancellation error for query1 but got none")
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require.Equal(t, errQueryCanceled, res.Err)
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2015-03-30 10:13:36 -07:00
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2015-08-10 05:21:24 -07:00
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// Canceling a query before starting it must have no effect.
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query2 := engine.newTestQuery(func(ctx context.Context) error {
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return contextDone(ctx, "test statement execution")
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})
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2015-03-30 10:13:36 -07:00
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query2.Cancel()
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promql: Allow per-query contexts.
For Weaveworks' Frankenstein, we need to support multitenancy. In
Frankenstein, we initially solved this without modifying the promql
package at all: we constructed a new promql.Engine for every
query and injected a storage implementation into that engine which would
be primed to only collect data for a given user.
This is problematic to upstream, however. Prometheus assumes that there
is only one engine: the query concurrency gate is part of the engine,
and the engine contains one central cancellable context to shut down all
queries. Also, creating a new engine for every query seems like overkill.
Thus, we want to be able to pass per-query contexts into a single engine.
This change gets rid of the promql.Engine's built-in base context and
allows passing in a per-query context instead. Central cancellation of
all queries is still possible by deriving all passed-in contexts from
one central one, but this is now the responsibility of the caller. The
central query context is now created in main() and passed into the
relevant components (web handler / API, rule manager).
In a next step, the per-query context would have to be passed to the
storage implementation, so that the storage can implement multi-tenancy
or other features based on the contextual information.
2016-09-15 04:52:50 -07:00
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res = query2.Exec(ctx)
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2020-10-29 02:43:23 -07:00
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require.NoError(t, res.Err)
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2015-03-30 10:13:36 -07:00
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}
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2018-02-15 04:08:00 -08:00
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// errQuerier implements storage.Querier which always returns error.
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type errQuerier struct {
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err error
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}
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2023-09-12 03:37:38 -07:00
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func (q *errQuerier) Select(context.Context, bool, *storage.SelectHints, ...*labels.Matcher) storage.SeriesSet {
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2020-06-09 09:57:31 -07:00
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return errSeriesSet{err: q.err}
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2018-02-15 04:08:00 -08:00
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}
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2021-10-22 01:06:44 -07:00
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2023-09-14 09:57:31 -07:00
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func (*errQuerier) LabelValues(context.Context, string, ...*labels.Matcher) ([]string, annotations.Annotations, error) {
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2021-02-09 09:38:35 -08:00
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return nil, nil, nil
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}
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2021-10-22 01:06:44 -07:00
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2023-09-14 09:57:31 -07:00
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func (*errQuerier) LabelNames(context.Context, ...*labels.Matcher) ([]string, annotations.Annotations, error) {
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2021-07-20 05:38:08 -07:00
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return nil, nil, nil
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}
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func (*errQuerier) Close() error { return nil }
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2018-02-15 04:08:00 -08:00
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// errSeriesSet implements storage.SeriesSet which always returns error.
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type errSeriesSet struct {
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err error
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}
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2023-09-14 09:57:31 -07:00
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func (errSeriesSet) Next() bool { return false }
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func (errSeriesSet) At() storage.Series { return nil }
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func (e errSeriesSet) Err() error { return e.err }
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func (e errSeriesSet) Warnings() annotations.Annotations { return nil }
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2018-02-15 04:08:00 -08:00
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func TestQueryError(t *testing.T) {
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2018-10-02 04:59:19 -07:00
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opts := EngineOpts{
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2020-01-28 12:38:49 -08:00
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Logger: nil,
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Reg: nil,
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MaxSamples: 10,
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Timeout: 10 * time.Second,
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2018-10-02 04:59:19 -07:00
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}
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|
engine := NewEngine(opts)
|
2019-03-25 16:01:12 -07:00
|
|
|
errStorage := ErrStorage{errors.New("storage error")}
|
2023-09-12 03:37:38 -07:00
|
|
|
queryable := storage.QueryableFunc(func(mint, maxt int64) (storage.Querier, error) {
|
2018-02-15 04:08:00 -08:00
|
|
|
return &errQuerier{err: errStorage}, nil
|
|
|
|
})
|
|
|
|
ctx, cancelCtx := context.WithCancel(context.Background())
|
|
|
|
defer cancelCtx()
|
|
|
|
|
2023-04-17 21:32:38 -07:00
|
|
|
vectorQuery, err := engine.NewInstantQuery(ctx, queryable, nil, "foo", time.Unix(1, 0))
|
2020-10-29 02:43:23 -07:00
|
|
|
require.NoError(t, err)
|
2019-10-09 17:06:53 -07:00
|
|
|
|
2018-02-15 04:08:00 -08:00
|
|
|
res := vectorQuery.Exec(ctx)
|
2020-10-29 02:43:23 -07:00
|
|
|
require.Error(t, res.Err, "expected error on failed select but got none")
|
2023-12-07 03:35:01 -08:00
|
|
|
require.ErrorIs(t, res.Err, errStorage, "expected error doesn't match")
|
2018-02-15 04:08:00 -08:00
|
|
|
|
2023-04-17 21:32:38 -07:00
|
|
|
matrixQuery, err := engine.NewInstantQuery(ctx, queryable, nil, "foo[1m]", time.Unix(1, 0))
|
2020-10-29 02:43:23 -07:00
|
|
|
require.NoError(t, err)
|
2019-10-09 17:06:53 -07:00
|
|
|
|
2018-02-15 04:08:00 -08:00
|
|
|
res = matrixQuery.Exec(ctx)
|
2020-10-29 02:43:23 -07:00
|
|
|
require.Error(t, res.Err, "expected error on failed select but got none")
|
2023-12-07 03:35:01 -08:00
|
|
|
require.ErrorIs(t, res.Err, errStorage, "expected error doesn't match")
|
2018-02-15 04:08:00 -08:00
|
|
|
}
|
|
|
|
|
2020-07-23 15:05:43 -07:00
|
|
|
type noopHintRecordingQueryable struct {
|
|
|
|
hints []*storage.SelectHints
|
2019-04-17 05:52:41 -07:00
|
|
|
}
|
|
|
|
|
2023-09-12 03:37:38 -07:00
|
|
|
func (h *noopHintRecordingQueryable) Querier(int64, int64) (storage.Querier, error) {
|
2020-07-23 15:05:43 -07:00
|
|
|
return &hintRecordingQuerier{Querier: &errQuerier{}, h: h}, nil
|
|
|
|
}
|
|
|
|
|
|
|
|
type hintRecordingQuerier struct {
|
|
|
|
storage.Querier
|
2019-04-17 05:52:41 -07:00
|
|
|
|
2020-07-23 15:05:43 -07:00
|
|
|
h *noopHintRecordingQueryable
|
2019-04-17 05:52:41 -07:00
|
|
|
}
|
2020-07-23 15:05:43 -07:00
|
|
|
|
2023-09-12 03:37:38 -07:00
|
|
|
func (h *hintRecordingQuerier) Select(ctx context.Context, sortSeries bool, hints *storage.SelectHints, matchers ...*labels.Matcher) storage.SeriesSet {
|
2020-07-23 15:05:43 -07:00
|
|
|
h.h.hints = append(h.h.hints, hints)
|
2023-09-12 03:37:38 -07:00
|
|
|
return h.Querier.Select(ctx, sortSeries, hints, matchers...)
|
2019-06-17 00:31:17 -07:00
|
|
|
}
|
2019-04-17 05:52:41 -07:00
|
|
|
|
2020-07-23 15:05:43 -07:00
|
|
|
func TestSelectHintsSetCorrectly(t *testing.T) {
|
2019-04-17 05:52:41 -07:00
|
|
|
opts := EngineOpts{
|
2021-01-20 02:57:39 -08:00
|
|
|
Logger: nil,
|
|
|
|
Reg: nil,
|
|
|
|
MaxSamples: 10,
|
|
|
|
Timeout: 10 * time.Second,
|
|
|
|
LookbackDelta: 5 * time.Second,
|
|
|
|
EnableAtModifier: true,
|
2019-04-17 05:52:41 -07:00
|
|
|
}
|
|
|
|
|
2020-07-23 15:05:43 -07:00
|
|
|
for _, tc := range []struct {
|
2019-04-17 05:52:41 -07:00
|
|
|
query string
|
|
|
|
|
2020-07-23 15:05:43 -07:00
|
|
|
// All times are in milliseconds.
|
2019-04-17 05:52:41 -07:00
|
|
|
start int64
|
|
|
|
end int64
|
|
|
|
|
2020-07-23 15:05:43 -07:00
|
|
|
// TODO(bwplotka): Add support for better hints when subquerying.
|
|
|
|
expected []*storage.SelectHints
|
2021-10-22 01:06:44 -07:00
|
|
|
}{
|
|
|
|
{
|
|
|
|
query: "foo", start: 10000,
|
|
|
|
expected: []*storage.SelectHints{
|
|
|
|
{Start: 5000, End: 10000},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "foo @ 15", start: 10000,
|
|
|
|
expected: []*storage.SelectHints{
|
|
|
|
{Start: 10000, End: 15000},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "foo @ 1", start: 10000,
|
|
|
|
expected: []*storage.SelectHints{
|
|
|
|
{Start: -4000, End: 1000},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "foo[2m]", start: 200000,
|
|
|
|
expected: []*storage.SelectHints{
|
|
|
|
{Start: 80000, End: 200000, Range: 120000},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "foo[2m] @ 180", start: 200000,
|
|
|
|
expected: []*storage.SelectHints{
|
|
|
|
{Start: 60000, End: 180000, Range: 120000},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "foo[2m] @ 300", start: 200000,
|
|
|
|
expected: []*storage.SelectHints{
|
|
|
|
{Start: 180000, End: 300000, Range: 120000},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "foo[2m] @ 60", start: 200000,
|
|
|
|
expected: []*storage.SelectHints{
|
|
|
|
{Start: -60000, End: 60000, Range: 120000},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "foo[2m] offset 2m", start: 300000,
|
|
|
|
expected: []*storage.SelectHints{
|
|
|
|
{Start: 60000, End: 180000, Range: 120000},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "foo[2m] @ 200 offset 2m", start: 300000,
|
|
|
|
expected: []*storage.SelectHints{
|
|
|
|
{Start: -40000, End: 80000, Range: 120000},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "foo[2m:1s]", start: 300000,
|
|
|
|
expected: []*storage.SelectHints{
|
2022-09-30 07:43:38 -07:00
|
|
|
{Start: 175000, End: 300000, Step: 1000},
|
2021-10-22 01:06:44 -07:00
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "count_over_time(foo[2m:1s])", start: 300000,
|
|
|
|
expected: []*storage.SelectHints{
|
2022-09-30 07:43:38 -07:00
|
|
|
{Start: 175000, End: 300000, Func: "count_over_time", Step: 1000},
|
2021-10-22 01:06:44 -07:00
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "count_over_time(foo[2m:1s] @ 300)", start: 200000,
|
|
|
|
expected: []*storage.SelectHints{
|
2022-09-30 07:43:38 -07:00
|
|
|
{Start: 175000, End: 300000, Func: "count_over_time", Step: 1000},
|
2021-10-22 01:06:44 -07:00
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "count_over_time(foo[2m:1s] @ 200)", start: 200000,
|
|
|
|
expected: []*storage.SelectHints{
|
2022-09-30 07:43:38 -07:00
|
|
|
{Start: 75000, End: 200000, Func: "count_over_time", Step: 1000},
|
2021-10-22 01:06:44 -07:00
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "count_over_time(foo[2m:1s] @ 100)", start: 200000,
|
|
|
|
expected: []*storage.SelectHints{
|
2022-09-30 07:43:38 -07:00
|
|
|
{Start: -25000, End: 100000, Func: "count_over_time", Step: 1000},
|
2021-10-22 01:06:44 -07:00
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "count_over_time(foo[2m:1s] offset 10s)", start: 300000,
|
|
|
|
expected: []*storage.SelectHints{
|
2022-09-30 07:43:38 -07:00
|
|
|
{Start: 165000, End: 290000, Func: "count_over_time", Step: 1000},
|
2021-10-22 01:06:44 -07:00
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "count_over_time((foo offset 10s)[2m:1s] offset 10s)", start: 300000,
|
|
|
|
expected: []*storage.SelectHints{
|
2022-09-30 07:43:38 -07:00
|
|
|
{Start: 155000, End: 280000, Func: "count_over_time", Step: 1000},
|
2021-10-22 01:06:44 -07:00
|
|
|
},
|
|
|
|
}, {
|
|
|
|
// When the @ is on the vector selector, the enclosing subquery parameters
|
|
|
|
// don't affect the hint ranges.
|
|
|
|
query: "count_over_time((foo @ 200 offset 10s)[2m:1s] offset 10s)", start: 300000,
|
|
|
|
expected: []*storage.SelectHints{
|
2022-09-30 07:43:38 -07:00
|
|
|
{Start: 185000, End: 190000, Func: "count_over_time", Step: 1000},
|
2021-10-22 01:06:44 -07:00
|
|
|
},
|
|
|
|
}, {
|
|
|
|
// When the @ is on the vector selector, the enclosing subquery parameters
|
|
|
|
// don't affect the hint ranges.
|
|
|
|
query: "count_over_time((foo @ 200 offset 10s)[2m:1s] @ 100 offset 10s)", start: 300000,
|
|
|
|
expected: []*storage.SelectHints{
|
2022-09-30 07:43:38 -07:00
|
|
|
{Start: 185000, End: 190000, Func: "count_over_time", Step: 1000},
|
2021-10-22 01:06:44 -07:00
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "count_over_time((foo offset 10s)[2m:1s] @ 100 offset 10s)", start: 300000,
|
|
|
|
expected: []*storage.SelectHints{
|
2022-09-30 07:43:38 -07:00
|
|
|
{Start: -45000, End: 80000, Func: "count_over_time", Step: 1000},
|
2021-10-22 01:06:44 -07:00
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "foo", start: 10000, end: 20000,
|
|
|
|
expected: []*storage.SelectHints{
|
|
|
|
{Start: 5000, End: 20000, Step: 1000},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "foo @ 15", start: 10000, end: 20000,
|
|
|
|
expected: []*storage.SelectHints{
|
|
|
|
{Start: 10000, End: 15000, Step: 1000},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "foo @ 1", start: 10000, end: 20000,
|
|
|
|
expected: []*storage.SelectHints{
|
|
|
|
{Start: -4000, End: 1000, Step: 1000},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "rate(foo[2m] @ 180)", start: 200000, end: 500000,
|
|
|
|
expected: []*storage.SelectHints{
|
|
|
|
{Start: 60000, End: 180000, Range: 120000, Func: "rate", Step: 1000},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "rate(foo[2m] @ 300)", start: 200000, end: 500000,
|
|
|
|
expected: []*storage.SelectHints{
|
|
|
|
{Start: 180000, End: 300000, Range: 120000, Func: "rate", Step: 1000},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "rate(foo[2m] @ 60)", start: 200000, end: 500000,
|
|
|
|
expected: []*storage.SelectHints{
|
|
|
|
{Start: -60000, End: 60000, Range: 120000, Func: "rate", Step: 1000},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "rate(foo[2m])", start: 200000, end: 500000,
|
|
|
|
expected: []*storage.SelectHints{
|
|
|
|
{Start: 80000, End: 500000, Range: 120000, Func: "rate", Step: 1000},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "rate(foo[2m] offset 2m)", start: 300000, end: 500000,
|
|
|
|
expected: []*storage.SelectHints{
|
|
|
|
{Start: 60000, End: 380000, Range: 120000, Func: "rate", Step: 1000},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "rate(foo[2m:1s])", start: 300000, end: 500000,
|
|
|
|
expected: []*storage.SelectHints{
|
|
|
|
{Start: 175000, End: 500000, Func: "rate", Step: 1000},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "count_over_time(foo[2m:1s])", start: 300000, end: 500000,
|
|
|
|
expected: []*storage.SelectHints{
|
|
|
|
{Start: 175000, End: 500000, Func: "count_over_time", Step: 1000},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "count_over_time(foo[2m:1s] offset 10s)", start: 300000, end: 500000,
|
|
|
|
expected: []*storage.SelectHints{
|
|
|
|
{Start: 165000, End: 490000, Func: "count_over_time", Step: 1000},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "count_over_time(foo[2m:1s] @ 300)", start: 200000, end: 500000,
|
|
|
|
expected: []*storage.SelectHints{
|
|
|
|
{Start: 175000, End: 300000, Func: "count_over_time", Step: 1000},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "count_over_time(foo[2m:1s] @ 200)", start: 200000, end: 500000,
|
|
|
|
expected: []*storage.SelectHints{
|
|
|
|
{Start: 75000, End: 200000, Func: "count_over_time", Step: 1000},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "count_over_time(foo[2m:1s] @ 100)", start: 200000, end: 500000,
|
|
|
|
expected: []*storage.SelectHints{
|
|
|
|
{Start: -25000, End: 100000, Func: "count_over_time", Step: 1000},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "count_over_time((foo offset 10s)[2m:1s] offset 10s)", start: 300000, end: 500000,
|
|
|
|
expected: []*storage.SelectHints{
|
|
|
|
{Start: 155000, End: 480000, Func: "count_over_time", Step: 1000},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
// When the @ is on the vector selector, the enclosing subquery parameters
|
|
|
|
// don't affect the hint ranges.
|
|
|
|
query: "count_over_time((foo @ 200 offset 10s)[2m:1s] offset 10s)", start: 300000, end: 500000,
|
|
|
|
expected: []*storage.SelectHints{
|
|
|
|
{Start: 185000, End: 190000, Func: "count_over_time", Step: 1000},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
// When the @ is on the vector selector, the enclosing subquery parameters
|
|
|
|
// don't affect the hint ranges.
|
|
|
|
query: "count_over_time((foo @ 200 offset 10s)[2m:1s] @ 100 offset 10s)", start: 300000, end: 500000,
|
|
|
|
expected: []*storage.SelectHints{
|
|
|
|
{Start: 185000, End: 190000, Func: "count_over_time", Step: 1000},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "count_over_time((foo offset 10s)[2m:1s] @ 100 offset 10s)", start: 300000, end: 500000,
|
|
|
|
expected: []*storage.SelectHints{
|
|
|
|
{Start: -45000, End: 80000, Func: "count_over_time", Step: 1000},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "sum by (dim1) (foo)", start: 10000,
|
|
|
|
expected: []*storage.SelectHints{
|
|
|
|
{Start: 5000, End: 10000, Func: "sum", By: true, Grouping: []string{"dim1"}},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "sum without (dim1) (foo)", start: 10000,
|
|
|
|
expected: []*storage.SelectHints{
|
|
|
|
{Start: 5000, End: 10000, Func: "sum", Grouping: []string{"dim1"}},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "sum by (dim1) (avg_over_time(foo[1s]))", start: 10000,
|
|
|
|
expected: []*storage.SelectHints{
|
|
|
|
{Start: 9000, End: 10000, Func: "avg_over_time", Range: 1000},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "sum by (dim1) (max by (dim2) (foo))", start: 10000,
|
|
|
|
expected: []*storage.SelectHints{
|
|
|
|
{Start: 5000, End: 10000, Func: "max", By: true, Grouping: []string{"dim2"}},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "(max by (dim1) (foo))[5s:1s]", start: 10000,
|
|
|
|
expected: []*storage.SelectHints{
|
2022-09-30 07:43:38 -07:00
|
|
|
{Start: 0, End: 10000, Func: "max", By: true, Grouping: []string{"dim1"}, Step: 1000},
|
2021-10-22 01:06:44 -07:00
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "(sum(http_requests{group=~\"p.*\"})+max(http_requests{group=~\"c.*\"}))[20s:5s]", start: 120000,
|
|
|
|
expected: []*storage.SelectHints{
|
2022-09-30 07:43:38 -07:00
|
|
|
{Start: 95000, End: 120000, Func: "sum", By: true, Step: 5000},
|
|
|
|
{Start: 95000, End: 120000, Func: "max", By: true, Step: 5000},
|
2021-10-22 01:06:44 -07:00
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "foo @ 50 + bar @ 250 + baz @ 900", start: 100000, end: 500000,
|
|
|
|
expected: []*storage.SelectHints{
|
|
|
|
{Start: 45000, End: 50000, Step: 1000},
|
|
|
|
{Start: 245000, End: 250000, Step: 1000},
|
|
|
|
{Start: 895000, End: 900000, Step: 1000},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "foo @ 50 + bar + baz @ 900", start: 100000, end: 500000,
|
|
|
|
expected: []*storage.SelectHints{
|
|
|
|
{Start: 45000, End: 50000, Step: 1000},
|
|
|
|
{Start: 95000, End: 500000, Step: 1000},
|
|
|
|
{Start: 895000, End: 900000, Step: 1000},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "rate(foo[2s] @ 50) + bar @ 250 + baz @ 900", start: 100000, end: 500000,
|
|
|
|
expected: []*storage.SelectHints{
|
|
|
|
{Start: 48000, End: 50000, Step: 1000, Func: "rate", Range: 2000},
|
|
|
|
{Start: 245000, End: 250000, Step: 1000},
|
|
|
|
{Start: 895000, End: 900000, Step: 1000},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "rate(foo[2s:1s] @ 50) + bar + baz", start: 100000, end: 500000,
|
|
|
|
expected: []*storage.SelectHints{
|
|
|
|
{Start: 43000, End: 50000, Step: 1000, Func: "rate"},
|
|
|
|
{Start: 95000, End: 500000, Step: 1000},
|
|
|
|
{Start: 95000, End: 500000, Step: 1000},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "rate(foo[2s:1s] @ 50) + bar + rate(baz[2m:1s] @ 900 offset 2m) ", start: 100000, end: 500000,
|
|
|
|
expected: []*storage.SelectHints{
|
|
|
|
{Start: 43000, End: 50000, Step: 1000, Func: "rate"},
|
|
|
|
{Start: 95000, End: 500000, Step: 1000},
|
|
|
|
{Start: 655000, End: 780000, Step: 1000, Func: "rate"},
|
|
|
|
},
|
|
|
|
}, { // Hints are based on the inner most subquery timestamp.
|
|
|
|
query: `sum_over_time(sum_over_time(metric{job="1"}[100s])[100s:25s] @ 50)[3s:1s] @ 3000`, start: 100000,
|
|
|
|
expected: []*storage.SelectHints{
|
2022-09-30 07:43:38 -07:00
|
|
|
{Start: -150000, End: 50000, Range: 100000, Func: "sum_over_time", Step: 25000},
|
2021-10-22 01:06:44 -07:00
|
|
|
},
|
|
|
|
}, { // Hints are based on the inner most subquery timestamp.
|
|
|
|
query: `sum_over_time(sum_over_time(metric{job="1"}[100s])[100s:25s] @ 3000)[3s:1s] @ 50`,
|
|
|
|
expected: []*storage.SelectHints{
|
2022-09-30 07:43:38 -07:00
|
|
|
{Start: 2800000, End: 3000000, Range: 100000, Func: "sum_over_time", Step: 25000},
|
2021-10-22 01:06:44 -07:00
|
|
|
},
|
2021-01-20 02:57:39 -08:00
|
|
|
},
|
|
|
|
} {
|
2020-07-23 15:05:43 -07:00
|
|
|
t.Run(tc.query, func(t *testing.T) {
|
|
|
|
engine := NewEngine(opts)
|
|
|
|
hintsRecorder := &noopHintRecordingQueryable{}
|
|
|
|
|
|
|
|
var (
|
|
|
|
query Query
|
|
|
|
err error
|
|
|
|
)
|
2023-04-17 21:32:38 -07:00
|
|
|
ctx := context.Background()
|
2023-08-18 11:48:59 -07:00
|
|
|
|
2020-07-23 15:05:43 -07:00
|
|
|
if tc.end == 0 {
|
2023-04-17 21:32:38 -07:00
|
|
|
query, err = engine.NewInstantQuery(ctx, hintsRecorder, nil, tc.query, timestamp.Time(tc.start))
|
2020-07-23 15:05:43 -07:00
|
|
|
} else {
|
2023-04-17 21:32:38 -07:00
|
|
|
query, err = engine.NewRangeQuery(ctx, hintsRecorder, nil, tc.query, timestamp.Time(tc.start), timestamp.Time(tc.end), time.Second)
|
2020-07-23 15:05:43 -07:00
|
|
|
}
|
2020-10-29 02:43:23 -07:00
|
|
|
require.NoError(t, err)
|
2019-04-17 05:52:41 -07:00
|
|
|
|
2023-08-18 11:48:59 -07:00
|
|
|
res := query.Exec(context.Background())
|
2020-10-29 02:43:23 -07:00
|
|
|
require.NoError(t, res.Err)
|
2020-07-23 15:05:43 -07:00
|
|
|
|
2020-10-29 02:43:23 -07:00
|
|
|
require.Equal(t, tc.expected, hintsRecorder.hints)
|
2020-07-23 15:05:43 -07:00
|
|
|
})
|
2019-04-17 05:52:41 -07:00
|
|
|
}
|
|
|
|
}
|
|
|
|
|
2015-03-30 10:13:36 -07:00
|
|
|
func TestEngineShutdown(t *testing.T) {
|
2018-10-02 04:59:19 -07:00
|
|
|
opts := EngineOpts{
|
2020-01-28 12:38:49 -08:00
|
|
|
Logger: nil,
|
|
|
|
Reg: nil,
|
|
|
|
MaxSamples: 10,
|
|
|
|
Timeout: 10 * time.Second,
|
2018-10-02 04:59:19 -07:00
|
|
|
}
|
|
|
|
engine := NewEngine(opts)
|
2016-09-15 15:58:06 -07:00
|
|
|
ctx, cancelCtx := context.WithCancel(context.Background())
|
2015-03-30 10:13:36 -07:00
|
|
|
|
2015-08-10 05:21:24 -07:00
|
|
|
block := make(chan struct{})
|
|
|
|
processing := make(chan struct{})
|
|
|
|
|
2015-03-30 10:13:36 -07:00
|
|
|
// Shutdown engine on first handler execution. Should handler execution ever become
|
|
|
|
// concurrent this test has to be adjusted accordingly.
|
2015-08-10 05:21:24 -07:00
|
|
|
f := func(ctx context.Context) error {
|
|
|
|
processing <- struct{}{}
|
|
|
|
<-block
|
|
|
|
return contextDone(ctx, "test statement execution")
|
|
|
|
}
|
|
|
|
query1 := engine.newTestQuery(f)
|
2015-03-30 10:13:36 -07:00
|
|
|
|
2015-04-29 02:08:56 -07:00
|
|
|
// Stopping the engine must cancel the base context. While executing queries is
|
|
|
|
// still possible, their context is canceled from the beginning and execution should
|
2015-03-30 10:13:36 -07:00
|
|
|
// terminate immediately.
|
|
|
|
|
2015-08-10 05:21:24 -07:00
|
|
|
var res *Result
|
|
|
|
go func() {
|
promql: Allow per-query contexts.
For Weaveworks' Frankenstein, we need to support multitenancy. In
Frankenstein, we initially solved this without modifying the promql
package at all: we constructed a new promql.Engine for every
query and injected a storage implementation into that engine which would
be primed to only collect data for a given user.
This is problematic to upstream, however. Prometheus assumes that there
is only one engine: the query concurrency gate is part of the engine,
and the engine contains one central cancellable context to shut down all
queries. Also, creating a new engine for every query seems like overkill.
Thus, we want to be able to pass per-query contexts into a single engine.
This change gets rid of the promql.Engine's built-in base context and
allows passing in a per-query context instead. Central cancellation of
all queries is still possible by deriving all passed-in contexts from
one central one, but this is now the responsibility of the caller. The
central query context is now created in main() and passed into the
relevant components (web handler / API, rule manager).
In a next step, the per-query context would have to be passed to the
storage implementation, so that the storage can implement multi-tenancy
or other features based on the contextual information.
2016-09-15 04:52:50 -07:00
|
|
|
res = query1.Exec(ctx)
|
2015-08-10 05:21:24 -07:00
|
|
|
processing <- struct{}{}
|
|
|
|
}()
|
|
|
|
|
|
|
|
<-processing
|
2016-09-15 15:58:06 -07:00
|
|
|
cancelCtx()
|
2015-08-10 05:21:24 -07:00
|
|
|
block <- struct{}{}
|
|
|
|
<-processing
|
|
|
|
|
2020-10-29 02:43:23 -07:00
|
|
|
require.Error(t, res.Err, "expected error on shutdown during query but got none")
|
|
|
|
require.Equal(t, errQueryCanceled, res.Err)
|
2015-03-30 10:13:36 -07:00
|
|
|
|
2015-08-10 05:21:24 -07:00
|
|
|
query2 := engine.newTestQuery(func(context.Context) error {
|
2021-09-13 12:19:20 -07:00
|
|
|
require.FailNow(t, "reached query execution unexpectedly")
|
2015-08-10 05:21:24 -07:00
|
|
|
return nil
|
|
|
|
})
|
|
|
|
|
|
|
|
// The second query is started after the engine shut down. It must
|
|
|
|
// be canceled immediately.
|
promql: Allow per-query contexts.
For Weaveworks' Frankenstein, we need to support multitenancy. In
Frankenstein, we initially solved this without modifying the promql
package at all: we constructed a new promql.Engine for every
query and injected a storage implementation into that engine which would
be primed to only collect data for a given user.
This is problematic to upstream, however. Prometheus assumes that there
is only one engine: the query concurrency gate is part of the engine,
and the engine contains one central cancellable context to shut down all
queries. Also, creating a new engine for every query seems like overkill.
Thus, we want to be able to pass per-query contexts into a single engine.
This change gets rid of the promql.Engine's built-in base context and
allows passing in a per-query context instead. Central cancellation of
all queries is still possible by deriving all passed-in contexts from
one central one, but this is now the responsibility of the caller. The
central query context is now created in main() and passed into the
relevant components (web handler / API, rule manager).
In a next step, the per-query context would have to be passed to the
storage implementation, so that the storage can implement multi-tenancy
or other features based on the contextual information.
