prometheus/web/federate.go

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2015-08-24 17:19:21 +00:00
// Copyright 2015 The Prometheus Authors
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
package web
import (
"errors"
"fmt"
"net/http"
"sort"
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"strings"
"github.com/go-kit/log/level"
"github.com/gogo/protobuf/proto"
"github.com/prometheus/client_golang/prometheus"
dto "github.com/prometheus/client_model/go"
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"github.com/prometheus/common/expfmt"
"github.com/prometheus/common/model"
"golang.org/x/exp/slices"
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"github.com/prometheus/prometheus/model/histogram"
"github.com/prometheus/prometheus/model/labels"
"github.com/prometheus/prometheus/model/timestamp"
"github.com/prometheus/prometheus/model/value"
"github.com/prometheus/prometheus/promql"
"github.com/prometheus/prometheus/promql/parser"
"github.com/prometheus/prometheus/storage"
"github.com/prometheus/prometheus/tsdb"
"github.com/prometheus/prometheus/tsdb/chunkenc"
)
var (
federationErrors = prometheus.NewCounter(prometheus.CounterOpts{
Name: "prometheus_web_federation_errors_total",
Help: "Total number of errors that occurred while sending federation responses.",
})
federationWarnings = prometheus.NewCounter(prometheus.CounterOpts{
Name: "prometheus_web_federation_warnings_total",
Help: "Total number of warnings that occurred while sending federation responses.",
})
)
func registerFederationMetrics(r prometheus.Registerer) {
r.MustRegister(federationWarnings, federationErrors)
}
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func (h *Handler) federation(w http.ResponseWriter, req *http.Request) {
h.mtx.RLock()
defer h.mtx.RUnlock()
ctx := req.Context()
if err := req.ParseForm(); err != nil {
http.Error(w, fmt.Sprintf("error parsing form values: %v", err), http.StatusBadRequest)
return
}
var matcherSets [][]*labels.Matcher
for _, s := range req.Form["match[]"] {
matchers, err := parser.ParseMetricSelector(s)
if err != nil {
http.Error(w, err.Error(), http.StatusBadRequest)
return
}
matcherSets = append(matcherSets, matchers)
}
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var (
mint = timestamp.FromTime(h.now().Time().Add(-h.lookbackDelta))
maxt = timestamp.FromTime(h.now().Time())
format = expfmt.Negotiate(req.Header)
enc = expfmt.NewEncoder(w, format)
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)
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w.Header().Set("Content-Type", string(format))
q, err := h.localStorage.Querier(mint, maxt)
if err != nil {
federationErrors.Inc()
if errors.Is(err, tsdb.ErrNotReady) {
http.Error(w, err.Error(), http.StatusServiceUnavailable)
return
}
http.Error(w, err.Error(), http.StatusInternalServerError)
return
}
defer q.Close()
vec := make(promql.Vector, 0, 8000)
hints := &storage.SelectHints{Start: mint, End: maxt}
var sets []storage.SeriesSet
for _, mset := range matcherSets {
s := q.Select(ctx, true, hints, mset...)
sets = append(sets, s)
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}
storage: Added Chunks{Queryable/Querier/SeriesSet/Series/Iteratable. Added generic Merge{SeriesSet/Querier} implementation. (#7005) * storage: Added Chunks{Queryable/Querier/SeriesSet/Series/Iteratable. Added generic Merge{SeriesSet/Querier} implementation. ## Rationales: In many places (e.g. chunk Remote read, Thanos Receive fetching chunk from TSDB), we operate on encoded chunks not samples. This means that we unnecessary decode/encode, wasting CPU, time and memory. This PR adds chunk iterator interfaces and makes the merge code to be reused between both seriesSets I will make the use of it in following PR inside tsdb itself. For now fanout implements it and mergers. All merges now also allows passing series mergers. This opens doors for custom deduplications other than TSDB vertical ones (e.g. offline one we have in Thanos). ## Changes * Added Chunk versions of all iterating methods. It all starts in Querier/ChunkQuerier. The plan is that Storage will implement both chunked and samples. * Added Seek to chunks.Iterator interface for iterating over chunks. * NewMergeChunkQuerier was added; Both this and NewMergeQuerier are now using generigMergeQuerier to share the code. Generic code was added. * Improved tests. * Added some TODO for further simplifications in next PRs. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com> * Addressed Brian's comments. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com> * Moved s/Labeled/SeriesLabels as per Krasi suggestion. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com> * Addressed Krasi's comments. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com> * Second iteration of Krasi comments. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com> * Another round of comments. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com>
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set := storage.NewMergeSeriesSet(sets, storage.ChainedSeriesMerge)
it := storage.NewBuffer(int64(h.lookbackDelta / 1e6))
var chkIter chunkenc.Iterator
Loop:
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for set.Next() {
s := set.At()
// TODO(fabxc): allow fast path for most recent sample either
// in the storage itself or caching layer in Prometheus.