2016-09-15 04:52:50 -07:00
|
|
|
res2 := query2.Exec(ctx)
|
2020-10-29 02:43:23 -07:00
|
|
|
require.Error(t, res2.Err, "expected error on querying with canceled context but got none")
|
2019-10-09 17:06:53 -07:00
|
|
|
|
|
|
|
var e ErrQueryCanceled
|
2023-12-07 03:35:01 -08:00
|
|
|
require.ErrorAs(t, res2.Err, &e, "expected cancellation error but got: %s", res2.Err)
|
2015-03-30 10:13:36 -07:00
|
|
|
}
|
2015-08-19 06:28:53 -07:00
|
|
|
|
2017-04-18 06:51:10 -07:00
|
|
|
func TestEngineEvalStmtTimestamps(t *testing.T) {
|
2023-08-18 11:48:59 -07:00
|
|
|
storage := LoadedStorage(t, `
|
2017-04-18 06:51:10 -07:00
|
|
|
load 10s
|
|
|
|
metric 1 2
|
|
|
|
`)
|
2023-08-18 11:48:59 -07:00
|
|
|
t.Cleanup(func() { storage.Close() })
|
2017-04-18 06:51:10 -07:00
|
|
|
|
|
|
|
cases := []struct {
|
2018-09-13 02:57:36 -07:00
|
|
|
Query string
|
2020-02-03 08:32:23 -08:00
|
|
|
Result parser.Value
|
2018-09-13 02:57:36 -07:00
|
|
|
Start time.Time
|
|
|
|
End time.Time
|
|
|
|
Interval time.Duration
|
|
|
|
ShouldError bool
|
2017-04-18 06:51:10 -07:00
|
|
|
}{
|
|
|
|
// Instant queries.
|
|
|
|
{
|
|
|
|
Query: "1",
|
|
|
|
Result: Scalar{V: 1, T: 1000},
|
|
|
|
Start: time.Unix(1, 0),
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: "metric",
|
|
|
|
Result: Vector{
|
2021-10-22 01:06:44 -07:00
|
|
|
Sample{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
F: 1,
|
|
|
|
T: 1000,
|
2021-10-22 01:06:44 -07:00
|
|
|
Metric: labels.FromStrings("__name__", "metric"),
|
|
|
|
},
|
2017-04-18 06:51:10 -07:00
|
|
|
},
|
|
|
|
Start: time.Unix(1, 0),
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: "metric[20s]",
|
2021-10-22 01:06:44 -07:00
|
|
|
Result: Matrix{
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 1, T: 0}, {F: 2, T: 10000}},
|
2021-10-22 01:06:44 -07:00
|
|
|
Metric: labels.FromStrings("__name__", "metric"),
|
|
|
|
},
|
2017-04-18 06:51:10 -07:00
|
|
|
},
|
|
|
|
Start: time.Unix(10, 0),
|
|
|
|
},
|
|
|
|
// Range queries.
|
|
|
|
{
|
|
|
|
Query: "1",
|
2021-10-22 01:06:44 -07:00
|
|
|
Result: Matrix{
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 1, T: 0}, {F: 1, T: 1000}, {F: 1, T: 2000}},
|
2022-03-09 14:16:54 -08:00
|
|
|
Metric: labels.EmptyLabels(),
|
2021-10-22 01:06:44 -07:00
|
|
|
},
|
2017-04-18 06:51:10 -07:00
|
|
|
},
|
|
|
|
Start: time.Unix(0, 0),
|
|
|
|
End: time.Unix(2, 0),
|
|
|
|
Interval: time.Second,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: "metric",
|
2021-10-22 01:06:44 -07:00
|
|
|
Result: Matrix{
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 1, T: 0}, {F: 1, T: 1000}, {F: 1, T: 2000}},
|
2021-10-22 01:06:44 -07:00
|
|
|
Metric: labels.FromStrings("__name__", "metric"),
|
|
|
|
},
|
2017-04-18 06:51:10 -07:00
|
|
|
},
|
|
|
|
Start: time.Unix(0, 0),
|
|
|
|
End: time.Unix(2, 0),
|
|
|
|
Interval: time.Second,
|
|
|
|
},
|
2017-06-12 22:22:27 -07:00
|
|
|
{
|
|
|
|
Query: "metric",
|
2021-10-22 01:06:44 -07:00
|
|
|
Result: Matrix{
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 1, T: 0}, {F: 1, T: 5000}, {F: 2, T: 10000}},
|
2021-10-22 01:06:44 -07:00
|
|
|
Metric: labels.FromStrings("__name__", "metric"),
|
|
|
|
},
|
2017-06-12 22:22:27 -07:00
|
|
|
},
|
|
|
|
Start: time.Unix(0, 0),
|
|
|
|
End: time.Unix(10, 0),
|
|
|
|
Interval: 5 * time.Second,
|
|
|
|
},
|
2018-09-13 02:57:36 -07:00
|
|
|
{
|
|
|
|
Query: `count_values("wrong label!", metric)`,
|
|
|
|
ShouldError: true,
|
|
|
|
},
|
2017-04-18 06:51:10 -07:00
|
|
|
}
|
|
|
|
|
2022-05-25 07:22:47 -07:00
|
|
|
for i, c := range cases {
|
|
|
|
t.Run(fmt.Sprintf("%d query=%s", i, c.Query), func(t *testing.T) {
|
|
|
|
var err error
|
|
|
|
var qry Query
|
2023-08-18 11:48:59 -07:00
|
|
|
engine := newTestEngine()
|
2022-05-25 07:22:47 -07:00
|
|
|
if c.Interval == 0 {
|
2023-08-18 11:48:59 -07:00
|
|
|
qry, err = engine.NewInstantQuery(context.Background(), storage, nil, c.Query, c.Start)
|
2022-05-25 07:22:47 -07:00
|
|
|
} else {
|
2023-08-18 11:48:59 -07:00
|
|
|
qry, err = engine.NewRangeQuery(context.Background(), storage, nil, c.Query, c.Start, c.End, c.Interval)
|
2022-05-25 07:22:47 -07:00
|
|
|
}
|
|
|
|
require.NoError(t, err)
|
2019-10-09 17:06:53 -07:00
|
|
|
|
2023-08-18 11:48:59 -07:00
|
|
|
res := qry.Exec(context.Background())
|
2022-05-25 07:22:47 -07:00
|
|
|
if c.ShouldError {
|
|
|
|
require.Error(t, res.Err, "expected error for the query %q", c.Query)
|
|
|
|
return
|
|
|
|
}
|
2019-10-09 17:06:53 -07:00
|
|
|
|
2022-05-25 07:22:47 -07:00
|
|
|
require.NoError(t, res.Err)
|
|
|
|
require.Equal(t, c.Result, res.Value, "query %q failed", c.Query)
|
|
|
|
})
|
2017-04-18 06:51:10 -07:00
|
|
|
}
|
|
|
|
}
|
|
|
|
|
2022-02-01 18:07:23 -08:00
|
|
|
func TestQueryStatistics(t *testing.T) {
|
2023-08-18 11:48:59 -07:00
|
|
|
storage := LoadedStorage(t, `
|
2022-02-01 18:07:23 -08:00
|
|
|
load 10s
|
|
|
|
metricWith1SampleEvery10Seconds 1+1x100
|
|
|
|
metricWith3SampleEvery10Seconds{a="1",b="1"} 1+1x100
|
|
|
|
metricWith3SampleEvery10Seconds{a="2",b="2"} 1+1x100
|
|
|
|
metricWith3SampleEvery10Seconds{a="3",b="2"} 1+1x100
|
2024-02-28 09:10:46 -08:00
|
|
|
metricWith1HistogramEvery10Seconds {{schema:1 count:5 sum:20 buckets:[1 2 1 1]}}+{{schema:1 count:10 sum:5 buckets:[1 2 3 4]}}x100
|
2022-02-01 18:07:23 -08:00
|
|
|
`)
|
2023-08-18 11:48:59 -07:00
|
|
|
t.Cleanup(func() { storage.Close() })
|
2022-02-01 18:07:23 -08:00
|
|
|
|
|
|
|
cases := []struct {
|
|
|
|
Query string
|
2022-02-22 12:30:39 -08:00
|
|
|
SkipMaxCheck bool
|
2022-04-11 16:22:25 -07:00
|
|
|
TotalSamples int64
|
2022-02-01 18:07:23 -08:00
|
|
|
TotalSamplesPerStep stats.TotalSamplesPerStep
|
2022-02-22 12:30:39 -08:00
|
|
|
PeakSamples int
|
2022-02-01 18:07:23 -08:00
|
|
|
Start time.Time
|
|
|
|
End time.Time
|
|
|
|
Interval time.Duration
|
|
|
|
}{
|
|
|
|
{
|
|
|
|
Query: `"literal string"`,
|
2022-02-22 12:30:39 -08:00
|
|
|
SkipMaxCheck: true, // This can't fail from a max samples limit.
|
2022-02-01 18:07:23 -08:00
|
|
|
Start: time.Unix(21, 0),
|
|
|
|
TotalSamples: 0,
|
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
|
|
|
21000: 0,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: "1",
|
|
|
|
Start: time.Unix(21, 0),
|
|
|
|
TotalSamples: 0,
|
2022-02-22 12:30:39 -08:00
|
|
|
PeakSamples: 1,
|
2022-02-01 18:07:23 -08:00
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
|
|
|
21000: 0,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: "metricWith1SampleEvery10Seconds",
|
|
|
|
Start: time.Unix(21, 0),
|
2022-02-22 12:30:39 -08:00
|
|
|
PeakSamples: 1,
|
2022-02-01 18:07:23 -08:00
|
|
|
TotalSamples: 1, // 1 sample / 10 seconds
|
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
|
|
|
21000: 1,
|
|
|
|
},
|
|
|
|
},
|
2024-02-28 07:13:15 -08:00
|
|
|
{
|
|
|
|
Query: "metricWith1HistogramEvery10Seconds",
|
|
|
|
Start: time.Unix(21, 0),
|
2024-03-27 10:19:14 -07:00
|
|
|
PeakSamples: 13,
|
|
|
|
TotalSamples: 13, // 1 histogram HPoint of size 13 / 10 seconds
|
2024-02-28 07:13:15 -08:00
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
2024-03-27 10:19:14 -07:00
|
|
|
21000: 13,
|
2024-02-28 07:13:15 -08:00
|
|
|
},
|
|
|
|
},
|
2022-02-01 18:07:23 -08:00
|
|
|
{
|
|
|
|
// timestamp function has a special handling.
|
|
|
|
Query: "timestamp(metricWith1SampleEvery10Seconds)",
|
|
|
|
Start: time.Unix(21, 0),
|
2022-02-22 12:30:39 -08:00
|
|
|
PeakSamples: 2,
|
2022-02-01 18:07:23 -08:00
|
|
|
TotalSamples: 1, // 1 sample / 10 seconds
|
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
|
|
|
21000: 1,
|
|
|
|
},
|
|
|
|
},
|
2024-02-28 09:10:46 -08:00
|
|
|
{
|
|
|
|
Query: "timestamp(metricWith1HistogramEvery10Seconds)",
|
|
|
|
Start: time.Unix(21, 0),
|
2024-03-27 10:19:14 -07:00
|
|
|
PeakSamples: 15, // histogram size 13 + 1 extra because Sample overhead + 1 float result
|
2024-02-28 09:10:46 -08:00
|
|
|
TotalSamples: 1, // 1 float sample (because of timestamp) / 10 seconds
|
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
|
|
|
21000: 1,
|
|
|
|
},
|
|
|
|
},
|
2022-02-01 18:07:23 -08:00
|
|
|
{
|
|
|
|
Query: "metricWith1SampleEvery10Seconds",
|
|
|
|
Start: time.Unix(22, 0),
|
2022-02-22 12:30:39 -08:00
|
|
|
PeakSamples: 1,
|
2022-02-01 18:07:23 -08:00
|
|
|
TotalSamples: 1, // 1 sample / 10 seconds
|
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
|
|
|
22000: 1, // Aligned to the step time, not the sample time.
|
|
|
|
},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: "metricWith1SampleEvery10Seconds offset 10s",
|
|
|
|
Start: time.Unix(21, 0),
|
2022-02-22 12:30:39 -08:00
|
|
|
PeakSamples: 1,
|
2022-02-01 18:07:23 -08:00
|
|
|
TotalSamples: 1, // 1 sample / 10 seconds
|
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
|
|
|
21000: 1,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: "metricWith1SampleEvery10Seconds @ 15",
|
|
|
|
Start: time.Unix(21, 0),
|
2022-02-22 12:30:39 -08:00
|
|
|
PeakSamples: 1,
|
2022-02-01 18:07:23 -08:00
|
|
|
TotalSamples: 1, // 1 sample / 10 seconds
|
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
|
|
|
21000: 1,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: `metricWith3SampleEvery10Seconds{a="1"}`,
|
|
|
|
Start: time.Unix(21, 0),
|
2022-02-22 12:30:39 -08:00
|
|
|
PeakSamples: 1,
|
2022-02-01 18:07:23 -08:00
|
|
|
TotalSamples: 1, // 1 sample / 10 seconds
|
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
|
|
|
21000: 1,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: `metricWith3SampleEvery10Seconds{a="1"} @ 19`,
|
|
|
|
Start: time.Unix(21, 0),
|
2022-02-22 12:30:39 -08:00
|
|
|
PeakSamples: 1,
|
2022-02-01 18:07:23 -08:00
|
|
|
TotalSamples: 1, // 1 sample / 10 seconds
|
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
|
|
|
21000: 1,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: `metricWith3SampleEvery10Seconds{a="1"}[20s] @ 19`,
|
|
|
|
Start: time.Unix(21, 0),
|
2022-02-22 12:30:39 -08:00
|
|
|
PeakSamples: 2,
|
2022-02-01 18:07:23 -08:00
|
|
|
TotalSamples: 2, // (1 sample / 10 seconds) * 20s
|
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
|
|
|
21000: 2,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: "metricWith3SampleEvery10Seconds",
|
|
|
|
Start: time.Unix(21, 0),
|
2022-02-22 12:30:39 -08:00
|
|
|
PeakSamples: 3,
|
2022-02-01 18:07:23 -08:00
|
|
|
TotalSamples: 3, // 3 samples / 10 seconds
|
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
|
|
|
21000: 3,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: "metricWith1SampleEvery10Seconds[60s]",
|
|
|
|
Start: time.Unix(201, 0),
|
2022-02-22 12:30:39 -08:00
|
|
|
PeakSamples: 6,
|
2022-02-01 18:07:23 -08:00
|
|
|
TotalSamples: 6, // 1 sample / 10 seconds * 60 seconds
|
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
|
|
|
201000: 6,
|
|
|
|
},
|
|
|
|
},
|
2024-02-28 09:10:46 -08:00
|
|
|
{
|
|
|
|
Query: "metricWith1HistogramEvery10Seconds[60s]",
|
|
|
|
Start: time.Unix(201, 0),
|
2024-03-27 10:19:14 -07:00
|
|
|
PeakSamples: 78,
|
|
|
|
TotalSamples: 78, // 1 histogram (size 13 HPoint) / 10 seconds * 60 seconds
|
2024-02-28 09:10:46 -08:00
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
2024-03-27 10:19:14 -07:00
|
|
|
201000: 78,
|
2024-02-28 09:10:46 -08:00
|
|
|
},
|
|
|
|
},
|
2022-02-01 18:07:23 -08:00
|
|
|
{
|
|
|
|
Query: "max_over_time(metricWith1SampleEvery10Seconds[59s])[20s:5s]",
|
|
|
|
Start: time.Unix(201, 0),
|
2022-02-22 12:30:39 -08:00
|
|
|
PeakSamples: 10,
|
2024-02-28 09:10:46 -08:00
|
|
|
TotalSamples: 24, // (1 sample / 10 seconds * 60 seconds) * 20/5 (using 59s so we always return 6 samples
|
2022-02-01 18:07:23 -08:00
|
|
|
// as if we run a query on 00 looking back 60 seconds we will return 7 samples;
|
|
|
|
// see next test).
|
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
|
|
|
201000: 24,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: "max_over_time(metricWith1SampleEvery10Seconds[60s])[20s:5s]",
|
|
|
|
Start: time.Unix(201, 0),
|
2022-02-22 12:30:39 -08:00
|
|
|
PeakSamples: 11,
|
2024-02-28 09:10:46 -08:00
|
|
|
TotalSamples: 26, // (1 sample / 10 seconds * 60 seconds) * 4 + 2 as
|
2022-02-01 18:07:23 -08:00
|
|
|
// max_over_time(metricWith1SampleEvery10Seconds[60s]) @ 190 and 200 will return 7 samples.
|
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
|
|
|
201000: 26,
|
|
|
|
},
|
|
|
|
},
|
2024-02-28 09:10:46 -08:00
|
|
|
{
|
|
|
|
Query: "max_over_time(metricWith1HistogramEvery10Seconds[60s])[20s:5s]",
|
|
|
|
Start: time.Unix(201, 0),
|
2024-03-27 10:19:14 -07:00
|
|
|
PeakSamples: 78,
|
|
|
|
TotalSamples: 338, // (1 histogram (size 13 HPoint) / 10 seconds * 60 seconds) * 4 + 2 * 13 as
|
2024-02-28 09:10:46 -08:00
|
|
|
// max_over_time(metricWith1SampleEvery10Seconds[60s]) @ 190 and 200 will return 7 samples.
|
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
2024-03-27 10:19:14 -07:00
|
|
|
201000: 338,
|
2024-02-28 09:10:46 -08:00
|
|
|
},
|
|
|
|
},
|
2022-02-01 18:07:23 -08:00
|
|
|
{
|
|
|
|
Query: "metricWith1SampleEvery10Seconds[60s] @ 30",
|
|
|
|
Start: time.Unix(201, 0),
|
2022-02-22 12:30:39 -08:00
|
|
|
PeakSamples: 4,
|
2022-02-01 18:07:23 -08:00
|
|
|
TotalSamples: 4, // @ modifier force the evaluation to at 30 seconds - So it brings 4 datapoints (0, 10, 20, 30 seconds) * 1 series
|
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
|
|
|
201000: 4,
|
|
|
|
},
|
|
|
|
},
|
2024-02-28 09:10:46 -08:00
|
|
|
{
|
|
|
|
Query: "metricWith1HistogramEvery10Seconds[60s] @ 30",
|
|
|
|
Start: time.Unix(201, 0),
|
2024-03-27 10:19:14 -07:00
|
|
|
PeakSamples: 52,
|
|
|
|
TotalSamples: 52, // @ modifier force the evaluation to at 30 seconds - So it brings 4 datapoints (0, 10, 20, 30 seconds) * 1 series
|
2024-02-28 09:10:46 -08:00
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
2024-03-27 10:19:14 -07:00
|
|
|
201000: 52,
|
2024-02-28 09:10:46 -08:00
|
|
|
},
|
|
|
|
},
|
2022-02-01 18:07:23 -08:00
|
|
|
{
|
|
|
|
Query: "sum(max_over_time(metricWith3SampleEvery10Seconds[60s] @ 30))",
|
|
|
|
Start: time.Unix(201, 0),
|
2022-02-22 12:30:39 -08:00
|
|
|
PeakSamples: 7,
|
2022-02-01 18:07:23 -08:00
|
|
|
TotalSamples: 12, // @ modifier force the evaluation to at 30 seconds - So it brings 4 datapoints (0, 10, 20, 30 seconds) * 3 series
|
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
|
|
|
201000: 12,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: "sum by (b) (max_over_time(metricWith3SampleEvery10Seconds[60s] @ 30))",
|
|
|
|
Start: time.Unix(201, 0),
|
2022-02-22 12:30:39 -08:00
|
|
|
PeakSamples: 8,
|
2022-02-01 18:07:23 -08:00
|
|
|
TotalSamples: 12, // @ modifier force the evaluation to at 30 seconds - So it brings 4 datapoints (0, 10, 20, 30 seconds) * 3 series
|
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
|
|
|
201000: 12,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: "metricWith1SampleEvery10Seconds[60s] offset 10s",
|
|
|
|
Start: time.Unix(201, 0),
|
2022-02-22 12:30:39 -08:00
|
|
|
PeakSamples: 6,
|
2022-02-01 18:07:23 -08:00
|
|
|
TotalSamples: 6, // 1 sample / 10 seconds * 60 seconds
|
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
|
|
|
201000: 6,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: "metricWith3SampleEvery10Seconds[60s]",
|
|
|
|
Start: time.Unix(201, 0),
|
2022-02-22 12:30:39 -08:00
|
|
|
PeakSamples: 18,
|
2022-02-01 18:07:23 -08:00
|
|
|
TotalSamples: 18, // 3 sample / 10 seconds * 60 seconds
|
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
|
|
|
201000: 18,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: "max_over_time(metricWith1SampleEvery10Seconds[60s])",
|
|
|
|
Start: time.Unix(201, 0),
|
2022-02-22 12:30:39 -08:00
|
|
|
PeakSamples: 7,
|
2022-02-01 18:07:23 -08:00
|
|
|
TotalSamples: 6, // 1 sample / 10 seconds * 60 seconds
|
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
|
|
|
201000: 6,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: "absent_over_time(metricWith1SampleEvery10Seconds[60s])",
|
|
|
|
Start: time.Unix(201, 0),
|
2022-02-22 12:30:39 -08:00
|
|
|
PeakSamples: 7,
|
2022-02-01 18:07:23 -08:00
|
|
|
TotalSamples: 6, // 1 sample / 10 seconds * 60 seconds
|
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
|
|
|
201000: 6,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: "max_over_time(metricWith3SampleEvery10Seconds[60s])",
|
|
|
|
Start: time.Unix(201, 0),
|
2022-02-22 12:30:39 -08:00
|
|
|
PeakSamples: 9,
|
2022-02-01 18:07:23 -08:00
|
|
|
TotalSamples: 18, // 3 sample / 10 seconds * 60 seconds
|
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
|
|
|
201000: 18,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: "metricWith1SampleEvery10Seconds[60s:5s]",
|
|
|
|
Start: time.Unix(201, 0),
|
2022-02-22 12:30:39 -08:00
|
|
|
PeakSamples: 12,
|
2022-02-01 18:07:23 -08:00
|
|
|
TotalSamples: 12, // 1 sample per query * 12 queries (60/5)
|
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
|
|
|
201000: 12,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: "metricWith1SampleEvery10Seconds[60s:5s] offset 10s",
|
|
|
|
Start: time.Unix(201, 0),
|
2022-02-22 12:30:39 -08:00
|
|
|
PeakSamples: 12,
|
2022-02-01 18:07:23 -08:00
|
|
|
TotalSamples: 12, // 1 sample per query * 12 queries (60/5)
|
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
|
|
|
201000: 12,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: "max_over_time(metricWith3SampleEvery10Seconds[60s:5s])",
|
|
|
|
Start: time.Unix(201, 0),
|
2022-02-22 12:30:39 -08:00
|
|
|
PeakSamples: 51,
|
2022-02-01 18:07:23 -08:00
|
|
|
TotalSamples: 36, // 3 sample per query * 12 queries (60/5)
|
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
|
|
|
201000: 36,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: "sum(max_over_time(metricWith3SampleEvery10Seconds[60s:5s])) + sum(max_over_time(metricWith3SampleEvery10Seconds[60s:5s]))",
|
|
|
|
Start: time.Unix(201, 0),
|
2022-02-22 12:30:39 -08:00
|
|
|
PeakSamples: 52,
|
2022-02-01 18:07:23 -08:00
|
|
|
TotalSamples: 72, // 2 * (3 sample per query * 12 queries (60/5))
|
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
|
|
|
201000: 72,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: `metricWith3SampleEvery10Seconds{a="1"}`,
|
|
|
|
Start: time.Unix(201, 0),
|
|
|
|
End: time.Unix(220, 0),
|
|
|
|
Interval: 5 * time.Second,
|
2022-02-22 12:30:39 -08:00
|
|
|
PeakSamples: 4,
|
2022-02-01 18:07:23 -08:00
|
|
|
TotalSamples: 4, // 1 sample per query * 4 steps
|
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
|
|
|
201000: 1,
|
|
|
|
206000: 1,
|
|
|
|
211000: 1,
|
|
|
|
216000: 1,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: `metricWith3SampleEvery10Seconds{a="1"}`,
|
|
|
|
Start: time.Unix(204, 0),
|
|
|
|
End: time.Unix(223, 0),
|
|
|
|
Interval: 5 * time.Second,
|
2022-02-22 12:30:39 -08:00
|
|
|
PeakSamples: 4,
|
2022-02-01 18:07:23 -08:00
|
|
|
TotalSamples: 4, // 1 sample per query * 4 steps
|
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
|
|
|
204000: 1, // aligned to the step time, not the sample time
|
|
|
|
209000: 1,
|
|
|
|
214000: 1,
|
|
|
|
219000: 1,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
{
|
2024-02-28 09:10:46 -08:00
|
|
|
Query: `metricWith1HistogramEvery10Seconds`,
|
|
|
|
Start: time.Unix(204, 0),
|
|
|
|
End: time.Unix(223, 0),
|
|
|
|
Interval: 5 * time.Second,
|
2024-03-27 10:19:14 -07:00
|
|
|
PeakSamples: 52,
|
|
|
|
TotalSamples: 52, // 1 histogram (size 13 HPoint) per query * 4 steps
|
2024-02-28 09:10:46 -08:00
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
2024-03-27 10:19:14 -07:00
|
|
|
204000: 13, // aligned to the step time, not the sample time
|
|
|
|
209000: 13,
|
|
|
|
214000: 13,
|
|
|
|
219000: 13,
|
2024-02-28 09:10:46 -08:00
|
|
|
},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
// timestamp function has a special handling
|
2022-02-01 18:07:23 -08:00
|
|
|
Query: "timestamp(metricWith1SampleEvery10Seconds)",
|
|
|
|
Start: time.Unix(201, 0),
|
|
|
|
End: time.Unix(220, 0),
|
|
|
|
Interval: 5 * time.Second,
|
2022-02-22 12:30:39 -08:00
|
|
|
PeakSamples: 5,
|
2024-02-28 07:13:15 -08:00
|
|
|
TotalSamples: 4, // 1 sample per query * 4 steps
|
2022-02-01 18:07:23 -08:00
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
|
|
|
201000: 1,
|
|
|
|
206000: 1,
|
|
|
|
211000: 1,
|
|
|
|
216000: 1,
|
|
|
|
},
|
|
|
|
},
|
2024-02-28 09:10:46 -08:00
|
|
|
{
|
|
|
|
// timestamp function has a special handling
|
|
|
|
Query: "timestamp(metricWith1HistogramEvery10Seconds)",
|
|
|
|
Start: time.Unix(201, 0),
|
|
|
|
End: time.Unix(220, 0),
|
|
|
|
Interval: 5 * time.Second,
|
2024-03-27 10:19:14 -07:00
|
|
|
PeakSamples: 18, // 13 histogram size + 1 extra because of Sample overhead + 4 float results
|
|
|
|
TotalSamples: 4, // 1 sample per query * 4 steps
|
2024-02-28 09:10:46 -08:00
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
|
|
|
201000: 1,
|
|
|
|
206000: 1,
|
|
|
|
211000: 1,
|
|
|
|
216000: 1,
|
|
|
|
},
|
|
|
|
},
|
2022-02-01 18:07:23 -08:00
|
|
|
{
|
|
|
|
Query: `max_over_time(metricWith3SampleEvery10Seconds{a="1"}[10s])`,
|
|
|
|
Start: time.Unix(991, 0),
|
|
|
|
End: time.Unix(1021, 0),
|
|
|
|
Interval: 10 * time.Second,
|
2022-02-22 12:30:39 -08:00
|
|
|
PeakSamples: 2,
|
2022-02-01 18:07:23 -08:00
|
|
|
TotalSamples: 2, // 1 sample per query * 2 steps with data
|
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
|
|
|
991000: 1,
|
|
|
|
1001000: 1,
|
|
|
|
1011000: 0,
|
|
|
|
1021000: 0,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: `metricWith3SampleEvery10Seconds{a="1"} offset 10s`,
|
|
|
|
Start: time.Unix(201, 0),
|
|
|
|
End: time.Unix(220, 0),
|
|
|
|
Interval: 5 * time.Second,
|
2022-02-22 12:30:39 -08:00
|
|
|
PeakSamples: 4,
|
2022-02-01 18:07:23 -08:00
|
|
|
TotalSamples: 4, // 1 sample per query * 4 steps
|
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
|
|
|
201000: 1,
|
|
|
|
206000: 1,
|
|
|
|
211000: 1,
|
|
|
|
216000: 1,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: "max_over_time(metricWith3SampleEvery10Seconds[60s] @ 30)",
|
|
|
|
Start: time.Unix(201, 0),
|
|
|
|
End: time.Unix(220, 0),
|
|
|
|
Interval: 5 * time.Second,
|
2022-02-22 12:30:39 -08:00
|
|
|
PeakSamples: 12,
|
2022-02-01 18:07:23 -08:00
|
|
|
TotalSamples: 48, // @ modifier force the evaluation timestamp at 30 seconds - So it brings 4 datapoints (0, 10, 20, 30 seconds) * 3 series * 4 steps
|
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
|
|
|
201000: 12,
|
|
|
|
206000: 12,
|
|
|
|
211000: 12,
|
|
|
|
216000: 12,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: `metricWith3SampleEvery10Seconds`,
|
|
|
|
Start: time.Unix(201, 0),
|
|
|
|
End: time.Unix(220, 0),
|
2022-02-22 12:30:39 -08:00
|
|
|
PeakSamples: 12,
|
2022-02-01 18:07:23 -08:00
|
|
|
Interval: 5 * time.Second,
|
|
|
|
TotalSamples: 12, // 3 sample per query * 4 steps
|
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
|
|
|
201000: 3,
|
|
|
|
206000: 3,
|
|
|
|
211000: 3,
|
|
|
|
216000: 3,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: `max_over_time(metricWith3SampleEvery10Seconds[60s])`,
|
|
|
|
Start: time.Unix(201, 0),
|
|
|
|
End: time.Unix(220, 0),
|
|
|
|
Interval: 5 * time.Second,
|
2022-02-22 12:30:39 -08:00
|
|
|
PeakSamples: 18,
|
2022-02-01 18:07:23 -08:00
|
|
|
TotalSamples: 72, // (3 sample / 10 seconds * 60 seconds) * 4 steps = 72
|
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
|
|
|
201000: 18,
|
|
|
|
206000: 18,
|
|
|
|
211000: 18,
|
|
|
|
216000: 18,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: "max_over_time(metricWith3SampleEvery10Seconds[60s:5s])",
|
|
|
|
Start: time.Unix(201, 0),
|
|
|
|
End: time.Unix(220, 0),
|
|
|
|
Interval: 5 * time.Second,
|
2022-02-22 12:30:39 -08:00
|
|
|
PeakSamples: 72,
|
2022-02-01 18:07:23 -08:00
|
|
|
TotalSamples: 144, // 3 sample per query * 12 queries (60/5) * 4 steps
|
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
|
|
|
201000: 36,
|
|
|
|
206000: 36,
|
|
|
|
211000: 36,
|
|
|
|
216000: 36,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: "max_over_time(metricWith1SampleEvery10Seconds[60s:5s])",
|
|
|
|
Start: time.Unix(201, 0),
|
|
|
|
End: time.Unix(220, 0),
|
|
|
|
Interval: 5 * time.Second,
|
2022-02-22 12:30:39 -08:00
|
|
|
PeakSamples: 32,
|
2022-02-01 18:07:23 -08:00
|
|
|
TotalSamples: 48, // 1 sample per query * 12 queries (60/5) * 4 steps
|
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
|
|
|
201000: 12,
|
|
|
|
206000: 12,
|
|
|
|
211000: 12,
|
|
|
|
216000: 12,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: "sum by (b) (max_over_time(metricWith1SampleEvery10Seconds[60s:5s]))",
|
|
|
|
Start: time.Unix(201, 0),
|
|
|
|
End: time.Unix(220, 0),
|
|
|
|
Interval: 5 * time.Second,
|
2022-02-22 12:30:39 -08:00
|
|
|
PeakSamples: 32,
|
2022-02-01 18:07:23 -08:00
|
|
|
TotalSamples: 48, // 1 sample per query * 12 queries (60/5) * 4 steps
|
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
|
|
|
201000: 12,
|
|
|
|
206000: 12,
|
|
|
|
211000: 12,
|
|
|
|
216000: 12,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: "sum(max_over_time(metricWith3SampleEvery10Seconds[60s:5s])) + sum(max_over_time(metricWith3SampleEvery10Seconds[60s:5s]))",
|
|
|
|
Start: time.Unix(201, 0),
|
|
|
|
End: time.Unix(220, 0),
|
|
|
|
Interval: 5 * time.Second,
|
2022-02-22 12:30:39 -08:00
|
|
|
PeakSamples: 76,
|
2022-02-01 18:07:23 -08:00
|
|
|
TotalSamples: 288, // 2 * (3 sample per query * 12 queries (60/5) * 4 steps)
|
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
|
|
|
201000: 72,
|
|
|
|
206000: 72,
|
|
|
|
211000: 72,
|
|
|
|
216000: 72,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: "sum(max_over_time(metricWith3SampleEvery10Seconds[60s:5s])) + sum(max_over_time(metricWith1SampleEvery10Seconds[60s:5s]))",
|
|
|
|
Start: time.Unix(201, 0),
|
|
|
|
End: time.Unix(220, 0),
|
|
|
|
Interval: 5 * time.Second,
|
2022-02-22 12:30:39 -08:00
|
|
|
PeakSamples: 72,
|
2022-02-01 18:07:23 -08:00
|
|
|
TotalSamples: 192, // (1 sample per query * 12 queries (60/5) + 3 sample per query * 12 queries (60/5)) * 4 steps
|
|
|
|
TotalSamplesPerStep: stats.TotalSamplesPerStep{
|
|
|
|
201000: 48,
|
|
|
|
206000: 48,
|
|
|
|
211000: 48,
|
|
|
|
216000: 48,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
}
|
|
|
|
|
2023-08-18 11:48:59 -07:00
|
|
|
engine := newTestEngine()
|
2022-02-01 18:07:23 -08:00
|
|
|
engine.enablePerStepStats = true
|
2022-02-22 12:30:39 -08:00
|
|
|
origMaxSamples := engine.maxSamplesPerQuery
|
2022-02-01 18:07:23 -08:00
|
|
|
for _, c := range cases {
|
|
|
|
t.Run(c.Query, func(t *testing.T) {
|
2023-07-03 05:56:06 -07:00
|
|
|
opts := NewPrometheusQueryOpts(true, 0)
|
2022-02-22 12:30:39 -08:00
|
|
|
engine.maxSamplesPerQuery = origMaxSamples
|
2022-02-01 18:07:23 -08:00
|
|
|
|
2022-02-22 12:30:39 -08:00
|
|
|
runQuery := func(expErr error) *stats.Statistics {
|
|
|
|
var err error
|
|
|
|
var qry Query
|
|
|
|
if c.Interval == 0 {
|
2023-08-18 11:48:59 -07:00
|
|
|
qry, err = engine.NewInstantQuery(context.Background(), storage, opts, c.Query, c.Start)
|
2022-02-22 12:30:39 -08:00
|
|
|
} else {
|
2023-08-18 11:48:59 -07:00
|
|
|
qry, err = engine.NewRangeQuery(context.Background(), storage, opts, c.Query, c.Start, c.End, c.Interval)
|
2022-02-22 12:30:39 -08:00
|
|
|
}
|
|
|
|
require.NoError(t, err)
|
|
|
|
|
2023-08-18 11:48:59 -07:00
|
|
|
res := qry.Exec(context.Background())
|
2022-02-22 12:30:39 -08:00
|
|
|
require.Equal(t, expErr, res.Err)
|
2022-02-01 18:07:23 -08:00
|
|
|
|
2022-02-22 12:30:39 -08:00
|
|
|
return qry.Stats()
|
|
|
|
}
|
2022-02-01 18:07:23 -08:00
|
|
|
|
2022-02-22 12:30:39 -08:00
|
|
|
stats := runQuery(nil)
|
2022-02-01 18:07:23 -08:00
|
|
|
require.Equal(t, c.TotalSamples, stats.Samples.TotalSamples, "Total samples mismatch")
|
|
|
|
require.Equal(t, &c.TotalSamplesPerStep, stats.Samples.TotalSamplesPerStepMap(), "Total samples per time mismatch")
|
2022-02-22 12:30:39 -08:00
|
|
|
require.Equal(t, c.PeakSamples, stats.Samples.PeakSamples, "Peak samples mismatch")
|
|
|
|
|
|
|
|
// Check that the peak is correct by setting the max to one less.
|
|
|
|
if c.SkipMaxCheck {
|
|
|
|
return
|
|
|
|
}
|
|
|
|
engine.maxSamplesPerQuery = stats.Samples.PeakSamples - 1
|
|
|
|
runQuery(ErrTooManySamples(env))
|
2022-02-01 18:07:23 -08:00
|
|
|
})
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
2018-10-02 04:59:19 -07:00
|
|
|
func TestMaxQuerySamples(t *testing.T) {
|
2023-08-18 11:48:59 -07:00
|
|
|
storage := LoadedStorage(t, `
|
2018-10-02 04:59:19 -07:00
|
|
|
load 10s
|
2021-01-20 02:57:39 -08:00
|
|
|
metric 1+1x100
|
|
|
|
bigmetric{a="1"} 1+1x100
|
|
|
|
bigmetric{a="2"} 1+1x100
|
2018-10-02 04:59:19 -07:00
|
|
|
`)
|
2023-08-18 11:48:59 -07:00
|
|
|
t.Cleanup(func() { storage.Close() })
|
2018-10-02 04:59:19 -07:00
|
|
|
|
2021-01-04 01:03:36 -08:00
|
|
|
// These test cases should be touching the limit exactly (hence no exceeding).
|
|
|
|
// Exceeding the limit will be tested by doing -1 to the MaxSamples.
|
2018-10-02 04:59:19 -07:00
|
|
|
cases := []struct {
|
|
|
|
Query string
|
|
|
|
MaxSamples int
|
|
|
|
Start time.Time
|
|
|
|
End time.Time
|
|
|
|
Interval time.Duration
|
|
|
|
}{
|
|
|
|
// Instant queries.
|
|
|
|
{
|
|
|
|
Query: "1",
|
|
|
|
MaxSamples: 1,
|
2021-01-04 01:03:36 -08:00
|
|
|
Start: time.Unix(1, 0),
|
2021-10-22 01:06:44 -07:00
|
|
|
},
|
|
|
|
{
|
2018-10-02 04:59:19 -07:00
|
|
|
Query: "metric",
|
|
|
|
MaxSamples: 1,
|
2021-01-04 01:03:36 -08:00
|
|
|
Start: time.Unix(1, 0),
|
2021-10-22 01:06:44 -07:00
|
|
|
},
|
|
|
|
{
|
2018-10-02 04:59:19 -07:00
|
|
|
Query: "metric[20s]",
|
|
|
|
MaxSamples: 2,
|
2021-01-04 01:03:36 -08:00
|
|
|
Start: time.Unix(10, 0),
|
2021-10-22 01:06:44 -07:00
|
|
|
},
|
|
|
|
{
|
2018-12-22 05:47:13 -08:00
|
|
|
Query: "rate(metric[20s])",
|
|
|
|
MaxSamples: 3,
|
2021-01-04 01:03:36 -08:00
|
|
|
Start: time.Unix(10, 0),
|
2021-10-22 01:06:44 -07:00
|
|
|
},
|
|
|
|
{
|
2018-12-22 05:47:13 -08:00
|
|
|
Query: "metric[20s:5s]",
|
|
|
|
MaxSamples: 3,
|
2021-01-04 01:03:36 -08:00
|
|
|
Start: time.Unix(10, 0),
|
2021-10-22 01:06:44 -07:00
|
|
|
},
|
|
|
|
{
|
2021-01-20 02:57:39 -08:00
|
|
|
Query: "metric[20s] @ 10",
|
|
|
|
MaxSamples: 2,
|
|
|
|
Start: time.Unix(0, 0),
|
2018-10-02 04:59:19 -07:00
|
|
|
},
|
|
|
|
// Range queries.
|
|
|
|
{
|
|
|
|
Query: "1",
|
|
|
|
MaxSamples: 3,
|
2021-01-04 01:03:36 -08:00
|
|
|
Start: time.Unix(0, 0),
|
|
|
|
End: time.Unix(2, 0),
|
|
|
|
Interval: time.Second,
|
2021-10-22 01:06:44 -07:00
|
|
|
},
|
|
|
|
{
|
2018-10-02 04:59:19 -07:00
|
|
|
Query: "1",
|
|
|
|
MaxSamples: 3,
|
2021-01-04 01:03:36 -08:00
|
|
|
Start: time.Unix(0, 0),
|
|
|
|
End: time.Unix(2, 0),
|
|
|
|
Interval: time.Second,
|
2021-10-22 01:06:44 -07:00
|
|
|
},
|
|
|
|
{
|
2018-10-02 04:59:19 -07:00
|
|
|
Query: "metric",
|
|
|
|
MaxSamples: 3,
|
2021-01-04 01:03:36 -08:00
|
|
|
Start: time.Unix(0, 0),
|
|
|
|
End: time.Unix(2, 0),
|
|
|
|
Interval: time.Second,
|
2021-10-22 01:06:44 -07:00
|
|
|
},
|
|
|
|
{
|
2018-10-02 04:59:19 -07:00
|
|
|
Query: "metric",
|
2021-01-04 01:03:36 -08:00
|
|
|
MaxSamples: 3,
|
|
|
|
Start: time.Unix(0, 0),
|
|
|
|
End: time.Unix(10, 0),
|
|
|
|
Interval: 5 * time.Second,
|
2021-10-22 01:06:44 -07:00
|
|
|
},
|
|
|
|
{
|
2020-05-28 05:36:30 -07:00
|
|
|
Query: "rate(bigmetric[1s])",
|
|
|
|
MaxSamples: 1,
|
2021-01-04 01:03:36 -08:00
|
|
|
Start: time.Unix(0, 0),
|
|
|
|
End: time.Unix(10, 0),
|
|
|
|
Interval: 5 * time.Second,
|
2021-10-22 01:06:44 -07:00
|
|
|
},
|
|
|
|
{
|
2021-01-20 02:57:39 -08:00
|
|
|
// Result is duplicated, so @ also produces 3 samples.
|
|
|
|
Query: "metric @ 10",
|
|
|
|
MaxSamples: 3,
|
|
|
|
Start: time.Unix(0, 0),
|
|
|
|
End: time.Unix(10, 0),
|
|
|
|
Interval: 5 * time.Second,
|
2021-10-22 01:06:44 -07:00
|
|
|
},
|
|
|
|
{
|
2021-01-20 02:57:39 -08:00
|
|
|
// The peak samples in memory is during the first evaluation:
|
|
|
|
// - Subquery takes 22 samples, 11 for each bigmetric,
|
|
|
|
// - Result is calculated per series where the series samples is buffered, hence 11 more here.
|
|
|
|
// - The result of two series is added before the last series buffer is discarded, so 2 more here.
|
|
|
|
// Hence at peak it is 22 (subquery) + 11 (buffer of a series) + 2 (result from 2 series).
|
|
|
|
// The subquery samples and the buffer is discarded before duplicating.
|
|
|
|
Query: `rate(bigmetric[10s:1s] @ 10)`,
|
|
|
|
MaxSamples: 35,
|
|
|
|
Start: time.Unix(0, 0),
|
|
|
|
End: time.Unix(10, 0),
|
|
|
|
Interval: 5 * time.Second,
|
2021-10-22 01:06:44 -07:00
|
|
|
},
|
|
|
|
{
|
2021-01-20 02:57:39 -08:00
|
|
|
// Here the reasoning is same as above. But LHS and RHS are done one after another.
|
|
|
|
// So while one of them takes 35 samples at peak, we need to hold the 2 sample
|
|
|
|
// result of the other till then.
|
|
|
|
Query: `rate(bigmetric[10s:1s] @ 10) + rate(bigmetric[10s:1s] @ 30)`,
|
|
|
|
MaxSamples: 37,
|
|
|
|
Start: time.Unix(0, 0),
|
|
|
|
End: time.Unix(10, 0),
|
|
|
|
Interval: 5 * time.Second,
|
2021-10-22 01:06:44 -07:00
|
|
|
},
|
|
|
|
{
|
2021-01-20 02:57:39 -08:00
|
|
|
// Sample as above but with only 1 part as step invariant.
|
|
|
|
// Here the peak is caused by the non-step invariant part as it touches more time range.
|
|
|
|
// Hence at peak it is 2*21 (subquery from 0s to 20s)
|
|
|
|
// + 11 (buffer of a series per evaluation)
|
|
|
|
// + 6 (result from 2 series at 3 eval times).
|
|
|
|
Query: `rate(bigmetric[10s:1s]) + rate(bigmetric[10s:1s] @ 30)`,
|
|
|
|
MaxSamples: 59,
|
|
|
|
Start: time.Unix(10, 0),
|
|
|
|
End: time.Unix(20, 0),
|
|
|
|
Interval: 5 * time.Second,
|
2021-10-22 01:06:44 -07:00
|
|
|
},
|
|
|
|
{
|
2021-01-20 02:57:39 -08:00
|
|
|
// Nested subquery.
|
|
|
|
// We saw that innermost rate takes 35 samples which is still the peak
|
|
|
|
// since the other two subqueries just duplicate the result.
|
|
|
|
Query: `rate(rate(bigmetric[10s:1s] @ 10)[100s:25s] @ 1000)[100s:20s] @ 2000`,
|
|
|
|
MaxSamples: 35,
|
|
|
|
Start: time.Unix(10, 0),
|
2021-10-22 01:06:44 -07:00
|
|
|
},
|
|
|
|
{
|
2021-01-20 02:57:39 -08:00
|
|
|
// Nested subquery.
|
|
|
|
// Now the outmost subquery produces more samples than inner most rate.
|
|
|
|
Query: `rate(rate(bigmetric[10s:1s] @ 10)[100s:25s] @ 1000)[17s:1s] @ 2000`,
|
|
|
|
MaxSamples: 36,
|
|
|
|
Start: time.Unix(10, 0),
|
2020-05-28 05:36:30 -07:00
|
|
|
},
|
2018-10-02 04:59:19 -07:00
|
|
|
}
|
|
|
|
|
|
|
|
for _, c := range cases {
|
2021-01-04 01:03:36 -08:00
|
|
|
t.Run(c.Query, func(t *testing.T) {
|
2023-08-18 11:48:59 -07:00
|
|
|
engine := newTestEngine()
|
2021-01-20 02:57:39 -08:00
|
|
|
testFunc := func(expError error) {
|
|
|
|
var err error
|
|
|
|
var qry Query
|
|
|
|
if c.Interval == 0 {
|
2023-08-18 11:48:59 -07:00
|
|
|
qry, err = engine.NewInstantQuery(context.Background(), storage, nil, c.Query, c.Start)
|
2021-01-20 02:57:39 -08:00
|
|
|
} else {
|
2023-08-18 11:48:59 -07:00
|
|
|
qry, err = engine.NewRangeQuery(context.Background(), storage, nil, c.Query, c.Start, c.End, c.Interval)
|
2021-01-20 02:57:39 -08:00
|
|
|
}
|
|
|
|
require.NoError(t, err)
|
|
|
|
|
2023-08-18 11:48:59 -07:00
|
|
|
res := qry.Exec(context.Background())
|
2022-02-01 18:07:23 -08:00
|
|
|
stats := qry.Stats()
|
2021-01-20 02:57:39 -08:00
|
|
|
require.Equal(t, expError, res.Err)
|
2022-02-01 18:07:23 -08:00
|
|
|
require.NotNil(t, stats)
|
2022-02-22 12:30:39 -08:00
|
|
|
if expError == nil {
|
|
|
|
require.Equal(t, c.MaxSamples, stats.Samples.PeakSamples, "peak samples mismatch for query %q", c.Query)
|
|
|
|
}
|
2021-01-20 02:57:39 -08:00
|
|
|
}
|
2018-10-02 04:59:19 -07:00
|
|
|
|
2021-01-20 02:57:39 -08:00
|
|
|
// Within limit.
|
2021-01-04 01:03:36 -08:00
|
|
|
engine.maxSamplesPerQuery = c.MaxSamples
|
2021-01-20 02:57:39 -08:00
|
|
|
testFunc(nil)
|
|
|
|
|
|
|
|
// Exceeding limit.
|
|
|
|
engine.maxSamplesPerQuery = c.MaxSamples - 1
|
|
|
|
testFunc(ErrTooManySamples(env))
|
|
|
|
})
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
func TestAtModifier(t *testing.T) {
|
2023-08-18 11:48:59 -07:00
|
|
|
engine := newTestEngine()
|
|
|
|
storage := LoadedStorage(t, `
|
2021-01-20 02:57:39 -08:00
|
|
|
load 10s
|
|
|
|
metric{job="1"} 0+1x1000
|
|
|
|
metric{job="2"} 0+2x1000
|
|
|
|
metric_topk{instance="1"} 0+1x1000
|
|
|
|
metric_topk{instance="2"} 0+2x1000
|
|
|
|
metric_topk{instance="3"} 1000-1x1000
|
|
|
|
|
|
|
|
load 1ms
|
|
|
|
metric_ms 0+1x10000
|
|
|
|
`)
|
2023-08-18 11:48:59 -07:00
|
|
|
t.Cleanup(func() { storage.Close() })
|
2021-01-20 02:57:39 -08:00
|
|
|
|
|
|
|
lbls1 := labels.FromStrings("__name__", "metric", "job", "1")
|
|
|
|
lbls2 := labels.FromStrings("__name__", "metric", "job", "2")
|
|
|
|
lblstopk2 := labels.FromStrings("__name__", "metric_topk", "instance", "2")
|
|
|
|
lblstopk3 := labels.FromStrings("__name__", "metric_topk", "instance", "3")
|
|
|
|
lblsms := labels.FromStrings("__name__", "metric_ms")
|
|
|
|
lblsneg := labels.FromStrings("__name__", "metric_neg")
|
|
|
|
|
|
|
|
// Add some samples with negative timestamp.
|
2023-08-18 11:48:59 -07:00
|
|
|
db := storage.DB
|
2021-01-20 02:57:39 -08:00
|
|
|
app := db.Appender(context.Background())
|
2021-02-18 04:07:00 -08:00
|
|
|
ref, err := app.Append(0, lblsneg, -1000000, 1000)
|
2021-01-20 02:57:39 -08:00
|
|
|
require.NoError(t, err)
|
|
|
|
for ts := int64(-1000000 + 1000); ts <= 0; ts += 1000 {
|
2022-07-21 09:23:30 -07:00
|
|
|
_, err := app.Append(ref, labels.EmptyLabels(), ts, -float64(ts/1000)+1)
|
2021-02-18 04:07:00 -08:00
|
|
|
require.NoError(t, err)
|
2021-01-20 02:57:39 -08:00
|
|
|
}
|
2021-02-03 05:43:12 -08:00
|
|
|
|
|
|
|
// To test the fix for https://github.com/prometheus/prometheus/issues/8433.
|
2021-02-18 04:07:00 -08:00
|
|
|
_, err = app.Append(0, labels.FromStrings("__name__", "metric_timestamp"), 3600*1000, 1000)
|
2021-02-03 05:43:12 -08:00
|
|
|
require.NoError(t, err)
|
|
|
|
|
2021-01-20 02:57:39 -08:00
|
|
|
require.NoError(t, app.Commit())
|
2018-10-02 04:59:19 -07:00
|
|
|
|
2021-01-20 02:57:39 -08:00
|
|
|
cases := []struct {
|
|
|
|
query string
|
|
|
|
start, end, interval int64 // Time in seconds.
|
|
|
|
result parser.Value
|
|
|
|
}{
|
|
|
|
{ // Time of the result is the evaluation time.
|
|
|
|
query: `metric_neg @ 0`,
|
|
|
|
start: 100,
|
|
|
|
result: Vector{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Sample{F: 1, T: 100000, Metric: lblsneg},
|
2021-01-20 02:57:39 -08:00
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: `metric_neg @ -200`,
|
|
|
|
start: 100,
|
|
|
|
result: Vector{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Sample{F: 201, T: 100000, Metric: lblsneg},
|
2021-01-20 02:57:39 -08:00
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: `metric{job="2"} @ 50`,
|
|
|
|
start: -2, end: 2, interval: 1,
|
|
|
|
result: Matrix{
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 10, T: -2000}, {F: 10, T: -1000}, {F: 10, T: 0}, {F: 10, T: 1000}, {F: 10, T: 2000}},
|
2021-01-20 02:57:39 -08:00
|
|
|
Metric: lbls2,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
}, { // Timestamps for matrix selector does not depend on the evaluation time.
|
|
|
|
query: "metric[20s] @ 300",
|
|
|
|
start: 10,
|
|
|
|
result: Matrix{
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 28, T: 280000}, {F: 29, T: 290000}, {F: 30, T: 300000}},
|
2021-01-20 02:57:39 -08:00
|
|
|
Metric: lbls1,
|
|
|
|
},
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 56, T: 280000}, {F: 58, T: 290000}, {F: 60, T: 300000}},
|
2021-01-20 02:57:39 -08:00
|
|
|
Metric: lbls2,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: `metric_neg[2s] @ 0`,
|
|
|
|
start: 100,
|
|
|
|
result: Matrix{
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 3, T: -2000}, {F: 2, T: -1000}, {F: 1, T: 0}},
|
2021-01-20 02:57:39 -08:00
|
|
|
Metric: lblsneg,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: `metric_neg[3s] @ -500`,
|
|
|
|
start: 100,
|
|
|
|
result: Matrix{
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 504, T: -503000}, {F: 503, T: -502000}, {F: 502, T: -501000}, {F: 501, T: -500000}},
|
2021-01-20 02:57:39 -08:00
|
|
|
Metric: lblsneg,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: `metric_ms[3ms] @ 2.345`,
|
|
|
|
start: 100,
|
|
|
|
result: Matrix{
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 2342, T: 2342}, {F: 2343, T: 2343}, {F: 2344, T: 2344}, {F: 2345, T: 2345}},
|
2021-01-20 02:57:39 -08:00
|
|
|
Metric: lblsms,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "metric[100s:25s] @ 300",
|
|
|
|
start: 100,
|
|
|
|
result: Matrix{
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 20, T: 200000}, {F: 22, T: 225000}, {F: 25, T: 250000}, {F: 27, T: 275000}, {F: 30, T: 300000}},
|
2021-01-20 02:57:39 -08:00
|
|
|
Metric: lbls1,
|
|
|
|
},
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 40, T: 200000}, {F: 44, T: 225000}, {F: 50, T: 250000}, {F: 54, T: 275000}, {F: 60, T: 300000}},
|
2021-01-20 02:57:39 -08:00
|
|
|
Metric: lbls2,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "metric_neg[50s:25s] @ 0",
|
|
|
|
start: 100,
|
|
|
|
result: Matrix{
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 51, T: -50000}, {F: 26, T: -25000}, {F: 1, T: 0}},
|
2021-01-20 02:57:39 -08:00
|
|
|
Metric: lblsneg,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: "metric_neg[50s:25s] @ -100",
|
|
|
|
start: 100,
|
|
|
|
result: Matrix{
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 151, T: -150000}, {F: 126, T: -125000}, {F: 101, T: -100000}},
|
2021-01-20 02:57:39 -08:00
|
|
|
Metric: lblsneg,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: `metric_ms[100ms:25ms] @ 2.345`,
|
|
|
|
start: 100,
|
|
|
|
result: Matrix{
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 2250, T: 2250}, {F: 2275, T: 2275}, {F: 2300, T: 2300}, {F: 2325, T: 2325}},
|
2021-01-20 02:57:39 -08:00
|
|
|
Metric: lblsms,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: `metric_topk and topk(1, sum_over_time(metric_topk[50s] @ 100))`,
|
|
|
|
start: 50, end: 80, interval: 10,
|
|
|
|
result: Matrix{
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 995, T: 50000}, {F: 994, T: 60000}, {F: 993, T: 70000}, {F: 992, T: 80000}},
|
2021-01-20 02:57:39 -08:00
|
|
|
Metric: lblstopk3,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: `metric_topk and topk(1, sum_over_time(metric_topk[50s] @ 5000))`,
|
|
|
|
start: 50, end: 80, interval: 10,
|
|
|
|
result: Matrix{
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 10, T: 50000}, {F: 12, T: 60000}, {F: 14, T: 70000}, {F: 16, T: 80000}},
|
2021-01-20 02:57:39 -08:00
|
|
|
Metric: lblstopk2,
|
|
|
|
},
|
|
|
|
},
|
2021-02-09 08:03:16 -08:00
|
|
|
}, {
|
|
|
|
query: `metric_topk and topk(1, sum_over_time(metric_topk[50s] @ end()))`,
|
|
|
|
start: 70, end: 100, interval: 10,
|
|
|
|
result: Matrix{
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 993, T: 70000}, {F: 992, T: 80000}, {F: 991, T: 90000}, {F: 990, T: 100000}},
|
2021-02-09 08:03:16 -08:00
|
|
|
Metric: lblstopk3,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
query: `metric_topk and topk(1, sum_over_time(metric_topk[50s] @ start()))`,
|
|
|
|
start: 100, end: 130, interval: 10,
|
|
|
|
result: Matrix{
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 990, T: 100000}, {F: 989, T: 110000}, {F: 988, T: 120000}, {F: 987, T: 130000}},
|
2021-02-09 08:03:16 -08:00
|
|
|
Metric: lblstopk3,
|
|
|
|
},
|
|
|
|
},
|
2021-02-03 05:43:12 -08:00
|
|
|
}, {
|
|
|
|
// Tests for https://github.com/prometheus/prometheus/issues/8433.
|
|
|
|
// The trick here is that the query range should be > lookback delta.
|
|
|
|
query: `timestamp(metric_timestamp @ 3600)`,
|
|
|
|
start: 0, end: 7 * 60, interval: 60,
|
|
|
|
result: Matrix{
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{
|
|
|
|
{F: 3600, T: 0},
|
|
|
|
{F: 3600, T: 60 * 1000},
|
|
|
|
{F: 3600, T: 2 * 60 * 1000},
|
|
|
|
{F: 3600, T: 3 * 60 * 1000},
|
|
|
|
{F: 3600, T: 4 * 60 * 1000},
|
|
|
|
{F: 3600, T: 5 * 60 * 1000},
|
|
|
|
{F: 3600, T: 6 * 60 * 1000},
|
|
|
|
{F: 3600, T: 7 * 60 * 1000},
|
2021-02-03 05:43:12 -08:00
|
|
|
},
|
2022-07-21 09:23:30 -07:00
|
|
|
Metric: labels.EmptyLabels(),
|
2021-02-03 05:43:12 -08:00
|
|
|
},
|
|
|
|
},
|
2021-01-20 02:57:39 -08:00
|
|
|
},
|
|
|
|
}
|
|
|
|
|
|
|
|
for _, c := range cases {
|
|
|
|
t.Run(c.query, func(t *testing.T) {
|
|
|
|
if c.interval == 0 {
|
|
|
|
c.interval = 1
|
|
|
|
}
|
|
|
|
start, end, interval := time.Unix(c.start, 0), time.Unix(c.end, 0), time.Duration(c.interval)*time.Second
|
|
|
|
var err error
|
|
|
|
var qry Query
|
|
|
|
if c.end == 0 {
|
2023-08-18 11:48:59 -07:00
|
|
|
qry, err = engine.NewInstantQuery(context.Background(), storage, nil, c.query, start)
|
2021-01-04 01:03:36 -08:00
|
|
|
} else {
|
2023-08-18 11:48:59 -07:00
|
|
|
qry, err = engine.NewRangeQuery(context.Background(), storage, nil, c.query, start, end, interval)
|
2021-01-04 01:03:36 -08:00
|
|
|
}
|
|
|
|
require.NoError(t, err)
|
2019-10-09 17:06:53 -07:00
|
|
|
|
2023-08-18 11:48:59 -07:00
|
|
|
res := qry.Exec(context.Background())
|
2021-01-04 01:03:36 -08:00
|
|
|
require.NoError(t, res.Err)
|
2021-01-24 06:53:30 -08:00
|
|
|
if expMat, ok := c.result.(Matrix); ok {
|
|
|
|
sort.Sort(expMat)
|
|
|
|
sort.Sort(res.Value.(Matrix))
|
|
|
|
}
|
2023-04-16 05:13:31 -07:00
|
|
|
testutil.RequireEqual(t, c.result, res.Value, "query %q failed", c.query)
|
2021-01-04 01:03:36 -08:00
|
|
|
})
|
2018-10-02 04:59:19 -07:00
|
|
|
}
|
|
|
|
}
|
2021-02-03 05:43:12 -08:00
|
|
|
|
2015-08-19 06:28:53 -07:00
|
|
|
func TestRecoverEvaluatorRuntime(t *testing.T) {
|
2022-07-14 02:34:15 -07:00
|
|
|
var output []interface{}
|
|
|
|
logger := log.Logger(log.LoggerFunc(func(keyvals ...interface{}) error {
|
|
|
|
output = append(output, keyvals...)
|
|
|
|
return nil
|
|
|
|
}))
|
|
|
|
ev := &evaluator{logger: logger}
|
|
|
|
|
|
|
|
expr, _ := parser.ParseExpr("sum(up)")
|
2017-08-11 11:45:52 -07:00
|
|
|
|
2015-08-19 06:28:53 -07:00
|
|
|
var err error
|
2022-07-14 02:34:15 -07:00
|
|
|
|
|
|
|
defer func() {
|
|
|
|
require.EqualError(t, err, "unexpected error: runtime error: index out of range [123] with length 0")
|
|
|
|
require.Contains(t, output, "sum(up)")
|
|
|
|
}()
|
|
|
|
defer ev.recover(expr, nil, &err)
|
2015-08-19 06:28:53 -07:00
|
|
|
|
|
|
|
// Cause a runtime panic.