chkIter = s.Iterator(chkIter)
it.Reset(chkIter)
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var (
t int64
f float64
fh *histogram.FloatHistogram
)
valueType := it.Seek(maxt)
switch valueType {
case chunkenc.ValFloat:
t, f = it.At()
case chunkenc.ValFloatHistogram, chunkenc.ValHistogram:
t, fh = it.AtFloatHistogram()
default:
sample, ok := it.PeekBack(1)
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if !ok {
continue Loop
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}
t = sample.T()
switch sample.Type() {
case chunkenc.ValFloat:
f = sample.F()
case chunkenc.ValHistogram:
fh = sample.H().ToFloat()
case chunkenc.ValFloatHistogram:
fh = sample.FH()
default:
continue Loop
}
}
// The exposition formats do not support stale markers, so drop them. This
// is good enough for staleness handling of federated data, as the
// interval-based limits on staleness will do the right thing for supported
// use cases (which is to say federating aggregated time series).
if value.IsStaleNaN(f) || (fh != nil && value.IsStaleNaN(fh.Sum)) {
continue
}
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vec = append(vec, promql.Sample{
Metric: s.Labels(),
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>
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T: t,
F: f,
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>
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H: fh,
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})
}
*: Consistent Error/Warning handling for SeriesSet iterator: Allowing Async Select (#7251) * Add errors and Warnings to SeriesSet Signed-off-by: Kemal Akkoyun <kakkoyun@gmail.com> * Change Querier interface and refactor accordingly Signed-off-by: Kemal Akkoyun <kakkoyun@gmail.com> * Refactor promql/engine to propagate warnings at eval stage Signed-off-by: Kemal Akkoyun <kakkoyun@gmail.com> * Address review issues Signed-off-by: Kemal Akkoyun <kakkoyun@gmail.com> * Make sure all the series from all Selects are pre-advanced Signed-off-by: Kemal Akkoyun <kakkoyun@gmail.com> * Address review issues Signed-off-by: Kemal Akkoyun <kakkoyun@gmail.com> * Separate merge series sets Signed-off-by: Kemal Akkoyun <kakkoyun@gmail.com> * Clean Signed-off-by: Kemal Akkoyun <kakkoyun@gmail.com> * Refactor merge querier failure handling Signed-off-by: Kemal Akkoyun <kakkoyun@gmail.com> * Refactored and simplified fanout with improvements from incoming chunk iterator PRs. * Secondary logic is hidden, instead of weird failed series set logic we had. * Fanout is well commented * Fanout closing record all errors * MergeQuerier improved API (clearer) * deferredGenericMergeSeriesSet is not needed as we return no samples anyway for failed series sets (next = false). Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com> * Fix formatting Signed-off-by: Kemal Akkoyun <kakkoyun@gmail.com> * Fix CI issues Signed-off-by: Kemal Akkoyun <kakkoyun@gmail.com> * Added final tests for error handling. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com> * Addressed Brian's comments. * Moved hints in populate to be allocated only when needed. * Used sync.Once in secondary Querier to achieve all-or-nothing partial response logic. * Select after first Next is done will panic. NOTE: in lazySeriesSet in theory we could just panic, I think however we can totally just return error, it will panic in expand anyway. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com> * Utilize errWithWarnings Signed-off-by: Kemal Akkoyun <kakkoyun@gmail.com> * Fix recently introduced expansion issue Signed-off-by: Kemal Akkoyun <kakkoyun@gmail.com> * Add tests for secondary querier error handling Signed-off-by: Kemal Akkoyun <kakkoyun@gmail.com> * Implement lazy merge Signed-off-by: Kemal Akkoyun <kakkoyun@gmail.com> * Add name to test cases Signed-off-by: Kemal Akkoyun <kakkoyun@gmail.com> * Reorganize Signed-off-by: Kemal Akkoyun <kakkoyun@gmail.com> * Address review comments Signed-off-by: Kemal Akkoyun <kakkoyun@gmail.com> * Address review comments Signed-off-by: Kemal Akkoyun <kakkoyun@gmail.com> * Remove redundant warnings Signed-off-by: Kemal Akkoyun <kakkoyun@gmail.com> * Fix rebase mistake Signed-off-by: Kemal Akkoyun <kakkoyun@gmail.com> Co-authored-by: Bartlomiej Plotka <bwplotka@gmail.com>
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if ws := set.Warnings(); len(ws) > 0 {
level.Debug(h.logger).Log("msg", "Federation select returned warnings", "warnings", ws)
federationWarnings.Add(float64(len(ws)))
}
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if set.Err() != nil {
federationErrors.Inc()
http.Error(w, set.Err().Error(), http.StatusInternalServerError)
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return
}
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slices.SortFunc(vec, func(a, b promql.Sample) int {
ni := a.Metric.Get(labels.MetricName)
nj := b.Metric.Get(labels.MetricName)
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return strings.Compare(ni, nj)
})
externalLabels := h.config.GlobalConfig.ExternalLabels.Map()
if _, ok := externalLabels[model.InstanceLabel]; !ok {
externalLabels[model.InstanceLabel] = ""
}
externalLabelNames := make([]string, 0, len(externalLabels))
for ln := range externalLabels {
externalLabelNames = append(externalLabelNames, ln)
}
sort.Strings(externalLabelNames)
var (
lastMetricName string
lastWasHistogram, lastHistogramWasGauge bool
protMetricFam *dto.MetricFamily
)
for _, s := range vec {
isHistogram := s.H != nil
if isHistogram &&
format != expfmt.FmtProtoDelim && format != expfmt.FmtProtoText && format != expfmt.FmtProtoCompact {
// Can't serve the native histogram.