|
|
|
|
var a []int
|
|
|
|
a[123] = 1
|
|
|
|
}
|
|
|
|
|
|
|
|
func TestRecoverEvaluatorError(t *testing.T) {
|
2017-08-11 11:45:52 -07:00
|
|
|
ev := &evaluator{logger: log.NewNopLogger()}
|
2015-08-19 06:28:53 -07:00
|
|
|
var err error
|
|
|
|
|
2019-03-25 16:01:12 -07:00
|
|
|
e := errors.New("custom error")
|
2015-08-19 06:28:53 -07:00
|
|
|
|
2015-08-25 17:04:01 -07:00
|
|
|
defer func() {
|
2021-09-13 12:19:20 -07:00
|
|
|
require.EqualError(t, err, e.Error())
|
2015-08-25 17:04:01 -07:00
|
|
|
}()
|
2022-07-14 02:34:15 -07:00
|
|
|
defer ev.recover(nil, nil, &err)
|
2020-06-09 09:57:31 -07:00
|
|
|
|
|
|
|
panic(e)
|
|
|
|
}
|
|
|
|
|
|
|
|
func TestRecoverEvaluatorErrorWithWarnings(t *testing.T) {
|
|
|
|
ev := &evaluator{logger: log.NewNopLogger()}
|
|
|
|
var err error
|
2023-09-14 09:57:31 -07:00
|
|
|
var ws annotations.Annotations
|
2020-06-09 09:57:31 -07:00
|
|
|
|
2023-09-14 09:57:31 -07:00
|
|
|
warnings := annotations.New().Add(errors.New("custom warning"))
|
2020-06-09 09:57:31 -07:00
|
|
|
e := errWithWarnings{
|
|
|
|
err: errors.New("custom error"),
|
|
|
|
warnings: warnings,
|
|
|
|
}
|
|
|
|
|
|
|
|
defer func() {
|
2021-09-13 12:19:20 -07:00
|
|
|
require.EqualError(t, err, e.Error())
|
|
|
|
require.Equal(t, warnings, ws, "wrong warning message")
|
2020-06-09 09:57:31 -07:00
|
|
|
}()
|
2022-07-14 02:34:15 -07:00
|
|
|
defer ev.recover(nil, &ws, &err)
|
2015-08-25 17:04:01 -07:00
|
|
|
|
|
|
|
panic(e)
|
2015-08-19 06:28:53 -07:00
|
|
|
}
|
2018-12-22 05:47:13 -08:00
|
|
|
|
|
|
|
func TestSubquerySelector(t *testing.T) {
|
2020-07-23 15:05:43 -07:00
|
|
|
type caseType struct {
|
|
|
|
Query string
|
|
|
|
Result Result
|
|
|
|
Start time.Time
|
|
|
|
}
|
|
|
|
|
|
|
|
for _, tst := range []struct {
|
2018-12-22 05:47:13 -08:00
|
|
|
loadString string
|
2020-07-23 15:05:43 -07:00
|
|
|
cases []caseType
|
2018-12-22 05:47:13 -08:00
|
|
|
}{
|
|
|
|
{
|
|
|
|
loadString: `load 10s
|
|
|
|
metric 1 2`,
|
2020-07-23 15:05:43 -07:00
|
|
|
cases: []caseType{
|
2018-12-22 05:47:13 -08:00
|
|
|
{
|
|
|
|
Query: "metric[20s:10s]",
|
|
|
|
Result: Result{
|
|
|
|
nil,
|
2021-10-22 01:06:44 -07:00
|
|
|
Matrix{
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 1, T: 0}, {F: 2, T: 10000}},
|
2021-10-22 01:06:44 -07:00
|
|
|
Metric: labels.FromStrings("__name__", "metric"),
|
|
|
|
},
|
2018-12-22 05:47:13 -08:00
|
|
|
},
|
|
|
|
nil,
|
|
|
|
},
|
|
|
|
Start: time.Unix(10, 0),
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: "metric[20s:5s]",
|
|
|
|
Result: Result{
|
|
|
|
nil,
|
2021-10-22 01:06:44 -07:00
|
|
|
Matrix{
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 1, T: 0}, {F: 1, T: 5000}, {F: 2, T: 10000}},
|
2021-10-22 01:06:44 -07:00
|
|
|
Metric: labels.FromStrings("__name__", "metric"),
|
|
|
|
},
|
2018-12-22 05:47:13 -08:00
|
|
|
},
|
|
|
|
nil,
|
|
|
|
},
|
|
|
|
Start: time.Unix(10, 0),
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: "metric[20s:5s] offset 2s",
|
|
|
|
Result: Result{
|
|
|
|
nil,
|
2021-10-22 01:06:44 -07:00
|
|
|
Matrix{
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 1, T: 0}, {F: 1, T: 5000}, {F: 2, T: 10000}},
|
2021-10-22 01:06:44 -07:00
|
|
|
Metric: labels.FromStrings("__name__", "metric"),
|
|
|
|
},
|
2018-12-22 05:47:13 -08:00
|
|
|
},
|
|
|
|
nil,
|
|
|
|
},
|
|
|
|
Start: time.Unix(12, 0),
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: "metric[20s:5s] offset 6s",
|
|
|
|
Result: Result{
|
|
|
|
nil,
|
2021-10-22 01:06:44 -07:00
|
|
|
Matrix{
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 1, T: 0}, {F: 1, T: 5000}, {F: 2, T: 10000}},
|
2021-10-22 01:06:44 -07:00
|
|
|
Metric: labels.FromStrings("__name__", "metric"),
|
|
|
|
},
|
2018-12-22 05:47:13 -08:00
|
|
|
},
|
|
|
|
nil,
|
|
|
|
},
|
|
|
|
Start: time.Unix(20, 0),
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: "metric[20s:5s] offset 4s",
|
|
|
|
Result: Result{
|
|
|
|
nil,
|
2021-10-22 01:06:44 -07:00
|
|
|
Matrix{
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 2, T: 15000}, {F: 2, T: 20000}, {F: 2, T: 25000}, {F: 2, T: 30000}},
|
2021-10-22 01:06:44 -07:00
|
|
|
Metric: labels.FromStrings("__name__", "metric"),
|
|
|
|
},
|
2018-12-22 05:47:13 -08:00
|
|
|
},
|
|
|
|
nil,
|
|
|
|
},
|
|
|
|
Start: time.Unix(35, 0),
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: "metric[20s:5s] offset 5s",
|
|
|
|
Result: Result{
|
|
|
|
nil,
|
2021-10-22 01:06:44 -07:00
|
|
|
Matrix{
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 2, T: 10000}, {F: 2, T: 15000}, {F: 2, T: 20000}, {F: 2, T: 25000}, {F: 2, T: 30000}},
|
2021-10-22 01:06:44 -07:00
|
|
|
Metric: labels.FromStrings("__name__", "metric"),
|
|
|
|
},
|
2018-12-22 05:47:13 -08:00
|
|
|
},
|
|
|
|
nil,
|
|
|
|
},
|
|
|
|
Start: time.Unix(35, 0),
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: "metric[20s:5s] offset 6s",
|
|
|
|
Result: Result{
|
|
|
|
nil,
|
2021-10-22 01:06:44 -07:00
|
|
|
Matrix{
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 2, T: 10000}, {F: 2, T: 15000}, {F: 2, T: 20000}, {F: 2, T: 25000}},
|
2021-10-22 01:06:44 -07:00
|
|
|
Metric: labels.FromStrings("__name__", "metric"),
|
|
|
|
},
|
2018-12-22 05:47:13 -08:00
|
|
|
},
|
|
|
|
nil,
|
|
|
|
},
|
|
|
|
Start: time.Unix(35, 0),
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: "metric[20s:5s] offset 7s",
|
|
|
|
Result: Result{
|
|
|
|
nil,
|
2021-10-22 01:06:44 -07:00
|
|
|
Matrix{
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 2, T: 10000}, {F: 2, T: 15000}, {F: 2, T: 20000}, {F: 2, T: 25000}},
|
2021-10-22 01:06:44 -07:00
|
|
|
Metric: labels.FromStrings("__name__", "metric"),
|
|
|
|
},
|
2018-12-22 05:47:13 -08:00
|
|
|
},
|
|
|
|
nil,
|
|
|
|
},
|
|
|
|
Start: time.Unix(35, 0),
|
|
|
|
},
|
|
|
|
},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
loadString: `load 10s
|
|
|
|
http_requests{job="api-server", instance="0", group="production"} 0+10x1000 100+30x1000
|
|
|
|
http_requests{job="api-server", instance="1", group="production"} 0+20x1000 200+30x1000
|
|
|
|
http_requests{job="api-server", instance="0", group="canary"} 0+30x1000 300+80x1000
|
|
|
|
http_requests{job="api-server", instance="1", group="canary"} 0+40x2000`,
|
2020-07-23 15:05:43 -07:00
|
|
|
cases: []caseType{
|
2018-12-22 05:47:13 -08:00
|
|
|
{ // Normal selector.
|
|
|
|
Query: `http_requests{group=~"pro.*",instance="0"}[30s:10s]`,
|
|
|
|
Result: Result{
|
|
|
|
nil,
|
2021-10-22 01:06:44 -07:00
|
|
|
Matrix{
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 9990, T: 9990000}, {F: 10000, T: 10000000}, {F: 100, T: 10010000}, {F: 130, T: 10020000}},
|
2021-10-22 01:06:44 -07:00
|
|
|
Metric: labels.FromStrings("__name__", "http_requests", "job", "api-server", "instance", "0", "group", "production"),
|
|
|
|
},
|
2018-12-22 05:47:13 -08:00
|
|
|
},
|
|
|
|
nil,
|
|
|
|
},
|
|
|
|
Start: time.Unix(10020, 0),
|
|
|
|
},
|
|
|
|
{ // Default step.
|
|
|
|
Query: `http_requests{group=~"pro.*",instance="0"}[5m:]`,
|
|
|
|
Result: Result{
|
|
|
|
nil,
|
2021-10-22 01:06:44 -07:00
|
|
|
Matrix{
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 9840, T: 9840000}, {F: 9900, T: 9900000}, {F: 9960, T: 9960000}, {F: 130, T: 10020000}, {F: 310, T: 10080000}},
|
2021-10-22 01:06:44 -07:00
|
|
|
Metric: labels.FromStrings("__name__", "http_requests", "job", "api-server", "instance", "0", "group", "production"),
|
|
|
|
},
|
2018-12-22 05:47:13 -08:00
|
|
|
},
|
|
|
|
nil,
|
|
|
|
},
|
|
|
|
Start: time.Unix(10100, 0),
|
|
|
|
},
|
|
|
|
{ // Checking if high offset (>LookbackDelta) is being taken care of.
|
|
|
|
Query: `http_requests{group=~"pro.*",instance="0"}[5m:] offset 20m`,
|
|
|
|
Result: Result{
|
|
|
|
nil,
|
2021-10-22 01:06:44 -07:00
|
|
|
Matrix{
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 8640, T: 8640000}, {F: 8700, T: 8700000}, {F: 8760, T: 8760000}, {F: 8820, T: 8820000}, {F: 8880, T: 8880000}},
|
2021-10-22 01:06:44 -07:00
|
|
|
Metric: labels.FromStrings("__name__", "http_requests", "job", "api-server", "instance", "0", "group", "production"),
|
|
|
|
},
|
2018-12-22 05:47:13 -08:00
|
|
|
},
|
|
|
|
nil,
|
|
|
|
},
|
|
|
|
Start: time.Unix(10100, 0),
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: `rate(http_requests[1m])[15s:5s]`,
|
|
|
|
Result: Result{
|
|
|
|
nil,
|
|
|
|
Matrix{
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 3, T: 7985000}, {F: 3, T: 7990000}, {F: 3, T: 7995000}, {F: 3, T: 8000000}},
|
2018-12-22 05:47:13 -08:00
|
|
|
Metric: labels.FromStrings("job", "api-server", "instance", "0", "group", "canary"),
|
|
|
|
},
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 4, T: 7985000}, {F: 4, T: 7990000}, {F: 4, T: 7995000}, {F: 4, T: 8000000}},
|
2018-12-22 05:47:13 -08:00
|
|
|
Metric: labels.FromStrings("job", "api-server", "instance", "1", "group", "canary"),
|
|
|
|
},
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 1, T: 7985000}, {F: 1, T: 7990000}, {F: 1, T: 7995000}, {F: 1, T: 8000000}},
|
2018-12-22 05:47:13 -08:00
|
|
|
Metric: labels.FromStrings("job", "api-server", "instance", "0", "group", "production"),
|
|
|
|
},
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 2, T: 7985000}, {F: 2, T: 7990000}, {F: 2, T: 7995000}, {F: 2, T: 8000000}},
|
2018-12-22 05:47:13 -08:00
|
|
|
Metric: labels.FromStrings("job", "api-server", "instance", "1", "group", "production"),
|
|
|
|
},
|
|
|
|
},
|
|
|
|
nil,
|
|
|
|
},
|
|
|
|
Start: time.Unix(8000, 0),
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: `sum(http_requests{group=~"pro.*"})[30s:10s]`,
|
|
|
|
Result: Result{
|
|
|
|
nil,
|
2021-10-22 01:06:44 -07:00
|
|
|
Matrix{
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 270, T: 90000}, {F: 300, T: 100000}, {F: 330, T: 110000}, {F: 360, T: 120000}},
|
2022-07-21 09:23:30 -07:00
|
|
|
Metric: labels.EmptyLabels(),
|
2021-10-22 01:06:44 -07:00
|
|
|
},
|
2018-12-22 05:47:13 -08:00
|
|
|
},
|
|
|
|
nil,
|
|
|
|
},
|
|
|
|
Start: time.Unix(120, 0),
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: `sum(http_requests)[40s:10s]`,
|
|
|
|
Result: Result{
|
|
|
|
nil,
|
2021-10-22 01:06:44 -07:00
|
|
|
Matrix{
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 800, T: 80000}, {F: 900, T: 90000}, {F: 1000, T: 100000}, {F: 1100, T: 110000}, {F: 1200, T: 120000}},
|
2022-07-21 09:23:30 -07:00
|
|
|
Metric: labels.EmptyLabels(),
|
2021-10-22 01:06:44 -07:00
|
|
|
},
|
2018-12-22 05:47:13 -08:00
|
|
|
},
|
|
|
|
nil,
|
|
|
|
},
|
|
|
|
Start: time.Unix(120, 0),
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Query: `(sum(http_requests{group=~"p.*"})+sum(http_requests{group=~"c.*"}))[20s:5s]`,
|
|
|
|
Result: Result{
|
|
|
|
nil,
|
2021-10-22 01:06:44 -07:00
|
|
|
Matrix{
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 1000, T: 100000}, {F: 1000, T: 105000}, {F: 1100, T: 110000}, {F: 1100, T: 115000}, {F: 1200, T: 120000}},
|
2022-07-21 09:23:30 -07:00
|
|
|
Metric: labels.EmptyLabels(),
|
2021-10-22 01:06:44 -07:00
|
|
|
},
|
2018-12-22 05:47:13 -08:00
|
|
|
},
|
|
|
|
nil,
|
|
|
|
},
|
|
|
|
Start: time.Unix(120, 0),
|
|
|
|
},
|
|
|
|
},
|
|
|
|
},
|
2020-07-23 15:05:43 -07:00
|
|
|
} {
|
2020-07-22 06:39:51 -07:00
|
|
|
t.Run("", func(t *testing.T) {
|
2023-08-18 11:48:59 -07:00
|
|
|
engine := newTestEngine()
|
|
|
|
storage := LoadedStorage(t, tst.loadString)
|
|
|
|
t.Cleanup(func() { storage.Close() })
|
2020-07-22 06:39:51 -07:00
|
|
|
|
|
|
|
for _, c := range tst.cases {
|
|
|
|
t.Run(c.Query, func(t *testing.T) {
|
2023-08-18 11:48:59 -07:00
|
|
|
qry, err := engine.NewInstantQuery(context.Background(), storage, nil, c.Query, c.Start)
|
2020-10-29 02:43:23 -07:00
|
|
|
require.NoError(t, err)
|
2020-07-22 06:39:51 -07:00
|
|
|
|
2023-08-18 11:48:59 -07:00
|
|
|
res := qry.Exec(context.Background())
|
2020-10-29 02:43:23 -07:00
|
|
|
require.Equal(t, c.Result.Err, res.Err)
|
2020-07-22 06:39:51 -07:00
|
|
|
mat := res.Value.(Matrix)
|
|
|
|
sort.Sort(mat)
|
2023-04-16 05:13:31 -07:00
|
|
|
testutil.RequireEqual(t, c.Result.Value, mat)
|
2020-07-22 06:39:51 -07:00
|
|
|
})
|
|
|
|
}
|
|
|
|
})
|
2018-12-22 05:47:13 -08:00
|
|
|
}
|
|
|
|
}
|
2020-01-08 05:28:43 -08:00
|
|
|
|
2023-06-28 20:34:26 -07:00
|
|
|
func TestTimestampFunction_StepsMoreOftenThanSamples(t *testing.T) {
|
2023-08-18 11:48:59 -07:00
|
|
|
engine := newTestEngine()
|
|
|
|
storage := LoadedStorage(t, `
|
2023-06-28 20:34:26 -07:00
|
|
|
load 1m
|
|
|
|
metric 0+1x1000
|
|
|
|
`)
|
2023-08-18 11:48:59 -07:00
|
|
|
t.Cleanup(func() { storage.Close() })
|
2023-06-28 20:34:26 -07:00
|
|
|
|
|
|
|
query := "timestamp(metric)"
|
|
|
|
start := time.Unix(0, 0)
|
|
|
|
end := time.Unix(61, 0)
|
|
|
|
interval := time.Second
|
|
|
|
|
2023-07-31 20:27:46 -07:00
|
|
|
// We expect the value to be 0 for t=0s to t=59s (inclusive), then 60 for t=60s and t=61s.
|
|
|
|
expectedPoints := []FPoint{}
|
|
|
|
|
|
|
|
for t := 0; t <= 59; t++ {
|
|
|
|
expectedPoints = append(expectedPoints, FPoint{F: 0, T: int64(t * 1000)})
|
|
|
|
}
|
|
|
|
|
|
|
|
expectedPoints = append(
|
|
|
|
expectedPoints,
|
|
|
|
FPoint{F: 60, T: 60_000},
|
|
|
|
FPoint{F: 60, T: 61_000},
|
|
|
|
)
|
|
|
|
|
2023-06-28 20:34:26 -07:00
|
|
|
expectedResult := Matrix{
|
|
|
|
Series{
|
2023-07-31 20:27:46 -07:00
|
|
|
Floats: expectedPoints,
|
2023-06-28 20:34:26 -07:00
|
|
|
Metric: labels.EmptyLabels(),
|
|
|
|
},
|
|
|
|
}
|
|
|
|
|
2023-08-18 11:48:59 -07:00
|
|
|
qry, err := engine.NewRangeQuery(context.Background(), storage, nil, query, start, end, interval)
|
2023-06-28 20:34:26 -07:00
|
|
|
require.NoError(t, err)
|
|
|
|
|
2023-08-18 11:48:59 -07:00
|
|
|
res := qry.Exec(context.Background())
|
2023-06-28 20:34:26 -07:00
|
|
|
require.NoError(t, res.Err)
|
2023-04-16 05:13:31 -07:00
|
|
|
testutil.RequireEqual(t, expectedResult, res.Value)
|
2023-06-28 20:34:26 -07:00
|
|
|
}
|
|
|
|
|
2020-01-08 05:28:43 -08:00
|
|
|
type FakeQueryLogger struct {
|
|
|
|
closed bool
|
|
|
|
logs []interface{}
|
|
|
|
}
|
|
|
|
|
|
|
|
func NewFakeQueryLogger() *FakeQueryLogger {
|
|
|
|
return &FakeQueryLogger{
|
|
|
|
closed: false,
|
|
|
|
logs: make([]interface{}, 0),
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
func (f *FakeQueryLogger) Close() error {
|
|
|
|
f.closed = true
|
|
|
|
return nil
|
|
|
|
}
|
|
|
|
|
|
|
|
func (f *FakeQueryLogger) Log(l ...interface{}) error {
|
|
|
|
f.logs = append(f.logs, l...)
|
|
|
|
return nil
|
|
|
|
}
|
|
|
|
|
|
|
|
func TestQueryLogger_basic(t *testing.T) {
|
|
|
|
opts := EngineOpts{
|
2020-01-28 12:38:49 -08:00
|
|
|
Logger: nil,
|
|
|
|
Reg: nil,
|
|
|
|
MaxSamples: 10,
|
|
|
|
Timeout: 10 * time.Second,
|
2020-01-08 05:28:43 -08:00
|
|
|
}
|
|
|
|
engine := NewEngine(opts)
|
|
|
|
|
|
|
|
queryExec := func() {
|
|
|
|
ctx, cancelCtx := context.WithCancel(context.Background())
|
|
|
|
defer cancelCtx()
|
|
|
|
query := engine.newTestQuery(func(ctx context.Context) error {
|
|
|
|
return contextDone(ctx, "test statement execution")
|
|
|
|
})
|
|
|
|
res := query.Exec(ctx)
|
2020-10-29 02:43:23 -07:00
|
|
|
require.NoError(t, res.Err)
|
2020-01-08 05:28:43 -08:00
|
|
|
}
|
|
|
|
|
2020-02-17 03:45:11 -08:00
|
|
|
// Query works without query log initialized.
|
2020-01-08 05:28:43 -08:00
|
|
|
queryExec()
|
|
|
|
|
|
|
|
f1 := NewFakeQueryLogger()
|
|
|
|
engine.SetQueryLogger(f1)
|
|
|
|
queryExec()
|
2020-01-27 01:53:10 -08:00
|
|
|
for i, field := range []interface{}{"params", map[string]interface{}{"query": "test statement"}} {
|
2020-10-29 02:43:23 -07:00
|
|
|
require.Equal(t, field, f1.logs[i])
|
2020-01-08 05:28:43 -08:00
|
|
|
}
|
|
|
|
|
|
|
|
l := len(f1.logs)
|
|
|
|
queryExec()
|
2023-12-07 03:35:01 -08:00
|
|
|
require.Len(t, f1.logs, 2*l)
|
2020-01-08 05:28:43 -08:00
|
|
|
|
|
|
|
// Test that we close the query logger when unsetting it.
|
2020-10-29 02:43:23 -07:00
|
|
|
require.False(t, f1.closed, "expected f1 to be open, got closed")
|
2020-01-08 05:28:43 -08:00
|
|
|
engine.SetQueryLogger(nil)
|
2020-10-29 02:43:23 -07:00
|
|
|
require.True(t, f1.closed, "expected f1 to be closed, got open")
|
2020-01-08 05:28:43 -08:00
|
|
|
queryExec()
|
|
|
|
|
|
|
|
// Test that we close the query logger when swapping.
|
|
|
|
f2 := NewFakeQueryLogger()
|
|
|
|
f3 := NewFakeQueryLogger()
|
|
|
|
engine.SetQueryLogger(f2)
|
2020-10-29 02:43:23 -07:00
|
|
|
require.False(t, f2.closed, "expected f2 to be open, got closed")
|
2020-01-08 05:28:43 -08:00
|
|
|
queryExec()
|
|
|
|
engine.SetQueryLogger(f3)
|
2020-10-29 02:43:23 -07:00
|
|
|
require.True(t, f2.closed, "expected f2 to be closed, got open")
|
|
|
|
require.False(t, f3.closed, "expected f3 to be open, got closed")
|
2020-01-08 05:28:43 -08:00
|
|
|
queryExec()
|
|
|
|
}
|
|
|
|
|
|
|
|
func TestQueryLogger_fields(t *testing.T) {
|
|
|
|
opts := EngineOpts{
|
2020-01-28 12:38:49 -08:00
|
|
|
Logger: nil,
|
|
|
|
Reg: nil,
|
|
|
|
MaxSamples: 10,
|
|
|
|
Timeout: 10 * time.Second,
|
2020-01-08 05:28:43 -08:00
|
|
|
}
|
|
|
|
engine := NewEngine(opts)
|
|
|
|
|
|
|
|
f1 := NewFakeQueryLogger()
|
|
|
|
engine.SetQueryLogger(f1)
|
|
|
|
|
|
|
|
ctx, cancelCtx := context.WithCancel(context.Background())
|
2020-01-27 01:53:10 -08:00
|
|
|
ctx = NewOriginContext(ctx, map[string]interface{}{"foo": "bar"})
|
2020-01-08 05:28:43 -08:00
|
|
|
defer cancelCtx()
|
|
|
|
query := engine.newTestQuery(func(ctx context.Context) error {
|
|
|
|
return contextDone(ctx, "test statement execution")
|
|
|
|
})
|
|
|
|
|
|
|
|
res := query.Exec(ctx)
|
2020-10-29 02:43:23 -07:00
|
|
|
require.NoError(t, res.Err)
|
2020-01-08 05:28:43 -08:00
|
|
|
|
|
|
|
expected := []string{"foo", "bar"}
|
|
|
|
for i, field := range expected {
|
|
|
|
v := f1.logs[len(f1.logs)-len(expected)+i].(string)
|
2020-10-29 02:43:23 -07:00
|
|
|
require.Equal(t, field, v)
|
2020-01-08 05:28:43 -08:00
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
func TestQueryLogger_error(t *testing.T) {
|
|
|
|
opts := EngineOpts{
|
2020-01-28 12:38:49 -08:00
|
|
|
Logger: nil,
|
|
|
|
Reg: nil,
|
|
|
|
MaxSamples: 10,
|
|
|
|
Timeout: 10 * time.Second,
|
2020-01-08 05:28:43 -08:00
|
|
|
}
|
|
|
|
engine := NewEngine(opts)
|
|
|
|
|
|
|
|
f1 := NewFakeQueryLogger()
|
|
|
|
engine.SetQueryLogger(f1)
|
|
|
|
|
|
|
|
ctx, cancelCtx := context.WithCancel(context.Background())
|
2020-01-27 01:53:10 -08:00
|
|
|
ctx = NewOriginContext(ctx, map[string]interface{}{"foo": "bar"})
|
2020-01-08 05:28:43 -08:00
|
|
|
defer cancelCtx()
|
|
|
|
testErr := errors.New("failure")
|
|
|
|
query := engine.newTestQuery(func(ctx context.Context) error {
|
|
|
|
return testErr
|
|
|
|
})
|
|
|
|
|
|
|
|
res := query.Exec(ctx)
|
2020-10-29 02:43:23 -07:00
|
|
|
require.Error(t, res.Err, "query should have failed")
|
2020-01-08 05:28:43 -08:00
|
|
|
|
2020-01-27 01:53:10 -08:00
|
|
|
for i, field := range []interface{}{"params", map[string]interface{}{"query": "test statement"}, "error", testErr} {
|
2020-10-29 02:43:23 -07:00
|
|
|
require.Equal(t, f1.logs[i], field)
|
2020-01-08 05:28:43 -08:00
|
|
|
}
|
|
|
|
}
|
2021-01-20 02:57:39 -08:00
|
|
|
|
2021-02-09 08:03:16 -08:00
|
|
|
func TestPreprocessAndWrapWithStepInvariantExpr(t *testing.T) {
|
|
|
|
startTime := time.Unix(1000, 0)
|
|
|
|
endTime := time.Unix(9999, 0)
|
2021-10-22 01:06:44 -07:00
|
|
|
testCases := []struct {
|
2021-11-10 06:46:24 -08:00
|
|
|
input string // The input to be parsed.
|
|
|
|
expected parser.Expr // The expected expression AST.
|
|
|
|
outputTest bool
|
2021-01-20 02:57:39 -08:00
|
|
|
}{
|
|
|
|
{
|
|
|
|
input: "123.4567",
|
|
|
|
expected: &parser.StepInvariantExpr{
|
|
|
|
Expr: &parser.NumberLiteral{
|
|
|
|
Val: 123.4567,
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{Start: 0, End: 8},
|
2021-01-20 02:57:39 -08:00
|
|
|
},
|
|
|
|
},
|
2021-11-10 06:46:24 -08:00
|
|
|
},
|
|
|
|
{
|
2021-01-20 02:57:39 -08:00
|
|
|
input: `"foo"`,
|
|
|
|
expected: &parser.StepInvariantExpr{
|
|
|
|
Expr: &parser.StringLiteral{
|
|
|
|
Val: "foo",
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{Start: 0, End: 5},
|
2021-01-20 02:57:39 -08:00
|
|
|
},
|
|
|
|
},
|
2021-11-10 06:46:24 -08:00
|
|
|
},
|
|
|
|
{
|
2021-01-20 02:57:39 -08:00
|
|
|
input: "foo * bar",
|
|
|
|
expected: &parser.BinaryExpr{
|
|
|
|
Op: parser.MUL,
|
|
|
|
LHS: &parser.VectorSelector{
|
|
|
|
Name: "foo",
|
|
|
|
LabelMatchers: []*labels.Matcher{
|
|
|
|
parser.MustLabelMatcher(labels.MatchEqual, "__name__", "foo"),
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-01-20 02:57:39 -08:00
|
|
|
Start: 0,
|
|
|
|
End: 3,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
RHS: &parser.VectorSelector{
|
|
|
|
Name: "bar",
|
|
|
|
LabelMatchers: []*labels.Matcher{
|
|
|
|
parser.MustLabelMatcher(labels.MatchEqual, "__name__", "bar"),
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-01-20 02:57:39 -08:00
|
|
|
Start: 6,
|
|
|
|
End: 9,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
VectorMatching: &parser.VectorMatching{Card: parser.CardOneToOne},
|
|
|
|
},
|
2021-11-10 06:46:24 -08:00
|
|
|
},
|
|
|
|
{
|
2021-01-20 02:57:39 -08:00
|
|
|
input: "foo * bar @ 10",
|
|
|
|
expected: &parser.BinaryExpr{
|
|
|
|
Op: parser.MUL,
|
|
|
|
LHS: &parser.VectorSelector{
|
|
|
|
Name: "foo",
|
|
|
|
LabelMatchers: []*labels.Matcher{
|
|
|
|
parser.MustLabelMatcher(labels.MatchEqual, "__name__", "foo"),
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-01-20 02:57:39 -08:00
|
|
|
Start: 0,
|
|
|
|
End: 3,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
RHS: &parser.StepInvariantExpr{
|
|
|
|
Expr: &parser.VectorSelector{
|
|
|
|
Name: "bar",
|
|
|
|
LabelMatchers: []*labels.Matcher{
|
|
|
|
parser.MustLabelMatcher(labels.MatchEqual, "__name__", "bar"),
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-01-20 02:57:39 -08:00
|
|
|
Start: 6,
|
|
|
|
End: 14,
|
|
|
|
},
|
|
|
|
Timestamp: makeInt64Pointer(10000),
|
|
|
|
},
|
|
|
|
},
|
|
|
|
VectorMatching: &parser.VectorMatching{Card: parser.CardOneToOne},
|
|
|
|
},
|
2021-11-10 06:46:24 -08:00
|
|
|
},
|
|
|
|
{
|
2021-01-20 02:57:39 -08:00
|
|
|
input: "foo @ 20 * bar @ 10",
|
|
|
|
expected: &parser.StepInvariantExpr{
|
|
|
|
Expr: &parser.BinaryExpr{
|
|
|
|
Op: parser.MUL,
|
|
|
|
LHS: &parser.VectorSelector{
|
|
|
|
Name: "foo",
|
|
|
|
LabelMatchers: []*labels.Matcher{
|
|
|
|
parser.MustLabelMatcher(labels.MatchEqual, "__name__", "foo"),
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-01-20 02:57:39 -08:00
|
|
|
Start: 0,
|
|
|
|
End: 8,
|
|
|
|
},
|
|
|
|
Timestamp: makeInt64Pointer(20000),
|
|
|
|
},
|
|
|
|
RHS: &parser.VectorSelector{
|
|
|
|
Name: "bar",
|
|
|
|
LabelMatchers: []*labels.Matcher{
|
|
|
|
parser.MustLabelMatcher(labels.MatchEqual, "__name__", "bar"),
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-01-20 02:57:39 -08:00
|
|
|
Start: 11,
|
|
|
|
End: 19,
|
|
|
|
},
|
|
|
|
Timestamp: makeInt64Pointer(10000),
|
|
|
|
},
|
|
|
|
VectorMatching: &parser.VectorMatching{Card: parser.CardOneToOne},
|
|
|
|
},
|
|
|
|
},
|
2021-11-10 06:46:24 -08:00
|
|
|
},
|
|
|
|
{
|
2021-01-20 02:57:39 -08:00
|
|
|
input: "test[5s]",
|
|
|
|
expected: &parser.MatrixSelector{
|
|
|
|
VectorSelector: &parser.VectorSelector{
|
|
|
|
Name: "test",
|
|
|
|
LabelMatchers: []*labels.Matcher{
|
|
|
|
parser.MustLabelMatcher(labels.MatchEqual, "__name__", "test"),
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-01-20 02:57:39 -08:00
|
|
|
Start: 0,
|
|
|
|
End: 4,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
Range: 5 * time.Second,
|
|
|
|
EndPos: 8,
|
|
|
|
},
|
2021-11-10 06:46:24 -08:00
|
|
|
},
|
|
|
|
{
|
2021-01-20 02:57:39 -08:00
|
|
|
input: `test{a="b"}[5y] @ 1603774699`,
|
|
|
|
expected: &parser.StepInvariantExpr{
|
|
|
|
Expr: &parser.MatrixSelector{
|
|
|
|
VectorSelector: &parser.VectorSelector{
|
|
|
|
Name: "test",
|
|
|
|
Timestamp: makeInt64Pointer(1603774699000),
|
|
|
|
LabelMatchers: []*labels.Matcher{
|
|
|
|
parser.MustLabelMatcher(labels.MatchEqual, "a", "b"),
|
|
|
|
parser.MustLabelMatcher(labels.MatchEqual, "__name__", "test"),
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-01-20 02:57:39 -08:00
|
|
|
Start: 0,
|
|
|
|
End: 11,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
Range: 5 * 365 * 24 * time.Hour,
|
|
|
|
EndPos: 28,
|
|
|
|
},
|
|
|
|
},
|
2021-11-10 06:46:24 -08:00
|
|
|
},
|
|
|
|
{
|
2021-01-20 02:57:39 -08:00
|
|
|
input: "sum by (foo)(some_metric)",
|
|
|
|
expected: &parser.AggregateExpr{
|
|
|
|
Op: parser.SUM,
|
|
|
|
Expr: &parser.VectorSelector{
|
|
|
|
Name: "some_metric",
|
|
|
|
LabelMatchers: []*labels.Matcher{
|
|
|
|
parser.MustLabelMatcher(labels.MatchEqual, "__name__", "some_metric"),
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-01-20 02:57:39 -08:00
|
|
|
Start: 13,
|
|
|
|
End: 24,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
Grouping: []string{"foo"},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-01-20 02:57:39 -08:00
|
|
|
Start: 0,
|
|
|
|
End: 25,
|
|
|
|
},
|
|
|
|
},
|
2021-11-10 06:46:24 -08:00
|
|
|
},
|
|
|
|
{
|
2021-01-20 02:57:39 -08:00
|
|
|
input: "sum by (foo)(some_metric @ 10)",
|
|
|
|
expected: &parser.StepInvariantExpr{
|
|
|
|
Expr: &parser.AggregateExpr{
|
|
|
|
Op: parser.SUM,
|
|
|
|
Expr: &parser.VectorSelector{
|
|
|
|
Name: "some_metric",
|
|
|
|
LabelMatchers: []*labels.Matcher{
|
|
|
|
parser.MustLabelMatcher(labels.MatchEqual, "__name__", "some_metric"),
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-01-20 02:57:39 -08:00
|
|
|
Start: 13,
|
|
|
|
End: 29,
|
|
|
|
},
|
|
|
|
Timestamp: makeInt64Pointer(10000),
|
|
|
|
},
|
|
|
|
Grouping: []string{"foo"},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-01-20 02:57:39 -08:00
|
|
|
Start: 0,
|
|
|
|
End: 30,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
},
|
2021-11-10 06:46:24 -08:00
|
|
|
},
|
|
|
|
{
|
2021-01-20 02:57:39 -08:00
|
|
|
input: "sum(some_metric1 @ 10) + sum(some_metric2 @ 20)",
|
|
|
|
expected: &parser.StepInvariantExpr{
|
|
|
|
Expr: &parser.BinaryExpr{
|
|
|
|
Op: parser.ADD,
|
|
|
|
VectorMatching: &parser.VectorMatching{},
|
|
|
|
LHS: &parser.AggregateExpr{
|
|
|
|
Op: parser.SUM,
|
|
|
|
Expr: &parser.VectorSelector{
|
|
|
|
Name: "some_metric1",
|
|
|
|
LabelMatchers: []*labels.Matcher{
|
|
|
|
parser.MustLabelMatcher(labels.MatchEqual, "__name__", "some_metric1"),
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-01-20 02:57:39 -08:00
|
|
|
Start: 4,
|
|
|
|
End: 21,
|
|
|
|
},
|
|
|
|
Timestamp: makeInt64Pointer(10000),
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-01-20 02:57:39 -08:00
|
|
|
Start: 0,
|
|
|
|
End: 22,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
RHS: &parser.AggregateExpr{
|
|
|
|
Op: parser.SUM,
|
|
|
|
Expr: &parser.VectorSelector{
|
|
|
|
Name: "some_metric2",
|
|
|
|
LabelMatchers: []*labels.Matcher{
|
|
|
|
parser.MustLabelMatcher(labels.MatchEqual, "__name__", "some_metric2"),
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-01-20 02:57:39 -08:00
|
|
|
Start: 29,
|
|
|
|
End: 46,
|
|
|
|
},
|
|
|
|
Timestamp: makeInt64Pointer(20000),
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-01-20 02:57:39 -08:00
|
|
|
Start: 25,
|
|
|
|
End: 47,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
},
|
|
|
|
},
|
2021-11-10 06:46:24 -08:00
|
|
|
},
|
|
|
|
{
|
2021-01-20 02:57:39 -08:00
|
|
|
input: "some_metric and topk(5, rate(some_metric[1m] @ 20))",
|
|
|
|
expected: &parser.BinaryExpr{
|
|
|
|
Op: parser.LAND,
|
|
|
|
VectorMatching: &parser.VectorMatching{
|
|
|
|
Card: parser.CardManyToMany,
|
|
|
|
},
|
|
|
|
LHS: &parser.VectorSelector{
|
|
|
|
Name: "some_metric",
|
|
|
|
LabelMatchers: []*labels.Matcher{
|
|
|
|
parser.MustLabelMatcher(labels.MatchEqual, "__name__", "some_metric"),
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-01-20 02:57:39 -08:00
|
|
|
Start: 0,
|
|
|
|
End: 11,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
RHS: &parser.StepInvariantExpr{
|
|
|
|
Expr: &parser.AggregateExpr{
|
|
|
|
Op: parser.TOPK,
|
|
|
|
Expr: &parser.Call{
|
|
|
|
Func: parser.MustGetFunction("rate"),
|
|
|
|
Args: parser.Expressions{
|
|
|
|
&parser.MatrixSelector{
|
|
|
|
VectorSelector: &parser.VectorSelector{
|
|
|
|
Name: "some_metric",
|
|
|
|
LabelMatchers: []*labels.Matcher{
|
|
|
|
parser.MustLabelMatcher(labels.MatchEqual, "__name__", "some_metric"),
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-01-20 02:57:39 -08:00
|
|
|
Start: 29,
|
|
|
|
End: 40,
|
|
|
|
},
|
|
|
|
Timestamp: makeInt64Pointer(20000),
|
|
|
|
},
|
|
|
|
Range: 1 * time.Minute,
|
|
|
|
EndPos: 49,
|
|
|
|
},
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-01-20 02:57:39 -08:00
|
|
|
Start: 24,
|
|
|
|
End: 50,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
Param: &parser.NumberLiteral{
|
|
|
|
Val: 5,
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-01-20 02:57:39 -08:00
|
|
|
Start: 21,
|
|
|
|
End: 22,
|
|
|
|
},
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-01-20 02:57:39 -08:00
|
|
|
Start: 16,
|
|
|
|
End: 51,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
},
|
|
|
|
},
|
2021-11-10 06:46:24 -08:00
|
|
|
},
|
|
|
|
{
|
2021-01-20 02:57:39 -08:00
|
|
|
input: "time()",
|
|
|
|
expected: &parser.Call{
|
|
|
|
Func: parser.MustGetFunction("time"),
|
|
|
|
Args: parser.Expressions{},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-01-20 02:57:39 -08:00
|
|
|
Start: 0,
|
|
|
|
End: 6,
|
|
|
|
},
|
|
|
|
},
|
2021-11-10 06:46:24 -08:00
|
|
|
},
|
|
|
|
{
|
2021-01-20 02:57:39 -08:00
|
|
|
input: `foo{bar="baz"}[10m:6s]`,
|
|
|
|
expected: &parser.SubqueryExpr{
|
|
|
|
Expr: &parser.VectorSelector{
|
|
|
|
Name: "foo",
|
|
|
|
LabelMatchers: []*labels.Matcher{
|
|
|
|
parser.MustLabelMatcher(labels.MatchEqual, "bar", "baz"),
|
|
|
|
parser.MustLabelMatcher(labels.MatchEqual, "__name__", "foo"),
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-01-20 02:57:39 -08:00
|
|
|
Start: 0,
|
|
|
|
End: 14,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
Range: 10 * time.Minute,
|
|
|
|
Step: 6 * time.Second,
|
|
|
|
EndPos: 22,
|
|
|
|
},
|
2021-11-10 06:46:24 -08:00
|
|
|
},
|
|
|
|
{
|
2021-01-20 02:57:39 -08:00
|
|
|
input: `foo{bar="baz"}[10m:6s] @ 10`,
|
|
|
|
expected: &parser.StepInvariantExpr{
|
|
|
|
Expr: &parser.SubqueryExpr{
|
|
|
|
Expr: &parser.VectorSelector{
|
|
|
|
Name: "foo",
|
|
|
|
LabelMatchers: []*labels.Matcher{
|
|
|
|
parser.MustLabelMatcher(labels.MatchEqual, "bar", "baz"),
|
|
|
|
parser.MustLabelMatcher(labels.MatchEqual, "__name__", "foo"),
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-01-20 02:57:39 -08:00
|
|
|
Start: 0,
|
|
|
|
End: 14,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
Range: 10 * time.Minute,
|
|
|
|
Step: 6 * time.Second,
|
|
|
|
Timestamp: makeInt64Pointer(10000),
|
|
|
|
EndPos: 27,
|
|
|
|
},
|
|
|
|
},
|
2021-11-10 06:46:24 -08:00
|
|
|
},
|
|
|
|
{ // Even though the subquery is step invariant, the inside is also wrapped separately.
|
2021-01-20 02:57:39 -08:00
|
|
|
input: `sum(foo{bar="baz"} @ 20)[10m:6s] @ 10`,
|
|
|
|
expected: &parser.StepInvariantExpr{
|
|
|
|
Expr: &parser.SubqueryExpr{
|
|
|
|
Expr: &parser.StepInvariantExpr{
|
|
|
|
Expr: &parser.AggregateExpr{
|
|
|
|
Op: parser.SUM,
|
|
|
|
Expr: &parser.VectorSelector{
|
|
|
|
Name: "foo",
|
|
|
|
LabelMatchers: []*labels.Matcher{
|
|
|
|
parser.MustLabelMatcher(labels.MatchEqual, "bar", "baz"),
|
|
|
|
parser.MustLabelMatcher(labels.MatchEqual, "__name__", "foo"),
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-01-20 02:57:39 -08:00
|
|
|
Start: 4,
|
|
|
|
End: 23,
|
|
|
|
},
|
|
|
|
Timestamp: makeInt64Pointer(20000),
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-01-20 02:57:39 -08:00
|
|
|
Start: 0,
|
|
|
|
End: 24,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
},
|
|
|
|
Range: 10 * time.Minute,
|
|
|
|
Step: 6 * time.Second,
|
|
|
|
Timestamp: makeInt64Pointer(10000),
|
|
|
|
EndPos: 37,
|
|
|
|
},
|
|
|
|
},
|
2021-11-10 06:46:24 -08:00
|
|
|
},
|
|
|
|
{
|
2021-01-20 02:57:39 -08:00
|
|
|
input: `min_over_time(rate(foo{bar="baz"}[2s])[5m:] @ 1603775091)[4m:3s]`,
|
|
|
|
expected: &parser.SubqueryExpr{
|
|
|
|
Expr: &parser.StepInvariantExpr{
|
|
|
|
Expr: &parser.Call{
|
|
|
|
Func: parser.MustGetFunction("min_over_time"),
|
|
|
|
Args: parser.Expressions{
|
|
|
|
&parser.SubqueryExpr{
|
|
|
|
Expr: &parser.Call{
|
|
|
|
Func: parser.MustGetFunction("rate"),
|
|
|
|
Args: parser.Expressions{
|
|
|
|
&parser.MatrixSelector{
|
|
|
|
VectorSelector: &parser.VectorSelector{
|
|
|
|
Name: "foo",
|
|
|
|
LabelMatchers: []*labels.Matcher{
|
|
|
|
parser.MustLabelMatcher(labels.MatchEqual, "bar", "baz"),
|
|
|
|
parser.MustLabelMatcher(labels.MatchEqual, "__name__", "foo"),
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-01-20 02:57:39 -08:00
|
|
|
Start: 19,
|
|
|
|
End: 33,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
Range: 2 * time.Second,
|
|
|
|
EndPos: 37,
|
|
|
|
},
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-01-20 02:57:39 -08:00
|
|
|
Start: 14,
|
|
|
|
End: 38,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
Range: 5 * time.Minute,
|
|
|
|
Timestamp: makeInt64Pointer(1603775091000),
|
|
|
|
EndPos: 56,
|
|
|
|
},
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-01-20 02:57:39 -08:00
|
|
|
Start: 0,
|
|
|
|
End: 57,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
},
|
|
|
|
Range: 4 * time.Minute,
|
|
|
|
Step: 3 * time.Second,
|
|
|
|
EndPos: 64,
|
|
|
|
},
|
2021-11-10 06:46:24 -08:00
|
|
|
},
|
|
|
|
{
|
2021-01-20 02:57:39 -08:00
|
|
|
input: `some_metric @ 123 offset 1m [10m:5s]`,
|
|
|
|
expected: &parser.SubqueryExpr{
|
|
|
|
Expr: &parser.StepInvariantExpr{
|
|
|
|
Expr: &parser.VectorSelector{
|
|
|
|
Name: "some_metric",
|
|
|
|
LabelMatchers: []*labels.Matcher{
|
|
|
|
parser.MustLabelMatcher(labels.MatchEqual, "__name__", "some_metric"),
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-01-20 02:57:39 -08:00
|
|
|
Start: 0,
|
|
|
|
End: 27,
|
|
|
|
},
|
|
|
|
Timestamp: makeInt64Pointer(123000),
|
|
|
|
OriginalOffset: 1 * time.Minute,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
Range: 10 * time.Minute,
|
|
|
|
Step: 5 * time.Second,
|
|
|
|
EndPos: 36,
|
|
|
|
},
|
2021-11-10 06:46:24 -08:00
|
|
|
},
|
|
|
|
{
|
2021-01-20 02:57:39 -08:00
|
|
|
input: `some_metric[10m:5s] offset 1m @ 123`,
|
|
|
|
expected: &parser.StepInvariantExpr{
|
|
|
|
Expr: &parser.SubqueryExpr{
|
|
|
|
Expr: &parser.VectorSelector{
|
|
|
|
Name: "some_metric",
|
|
|
|
LabelMatchers: []*labels.Matcher{
|
|
|
|
parser.MustLabelMatcher(labels.MatchEqual, "__name__", "some_metric"),
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-01-20 02:57:39 -08:00
|
|
|
Start: 0,
|
|
|
|
End: 11,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
Timestamp: makeInt64Pointer(123000),
|
|
|
|
OriginalOffset: 1 * time.Minute,
|
|
|
|
Range: 10 * time.Minute,
|
|
|
|
Step: 5 * time.Second,
|
|
|
|
EndPos: 35,
|
|
|
|
},
|
|
|
|
},
|
2021-11-10 06:46:24 -08:00
|
|
|
},
|
|
|
|
{
|
2021-01-20 02:57:39 -08:00
|
|
|
input: `(foo + bar{nm="val"} @ 1234)[5m:] @ 1603775019`,
|
|
|
|
expected: &parser.StepInvariantExpr{
|
|
|
|
Expr: &parser.SubqueryExpr{
|
|
|
|
Expr: &parser.ParenExpr{
|
|
|
|
Expr: &parser.BinaryExpr{
|
|
|
|
Op: parser.ADD,
|
|
|
|
VectorMatching: &parser.VectorMatching{
|
|
|
|
Card: parser.CardOneToOne,
|
|
|
|
},
|
|
|
|
LHS: &parser.VectorSelector{
|
|
|
|
Name: "foo",
|
|
|
|
LabelMatchers: []*labels.Matcher{
|
|
|
|
parser.MustLabelMatcher(labels.MatchEqual, "__name__", "foo"),
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-01-20 02:57:39 -08:00
|
|
|
Start: 1,
|
|
|
|
End: 4,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
RHS: &parser.StepInvariantExpr{
|
|
|
|
Expr: &parser.VectorSelector{
|
|
|
|
Name: "bar",
|
|
|
|
LabelMatchers: []*labels.Matcher{
|
|
|
|
parser.MustLabelMatcher(labels.MatchEqual, "nm", "val"),
|
|
|
|
parser.MustLabelMatcher(labels.MatchEqual, "__name__", "bar"),
|
|
|
|
},
|
|
|
|
Timestamp: makeInt64Pointer(1234000),
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-01-20 02:57:39 -08:00
|
|
|
Start: 7,
|
|
|
|
End: 27,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
},
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-01-20 02:57:39 -08:00
|
|
|
Start: 0,
|
|
|
|
End: 28,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
Range: 5 * time.Minute,
|
|
|
|
Timestamp: makeInt64Pointer(1603775019000),
|
|
|
|
EndPos: 46,
|
|
|
|
},
|
|
|
|
},
|
2021-11-10 06:46:24 -08:00
|
|
|
},
|
|
|
|
{
|
2021-01-20 02:57:39 -08:00
|
|
|
input: "abs(abs(metric @ 10))",
|
|
|
|
expected: &parser.StepInvariantExpr{
|
|
|
|
Expr: &parser.Call{
|
|
|
|
Func: &parser.Function{
|
|
|
|
Name: "abs",
|
|
|
|
ArgTypes: []parser.ValueType{parser.ValueTypeVector},
|
|
|
|
ReturnType: parser.ValueTypeVector,
|
|
|
|
},
|
|
|
|
Args: parser.Expressions{&parser.Call{
|
|
|
|
Func: &parser.Function{
|
|
|
|
Name: "abs",
|
|
|
|
ArgTypes: []parser.ValueType{parser.ValueTypeVector},
|
|
|
|
ReturnType: parser.ValueTypeVector,
|
|
|
|
},
|
|
|
|
Args: parser.Expressions{&parser.VectorSelector{
|
|
|
|
Name: "metric",
|
|
|
|
LabelMatchers: []*labels.Matcher{
|
|
|
|
parser.MustLabelMatcher(labels.MatchEqual, "__name__", "metric"),
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-01-20 02:57:39 -08:00
|
|
|
Start: 8,
|
|
|
|
End: 19,
|
|
|
|
},
|
|
|
|
Timestamp: makeInt64Pointer(10000),
|
|
|
|
}},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-01-20 02:57:39 -08:00
|
|
|
Start: 4,
|
|
|
|
End: 20,
|
|
|
|
},
|
|
|
|
}},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-01-20 02:57:39 -08:00
|
|
|
Start: 0,
|
|
|
|
End: 21,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
},
|
2021-11-10 06:46:24 -08:00
|
|
|
},
|
|
|
|
{
|
2021-01-20 02:57:39 -08:00
|
|
|
input: "sum(sum(some_metric1 @ 10) + sum(some_metric2 @ 20))",
|
|
|
|
expected: &parser.StepInvariantExpr{
|
|
|
|
Expr: &parser.AggregateExpr{
|
|
|
|
Op: parser.SUM,
|
|
|
|
Expr: &parser.BinaryExpr{
|
|
|
|
Op: parser.ADD,
|
|
|
|
VectorMatching: &parser.VectorMatching{},
|
|
|
|
LHS: &parser.AggregateExpr{
|
|
|
|
Op: parser.SUM,
|
|
|
|
Expr: &parser.VectorSelector{
|
|
|
|
Name: "some_metric1",
|
|
|
|
LabelMatchers: []*labels.Matcher{
|
|
|
|
parser.MustLabelMatcher(labels.MatchEqual, "__name__", "some_metric1"),
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-01-20 02:57:39 -08:00
|
|
|
Start: 8,
|
|
|
|
End: 25,
|
|
|
|
},
|
|
|
|
Timestamp: makeInt64Pointer(10000),
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-01-20 02:57:39 -08:00
|
|
|
Start: 4,
|
|
|
|
End: 26,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
RHS: &parser.AggregateExpr{
|
|
|
|
Op: parser.SUM,
|
|
|
|
Expr: &parser.VectorSelector{
|
|
|
|
Name: "some_metric2",
|
|
|
|
LabelMatchers: []*labels.Matcher{
|
|
|
|
parser.MustLabelMatcher(labels.MatchEqual, "__name__", "some_metric2"),
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-01-20 02:57:39 -08:00
|
|
|
Start: 33,
|
|
|
|
End: 50,
|
|
|
|
},
|
|
|
|
Timestamp: makeInt64Pointer(20000),
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-01-20 02:57:39 -08:00
|
|
|
Start: 29,
|
|
|
|
End: 52,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-01-20 02:57:39 -08:00
|
|
|
Start: 0,
|
|
|
|
End: 52,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
},
|
2021-11-10 06:46:24 -08:00
|
|
|
},
|
|
|
|
{
|
2021-02-09 08:03:16 -08:00
|
|
|
input: `foo @ start()`,
|
|
|
|
expected: &parser.StepInvariantExpr{
|
|
|
|
Expr: &parser.VectorSelector{
|
|
|
|
Name: "foo",
|
|
|
|
LabelMatchers: []*labels.Matcher{
|
|
|
|
parser.MustLabelMatcher(labels.MatchEqual, "__name__", "foo"),
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-02-09 08:03:16 -08:00
|
|
|
Start: 0,
|
|
|
|
End: 13,
|
|
|
|
},
|
|
|
|
Timestamp: makeInt64Pointer(timestamp.FromTime(startTime)),
|
|
|
|
StartOrEnd: parser.START,
|
|
|
|
},
|
|
|
|
},
|
2021-11-10 06:46:24 -08:00
|
|
|
},
|
|
|
|
{
|
2021-02-09 08:03:16 -08:00
|
|
|
input: `foo @ end()`,
|
|
|
|
expected: &parser.StepInvariantExpr{
|
|
|
|
Expr: &parser.VectorSelector{
|
|
|
|
Name: "foo",
|
|
|
|
LabelMatchers: []*labels.Matcher{
|
|
|
|
parser.MustLabelMatcher(labels.MatchEqual, "__name__", "foo"),
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-02-09 08:03:16 -08:00
|
|
|
Start: 0,
|
|
|
|
End: 11,
|
|
|
|
},
|
|
|
|
Timestamp: makeInt64Pointer(timestamp.FromTime(endTime)),
|
|
|
|
StartOrEnd: parser.END,
|
|
|
|
},
|
|
|
|
},
|
2021-11-10 06:46:24 -08:00
|
|
|
},
|
|
|
|
{
|
2021-02-09 08:03:16 -08:00
|
|
|
input: `test[5y] @ start()`,
|
|
|
|
expected: &parser.StepInvariantExpr{
|
|
|
|
Expr: &parser.MatrixSelector{
|
|
|
|
VectorSelector: &parser.VectorSelector{
|
|
|
|
Name: "test",
|
|
|
|
Timestamp: makeInt64Pointer(timestamp.FromTime(startTime)),
|
|
|
|
StartOrEnd: parser.START,
|
|
|
|
LabelMatchers: []*labels.Matcher{
|
|
|
|
parser.MustLabelMatcher(labels.MatchEqual, "__name__", "test"),
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-02-09 08:03:16 -08:00
|
|
|
Start: 0,
|
|
|
|
End: 4,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
Range: 5 * 365 * 24 * time.Hour,
|
|
|
|
EndPos: 18,
|
|
|
|
},
|
|
|
|
},
|
2021-11-10 06:46:24 -08:00
|
|
|
},
|
|
|
|
{
|
2021-02-09 08:03:16 -08:00
|
|
|
input: `test[5y] @ end()`,
|
|
|
|
expected: &parser.StepInvariantExpr{
|
|
|
|
Expr: &parser.MatrixSelector{
|
|
|
|
VectorSelector: &parser.VectorSelector{
|
|
|
|
Name: "test",
|
|
|
|
Timestamp: makeInt64Pointer(timestamp.FromTime(endTime)),
|
|
|
|
StartOrEnd: parser.END,
|
|
|
|
LabelMatchers: []*labels.Matcher{
|
|
|
|
parser.MustLabelMatcher(labels.MatchEqual, "__name__", "test"),
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-02-09 08:03:16 -08:00
|
|
|
Start: 0,
|
|
|
|
End: 4,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
Range: 5 * 365 * 24 * time.Hour,
|
|
|
|
EndPos: 16,
|
|
|
|
},
|
|
|
|
},
|
2021-11-10 06:46:24 -08:00
|
|
|
},
|
|
|
|
{
|
2021-02-09 08:03:16 -08:00
|
|
|
input: `some_metric[10m:5s] @ start()`,
|
|
|
|
expected: &parser.StepInvariantExpr{
|
|
|
|
Expr: &parser.SubqueryExpr{
|
|
|
|
Expr: &parser.VectorSelector{
|
|
|
|
Name: "some_metric",
|
|
|
|
LabelMatchers: []*labels.Matcher{
|
|
|
|
parser.MustLabelMatcher(labels.MatchEqual, "__name__", "some_metric"),
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-02-09 08:03:16 -08:00
|
|
|
Start: 0,
|
|
|
|
End: 11,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
Timestamp: makeInt64Pointer(timestamp.FromTime(startTime)),
|
|
|
|
StartOrEnd: parser.START,
|
|
|
|
Range: 10 * time.Minute,
|
|
|
|
Step: 5 * time.Second,
|
|
|
|
EndPos: 29,
|
|
|
|
},
|
|
|
|
},
|
2021-11-10 06:46:24 -08:00
|
|
|
},
|
|
|
|
{
|
2021-02-09 08:03:16 -08:00
|
|
|
input: `some_metric[10m:5s] @ end()`,
|
|
|
|
expected: &parser.StepInvariantExpr{
|
|
|
|
Expr: &parser.SubqueryExpr{
|
|
|
|
Expr: &parser.VectorSelector{
|
|
|
|
Name: "some_metric",
|
|
|
|
LabelMatchers: []*labels.Matcher{
|
|
|
|
parser.MustLabelMatcher(labels.MatchEqual, "__name__", "some_metric"),
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-02-09 08:03:16 -08:00
|
|
|
Start: 0,
|
|
|
|
End: 11,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
Timestamp: makeInt64Pointer(timestamp.FromTime(endTime)),
|
|
|
|
StartOrEnd: parser.END,
|
|
|
|
Range: 10 * time.Minute,
|
|
|
|
Step: 5 * time.Second,
|
|
|
|
EndPos: 27,
|
|
|
|
},
|
|
|
|
},
|
2021-01-20 02:57:39 -08:00
|
|
|
},
|
2021-11-10 06:46:24 -08:00
|
|
|
{
|
|
|
|
input: `floor(some_metric / (3 * 1024))`,
|
|
|
|
outputTest: true,
|
|
|
|
expected: &parser.Call{
|
|
|
|
Func: &parser.Function{
|
|
|
|
Name: "floor",
|
|
|
|
ArgTypes: []parser.ValueType{parser.ValueTypeVector},
|
|
|
|
ReturnType: parser.ValueTypeVector,
|
|
|
|
},
|
|
|
|
Args: parser.Expressions{
|
|
|
|
&parser.BinaryExpr{
|
|
|
|
Op: parser.DIV,
|
|
|
|
LHS: &parser.VectorSelector{
|
|
|
|
Name: "some_metric",
|
|
|
|
LabelMatchers: []*labels.Matcher{
|
|
|
|
parser.MustLabelMatcher(labels.MatchEqual, "__name__", "some_metric"),
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-11-10 06:46:24 -08:00
|
|
|
Start: 6,
|
|
|
|
End: 17,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
RHS: &parser.StepInvariantExpr{
|
|
|
|
Expr: &parser.ParenExpr{
|
|
|
|
Expr: &parser.BinaryExpr{
|
|
|
|
Op: parser.MUL,
|
|
|
|
LHS: &parser.NumberLiteral{
|
|
|
|
Val: 3,
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-11-10 06:46:24 -08:00
|
|
|
Start: 21,
|
|
|
|
End: 22,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
RHS: &parser.NumberLiteral{
|
|
|
|
Val: 1024,
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-11-10 06:46:24 -08:00
|
|
|
Start: 25,
|
|
|
|
End: 29,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-11-10 06:46:24 -08:00
|
|
|
Start: 20,
|
|
|
|
End: 30,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
},
|
|
|
|
},
|
|
|
|
},
|
2023-09-14 09:57:31 -07:00
|
|
|
PosRange: posrange.PositionRange{
|
2021-11-10 06:46:24 -08:00
|
|
|
Start: 0,
|
|
|
|
End: 31,
|
|
|
|
},
|
|
|
|
},
|
|
|
|
},
|
2021-01-20 02:57:39 -08:00
|
|
|
}
|
|
|
|
|
|
|
|
for _, test := range testCases {
|
|
|
|
t.Run(test.input, func(t *testing.T) {
|
|
|
|
expr, err := parser.ParseExpr(test.input)
|
|
|
|
require.NoError(t, err)
|
2021-02-09 08:03:16 -08:00
|
|
|
expr = PreprocessExpr(expr, startTime, endTime)
|
2021-11-10 06:46:24 -08:00
|
|
|
if test.outputTest {
|
|
|
|
require.Equal(t, test.input, expr.String(), "error on input '%s'", test.input)
|
|
|
|
}
|
2021-01-20 02:57:39 -08:00
|
|
|
require.Equal(t, test.expected, expr, "error on input '%s'", test.input)
|
|
|
|
})
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
func TestEngineOptsValidation(t *testing.T) {
|
|
|
|
cases := []struct {
|
|
|
|
opts EngineOpts
|
|
|
|
query string
|
|
|
|
fail bool
|
2021-02-09 08:03:16 -08:00
|
|
|
expError error
|
2021-01-20 02:57:39 -08:00
|
|
|
}{
|
|
|
|
{
|
2021-02-09 08:03:16 -08:00
|
|
|
opts: EngineOpts{EnableAtModifier: false},
|
|
|
|
query: "metric @ 100", fail: true, expError: ErrValidationAtModifierDisabled,
|
|
|
|
}, {
|
|
|
|
opts: EngineOpts{EnableAtModifier: false},
|
|
|
|
query: "rate(metric[1m] @ 100)", fail: true, expError: ErrValidationAtModifierDisabled,
|
|
|
|
}, {
|
|
|
|
opts: EngineOpts{EnableAtModifier: false},
|
|
|
|
query: "rate(metric[1h:1m] @ 100)", fail: true, expError: ErrValidationAtModifierDisabled,
|
|
|
|
}, {
|
|
|
|
opts: EngineOpts{EnableAtModifier: false},
|
|
|
|
query: "metric @ start()", fail: true, expError: ErrValidationAtModifierDisabled,
|
|
|
|
}, {
|
|
|
|
opts: EngineOpts{EnableAtModifier: false},
|
|
|
|
query: "rate(metric[1m] @ start())", fail: true, expError: ErrValidationAtModifierDisabled,
|
|
|
|
}, {
|
|
|
|
opts: EngineOpts{EnableAtModifier: false},
|
|
|
|
query: "rate(metric[1h:1m] @ start())", fail: true, expError: ErrValidationAtModifierDisabled,
|
|
|
|
}, {
|
|
|
|
opts: EngineOpts{EnableAtModifier: false},
|
|
|
|
query: "metric @ end()", fail: true, expError: ErrValidationAtModifierDisabled,
|
|
|
|
}, {
|
|
|
|
opts: EngineOpts{EnableAtModifier: false},
|
|
|
|
query: "rate(metric[1m] @ end())", fail: true, expError: ErrValidationAtModifierDisabled,
|
|
|
|
}, {
|
|
|
|
opts: EngineOpts{EnableAtModifier: false},
|
|
|
|
query: "rate(metric[1h:1m] @ end())", fail: true, expError: ErrValidationAtModifierDisabled,
|
2021-01-20 02:57:39 -08:00
|
|
|
}, {
|
|
|
|
opts: EngineOpts{EnableAtModifier: true},
|
|
|
|
query: "metric @ 100",
|
2021-02-09 08:03:16 -08:00
|
|
|
}, {
|
|
|
|
opts: EngineOpts{EnableAtModifier: true},
|
|
|
|
query: "rate(metric[1m] @ start())",
|
|
|
|
}, {
|
|
|
|
opts: EngineOpts{EnableAtModifier: true},
|
|
|
|
query: "rate(metric[1h:1m] @ end())",
|
2021-02-21 10:03:58 -08:00
|
|
|
}, {
|
|
|
|
opts: EngineOpts{EnableNegativeOffset: false},
|
|
|
|
query: "metric offset -1s", fail: true, expError: ErrValidationNegativeOffsetDisabled,
|
|
|
|
}, {
|
|
|
|
opts: EngineOpts{EnableNegativeOffset: true},
|
|
|
|
query: "metric offset -1s",
|
|
|
|
}, {
|
|
|
|
opts: EngineOpts{EnableAtModifier: true, EnableNegativeOffset: true},
|
|
|
|
query: "metric @ 100 offset -2m",
|
|
|
|
}, {
|
|
|
|
opts: EngineOpts{EnableAtModifier: true, EnableNegativeOffset: true},
|
|
|
|
query: "metric offset -2m @ 100",
|
2021-01-20 02:57:39 -08:00
|
|
|
},
|
|
|
|
}
|
|
|
|
|
|
|
|
for _, c := range cases {
|
|
|
|
eng := NewEngine(c.opts)
|
2023-08-18 11:48:59 -07:00
|
|
|
_, err1 := eng.NewInstantQuery(context.Background(), nil, nil, c.query, time.Unix(10, 0))
|
|
|
|
_, err2 := eng.NewRangeQuery(context.Background(), nil, nil, c.query, time.Unix(0, 0), time.Unix(10, 0), time.Second)
|
2021-01-20 02:57:39 -08:00
|
|
|
if c.fail {
|
2021-02-09 08:03:16 -08:00
|
|
|
require.Equal(t, c.expError, err1)
|
|
|
|
require.Equal(t, c.expError, err2)
|
2021-01-20 02:57:39 -08:00
|
|
|
} else {
|
2023-12-07 03:35:01 -08:00
|
|
|
require.NoError(t, err1)
|
|
|
|
require.NoError(t, err2)
|
2021-01-20 02:57:39 -08:00
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
2021-04-26 15:43:22 -07:00
|
|
|
|
|
|
|
func TestRangeQuery(t *testing.T) {
|
|
|
|
cases := []struct {
|
|
|
|
Name string
|
|
|
|
Load string
|
|
|
|
Query string
|
|
|
|
Result parser.Value
|
|
|
|
Start time.Time
|
|
|
|
End time.Time
|
|
|
|
Interval time.Duration
|
|
|
|
}{
|
|
|
|
{
|
|
|
|
Name: "sum_over_time with all values",
|
|
|
|
Load: `load 30s
|
|
|
|
bar 0 1 10 100 1000`,
|
|
|
|
Query: "sum_over_time(bar[30s])",
|
2021-10-22 01:06:44 -07:00
|
|
|
Result: Matrix{
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 0, T: 0}, {F: 11, T: 60000}, {F: 1100, T: 120000}},
|
2022-07-21 09:23:30 -07:00
|
|
|
Metric: labels.EmptyLabels(),
|
2021-10-22 01:06:44 -07:00
|
|
|
},
|
2021-04-26 15:43:22 -07:00
|
|
|
},
|
|
|
|
Start: time.Unix(0, 0),
|
|
|
|
End: time.Unix(120, 0),
|
|
|
|
Interval: 60 * time.Second,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Name: "sum_over_time with trailing values",
|
|
|
|
Load: `load 30s
|
|
|
|
bar 0 1 10 100 1000 0 0 0 0`,
|
|
|
|
Query: "sum_over_time(bar[30s])",
|
2021-10-22 01:06:44 -07:00
|
|
|
Result: Matrix{
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 0, T: 0}, {F: 11, T: 60000}, {F: 1100, T: 120000}},
|
2022-07-21 09:23:30 -07:00
|
|
|
Metric: labels.EmptyLabels(),
|
2021-10-22 01:06:44 -07:00
|
|
|
},
|
2021-04-26 15:43:22 -07:00
|
|
|
},
|
|
|
|
Start: time.Unix(0, 0),
|
|
|
|
End: time.Unix(120, 0),
|
|
|
|
Interval: 60 * time.Second,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Name: "sum_over_time with all values long",
|
|
|
|
Load: `load 30s
|
|
|
|
bar 0 1 10 100 1000 10000 100000 1000000 10000000`,
|
|
|
|
Query: "sum_over_time(bar[30s])",
|
2021-10-22 01:06:44 -07:00
|
|
|
Result: Matrix{
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 0, T: 0}, {F: 11, T: 60000}, {F: 1100, T: 120000}, {F: 110000, T: 180000}, {F: 11000000, T: 240000}},
|
2022-07-21 09:23:30 -07:00
|
|
|
Metric: labels.EmptyLabels(),
|
2021-10-22 01:06:44 -07:00
|
|
|
},
|
2021-04-26 15:43:22 -07:00
|
|
|
},
|
|
|
|
Start: time.Unix(0, 0),
|
|
|
|
End: time.Unix(240, 0),
|
|
|
|
Interval: 60 * time.Second,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Name: "sum_over_time with all values random",
|
|
|
|
Load: `load 30s
|
|
|
|
bar 5 17 42 2 7 905 51`,
|
|
|
|
Query: "sum_over_time(bar[30s])",
|
2021-10-22 01:06:44 -07:00
|
|
|
Result: Matrix{
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 5, T: 0}, {F: 59, T: 60000}, {F: 9, T: 120000}, {F: 956, T: 180000}},
|
2022-07-21 09:23:30 -07:00
|
|
|
Metric: labels.EmptyLabels(),
|
2021-10-22 01:06:44 -07:00
|
|
|
},
|
2021-04-26 15:43:22 -07:00
|
|
|
},
|
|
|
|
Start: time.Unix(0, 0),
|
|
|
|
End: time.Unix(180, 0),
|
|
|
|
Interval: 60 * time.Second,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Name: "metric query",
|
|
|
|
Load: `load 30s
|
|
|
|
metric 1+1x4`,
|
|
|
|
Query: "metric",
|
2021-10-22 01:06:44 -07:00
|
|
|
Result: Matrix{
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 1, T: 0}, {F: 3, T: 60000}, {F: 5, T: 120000}},
|
2022-07-21 09:23:30 -07:00
|
|
|
Metric: labels.FromStrings("__name__", "metric"),
|
2021-10-22 01:06:44 -07:00
|
|
|
},
|
2021-04-26 15:43:22 -07:00
|
|
|
},
|
|
|
|
Start: time.Unix(0, 0),
|
|
|
|
End: time.Unix(120, 0),
|
|
|
|
Interval: 1 * time.Minute,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Name: "metric query with trailing values",
|
|
|
|
Load: `load 30s
|
|
|
|
metric 1+1x8`,
|
|
|
|
Query: "metric",
|
2021-10-22 01:06:44 -07:00
|
|
|
Result: Matrix{
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 1, T: 0}, {F: 3, T: 60000}, {F: 5, T: 120000}},
|
2022-07-21 09:23:30 -07:00
|
|
|
Metric: labels.FromStrings("__name__", "metric"),
|
2021-10-22 01:06:44 -07:00
|
|
|
},
|
2021-04-26 15:43:22 -07:00
|
|
|
},
|
|
|
|
Start: time.Unix(0, 0),
|
|
|
|
End: time.Unix(120, 0),
|
|
|
|
Interval: 1 * time.Minute,
|
|
|
|
},
|
2021-10-20 07:03:02 -07:00
|
|
|
{
|
|
|
|
Name: "short-circuit",
|
|
|
|
Load: `load 30s
|
|
|
|
foo{job="1"} 1+1x4
|
|
|
|
bar{job="2"} 1+1x4`,
|
|
|
|
Query: `foo > 2 or bar`,
|
|
|
|
Result: Matrix{
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 1, T: 0}, {F: 3, T: 60000}, {F: 5, T: 120000}},
|
2022-07-21 09:23:30 -07:00
|
|
|
Metric: labels.FromStrings(
|
|
|
|
"__name__", "bar",
|
|
|
|
"job", "2",
|
|
|
|
),
|
2021-10-20 07:03:02 -07:00
|
|
|
},
|
|
|
|
Series{
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
Floats: []FPoint{{F: 3, T: 60000}, {F: 5, T: 120000}},
|
2022-07-21 09:23:30 -07:00
|
|
|
Metric: labels.FromStrings(
|
|
|
|
"__name__", "foo",
|
|
|
|
"job", "1",
|
|
|
|
),
|
2021-10-20 07:03:02 -07:00
|
|
|
},
|
2021-04-26 15:43:22 -07:00
|
|
|
},
|
|
|
|
Start: time.Unix(0, 0),
|
|
|
|
End: time.Unix(120, 0),
|
|
|
|
Interval: 1 * time.Minute,
|
|
|
|
},
|
|
|
|
}
|
|
|
|
for _, c := range cases {
|
|
|
|
t.Run(c.Name, func(t *testing.T) {
|
2023-08-18 11:48:59 -07:00
|
|
|
engine := newTestEngine()
|
|
|
|
storage := LoadedStorage(t, c.Load)
|
|
|
|
t.Cleanup(func() { storage.Close() })
|
2021-04-26 15:43:22 -07:00
|
|
|
|
2023-08-18 11:48:59 -07:00
|
|
|
qry, err := engine.NewRangeQuery(context.Background(), storage, nil, c.Query, c.Start, c.End, c.Interval)
|
2021-04-26 15:43:22 -07:00
|
|
|
require.NoError(t, err)
|
|
|
|
|
2023-08-18 11:48:59 -07:00
|
|
|
res := qry.Exec(context.Background())
|
2021-04-26 15:43:22 -07:00
|
|
|
require.NoError(t, res.Err)
|
2023-04-16 05:13:31 -07:00
|
|
|
testutil.RequireEqual(t, c.Result, res.Value)
|
2021-04-26 15:43:22 -07:00
|
|
|
})
|
|
|
|
}
|
|
|
|
}
|
2021-11-02 08:01:32 -07:00
|
|
|
|
2023-01-18 08:59:29 -08:00
|
|
|
func TestNativeHistogramRate(t *testing.T) {
|
2021-12-06 04:49:18 -08:00
|
|
|
// TODO(beorn7): Integrate histograms into the PromQL testing framework
|
|
|
|
// and write more tests there.