// TODO(codesome): Serve them when other protocols get the native histogram support.
continue
}
nameSeen := false
globalUsed := map[string]struct{}{}
protMetric := &dto.Metric{}
err := s.Metric.Validate(func(l labels.Label) error {
if l.Value == "" {
// No value means unset. Never consider those labels.
// This is also important to protect against nameless metrics.
return nil
}
if l.Name == labels.MetricName {
nameSeen = true
if l.Value == lastMetricName && // We already have the name in the current MetricFamily, and we ignore nameless metrics.
lastWasHistogram == isHistogram && // The sample type matches (float vs histogram).
// If it was a histogram, the histogram type (counter vs gauge) also matches.
(!isHistogram || lastHistogramWasGauge == (s.H.CounterResetHint == histogram.GaugeType)) {
return nil
}
// Since we now check for the sample type and type of histogram above, we will end up
// creating multiple metric families for the same metric name. This would technically be
// an invalid exposition. But since the consumer of this is Prometheus, and Prometheus can
// parse it fine, we allow it and bend the rules to make federation possible in those cases.
// Need to start a new MetricFamily. Ship off the old one (if any) before
// creating the new one.
if protMetricFam != nil {
if err := enc.Encode(protMetricFam); err != nil {
return err
}
}
protMetricFam = &dto.MetricFamily{
Type: dto.MetricType_UNTYPED.Enum(),
Name: proto.String(l.Value),
}
if isHistogram {
if s.H.CounterResetHint == histogram.GaugeType {
protMetricFam.Type = dto.MetricType_GAUGE_HISTOGRAM.Enum()
} else {
protMetricFam.Type = dto.MetricType_HISTOGRAM.Enum()
}
}
lastMetricName = l.Value
return nil
}
protMetric.Label = append(protMetric.Label, &dto.LabelPair{
Name: proto.String(l.Name),
Value: proto.String(l.Value),
})
if _, ok := externalLabels[l.Name]; ok {
globalUsed[l.Name] = struct{}{}
}
return nil
})
if err != nil {
federationErrors.Inc()
level.Error(h.logger).Log("msg", "federation failed", "err", err)
return
}
if !nameSeen {
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level.Warn(h.logger).Log("msg", "Ignoring nameless metric during federation", "metric", s.Metric)
continue
}
// Attach global labels if they do not exist yet.
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for _, ln := range externalLabelNames {
lv := externalLabels[ln]
if _, ok := globalUsed[ln]; !ok {
protMetric.Label = append(protMetric.Label, &dto.LabelPair{
Name: proto.String(ln),
Value: proto.String(lv),
})
}
}
protMetric.TimestampMs = proto.Int64(s.T)
if !isHistogram {
lastHistogramWasGauge = false
protMetric.Untyped = &dto.Untyped{
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 14:58:40 +00:00
Value: proto.Float64(s.F),
}
} else {
lastHistogramWasGauge = s.H.CounterResetHint == histogram.GaugeType
protMetric.Histogram = &dto.Histogram{
SampleCountFloat: proto.Float64(s.H.Count),
SampleSum: proto.Float64(s.H.Sum),
Schema: proto.Int32(s.H.Schema),
ZeroThreshold: proto.Float64(s.H.ZeroThreshold),
ZeroCountFloat: proto.Float64(s.H.ZeroCount),
NegativeCount: s.H.NegativeBuckets,
PositiveCount: s.H.PositiveBuckets,
}
if len(s.H.PositiveSpans) > 0 {
protMetric.Histogram.PositiveSpan = make([]*dto.BucketSpan, len(s.H.PositiveSpans))
for i, sp := range s.H.PositiveSpans {
protMetric.Histogram.PositiveSpan[i] = &dto.BucketSpan{
Offset: proto.Int32(sp.Offset),
Length: proto.Uint32(sp.Length),
}
}
}
if len(s.H.NegativeSpans) > 0 {
protMetric.Histogram.NegativeSpan = make([]*dto.BucketSpan, len(s.H.NegativeSpans))
for i, sp := range s.H.NegativeSpans {
protMetric.Histogram.NegativeSpan[i] = &dto.BucketSpan{
Offset: proto.Int32(sp.Offset),
Length: proto.Uint32(sp.Length),
}
}
}
}
lastWasHistogram = isHistogram
protMetricFam.Metric = append(protMetricFam.Metric, protMetric)
}
// Still have to ship off the last MetricFamily, if any.
if protMetricFam != nil {
if err := enc.Encode(protMetricFam); err != nil {
federationErrors.Inc()
2017-08-11 18:45:52 +00:00
level.Error(h.logger).Log("msg", "federation failed", "err", err)
}
}
}