|
2023-08-18 11:48:59 -07:00
|
|
|
engine := newTestEngine()
|
|
|
|
storage := teststorage.New(t)
|
|
|
|
t.Cleanup(func() { storage.Close() })
|
2021-11-02 08:01:32 -07:00
|
|
|
|
|
|
|
seriesName := "sparse_histogram_series"
|
|
|
|
lbls := labels.FromStrings("__name__", seriesName)
|
|
|
|
|
2023-08-18 11:48:59 -07:00
|
|
|
app := storage.Appender(context.Background())
|
2023-02-10 03:39:33 -08:00
|
|
|
for i, h := range tsdbutil.GenerateTestHistograms(100) {
|
2022-12-28 00:55:07 -08:00
|
|
|
_, err := app.AppendHistogram(0, lbls, int64(i)*int64(15*time.Second/time.Millisecond), h, nil)
|
2021-11-02 08:01:32 -07:00
|
|
|
require.NoError(t, err)
|
|
|
|
}
|
|
|
|
require.NoError(t, app.Commit())
|
|
|
|
|
2023-12-14 02:28:15 -08:00
|
|
|
queryString := fmt.Sprintf("rate(%s[45s])", seriesName)
|
|
|
|
t.Run("instant_query", func(t *testing.T) {
|
|
|
|
qry, err := engine.NewInstantQuery(context.Background(), storage, nil, queryString, timestamp.Time(int64(5*time.Minute/time.Millisecond)))
|
|
|
|
require.NoError(t, err)
|
|
|
|
res := qry.Exec(context.Background())
|
|
|
|
require.NoError(t, res.Err)
|
|
|
|
vector, err := res.Vector()
|
|
|
|
require.NoError(t, err)
|
|
|
|
require.Len(t, vector, 1)
|
|
|
|
actualHistogram := vector[0].H
|
|
|
|
expectedHistogram := &histogram.FloatHistogram{
|
|
|
|
CounterResetHint: histogram.GaugeType,
|
|
|
|
Schema: 1,
|
|
|
|
ZeroThreshold: 0.001,
|
|
|
|
ZeroCount: 1. / 15.,
|
|
|
|
Count: 9. / 15.,
|
|
|
|
Sum: 1.2266666666666663,
|
|
|
|
PositiveSpans: []histogram.Span{{Offset: 0, Length: 2}, {Offset: 1, Length: 2}},
|
|
|
|
PositiveBuckets: []float64{1. / 15., 1. / 15., 1. / 15., 1. / 15.},
|
|
|
|
NegativeSpans: []histogram.Span{{Offset: 0, Length: 2}, {Offset: 1, Length: 2}},
|
|
|
|
NegativeBuckets: []float64{1. / 15., 1. / 15., 1. / 15., 1. / 15.},
|
|
|
|
}
|
|
|
|
require.Equal(t, expectedHistogram, actualHistogram)
|
|
|
|
})
|
|
|
|
|
|
|
|
t.Run("range_query", func(t *testing.T) {
|
|
|
|
step := 30 * time.Second
|
|
|
|
start := timestamp.Time(int64(5 * time.Minute / time.Millisecond))
|
|
|
|
end := start.Add(step)
|
|
|
|
qry, err := engine.NewRangeQuery(context.Background(), storage, nil, queryString, start, end, step)
|
|
|
|
require.NoError(t, err)
|
|
|
|
res := qry.Exec(context.Background())
|
|
|
|
require.NoError(t, res.Err)
|
|
|
|
matrix, err := res.Matrix()
|
|
|
|
require.NoError(t, err)
|
|
|
|
require.Len(t, matrix, 1)
|
|
|
|
require.Len(t, matrix[0].Histograms, 2)
|
|
|
|
actualHistograms := matrix[0].Histograms
|
|
|
|
expectedHistograms := []HPoint{{
|
|
|
|
T: 300000,
|
|
|
|
H: &histogram.FloatHistogram{
|
|
|
|
CounterResetHint: histogram.GaugeType,
|
|
|
|
Schema: 1,
|
|
|
|
ZeroThreshold: 0.001,
|
|
|
|
ZeroCount: 1. / 15.,
|
|
|
|
Count: 9. / 15.,
|
|
|
|
Sum: 1.2266666666666663,
|
|
|
|
PositiveSpans: []histogram.Span{{Offset: 0, Length: 2}, {Offset: 1, Length: 2}},
|
|
|
|
PositiveBuckets: []float64{1. / 15., 1. / 15., 1. / 15., 1. / 15.},
|
|
|
|
NegativeSpans: []histogram.Span{{Offset: 0, Length: 2}, {Offset: 1, Length: 2}},
|
|
|
|
NegativeBuckets: []float64{1. / 15., 1. / 15., 1. / 15., 1. / 15.},
|
|
|
|
},
|
|
|
|
}, {
|
|
|
|
T: 330000,
|
|
|
|
H: &histogram.FloatHistogram{
|
|
|
|
CounterResetHint: histogram.GaugeType,
|
|
|
|
Schema: 1,
|
|
|
|
ZeroThreshold: 0.001,
|
|
|
|
ZeroCount: 1. / 15.,
|
|
|
|
Count: 9. / 15.,
|
|
|
|
Sum: 1.2266666666666663,
|
|
|
|
PositiveSpans: []histogram.Span{{Offset: 0, Length: 2}, {Offset: 1, Length: 2}},
|
|
|
|
PositiveBuckets: []float64{1. / 15., 1. / 15., 1. / 15., 1. / 15.},
|
|
|
|
NegativeSpans: []histogram.Span{{Offset: 0, Length: 2}, {Offset: 1, Length: 2}},
|
|
|
|
NegativeBuckets: []float64{1. / 15., 1. / 15., 1. / 15., 1. / 15.},
|
|
|
|
},
|
|
|
|
}}
|
|
|
|
require.Equal(t, expectedHistograms, actualHistograms)
|
|
|
|
})
|
2021-11-02 08:01:32 -07:00
|
|
|
}
|
2021-12-06 05:47:22 -08:00
|
|
|
|
2023-01-18 08:59:29 -08:00
|
|
|
func TestNativeFloatHistogramRate(t *testing.T) {
|
2022-12-28 05:45:47 -08:00
|
|
|
// TODO(beorn7): Integrate histograms into the PromQL testing framework
|
|
|
|
// and write more tests there.
|
2023-08-18 11:48:59 -07:00
|
|
|
engine := newTestEngine()
|
|
|
|
storage := teststorage.New(t)
|
|
|
|
t.Cleanup(func() { storage.Close() })
|
2022-12-28 05:45:47 -08:00
|
|
|
|
|
|
|
seriesName := "sparse_histogram_series"
|
|
|
|
lbls := labels.FromStrings("__name__", seriesName)
|
|
|
|
|
2023-08-18 11:48:59 -07:00
|
|
|
app := storage.Appender(context.Background())
|
2023-02-10 03:39:33 -08:00
|
|
|
for i, fh := range tsdbutil.GenerateTestFloatHistograms(100) {
|
2022-12-28 05:45:47 -08:00
|
|
|
_, err := app.AppendHistogram(0, lbls, int64(i)*int64(15*time.Second/time.Millisecond), nil, fh)
|
|
|
|
require.NoError(t, err)
|
|
|
|
}
|
|
|
|
require.NoError(t, app.Commit())
|
|
|
|
|
|
|
|
queryString := fmt.Sprintf("rate(%s[1m])", seriesName)
|
2023-08-18 11:48:59 -07:00
|
|
|
qry, err := engine.NewInstantQuery(context.Background(), storage, nil, queryString, timestamp.Time(int64(5*time.Minute/time.Millisecond)))
|
2022-12-28 05:45:47 -08:00
|
|
|
require.NoError(t, err)
|
2023-08-18 11:48:59 -07:00
|
|
|
res := qry.Exec(context.Background())
|
2022-12-28 05:45:47 -08:00
|
|
|
require.NoError(t, res.Err)
|
|
|
|
vector, err := res.Vector()
|
|
|
|
require.NoError(t, err)
|
|
|
|
require.Len(t, vector, 1)
|
|
|
|
actualHistogram := vector[0].H
|
|
|
|
expectedHistogram := &histogram.FloatHistogram{
|
2023-02-15 04:49:51 -08:00
|
|
|
CounterResetHint: histogram.GaugeType,
|
2023-01-18 08:59:29 -08:00
|
|
|
Schema: 1,
|
|
|
|
ZeroThreshold: 0.001,
|
|
|
|
ZeroCount: 1. / 15.,
|
2023-08-22 12:51:56 -07:00
|
|
|
Count: 9. / 15.,
|
2023-01-18 08:59:29 -08:00
|
|
|
Sum: 1.226666666666667,
|
|
|
|
PositiveSpans: []histogram.Span{{Offset: 0, Length: 2}, {Offset: 1, Length: 2}},
|
|
|
|
PositiveBuckets: []float64{1. / 15., 1. / 15., 1. / 15., 1. / 15.},
|
|
|
|
NegativeSpans: []histogram.Span{{Offset: 0, Length: 2}, {Offset: 1, Length: 2}},
|
|
|
|
NegativeBuckets: []float64{1. / 15., 1. / 15., 1. / 15., 1. / 15.},
|
2022-12-28 05:45:47 -08:00
|
|
|
}
|
|
|
|
require.Equal(t, expectedHistogram, actualHistogram)
|
|
|
|
}
|
|
|
|
|
2023-01-18 08:59:29 -08:00
|
|
|
func TestNativeHistogram_HistogramCountAndSum(t *testing.T) {
|
2022-06-28 07:43:58 -07:00
|
|
|
// TODO(codesome): Integrate histograms into the PromQL testing framework
|
|
|
|
// and write more tests there.
|
|
|
|
h := &histogram.Histogram{
|
|
|
|
Count: 24,
|
|
|
|
ZeroCount: 4,
|
|
|
|
ZeroThreshold: 0.001,
|
|
|
|
Sum: 100,
|
|
|
|
Schema: 0,
|
|
|
|
PositiveSpans: []histogram.Span{
|
|
|
|
{Offset: 0, Length: 2},
|
|
|
|
{Offset: 1, Length: 2},
|
|
|
|
},
|
|
|
|
PositiveBuckets: []int64{2, 1, -2, 3},
|
|
|
|
NegativeSpans: []histogram.Span{
|
|
|
|
{Offset: 0, Length: 2},
|
|
|
|
{Offset: 1, Length: 2},
|
|
|
|
},
|
|
|
|
NegativeBuckets: []int64{2, 1, -2, 3},
|
|
|
|
}
|
2022-12-28 05:45:47 -08:00
|
|
|
for _, floatHisto := range []bool{true, false} {
|
|
|
|
t.Run(fmt.Sprintf("floatHistogram=%t", floatHisto), func(t *testing.T) {
|
2023-08-18 11:48:59 -07:00
|
|
|
engine := newTestEngine()
|
|
|
|
storage := teststorage.New(t)
|
|
|
|
t.Cleanup(func() { storage.Close() })
|
2022-06-28 07:43:58 -07:00
|
|
|
|
2022-12-28 05:45:47 -08:00
|
|
|
seriesName := "sparse_histogram_series"
|
|
|
|
lbls := labels.FromStrings("__name__", seriesName)
|
2022-06-28 07:43:58 -07:00
|
|
|
|
2022-12-28 05:45:47 -08:00
|
|
|
ts := int64(10 * time.Minute / time.Millisecond)
|
2023-08-18 11:48:59 -07:00
|
|
|
app := storage.Appender(context.Background())
|
|
|
|
var err error
|
2022-12-28 05:45:47 -08:00
|
|
|
if floatHisto {
|
2023-11-29 06:15:57 -08:00
|
|
|
_, err = app.AppendHistogram(0, lbls, ts, nil, h.ToFloat(nil))
|
2022-12-28 05:45:47 -08:00
|
|
|
} else {
|
|
|
|
_, err = app.AppendHistogram(0, lbls, ts, h, nil)
|
|
|
|
}
|
|
|
|
require.NoError(t, err)
|
|
|
|
require.NoError(t, app.Commit())
|
2022-06-28 07:43:58 -07:00
|
|
|
|
2022-12-28 05:45:47 -08:00
|
|
|
queryString := fmt.Sprintf("histogram_count(%s)", seriesName)
|
2023-08-18 11:48:59 -07:00
|
|
|
qry, err := engine.NewInstantQuery(context.Background(), storage, nil, queryString, timestamp.Time(ts))
|
2022-12-28 05:45:47 -08:00
|
|
|
require.NoError(t, err)
|
2022-06-28 07:43:58 -07:00
|
|
|
|
2023-08-18 11:48:59 -07:00
|
|
|
res := qry.Exec(context.Background())
|
2022-12-28 05:45:47 -08:00
|
|
|
require.NoError(t, res.Err)
|
2022-06-28 07:43:58 -07:00
|
|
|
|
2022-12-28 05:45:47 -08:00
|
|
|
vector, err := res.Vector()
|
|
|
|
require.NoError(t, err)
|
2022-06-28 07:43:58 -07:00
|
|
|
|
2022-12-28 05:45:47 -08:00
|
|
|
require.Len(t, vector, 1)
|
|
|
|
require.Nil(t, vector[0].H)
|
|
|
|
if floatHisto {
|
2023-11-29 06:15:57 -08:00
|
|
|
require.Equal(t, h.ToFloat(nil).Count, vector[0].F)
|
2022-12-28 05:45:47 -08:00
|
|
|
} else {
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
require.Equal(t, float64(h.Count), vector[0].F)
|
2022-12-28 05:45:47 -08:00
|
|
|
}
|
2022-06-28 07:43:58 -07:00
|
|
|
|
2022-12-28 05:45:47 -08:00
|
|
|
queryString = fmt.Sprintf("histogram_sum(%s)", seriesName)
|
2023-08-18 11:48:59 -07:00
|
|
|
qry, err = engine.NewInstantQuery(context.Background(), storage, nil, queryString, timestamp.Time(ts))
|
2022-12-28 05:45:47 -08:00
|
|
|
require.NoError(t, err)
|
2022-06-28 07:43:58 -07:00
|
|
|
|
2023-08-18 11:48:59 -07:00
|
|
|
res = qry.Exec(context.Background())
|
2022-12-28 05:45:47 -08:00
|
|
|
require.NoError(t, res.Err)
|
2022-06-28 07:43:58 -07:00
|
|
|
|
2022-12-28 05:45:47 -08:00
|
|
|
vector, err = res.Vector()
|
|
|
|
require.NoError(t, err)
|
2022-06-28 07:43:58 -07:00
|
|
|
|
2022-12-28 05:45:47 -08:00
|
|
|
require.Len(t, vector, 1)
|
|
|
|
require.Nil(t, vector[0].H)
|
|
|
|
if floatHisto {
|
2023-11-29 06:15:57 -08:00
|
|
|
require.Equal(t, h.ToFloat(nil).Sum, vector[0].F)
|
2022-12-28 05:45:47 -08:00
|
|
|
} else {
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
require.Equal(t, h.Sum, vector[0].F)
|
2022-12-28 05:45:47 -08:00
|
|
|
}
|
|
|
|
})
|
|
|
|
}
|
2022-06-28 07:43:58 -07:00
|
|
|
}
|
|
|
|
|
2023-08-24 12:02:14 -07:00
|
|
|
func TestNativeHistogram_HistogramStdDevVar(t *testing.T) {
|
|
|
|
// TODO(codesome): Integrate histograms into the PromQL testing framework
|
|
|
|
// and write more tests there.
|
|
|
|
testCases := []struct {
|
|
|
|
name string
|
|
|
|
h *histogram.Histogram
|
|
|
|
stdVar float64
|
|
|
|
}{
|
|
|
|
{
|
|
|
|
name: "1, 2, 3, 4 low-res",
|
|
|
|
h: &histogram.Histogram{
|
|
|
|
Count: 4,
|
|
|
|
Sum: 10,
|
|
|
|
Schema: 2,
|
|
|
|
PositiveSpans: []histogram.Span{
|
|
|
|
{Offset: 0, Length: 1},
|
|
|
|
{Offset: 3, Length: 1},
|
|
|
|
{Offset: 2, Length: 2},
|
|
|
|
},
|
|
|
|
PositiveBuckets: []int64{1, 0, 0, 0},
|
|
|
|
},
|
|
|
|
stdVar: 1.163807968526718, // actual variance: 1.25
|
|
|
|
},
|
|
|
|
{
|
|
|
|
name: "1, 2, 3, 4 hi-res",
|
|
|
|
h: &histogram.Histogram{
|
|
|
|
Count: 4,
|
|
|
|
Sum: 10,
|
|
|
|
Schema: 8,
|
|
|
|
PositiveSpans: []histogram.Span{
|
|
|
|
{Offset: 0, Length: 1},
|
|
|
|
{Offset: 255, Length: 1},
|
|
|
|
{Offset: 149, Length: 1},
|
|
|
|
{Offset: 105, Length: 1},
|
|
|
|
},
|
|
|
|
PositiveBuckets: []int64{1, 0, 0, 0},
|
|
|
|
},
|
|
|
|
stdVar: 1.2471347737158793, // actual variance: 1.25
|
|
|
|
},
|
|
|
|
{
|
|
|
|
name: "-50, -8, 0, 3, 8, 9, 100",
|
|
|
|
h: &histogram.Histogram{
|
|
|
|
Count: 7,
|
|
|
|
ZeroCount: 1,
|
|
|
|
Sum: 62,
|
|
|
|
Schema: 3,
|
|
|
|
PositiveSpans: []histogram.Span{
|
|
|
|
{Offset: 13, Length: 1},
|
|
|
|
{Offset: 10, Length: 1},
|
|
|
|
{Offset: 1, Length: 1},
|
|
|
|
{Offset: 27, Length: 1},
|
|
|
|
},
|
|
|
|
PositiveBuckets: []int64{1, 0, 0, 0},
|
|
|
|
NegativeSpans: []histogram.Span{
|
|
|
|
{Offset: 24, Length: 1},
|
|
|
|
{Offset: 21, Length: 1},
|
|
|
|
},
|
|
|
|
NegativeBuckets: []int64{1, 0},
|
|
|
|
},
|
|
|
|
stdVar: 1544.8582535368798, // actual variance: 1738.4082
|
|
|
|
},
|
|
|
|
{
|
|
|
|
name: "-50, -8, 0, 3, 8, 9, 100, NaN",
|
|
|
|
h: &histogram.Histogram{
|
|
|
|
Count: 8,
|
|
|
|
ZeroCount: 1,
|
|
|
|
Sum: math.NaN(),
|
|
|
|
Schema: 3,
|
|
|
|
PositiveSpans: []histogram.Span{
|
|
|
|
{Offset: 13, Length: 1},
|
|
|
|
{Offset: 10, Length: 1},
|
|
|
|
{Offset: 1, Length: 1},
|
|
|
|
{Offset: 27, Length: 1},
|
|
|
|
},
|
|
|
|
PositiveBuckets: []int64{1, 0, 0, 0},
|
|
|
|
NegativeSpans: []histogram.Span{
|
|
|
|
{Offset: 24, Length: 1},
|
|
|
|
{Offset: 21, Length: 1},
|
|
|
|
},
|
|
|
|
NegativeBuckets: []int64{1, 0},
|
|
|
|
},
|
|
|
|
stdVar: math.NaN(),
|
|
|
|
},
|
|
|
|
{
|
|
|
|
name: "-50, -8, 0, 3, 8, 9, 100, +Inf",
|
|
|
|
h: &histogram.Histogram{
|
2023-10-13 00:58:26 -07:00
|
|
|
Count: 7,
|
2023-08-24 12:02:14 -07:00
|
|
|
ZeroCount: 1,
|
|
|
|
Sum: math.Inf(1),
|
|
|
|
Schema: 3,
|
|
|
|
PositiveSpans: []histogram.Span{
|
|
|
|
{Offset: 13, Length: 1},
|
|
|
|
{Offset: 10, Length: 1},
|
|
|
|
{Offset: 1, Length: 1},
|
|
|
|
{Offset: 27, Length: 1},
|
|
|
|
},
|
|
|
|
PositiveBuckets: []int64{1, 0, 0, 0},
|
|
|
|
NegativeSpans: []histogram.Span{
|
|
|
|
{Offset: 24, Length: 1},
|
|
|
|
{Offset: 21, Length: 1},
|
|
|
|
},
|
|
|
|
NegativeBuckets: []int64{1, 0},
|
|
|
|
},
|
|
|
|
stdVar: math.NaN(),
|
|
|
|
},
|
|
|
|
}
|
|
|
|
for _, tc := range testCases {
|
|
|
|
for _, floatHisto := range []bool{true, false} {
|
|
|
|
t.Run(fmt.Sprintf("%s floatHistogram=%t", tc.name, floatHisto), func(t *testing.T) {
|
|
|
|
engine := newTestEngine()
|
|
|
|
storage := teststorage.New(t)
|
|
|
|
t.Cleanup(func() { storage.Close() })
|
|
|
|
|
|
|
|
seriesName := "sparse_histogram_series"
|
|
|
|
lbls := labels.FromStrings("__name__", seriesName)
|
|
|
|
|
|
|
|
ts := int64(10 * time.Minute / time.Millisecond)
|
|
|
|
app := storage.Appender(context.Background())
|
|
|
|
var err error
|
|
|
|
if floatHisto {
|
2023-11-29 06:15:57 -08:00
|
|
|
_, err = app.AppendHistogram(0, lbls, ts, nil, tc.h.ToFloat(nil))
|
2023-08-24 12:02:14 -07:00
|
|
|
} else {
|
|
|
|
_, err = app.AppendHistogram(0, lbls, ts, tc.h, nil)
|
|
|
|
}
|
|
|
|
require.NoError(t, err)
|
|
|
|
require.NoError(t, app.Commit())
|
|
|
|
|
|
|
|
queryString := fmt.Sprintf("histogram_stdvar(%s)", seriesName)
|
|
|
|
qry, err := engine.NewInstantQuery(context.Background(), storage, nil, queryString, timestamp.Time(ts))
|
|
|
|
require.NoError(t, err)
|
|
|
|
|
|
|
|
res := qry.Exec(context.Background())
|
|
|
|
require.NoError(t, res.Err)
|
|
|
|
|
|
|
|
vector, err := res.Vector()
|
|
|
|
require.NoError(t, err)
|
|
|
|
|
|
|
|
require.Len(t, vector, 1)
|
|
|
|
require.Nil(t, vector[0].H)
|
|
|
|
require.InEpsilon(t, tc.stdVar, vector[0].F, 1e-12)
|
|
|
|
|
|
|
|
queryString = fmt.Sprintf("histogram_stddev(%s)", seriesName)
|
|
|
|
qry, err = engine.NewInstantQuery(context.Background(), storage, nil, queryString, timestamp.Time(ts))
|
|
|
|
require.NoError(t, err)
|
|
|
|
|
|
|
|
res = qry.Exec(context.Background())
|
|
|
|
require.NoError(t, res.Err)
|
|
|
|
|
|
|
|
vector, err = res.Vector()
|
|
|
|
require.NoError(t, err)
|
|
|
|
|
|
|
|
require.Len(t, vector, 1)
|
|
|
|
require.Nil(t, vector[0].H)
|
|
|
|
require.InEpsilon(t, math.Sqrt(tc.stdVar), vector[0].F, 1e-12)
|
|
|
|
})
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
2023-01-18 08:59:29 -08:00
|
|
|
func TestNativeHistogram_HistogramQuantile(t *testing.T) {
|
2021-12-06 05:47:22 -08:00
|
|
|
// TODO(codesome): Integrate histograms into the PromQL testing framework
|
|
|
|
// and write more tests there.
|
|
|
|
type subCase struct {
|
|
|
|
quantile string
|
|
|
|
value float64
|
|
|
|
}
|
|
|
|
|
|
|
|
cases := []struct {
|
|
|
|
text string
|
|
|
|
// Histogram to test.
|
|
|
|
h *histogram.Histogram
|
|
|
|
// Different quantiles to test for this histogram.
|
|
|
|
subCases []subCase
|
|
|
|
}{
|
|
|
|
{
|
|
|
|
text: "all positive buckets with zero bucket",
|
|
|
|
h: &histogram.Histogram{
|
|
|
|
Count: 12,
|
|
|
|
ZeroCount: 2,
|
|
|
|
ZeroThreshold: 0.001,
|
|
|
|
Sum: 100, // Does not matter.
|
|
|
|
Schema: 0,
|
|
|
|
PositiveSpans: []histogram.Span{
|
|
|
|
{Offset: 0, Length: 2},
|
|
|
|
{Offset: 1, Length: 2},
|
|
|
|
},
|
|
|
|
PositiveBuckets: []int64{2, 1, -2, 3},
|
|
|
|
},
|
|
|
|
subCases: []subCase{
|
|
|
|
{
|
|
|
|
quantile: "1.0001",
|
|
|
|
value: math.Inf(1),
|
|
|
|
},
|
|
|
|
{
|
|
|
|
quantile: "1",
|
|
|
|
value: 16,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
quantile: "0.99",
|
|
|
|
value: 15.759999999999998,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
quantile: "0.9",
|
|
|
|
value: 13.600000000000001,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
quantile: "0.6",
|
|
|
|
value: 4.799999999999997,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
quantile: "0.5",
|
|
|
|
value: 1.6666666666666665,
|
|
|
|
},
|
|
|
|
{ // Zero bucket.
|
|
|
|
quantile: "0.1",
|
|
|
|
value: 0.0006000000000000001,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
quantile: "0",
|
|
|
|
value: 0,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
quantile: "-1",
|
|
|
|
value: math.Inf(-1),
|
|
|
|
},
|
|
|
|
},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
text: "all negative buckets with zero bucket",
|
|
|
|
h: &histogram.Histogram{
|
|
|
|
Count: 12,
|
|
|
|
ZeroCount: 2,
|
|
|
|
ZeroThreshold: 0.001,
|
|
|
|
Sum: 100, // Does not matter.
|
|
|
|
Schema: 0,
|
|
|
|
NegativeSpans: []histogram.Span{
|
|
|
|
{Offset: 0, Length: 2},
|
|
|
|
{Offset: 1, Length: 2},
|
|
|
|
},
|
|
|
|
NegativeBuckets: []int64{2, 1, -2, 3},
|
|
|
|
},
|
|
|
|
subCases: []subCase{
|
|
|
|
{
|
|
|
|
quantile: "1.0001",
|
|
|
|
value: math.Inf(1),
|
|
|
|
},
|
|
|
|
{ // Zero bucket.
|
|
|
|
quantile: "1",
|
2022-06-16 09:54:28 -07:00
|
|
|
value: 0,
|
2021-12-06 05:47:22 -08:00
|
|
|
},
|
|
|
|
{ // Zero bucket.
|
|
|
|
quantile: "0.99",
|
2022-06-16 09:54:28 -07:00
|
|
|
value: -6.000000000000048e-05,
|
2021-12-06 05:47:22 -08:00
|
|
|
},
|
|
|
|
{ // Zero bucket.
|
|
|
|
quantile: "0.9",
|
2022-06-16 09:54:28 -07:00
|
|
|
value: -0.0005999999999999996,
|
2021-12-06 05:47:22 -08:00
|
|
|
},
|
|
|
|
{
|
|
|
|
quantile: "0.5",
|
|
|
|
value: -1.6666666666666667,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
quantile: "0.1",
|
|
|
|
value: -13.6,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
quantile: "0",
|
|
|
|
value: -16,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
quantile: "-1",
|
|
|
|
value: math.Inf(-1),
|
|
|
|
},
|
|
|
|
},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
text: "both positive and negative buckets with zero bucket",
|
|
|
|
h: &histogram.Histogram{
|
|
|
|
Count: 24,
|
|
|
|
ZeroCount: 4,
|
|
|
|
ZeroThreshold: 0.001,
|
|
|
|
Sum: 100, // Does not matter.
|
|
|
|
Schema: 0,
|
|
|
|
PositiveSpans: []histogram.Span{
|
|
|
|
{Offset: 0, Length: 2},
|
|
|
|
{Offset: 1, Length: 2},
|
|
|
|
},
|
|
|
|
PositiveBuckets: []int64{2, 1, -2, 3},
|
|
|
|
NegativeSpans: []histogram.Span{
|
|
|
|
{Offset: 0, Length: 2},
|
|
|
|
{Offset: 1, Length: 2},
|
|
|
|
},
|
|
|
|
NegativeBuckets: []int64{2, 1, -2, 3},
|
|
|
|
},
|
|
|
|
subCases: []subCase{
|
|
|
|
{
|
|
|
|
quantile: "1.0001",
|
|
|
|
value: math.Inf(1),
|
|
|
|
},
|
|
|
|
{
|
|
|
|
quantile: "1",
|
|
|
|
value: 16,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
quantile: "0.99",
|
|
|
|
value: 15.519999999999996,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
quantile: "0.9",
|
|
|
|
value: 11.200000000000003,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
quantile: "0.7",
|
|
|
|
value: 1.2666666666666657,
|
|
|
|
},
|
|
|
|
{ // Zero bucket.
|
|
|
|
quantile: "0.55",
|
|
|
|
value: 0.0006000000000000005,
|
|
|
|
},
|
|
|
|
{ // Zero bucket.
|
|
|
|
quantile: "0.5",
|
|
|
|
value: 0,
|
|
|
|
},
|
|
|
|
{ // Zero bucket.
|
|
|
|
quantile: "0.45",
|
|
|
|
value: -0.0005999999999999996,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
quantile: "0.3",
|
|
|
|
value: -1.266666666666667,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
quantile: "0.1",
|
|
|
|
value: -11.2,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
quantile: "0.01",
|
|
|
|
value: -15.52,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
quantile: "0",
|
|
|
|
value: -16,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
quantile: "-1",
|
|
|
|
value: math.Inf(-1),
|
|
|
|
},
|
|
|
|
},
|
|
|
|
},
|
|
|
|
}
|
|
|
|
|
2023-08-18 11:48:59 -07:00
|
|
|
engine := newTestEngine()
|
|
|
|
storage := teststorage.New(t)
|
|
|
|
t.Cleanup(func() { storage.Close() })
|
2022-12-28 05:45:47 -08:00
|
|
|
idx := int64(0)
|
|
|
|
for _, floatHisto := range []bool{true, false} {
|
|
|
|
for _, c := range cases {
|
|
|
|
t.Run(fmt.Sprintf("%s floatHistogram=%t", c.text, floatHisto), func(t *testing.T) {
|
|
|
|
seriesName := "sparse_histogram_series"
|
|
|
|
lbls := labels.FromStrings("__name__", seriesName)
|
|
|
|
ts := idx * int64(10*time.Minute/time.Millisecond)
|
2023-08-18 11:48:59 -07:00
|
|
|
app := storage.Appender(context.Background())
|
|
|
|
var err error
|
2022-12-28 05:45:47 -08:00
|
|
|
if floatHisto {
|
2023-11-29 06:15:57 -08:00
|
|
|
_, err = app.AppendHistogram(0, lbls, ts, nil, c.h.ToFloat(nil))
|
2022-12-28 05:45:47 -08:00
|
|
|
} else {
|
|
|
|
_, err = app.AppendHistogram(0, lbls, ts, c.h, nil)
|
|
|
|
}
|
|
|
|
require.NoError(t, err)
|
|
|
|
require.NoError(t, app.Commit())
|
2021-12-06 05:47:22 -08:00
|
|
|
|
2022-12-28 05:45:47 -08:00
|
|
|
for j, sc := range c.subCases {
|
|
|
|
t.Run(fmt.Sprintf("%d %s", j, sc.quantile), func(t *testing.T) {
|
|
|
|
queryString := fmt.Sprintf("histogram_quantile(%s, %s)", sc.quantile, seriesName)
|
2023-08-18 11:48:59 -07:00
|
|
|
qry, err := engine.NewInstantQuery(context.Background(), storage, nil, queryString, timestamp.Time(ts))
|
2022-12-28 05:45:47 -08:00
|
|
|
require.NoError(t, err)
|
2021-12-06 05:47:22 -08:00
|
|
|
|
2023-08-18 11:48:59 -07:00
|
|
|
res := qry.Exec(context.Background())
|
2022-12-28 05:45:47 -08:00
|
|
|
require.NoError(t, res.Err)
|
2021-12-06 05:47:22 -08:00
|
|
|
|
2022-12-28 05:45:47 -08:00
|
|
|
vector, err := res.Vector()
|
|
|
|
require.NoError(t, err)
|
2021-12-06 05:47:22 -08:00
|
|
|
|
2022-12-28 05:45:47 -08:00
|
|
|
require.Len(t, vector, 1)
|
|
|
|
require.Nil(t, vector[0].H)
|
2023-11-24 15:05:38 -08:00
|
|
|
require.True(t, almostEqual(sc.value, vector[0].F, defaultEpsilon))
|
2022-12-28 05:45:47 -08:00
|
|
|
})
|
|
|
|
}
|
|
|
|
idx++
|
|
|
|
})
|
|
|
|
}
|
2021-12-06 05:47:22 -08:00
|
|
|
}
|
|
|
|
}
|
2021-12-06 08:08:10 -08:00
|
|
|
|
2023-01-18 08:59:29 -08:00
|
|
|
func TestNativeHistogram_HistogramFraction(t *testing.T) {
|
2022-06-16 11:44:12 -07:00
|
|
|
// TODO(codesome): Integrate histograms into the PromQL testing framework
|
|
|
|
// and write more tests there.
|
|
|
|
type subCase struct {
|
|
|
|
lower, upper string
|
|
|
|
value float64
|
|
|
|
}
|
|
|
|
|
|
|
|
invariantCases := []subCase{
|
|
|
|
{
|
|
|
|
lower: "42",
|
|
|
|
upper: "3.1415",
|
|
|
|
value: 0,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "0",
|
|
|
|
upper: "0",
|
|
|
|
value: 0,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "0.000001",
|
|
|
|
upper: "0.000001",
|
|
|
|
value: 0,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "42",
|
|
|
|
upper: "42",
|
|
|
|
value: 0,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "-3.1",
|
|
|
|
upper: "-3.1",
|
|
|
|
value: 0,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "3.1415",
|
|
|
|
upper: "NaN",
|
|
|
|
value: math.NaN(),
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "NaN",
|
|
|
|
upper: "42",
|
|
|
|
value: math.NaN(),
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "NaN",
|
|
|
|
upper: "NaN",
|
|
|
|
value: math.NaN(),
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "-Inf",
|
|
|
|
upper: "+Inf",
|
|
|
|
value: 1,
|
|
|
|
},
|
|
|
|
}
|
|
|
|
|
|
|
|
cases := []struct {
|
|
|
|
text string
|
|
|
|
// Histogram to test.
|
|
|
|
h *histogram.Histogram
|
|
|
|
// Different ranges to test for this histogram.
|
|
|
|
subCases []subCase
|
|
|
|
}{
|
|
|
|
{
|
|
|
|
text: "empty histogram",
|
|
|
|
h: &histogram.Histogram{},
|
|
|
|
subCases: []subCase{
|
|
|
|
{
|
|
|
|
lower: "3.1415",
|
|
|
|
upper: "42",
|
|
|
|
value: math.NaN(),
|
|
|
|
},
|
|
|
|
},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
text: "all positive buckets with zero bucket",
|
|
|
|
h: &histogram.Histogram{
|
|
|
|
Count: 12,
|
|
|
|
ZeroCount: 2,
|
|
|
|
ZeroThreshold: 0.001,
|
|
|
|
Sum: 100, // Does not matter.
|
|
|
|
Schema: 0,
|
|
|
|
PositiveSpans: []histogram.Span{
|
|
|
|
{Offset: 0, Length: 2},
|
|
|
|
{Offset: 1, Length: 2},
|
|
|
|
},
|
|
|
|
PositiveBuckets: []int64{2, 1, -2, 3}, // Abs: 2, 3, 1, 4
|
|
|
|
},
|
|
|
|
subCases: append([]subCase{
|
|
|
|
{
|
|
|
|
lower: "0",
|
|
|
|
upper: "+Inf",
|
|
|
|
value: 1,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "-Inf",
|
|
|
|
upper: "0",
|
|
|
|
value: 0,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "-0.001",
|
|
|
|
upper: "0",
|
|
|
|
value: 0,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "0",
|
|
|
|
upper: "0.001",
|
|
|
|
value: 2. / 12.,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "0",
|
|
|
|
upper: "0.0005",
|
|
|
|
value: 1. / 12.,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "0.001",
|
|
|
|
upper: "inf",
|
|
|
|
value: 10. / 12.,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "-inf",
|
|
|
|
upper: "-0.001",
|
|
|
|
value: 0,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "1",
|
|
|
|
upper: "2",
|
|
|
|
value: 3. / 12.,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "1.5",
|
|
|
|
upper: "2",
|
|
|
|
value: 1.5 / 12.,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "1",
|
|
|
|
upper: "8",
|
|
|
|
value: 4. / 12.,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "1",
|
|
|
|
upper: "6",
|
|
|
|
value: 3.5 / 12.,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "1.5",
|
|
|
|
upper: "6",
|
|
|
|
value: 2. / 12.,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "-2",
|
|
|
|
upper: "-1",
|
|
|
|
value: 0,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "-2",
|
|
|
|
upper: "-1.5",
|
|
|
|
value: 0,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "-8",
|
|
|
|
upper: "-1",
|
|
|
|
value: 0,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "-6",
|
|
|
|
upper: "-1",
|
|
|
|
value: 0,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "-6",
|
|
|
|
upper: "-1.5",
|
|
|
|
value: 0,
|
|
|
|
},
|
|
|
|
}, invariantCases...),
|
|
|
|
},
|
|
|
|
{
|
|
|
|
text: "all negative buckets with zero bucket",
|
|
|
|
h: &histogram.Histogram{
|
|
|
|
Count: 12,
|
|
|
|
ZeroCount: 2,
|
|
|
|
ZeroThreshold: 0.001,
|
|
|
|
Sum: 100, // Does not matter.
|
|
|
|
Schema: 0,
|
|
|
|
NegativeSpans: []histogram.Span{
|
|
|
|
{Offset: 0, Length: 2},
|
|
|
|
{Offset: 1, Length: 2},
|
|
|
|
},
|
|
|
|
NegativeBuckets: []int64{2, 1, -2, 3},
|
|
|
|
},
|
|
|
|
subCases: append([]subCase{
|
|
|
|
{
|
|
|
|
lower: "0",
|
|
|
|
upper: "+Inf",
|
|
|
|
value: 0,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "-Inf",
|
|
|
|
upper: "0",
|
|
|
|
value: 1,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "-0.001",
|
|
|
|
upper: "0",
|
|
|
|
value: 2. / 12.,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "0",
|
|
|
|
upper: "0.001",
|
|
|
|
value: 0,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "-0.0005",
|
|
|
|
upper: "0",
|
|
|
|
value: 1. / 12.,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "0.001",
|
|
|
|
upper: "inf",
|
|
|
|
value: 0,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "-inf",
|
|
|
|
upper: "-0.001",
|
|
|
|
value: 10. / 12.,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "1",
|
|
|
|
upper: "2",
|
|
|
|
value: 0,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "1.5",
|
|
|
|
upper: "2",
|
|
|
|
value: 0,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "1",
|
|
|
|
upper: "8",
|
|
|
|
value: 0,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "1",
|
|
|
|
upper: "6",
|
|
|
|
value: 0,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "1.5",
|
|
|
|
upper: "6",
|
|
|
|
value: 0,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "-2",
|
|
|
|
upper: "-1",
|
|
|
|
value: 3. / 12.,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "-2",
|
|
|
|
upper: "-1.5",
|
|
|
|
value: 1.5 / 12.,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "-8",
|
|
|
|
upper: "-1",
|
|
|
|
value: 4. / 12.,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "-6",
|
|
|
|
upper: "-1",
|
|
|
|
value: 3.5 / 12.,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "-6",
|
|
|
|
upper: "-1.5",
|
|
|
|
value: 2. / 12.,
|
|
|
|
},
|
|
|
|
}, invariantCases...),
|
|
|
|
},
|
|
|
|
{
|
|
|
|
text: "both positive and negative buckets with zero bucket",
|
|
|
|
h: &histogram.Histogram{
|
|
|
|
Count: 24,
|
|
|
|
ZeroCount: 4,
|
|
|
|
ZeroThreshold: 0.001,
|
|
|
|
Sum: 100, // Does not matter.
|
|
|
|
Schema: 0,
|
|
|
|
PositiveSpans: []histogram.Span{
|
|
|
|
{Offset: 0, Length: 2},
|
|
|
|
{Offset: 1, Length: 2},
|
|
|
|
},
|
|
|
|
PositiveBuckets: []int64{2, 1, -2, 3},
|
|
|
|
NegativeSpans: []histogram.Span{
|
|
|
|
{Offset: 0, Length: 2},
|
|
|
|
{Offset: 1, Length: 2},
|
|
|
|
},
|
|
|
|
NegativeBuckets: []int64{2, 1, -2, 3},
|
|
|
|
},
|
|
|
|
subCases: append([]subCase{
|
|
|
|
{
|
|
|
|
lower: "0",
|
|
|
|
upper: "+Inf",
|
|
|
|
value: 0.5,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "-Inf",
|
|
|
|
upper: "0",
|
|
|
|
value: 0.5,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "-0.001",
|
|
|
|
upper: "0",
|
|
|
|
value: 2. / 24,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "0",
|
|
|
|
upper: "0.001",
|
|
|
|
value: 2. / 24.,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "-0.0005",
|
|
|
|
upper: "0.0005",
|
|
|
|
value: 2. / 24.,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "0.001",
|
|
|
|
upper: "inf",
|
|
|
|
value: 10. / 24.,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "-inf",
|
|
|
|
upper: "-0.001",
|
|
|
|
value: 10. / 24.,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "1",
|
|
|
|
upper: "2",
|
|
|
|
value: 3. / 24.,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "1.5",
|
|
|
|
upper: "2",
|
|
|
|
value: 1.5 / 24.,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "1",
|
|
|
|
upper: "8",
|
|
|
|
value: 4. / 24.,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "1",
|
|
|
|
upper: "6",
|
|
|
|
value: 3.5 / 24.,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "1.5",
|
|
|
|
upper: "6",
|
|
|
|
value: 2. / 24.,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "-2",
|
|
|
|
upper: "-1",
|
|
|
|
value: 3. / 24.,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "-2",
|
|
|
|
upper: "-1.5",
|
|
|
|
value: 1.5 / 24.,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "-8",
|
|
|
|
upper: "-1",
|
|
|
|
value: 4. / 24.,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "-6",
|
|
|
|
upper: "-1",
|
|
|
|
value: 3.5 / 24.,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
lower: "-6",
|
|
|
|
upper: "-1.5",
|
|
|
|
value: 2. / 24.,
|
|
|
|
},
|
|
|
|
}, invariantCases...),
|
|
|
|
},
|
|
|
|
}
|
2022-12-28 05:45:47 -08:00
|
|
|
idx := int64(0)
|
|
|
|
for _, floatHisto := range []bool{true, false} {
|
|
|
|
for _, c := range cases {
|
|
|
|
t.Run(fmt.Sprintf("%s floatHistogram=%t", c.text, floatHisto), func(t *testing.T) {
|
2023-08-18 11:48:59 -07:00
|
|
|
engine := newTestEngine()
|
|
|
|
storage := teststorage.New(t)
|
|
|
|
t.Cleanup(func() { storage.Close() })
|
2022-06-16 11:44:12 -07:00
|
|
|
|
2022-12-28 05:45:47 -08:00
|
|
|
seriesName := "sparse_histogram_series"
|
|
|
|
lbls := labels.FromStrings("__name__", seriesName)
|
2022-06-16 11:44:12 -07:00
|
|
|
|
2022-12-28 05:45:47 -08:00
|
|
|
ts := idx * int64(10*time.Minute/time.Millisecond)
|
2023-08-18 11:48:59 -07:00
|
|
|
app := storage.Appender(context.Background())
|
|
|
|
var err error
|
2022-12-28 05:45:47 -08:00
|
|
|
if floatHisto {
|
2023-11-29 06:15:57 -08:00
|
|
|
_, err = app.AppendHistogram(0, lbls, ts, nil, c.h.ToFloat(nil))
|
2022-12-28 05:45:47 -08:00
|
|
|
} else {
|
|
|
|
_, err = app.AppendHistogram(0, lbls, ts, c.h, nil)
|
|
|
|
}
|
|
|
|
require.NoError(t, err)
|
|
|
|
require.NoError(t, app.Commit())
|
|
|
|
|
|
|
|
for j, sc := range c.subCases {
|
|
|
|
t.Run(fmt.Sprintf("%d %s %s", j, sc.lower, sc.upper), func(t *testing.T) {
|
|
|
|
queryString := fmt.Sprintf("histogram_fraction(%s, %s, %s)", sc.lower, sc.upper, seriesName)
|
2023-08-18 11:48:59 -07:00
|
|
|
qry, err := engine.NewInstantQuery(context.Background(), storage, nil, queryString, timestamp.Time(ts))
|
2022-12-28 05:45:47 -08:00
|
|
|
require.NoError(t, err)
|
|
|
|
|
2023-08-18 11:48:59 -07:00
|
|
|
res := qry.Exec(context.Background())
|
2022-12-28 05:45:47 -08:00
|
|
|
require.NoError(t, res.Err)
|
|
|
|
|
|
|
|
vector, err := res.Vector()
|
|
|
|
require.NoError(t, err)
|
|
|
|
|
|
|
|
require.Len(t, vector, 1)
|
|
|
|
require.Nil(t, vector[0].H)
|
|
|
|
if math.IsNaN(sc.value) {
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
require.True(t, math.IsNaN(vector[0].F))
|
2022-12-28 05:45:47 -08:00
|
|
|
return
|
|
|
|
}
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
require.Equal(t, sc.value, vector[0].F)
|
2022-12-28 05:45:47 -08:00
|
|
|
})
|
|
|
|
}
|
|
|
|
idx++
|
|
|
|
})
|
|
|
|
}
|
2022-06-16 11:44:12 -07:00
|
|
|
}
|
|
|
|
}
|
|
|
|
|
2023-05-16 12:15:20 -07:00
|
|
|
func TestNativeHistogram_Sum_Count_Add_AvgOperator(t *testing.T) {
|
|
|
|
// TODO(codesome): Integrate histograms into the PromQL testing framework
|
|
|
|
// and write more tests there.
|
|
|
|
cases := []struct {
|
|
|
|
histograms []histogram.Histogram
|
|
|
|
expected histogram.FloatHistogram
|
|
|
|
expectedAvg histogram.FloatHistogram
|
|
|
|
}{
|
|
|
|
{
|
|
|
|
histograms: []histogram.Histogram{
|
|
|
|
{
|
|
|
|
CounterResetHint: histogram.GaugeType,
|
|
|
|
Schema: 0,
|
2023-08-22 12:51:56 -07:00
|
|
|
Count: 25,
|
2023-05-16 12:15:20 -07:00
|
|
|
Sum: 1234.5,
|
|
|
|
ZeroThreshold: 0.001,
|
|
|
|
ZeroCount: 4,
|
|
|
|
PositiveSpans: []histogram.Span{
|
|
|
|
{Offset: 0, Length: 2},
|
|
|
|
{Offset: 1, Length: 2},
|
|
|
|
},
|
|
|
|
PositiveBuckets: []int64{1, 1, -1, 0},
|
|
|
|
NegativeSpans: []histogram.Span{
|
|
|
|
{Offset: 0, Length: 2},
|
|
|
|
{Offset: 2, Length: 2},
|
|
|
|
},
|
|
|
|
NegativeBuckets: []int64{2, 2, -3, 8},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
CounterResetHint: histogram.GaugeType,
|
|
|
|
Schema: 0,
|
2023-08-22 12:51:56 -07:00
|
|
|
Count: 41,
|
2023-05-16 12:15:20 -07:00
|
|
|
Sum: 2345.6,
|
|
|
|
ZeroThreshold: 0.001,
|
|
|
|
ZeroCount: 5,
|
|
|
|
PositiveSpans: []histogram.Span{
|
|
|
|
{Offset: 0, Length: 4},
|
|
|
|
{Offset: 0, Length: 0},
|
|
|
|
{Offset: 0, Length: 3},
|
|
|
|
},
|
|
|
|
PositiveBuckets: []int64{1, 2, -2, 1, -1, 0, 0},
|
|
|
|
NegativeSpans: []histogram.Span{
|
|
|
|
{Offset: 1, Length: 4},
|
|
|
|
{Offset: 2, Length: 0},
|
|
|
|
{Offset: 2, Length: 3},
|
|
|
|
},
|
|
|
|
NegativeBuckets: []int64{1, 3, -2, 5, -2, 0, -3},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
CounterResetHint: histogram.GaugeType,
|
|
|
|
Schema: 0,
|
2023-08-22 12:51:56 -07:00
|
|
|
Count: 41,
|
2023-05-16 12:15:20 -07:00
|
|
|
Sum: 1111.1,
|
|
|
|
ZeroThreshold: 0.001,
|
|
|
|
ZeroCount: 5,
|
|
|
|
PositiveSpans: []histogram.Span{
|
|
|
|
{Offset: 0, Length: 4},
|
|
|
|
{Offset: 0, Length: 0},
|
|
|
|
{Offset: 0, Length: 3},
|
|
|
|
},
|
|
|
|
PositiveBuckets: []int64{1, 2, -2, 1, -1, 0, 0},
|
|
|
|
NegativeSpans: []histogram.Span{
|
|
|
|
{Offset: 1, Length: 4},
|
|
|
|
{Offset: 2, Length: 0},
|
|
|
|
{Offset: 2, Length: 3},
|
|
|
|
},
|
|
|
|
NegativeBuckets: []int64{1, 3, -2, 5, -2, 0, -3},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
CounterResetHint: histogram.GaugeType,
|
|
|
|
Schema: 1, // Everything is 0 just to make the count 4 so avg has nicer numbers.
|
|
|
|
},
|
|
|
|
},
|
|
|
|
expected: histogram.FloatHistogram{
|
|
|
|
CounterResetHint: histogram.GaugeType,
|
|
|
|
Schema: 0,
|
|
|
|
ZeroThreshold: 0.001,
|
|
|
|
ZeroCount: 14,
|
2023-08-22 12:51:56 -07:00
|
|
|
Count: 107,
|
2023-05-16 12:15:20 -07:00
|
|
|
Sum: 4691.2,
|
|
|
|
PositiveSpans: []histogram.Span{
|
|
|
|
{Offset: 0, Length: 7},
|
|
|
|
},
|
|
|
|
PositiveBuckets: []float64{3, 8, 2, 5, 3, 2, 2},
|
|
|
|
NegativeSpans: []histogram.Span{
|
|
|
|
{Offset: 0, Length: 6},
|
|
|
|
{Offset: 3, Length: 3},
|
|
|
|
},
|
|
|
|
NegativeBuckets: []float64{2, 6, 8, 4, 15, 9, 10, 10, 4},
|
|
|
|
},
|
|
|
|
expectedAvg: histogram.FloatHistogram{
|
|
|
|
CounterResetHint: histogram.GaugeType,
|
|
|
|
Schema: 0,
|
|
|
|
ZeroThreshold: 0.001,
|
|
|
|
ZeroCount: 3.5,
|
2023-08-22 12:51:56 -07:00
|
|
|
Count: 26.75,
|
2023-05-16 12:15:20 -07:00
|
|
|
Sum: 1172.8,
|
|
|
|
PositiveSpans: []histogram.Span{
|
|
|
|
{Offset: 0, Length: 7},
|
|
|
|
},
|
|
|
|
PositiveBuckets: []float64{0.75, 2, 0.5, 1.25, 0.75, 0.5, 0.5},
|
|
|
|
NegativeSpans: []histogram.Span{
|
|
|
|
{Offset: 0, Length: 6},
|
|
|
|
{Offset: 3, Length: 3},
|
|
|
|
},
|
|
|
|
NegativeBuckets: []float64{0.5, 1.5, 2, 1, 3.75, 2.25, 2.5, 2.5, 1},
|
|
|
|
},
|
|
|
|
},
|
|
|
|
}
|
|
|
|
|
|
|
|
idx0 := int64(0)
|
|
|
|
for _, c := range cases {
|
|
|
|
for _, floatHisto := range []bool{true, false} {
|
|
|
|
t.Run(fmt.Sprintf("floatHistogram=%t %d", floatHisto, idx0), func(t *testing.T) {
|
2023-08-18 11:48:59 -07:00
|
|
|
storage := teststorage.New(t)
|
|
|
|
t.Cleanup(func() { storage.Close() })
|
2023-05-16 12:15:20 -07:00
|
|
|
|
|
|
|
seriesName := "sparse_histogram_series"
|
|
|
|
seriesNameOverTime := "sparse_histogram_series_over_time"
|
|
|
|
|
2023-08-18 11:48:59 -07:00
|
|
|
engine := newTestEngine()
|
2023-05-16 12:15:20 -07:00
|
|
|
|
|
|
|
ts := idx0 * int64(10*time.Minute/time.Millisecond)
|
2023-08-18 11:48:59 -07:00
|
|
|
app := storage.Appender(context.Background())
|
2024-02-25 11:26:52 -08:00
|
|
|
_, err := app.Append(0, labels.FromStrings("__name__", "float_series", "idx", "0"), ts, 42)
|
|
|
|
require.NoError(t, err)
|
2023-05-16 12:15:20 -07:00
|
|
|
for idx1, h := range c.histograms {
|
|
|
|
lbls := labels.FromStrings("__name__", seriesName, "idx", fmt.Sprintf("%d", idx1))
|
|
|
|
// Since we mutate h later, we need to create a copy here.
|
2023-08-18 11:48:59 -07:00
|
|
|
var err error
|
2023-05-16 12:15:20 -07:00
|
|
|
if floatHisto {
|
2023-11-29 06:15:57 -08:00
|
|
|
_, err = app.AppendHistogram(0, lbls, ts, nil, h.Copy().ToFloat(nil))
|
2023-05-16 12:15:20 -07:00
|
|
|
} else {
|
|
|
|
_, err = app.AppendHistogram(0, lbls, ts, h.Copy(), nil)
|
|
|
|
}
|
|
|
|
require.NoError(t, err)
|
|
|
|
|
|
|
|
lbls = labels.FromStrings("__name__", seriesNameOverTime)
|
|
|
|
newTs := ts + int64(idx1)*int64(time.Minute/time.Millisecond)
|
|
|
|
// Since we mutate h later, we need to create a copy here.
|
|
|
|
if floatHisto {
|
2023-11-29 06:15:57 -08:00
|
|
|
_, err = app.AppendHistogram(0, lbls, newTs, nil, h.Copy().ToFloat(nil))
|
2023-05-16 12:15:20 -07:00
|
|
|
} else {
|
|
|
|
_, err = app.AppendHistogram(0, lbls, newTs, h.Copy(), nil)
|
|
|
|
}
|
|
|
|
require.NoError(t, err)
|
|
|
|
}
|
|
|
|
require.NoError(t, app.Commit())
|
|
|
|
|
|
|
|
queryAndCheck := func(queryString string, ts int64, exp Vector) {
|
2023-08-18 11:48:59 -07:00
|
|
|
qry, err := engine.NewInstantQuery(context.Background(), storage, nil, queryString, timestamp.Time(ts))
|
2023-05-16 12:15:20 -07:00
|
|
|
require.NoError(t, err)
|
|
|
|
|
2023-08-18 11:48:59 -07:00
|
|
|
res := qry.Exec(context.Background())
|
2023-05-16 12:15:20 -07:00
|
|
|
require.NoError(t, res.Err)
|
2024-02-25 11:26:52 -08:00
|
|
|
require.Empty(t, res.Warnings)
|
2023-05-16 12:15:20 -07:00
|
|
|
|
|
|
|
vector, err := res.Vector()
|
|
|
|
require.NoError(t, err)
|
|
|
|
|
2023-04-16 05:13:31 -07:00
|
|
|
testutil.RequireEqual(t, exp, vector)
|
2023-05-16 12:15:20 -07:00
|
|
|
}
|
2024-02-25 11:26:52 -08:00
|
|
|
queryAndCheckAnnotations := func(queryString string, ts int64, expWarnings annotations.Annotations) {
|
|
|
|
qry, err := engine.NewInstantQuery(context.Background(), storage, nil, queryString, timestamp.Time(ts))
|
|
|
|
require.NoError(t, err)
|
|
|
|
|
|
|
|
res := qry.Exec(context.Background())
|
|
|
|
require.NoError(t, res.Err)
|
|
|
|
require.Equal(t, expWarnings, res.Warnings)
|
|
|
|
}
|
2023-05-16 12:15:20 -07:00
|
|
|
|
|
|
|
// sum().
|
|
|
|
queryString := fmt.Sprintf("sum(%s)", seriesName)
|
|
|
|
queryAndCheck(queryString, ts, []Sample{{T: ts, H: &c.expected, Metric: labels.EmptyLabels()}})
|
|
|
|
|
2024-02-25 11:26:52 -08:00
|
|
|
queryString = `sum({idx="0"})`
|
|
|
|
var annos annotations.Annotations
|
|
|
|
annos.Add(annotations.NewMixedFloatsHistogramsAggWarning(posrange.PositionRange{Start: 4, End: 13}))
|
|
|
|
queryAndCheckAnnotations(queryString, ts, annos)
|
|
|
|
|
2023-05-16 12:15:20 -07:00
|
|
|
// + operator.
|
|
|
|
queryString = fmt.Sprintf(`%s{idx="0"}`, seriesName)
|
|
|
|
for idx := 1; idx < len(c.histograms); idx++ {
|
|
|
|
queryString += fmt.Sprintf(` + ignoring(idx) %s{idx="%d"}`, seriesName, idx)
|
|
|
|
}
|
|
|
|
queryAndCheck(queryString, ts, []Sample{{T: ts, H: &c.expected, Metric: labels.EmptyLabels()}})
|
|
|
|
|
|
|
|
// count().
|
|
|
|
queryString = fmt.Sprintf("count(%s)", seriesName)
|
|
|
|
queryAndCheck(queryString, ts, []Sample{{T: ts, F: 4, Metric: labels.EmptyLabels()}})
|
|
|
|
|
|
|
|
// avg().
|
|
|
|
queryString = fmt.Sprintf("avg(%s)", seriesName)
|
|
|
|
queryAndCheck(queryString, ts, []Sample{{T: ts, H: &c.expectedAvg, Metric: labels.EmptyLabels()}})
|
|
|
|
|
|
|
|
offset := int64(len(c.histograms) - 1)
|
|
|
|
newTs := ts + offset*int64(time.Minute/time.Millisecond)
|
|
|
|
|
|
|
|
// sum_over_time().
|
|
|
|
queryString = fmt.Sprintf("sum_over_time(%s[%dm:1m])", seriesNameOverTime, offset)
|
|
|
|
queryAndCheck(queryString, newTs, []Sample{{T: newTs, H: &c.expected, Metric: labels.EmptyLabels()}})
|
|
|
|
|
|
|
|
// avg_over_time().
|
|
|
|
queryString = fmt.Sprintf("avg_over_time(%s[%dm:1m])", seriesNameOverTime, offset)
|
|
|
|
queryAndCheck(queryString, newTs, []Sample{{T: newTs, H: &c.expectedAvg, Metric: labels.EmptyLabels()}})
|
|
|
|
})
|
|
|
|
idx0++
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
func TestNativeHistogram_SubOperator(t *testing.T) {
|
2021-12-06 08:08:10 -08:00
|
|
|
// TODO(codesome): Integrate histograms into the PromQL testing framework
|
|
|
|
// and write more tests there.
|
|
|
|
cases := []struct {
|
|
|
|
histograms []histogram.Histogram
|
|
|
|
expected histogram.FloatHistogram
|
|
|
|
}{
|
|
|
|
{
|
|
|
|
histograms: []histogram.Histogram{
|
|
|
|
{
|
|
|
|
Schema: 0,
|
2023-08-22 12:51:56 -07:00
|
|
|
Count: 41,
|
2023-05-16 12:15:20 -07:00
|
|
|
Sum: 2345.6,
|
|
|
|
ZeroThreshold: 0.001,
|
|
|
|
ZeroCount: 5,
|
|
|
|
PositiveSpans: []histogram.Span{
|
|
|
|
{Offset: 0, Length: 4},
|
|
|
|
{Offset: 0, Length: 0},
|
|
|
|
{Offset: 0, Length: 3},
|
|
|
|
},
|
|
|
|
PositiveBuckets: []int64{1, 2, -2, 1, -1, 0, 0},
|
|
|
|
NegativeSpans: []histogram.Span{
|
|
|
|
{Offset: 1, Length: 4},
|
|
|
|
{Offset: 2, Length: 0},
|
|
|
|
{Offset: 2, Length: 3},
|
|
|
|
},
|
|
|
|
NegativeBuckets: []int64{1, 3, -2, 5, -2, 0, -3},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
Schema: 0,
|
|
|
|
Count: 11,
|
2021-12-06 08:08:10 -08:00
|
|
|
Sum: 1234.5,
|
|
|
|
ZeroThreshold: 0.001,
|
2023-05-16 12:15:20 -07:00
|
|
|
ZeroCount: 3,
|
2021-12-06 08:08:10 -08:00
|
|
|
PositiveSpans: []histogram.Span{
|
|
|
|
{Offset: 1, Length: 2},
|
|
|
|
},
|
2023-05-16 12:15:20 -07:00
|
|
|
PositiveBuckets: []int64{2, -1},
|
2021-12-06 08:08:10 -08:00
|
|
|
NegativeSpans: []histogram.Span{
|
|
|
|
{Offset: 2, Length: 2},
|
|
|
|
},
|
2023-05-16 12:15:20 -07:00
|
|
|
NegativeBuckets: []int64{3, -1},
|
2021-12-06 08:08:10 -08:00
|
|
|
},
|
2023-05-16 12:15:20 -07:00
|
|
|
},
|
|
|
|
expected: histogram.FloatHistogram{
|
|
|
|
Schema: 0,
|
2023-08-22 12:51:56 -07:00
|
|
|
Count: 30,
|
2023-05-16 12:15:20 -07:00
|
|
|
Sum: 1111.1,
|
|
|
|
ZeroThreshold: 0.001,
|
|
|
|
ZeroCount: 2,
|
|
|
|
PositiveSpans: []histogram.Span{
|
|
|
|
{Offset: 0, Length: 2},
|
|
|
|
{Offset: 1, Length: 4},
|
|
|
|
},
|
|
|
|
PositiveBuckets: []float64{1, 1, 2, 1, 1, 1},
|
|
|
|
NegativeSpans: []histogram.Span{
|
|
|
|
{Offset: 1, Length: 2},
|
|
|
|
{Offset: 1, Length: 1},
|
|
|
|
{Offset: 4, Length: 3},
|
|
|
|
},
|
|
|
|
NegativeBuckets: []float64{1, 1, 7, 5, 5, 2},
|
|
|
|
},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
histograms: []histogram.Histogram{
|
2021-12-06 08:08:10 -08:00
|
|
|
{
|
|
|
|
Schema: 0,
|
2023-08-22 12:51:56 -07:00
|
|
|
Count: 41,
|
2021-12-06 08:08:10 -08:00
|
|
|
Sum: 2345.6,
|
|
|
|
ZeroThreshold: 0.001,
|
|
|
|
ZeroCount: 5,
|
|
|
|
PositiveSpans: []histogram.Span{
|
|
|
|
{Offset: 0, Length: 4},
|
|
|
|
{Offset: 0, Length: 0},
|
|
|
|
{Offset: 0, Length: 3},
|
|
|
|
},
|
|
|
|
PositiveBuckets: []int64{1, 2, -2, 1, -1, 0, 0},
|
|
|
|
NegativeSpans: []histogram.Span{
|
|
|
|
{Offset: 1, Length: 4},
|
|
|
|
{Offset: 2, Length: 0},
|
|
|
|
{Offset: 2, Length: 3},
|
|
|
|
},
|
|
|
|
NegativeBuckets: []int64{1, 3, -2, 5, -2, 0, -3},
|
|
|
|
},
|
2023-05-16 12:15:20 -07:00
|
|
|
{
|
|
|
|
Schema: 1,
|
|
|
|
Count: 11,
|
|
|
|
Sum: 1234.5,
|
|
|
|
ZeroThreshold: 0.001,
|
|
|
|
ZeroCount: 3,
|
|
|
|
PositiveSpans: []histogram.Span{
|
|
|
|
{Offset: 1, Length: 2},
|
|
|
|
},
|
|
|
|
PositiveBuckets: []int64{2, -1},
|
|
|
|
NegativeSpans: []histogram.Span{
|
|
|
|
{Offset: 2, Length: 2},
|
|
|
|
},
|
|
|
|
NegativeBuckets: []int64{3, -1},
|
|
|
|
},
|
|
|
|
},
|
|
|
|
expected: histogram.FloatHistogram{
|
|
|
|
Schema: 0,
|
2023-08-22 12:51:56 -07:00
|
|
|
Count: 30,
|
2023-05-16 12:15:20 -07:00
|
|
|
Sum: 1111.1,
|
|
|
|
ZeroThreshold: 0.001,
|
|
|
|
ZeroCount: 2,
|
|
|
|
PositiveSpans: []histogram.Span{
|
|
|
|
{Offset: 0, Length: 1},
|
|
|
|
{Offset: 1, Length: 5},
|
|
|
|
},
|
|
|
|
PositiveBuckets: []float64{1, 1, 2, 1, 1, 1},
|
|
|
|
NegativeSpans: []histogram.Span{
|
|
|
|
{Offset: 1, Length: 4},
|
|
|
|
{Offset: 4, Length: 3},
|
|
|
|
},
|
|
|
|
NegativeBuckets: []float64{-2, 2, 2, 7, 5, 5, 2},
|
|
|
|
},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
histograms: []histogram.Histogram{
|
|
|
|
{
|
|
|
|
Schema: 1,
|
|
|
|
Count: 11,
|
|
|
|
Sum: 1234.5,
|
|
|
|
ZeroThreshold: 0.001,
|
|
|
|
ZeroCount: 3,
|
|
|
|
PositiveSpans: []histogram.Span{
|
|
|
|
{Offset: 1, Length: 2},
|
|
|
|
},
|
|
|
|
PositiveBuckets: []int64{2, -1},
|
|
|
|
NegativeSpans: []histogram.Span{
|
|
|
|
{Offset: 2, Length: 2},
|
|
|
|
},
|
|
|
|
NegativeBuckets: []int64{3, -1},
|
|
|
|
},
|
2021-12-06 08:08:10 -08:00
|
|
|
{
|
|
|
|
Schema: 0,
|
2023-08-22 12:51:56 -07:00
|
|
|
Count: 41,
|
2023-05-16 12:15:20 -07:00
|
|
|
Sum: 2345.6,
|
2021-12-06 08:08:10 -08:00
|
|
|
ZeroThreshold: 0.001,
|
|
|
|
ZeroCount: 5,
|
|
|
|
PositiveSpans: []histogram.Span{
|
|
|
|
{Offset: 0, Length: 4},
|
|
|
|
{Offset: 0, Length: 0},
|
|
|
|
{Offset: 0, Length: 3},
|
|
|
|
},
|
|
|
|
PositiveBuckets: []int64{1, 2, -2, 1, -1, 0, 0},
|
|
|
|
NegativeSpans: []histogram.Span{
|
|
|
|
{Offset: 1, Length: 4},
|
|
|
|
{Offset: 2, Length: 0},
|
|
|
|
{Offset: 2, Length: 3},
|
|
|
|
},
|
|
|
|
NegativeBuckets: []int64{1, 3, -2, 5, -2, 0, -3},
|
|
|
|
},
|
|
|
|
},
|
|
|
|
expected: histogram.FloatHistogram{
|
|
|
|
Schema: 0,
|
2023-08-22 12:51:56 -07:00
|
|
|
Count: -30,
|
2023-05-16 12:15:20 -07:00
|
|
|
Sum: -1111.1,
|
2021-12-06 08:08:10 -08:00
|
|
|
ZeroThreshold: 0.001,
|
2023-05-16 12:15:20 -07:00
|
|
|
ZeroCount: -2,
|
2021-12-06 08:08:10 -08:00
|
|
|
PositiveSpans: []histogram.Span{
|
2023-05-16 12:15:20 -07:00
|
|
|
{Offset: 0, Length: 1},
|
|
|
|
{Offset: 1, Length: 5},
|
2021-12-06 08:08:10 -08:00
|
|
|
},
|
2023-05-16 12:15:20 -07:00
|
|
|
PositiveBuckets: []float64{-1, -1, -2, -1, -1, -1},
|
2021-12-06 08:08:10 -08:00
|
|
|
NegativeSpans: []histogram.Span{
|
2023-05-16 12:15:20 -07:00
|
|
|
{Offset: 1, Length: 4},
|
|
|
|
{Offset: 4, Length: 3},
|
2021-12-06 08:08:10 -08:00
|
|
|
},
|
2023-05-16 12:15:20 -07:00
|
|
|
NegativeBuckets: []float64{2, -2, -2, -7, -5, -5, -2},
|
2021-12-06 08:08:10 -08:00
|
|
|
},
|
|
|
|
},
|
|
|
|
}
|
|
|
|
|
2022-12-28 05:45:47 -08:00
|
|
|
idx0 := int64(0)
|
|
|
|
for _, c := range cases {
|
|
|
|
for _, floatHisto := range []bool{true, false} {
|
|
|
|
t.Run(fmt.Sprintf("floatHistogram=%t %d", floatHisto, idx0), func(t *testing.T) {
|
2023-08-18 11:48:59 -07:00
|
|
|
engine := newTestEngine()
|
|
|
|
storage := teststorage.New(t)
|
|
|
|
t.Cleanup(func() { storage.Close() })
|
2021-12-06 08:08:10 -08:00
|
|
|
|
2022-12-28 05:45:47 -08:00
|
|
|
seriesName := "sparse_histogram_series"
|
2021-12-06 08:08:10 -08:00
|
|
|
|
2022-12-28 05:45:47 -08:00
|
|
|
ts := idx0 * int64(10*time.Minute/time.Millisecond)
|
2023-08-18 11:48:59 -07:00
|
|
|
app := storage.Appender(context.Background())
|
2022-12-28 05:45:47 -08:00
|
|
|
for idx1, h := range c.histograms {
|
|
|
|
lbls := labels.FromStrings("__name__", seriesName, "idx", fmt.Sprintf("%d", idx1))
|
|
|
|
// Since we mutate h later, we need to create a copy here.
|
2023-08-18 11:48:59 -07:00
|
|
|
var err error
|
2022-12-28 05:45:47 -08:00
|
|
|
if floatHisto {
|
2023-11-29 06:15:57 -08:00
|
|
|
_, err = app.AppendHistogram(0, lbls, ts, nil, h.Copy().ToFloat(nil))
|
2022-12-28 05:45:47 -08:00
|
|
|
} else {
|
|
|
|
_, err = app.AppendHistogram(0, lbls, ts, h.Copy(), nil)
|
|
|
|
}
|
|
|
|
require.NoError(t, err)
|
|
|
|
}
|
|
|
|
require.NoError(t, app.Commit())
|
2021-12-06 08:08:10 -08:00
|
|
|
|
2022-12-28 05:45:47 -08:00
|
|
|
queryAndCheck := func(queryString string, exp Vector) {
|
2023-08-18 11:48:59 -07:00
|
|
|
qry, err := engine.NewInstantQuery(context.Background(), storage, nil, queryString, timestamp.Time(ts))
|
2022-12-28 05:45:47 -08:00
|
|
|
require.NoError(t, err)
|
2021-12-06 08:08:10 -08:00
|
|
|
|
2023-08-18 11:48:59 -07:00
|
|
|
res := qry.Exec(context.Background())
|
2022-12-28 05:45:47 -08:00
|
|
|
require.NoError(t, res.Err)
|
2021-12-06 08:08:10 -08:00
|
|
|
|
2022-12-28 05:45:47 -08:00
|
|
|
vector, err := res.Vector()
|
|
|
|
require.NoError(t, err)
|
2021-12-06 09:36:58 -08:00
|
|
|
|
2023-08-25 14:35:42 -07:00
|
|
|
if len(vector) == len(exp) {
|
|
|
|
for i, e := range exp {
|
|
|
|
got := vector[i].H
|
|
|
|
if got != e.H {
|
|
|
|
// Error messages are better if we compare structs, not pointers.
|
|
|
|
require.Equal(t, *e.H, *got)
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
2023-04-16 05:13:31 -07:00
|
|
|
testutil.RequireEqual(t, exp, vector)
|
2022-12-28 05:45:47 -08:00
|
|
|
}
|
2021-12-06 08:08:10 -08:00
|
|
|
|
2023-05-16 12:15:20 -07:00
|
|
|
// - operator.
|
|
|
|
queryString := fmt.Sprintf(`%s{idx="0"}`, seriesName)
|
2022-12-28 05:45:47 -08:00
|
|
|
for idx := 1; idx < len(c.histograms); idx++ {
|
2023-05-16 12:15:20 -07:00
|
|
|
queryString += fmt.Sprintf(` - ignoring(idx) %s{idx="%d"}`, seriesName, idx)
|
2022-12-28 05:45:47 -08:00
|
|
|
}
|
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>
2022-10-28 07:58:40 -07:00
|
|
|
queryAndCheck(queryString, []Sample{{T: ts, H: &c.expected, Metric: labels.EmptyLabels()}})
|
2023-05-16 12:15:20 -07:00
|
|
|
})
|
|
|
|
}
|
2023-08-25 14:35:42 -07:00
|
|
|
idx0++
|
2023-05-16 12:15:20 -07:00
|
|
|
}
|
|
|
|
}
|
2022-08-29 07:27:29 -07:00
|
|
|
|
2023-05-16 12:15:20 -07:00
|
|
|
func TestNativeHistogram_MulDivOperator(t *testing.T) {
|
|
|
|
// TODO(codesome): Integrate histograms into the PromQL testing framework
|
|
|
|
// and write more tests there.
|
|
|
|
originalHistogram := histogram.Histogram{
|
|
|
|
Schema: 0,
|
|
|
|
Count: 21,
|
|
|
|
Sum: 33,
|
|
|
|
ZeroThreshold: 0.001,
|
|
|
|
ZeroCount: 3,
|
|
|
|
PositiveSpans: []histogram.Span{
|
|
|
|
{Offset: 0, Length: 3},
|
|
|
|
},
|
|
|
|
PositiveBuckets: []int64{3, 0, 0},
|
|
|
|
NegativeSpans: []histogram.Span{
|
|
|
|
{Offset: 0, Length: 3},
|
|
|
|
},
|
|
|
|
NegativeBuckets: []int64{3, 0, 0},
|
|
|
|
}
|
|
|
|
|
|
|
|
cases := []struct {
|
|
|
|
scalar float64
|
|
|
|
histogram histogram.Histogram
|
|
|
|
expectedMul histogram.FloatHistogram
|
|
|
|
expectedDiv histogram.FloatHistogram
|
|
|
|
}{
|
|
|
|
{
|
|
|
|
scalar: 3,
|
|
|
|
histogram: originalHistogram,
|
|
|
|
expectedMul: histogram.FloatHistogram{
|
|
|
|
Schema: 0,
|
|
|
|
Count: 63,
|
|
|
|
Sum: 99,
|
|
|
|
ZeroThreshold: 0.001,
|
|
|
|
ZeroCount: 9,
|
|
|
|
PositiveSpans: []histogram.Span{
|
|
|
|
{Offset: 0, Length: 3},
|
|
|
|
},
|
|
|
|
PositiveBuckets: []float64{9, 9, 9},
|
|
|
|
NegativeSpans: []histogram.Span{
|
|
|
|
{Offset: 0, Length: 3},
|
|
|
|
},
|
|
|
|
NegativeBuckets: []float64{9, 9, 9},
|
|
|
|
},
|
|
|
|
expectedDiv: histogram.FloatHistogram{
|
|
|
|
Schema: 0,
|
|
|
|
Count: 7,
|
|
|
|
Sum: 11,
|
|
|
|
ZeroThreshold: 0.001,
|
|
|
|
ZeroCount: 1,
|
|
|
|
PositiveSpans: []histogram.Span{
|
|
|
|
{Offset: 0, Length: 3},
|
|
|
|
},
|
|
|
|
PositiveBuckets: []float64{1, 1, 1},
|
|
|
|
NegativeSpans: []histogram.Span{
|
|
|
|
{Offset: 0, Length: 3},
|
|
|
|
},
|
|
|
|
NegativeBuckets: []float64{1, 1, 1},
|
|
|
|
},
|
|
|
|
},
|
|
|
|
{
|
|
|
|
scalar: 0,
|
|
|
|
histogram: originalHistogram,
|
|
|
|
expectedMul: histogram.FloatHistogram{
|
|
|
|
Schema: 0,
|
|
|
|
Count: 0,
|
|
|
|
Sum: 0,
|
|
|
|
ZeroThreshold: 0.001,
|
|
|
|
ZeroCount: 0,
|
|
|
|
PositiveSpans: []histogram.Span{
|
|
|
|
{Offset: 0, Length: 3},
|
|
|
|
},
|
|
|
|
PositiveBuckets: []float64{0, 0, 0},
|
|
|
|
NegativeSpans: []histogram.Span{
|
|
|
|
{Offset: 0, Length: 3},
|
|
|
|
},
|
|
|
|
NegativeBuckets: []float64{0, 0, 0},
|
|
|
|
},
|
|
|
|
expectedDiv: histogram.FloatHistogram{
|
|
|
|
Schema: 0,
|
|
|
|
Count: math.Inf(1),
|
|
|
|
Sum: math.Inf(1),
|
|
|
|
ZeroThreshold: 0.001,
|
|
|
|
ZeroCount: math.Inf(1),
|
|
|
|
PositiveSpans: []histogram.Span{
|
|
|
|
{Offset: 0, Length: 3},
|
|
|
|
},
|
|
|
|
PositiveBuckets: []float64{math.Inf(1), math.Inf(1), math.Inf(1)},
|
|
|
|
NegativeSpans: []histogram.Span{
|
|
|
|
{Offset: 0, Length: 3},
|
|
|
|
},
|
|
|
|
NegativeBuckets: []float64{math.Inf(1), math.Inf(1), math.Inf(1)},
|
|
|
|
},
|
|
|
|
},
|
|
|
|
}
|
|
|
|
|
|
|
|
idx0 := int64(0)
|
|
|
|
for _, c := range cases {
|
|
|
|
for _, floatHisto := range []bool{true, false} {
|
|
|
|
t.Run(fmt.Sprintf("floatHistogram=%t %d", floatHisto, idx0), func(t *testing.T) {
|
2023-08-18 11:48:59 -07:00
|
|
|
storage := teststorage.New(t)
|
|
|
|
t.Cleanup(func() { storage.Close() })
|
2023-05-16 12:15:20 -07:00
|
|
|
|
|
|
|
seriesName := "sparse_histogram_series"
|
|
|
|
floatSeriesName := "float_series"
|
|
|
|
|
2023-08-18 11:48:59 -07:00
|
|
|
engine := newTestEngine()
|
2023-05-16 12:15:20 -07:00
|
|
|
|
|
|
|
ts := idx0 * int64(10*time.Minute/time.Millisecond)
|
2023-08-18 11:48:59 -07:00
|
|
|
app := storage.Appender(context.Background())
|
2023-05-16 12:15:20 -07:00
|
|
|
h := c.histogram
|
|
|
|
lbls := labels.FromStrings("__name__", seriesName)
|
|
|
|
// Since we mutate h later, we need to create a copy here.
|
2023-08-18 11:48:59 -07:00
|
|
|
var err error
|
2023-05-16 12:15:20 -07:00
|
|
|
if floatHisto {
|
2023-11-29 06:15:57 -08:00
|
|
|
_, err = app.AppendHistogram(0, lbls, ts, nil, h.Copy().ToFloat(nil))
|
2023-05-16 12:15:20 -07:00
|
|
|
} else {
|
|
|
|
_, err = app.AppendHistogram(0, lbls, ts, h.Copy(), nil)
|
|
|
|
}
|
|
|
|
require.NoError(t, err)
|
|
|
|
_, err = app.Append(0, labels.FromStrings("__name__", floatSeriesName), ts, c.scalar)
|
|
|
|
require.NoError(t, err)
|
|
|
|
require.NoError(t, app.Commit())
|
|
|
|
|
|
|
|
queryAndCheck := func(queryString string, exp Vector) {
|
2023-08-18 11:48:59 -07:00
|
|
|
qry, err := engine.NewInstantQuery(context.Background(), storage, nil, queryString, timestamp.Time(ts))
|
2023-05-16 12:15:20 -07:00
|
|
|
require.NoError(t, err)
|
|
|
|
|
2023-08-18 11:48:59 -07:00
|
|
|
res := qry.Exec(context.Background())
|
2023-05-16 12:15:20 -07:00
|
|
|
require.NoError(t, res.Err)
|
|
|
|
|
|
|
|
vector, err := res.Vector()
|
|
|
|
require.NoError(t, err)
|
|
|
|
|
2023-04-16 05:13:31 -07:00
|
|
|
testutil.RequireEqual(t, exp, vector)
|
2023-05-16 12:15:20 -07:00
|
|
|
}
|
|
|
|
|
|
|
|
// histogram * scalar.
|
|
|
|
queryString := fmt.Sprintf(`%s * %f`, seriesName, c.scalar)
|
|
|
|
queryAndCheck(queryString, []Sample{{T: ts, H: &c.expectedMul, Metric: labels.EmptyLabels()}})
|
|
|
|
|
|
|
|
// scalar * histogram.
|
|
|
|
queryString = fmt.Sprintf(`%f * %s`, c.scalar, seriesName)
|
|
|
|
queryAndCheck(queryString, []Sample{{T: ts, H: &c.expectedMul, Metric: labels.EmptyLabels()}})
|
|
|
|
|
|
|
|
// histogram * float.
|
|
|
|
queryString = fmt.Sprintf(`%s * %s`, seriesName, floatSeriesName)
|
|
|
|
queryAndCheck(queryString, []Sample{{T: ts, H: &c.expectedMul, Metric: labels.EmptyLabels()}})
|
|
|
|
|
|
|
|
// float * histogram.
|
|
|
|
queryString = fmt.Sprintf(`%s * %s`, floatSeriesName, seriesName)
|
|
|
|
queryAndCheck(queryString, []Sample{{T: ts, H: &c.expectedMul, Metric: labels.EmptyLabels()}})
|
|
|
|
|
|
|
|
// histogram / scalar.
|
|
|
|
queryString = fmt.Sprintf(`%s / %f`, seriesName, c.scalar)
|
|
|
|
queryAndCheck(queryString, []Sample{{T: ts, H: &c.expectedDiv, Metric: labels.EmptyLabels()}})
|
|
|
|
|
|
|
|
// histogram / float.
|
|
|
|
queryString = fmt.Sprintf(`%s / %s`, seriesName, floatSeriesName)
|
|
|
|
queryAndCheck(queryString, []Sample{{T: ts, H: &c.expectedDiv, Metric: labels.EmptyLabels()}})
|
2022-08-29 07:27:29 -07:00
|
|
|
})
|
2022-12-28 05:45:47 -08:00
|
|
|
idx0++
|
|
|
|
}
|
2021-12-06 08:08:10 -08:00
|
|
|
}
|
|
|
|
}
|
2022-08-10 08:54:37 -07:00
|
|
|
|
2022-08-02 02:15:39 -07:00
|
|
|
func TestQueryLookbackDelta(t *testing.T) {
|
|
|
|
var (
|
|
|
|
load = `load 5m
|
|
|
|
metric 0 1 2
|
|
|
|
`
|
|
|
|
query = "metric"
|
|
|
|
lastDatapointTs = time.Unix(600, 0)
|
|
|
|
)
|
|
|
|
|
|
|
|
cases := []struct {
|
|
|
|
name string
|
|
|
|
ts time.Time
|
|
|
|
engineLookback, queryLookback time.Duration
|
|
|
|
expectSamples bool
|
|
|
|
}{
|
|
|
|
{
|
|
|
|
name: "default lookback delta",
|
|
|
|
ts: lastDatapointTs.Add(defaultLookbackDelta),
|
|
|
|
expectSamples: true,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
name: "outside default lookback delta",
|
|
|
|
ts: lastDatapointTs.Add(defaultLookbackDelta + time.Millisecond),
|
|
|
|
expectSamples: false,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
name: "custom engine lookback delta",
|
|
|
|
ts: lastDatapointTs.Add(10 * time.Minute),
|
|
|
|
engineLookback: 10 * time.Minute,
|
|
|
|
expectSamples: true,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
name: "outside custom engine lookback delta",
|
|
|
|
ts: lastDatapointTs.Add(10*time.Minute + time.Millisecond),
|
|
|
|
engineLookback: 10 * time.Minute,
|
|
|
|
expectSamples: false,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
name: "custom query lookback delta",
|
|
|
|
ts: lastDatapointTs.Add(20 * time.Minute),
|
|
|
|
engineLookback: 10 * time.Minute,
|
|
|
|
queryLookback: 20 * time.Minute,
|
|
|
|
expectSamples: true,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
name: "outside custom query lookback delta",
|
|
|
|
ts: lastDatapointTs.Add(20*time.Minute + time.Millisecond),
|
|
|
|
engineLookback: 10 * time.Minute,
|
|
|
|
queryLookback: 20 * time.Minute,
|
|
|
|
expectSamples: false,
|
|
|
|
},
|
|
|
|
{
|
|
|
|
name: "negative custom query lookback delta",
|
|
|
|
ts: lastDatapointTs.Add(20 * time.Minute),
|
|
|
|
engineLookback: -10 * time.Minute,
|
|
|
|
queryLookback: 20 * time.Minute,
|
|
|
|
expectSamples: true,
|
|
|
|
},
|
|
|
|
}
|
|
|
|
|
|
|
|
for _, c := range cases {
|
|
|
|
c := c
|
|
|
|
t.Run(c.name, func(t *testing.T) {
|
2023-08-18 11:48:59 -07:00
|
|
|
engine := newTestEngine()
|
|
|
|
storage := LoadedStorage(t, load)
|
|
|
|
t.Cleanup(func() { storage.Close() })
|
2022-08-02 02:15:39 -07:00
|
|
|
|
|
|
|
if c.engineLookback != 0 {
|
2023-08-18 11:48:59 -07:00
|
|
|
engine.lookbackDelta = c.engineLookback
|
2022-08-02 02:15:39 -07:00
|
|
|
}
|
2023-07-03 05:56:06 -07:00
|
|
|
opts := NewPrometheusQueryOpts(false, c.queryLookback)
|
2023-08-18 11:48:59 -07:00
|
|
|
qry, err := engine.NewInstantQuery(context.Background(), storage, opts, query, c.ts)
|
2022-08-02 02:15:39 -07:00
|
|
|
require.NoError(t, err)
|
|
|
|
|
2023-08-18 11:48:59 -07:00
|
|
|
res := qry.Exec(context.Background())
|
2022-08-02 02:15:39 -07:00
|
|
|
require.NoError(t, res.Err)
|
|
|
|
vec, ok := res.Value.(Vector)
|
|
|
|
require.True(t, ok)
|
|
|
|
if c.expectSamples {
|
|
|
|
require.NotEmpty(t, vec)
|
|
|
|
} else {
|
|
|
|
require.Empty(t, vec)
|
|
|
|
}
|
|
|
|
})
|
|
|
|
}
|
|
|
|
}
|