prometheus/promql/analyzer.go
beorn7 836f1db04c Improve MetricsForLabelMatchers
WIP: This needs more tests.

It now gets a from and through value, which it may opportunistically
use to optimize the retrieval. With possible future range indices,
this could be used in a very efficient way. This change merely applies
some easy checks, which should nevertheless solve the use case of
heavy rule evaluations on servers with a lot of series churn.

Idea is the following:

- Only archive series that are at least as old as the headChunkTimeout
  (which was already extremely unlikely to happen).

- Then maintain a high watermark for the last archival, i.e. no
  archived series has a sample more recent than that watermark.

- Any query that doesn't reach to a time before that watermark doesn't
  have to touch the archive index at all. (A production server at
  Soundcloud with the aforementioned series churn and heavy rule
  evaluations spends 50% of its CPU time in archive index
  lookups. Since rule evaluations usually only touch very recent
  values, most of those lookup should disappear with this change.)

- Federation with a very broad label matcher will profit from this,
  too.

As a byproduct, the un-needed MetricForFingerprint method was removed
from the Storage interface.
2016-03-09 00:25:59 +01:00

182 lines
6.0 KiB
Go

// Copyright 2013 The Prometheus Authors
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
package promql
import (
"errors"
"time"
"github.com/prometheus/common/model"
"golang.org/x/net/context"
"github.com/prometheus/prometheus/storage/local"
)
// An Analyzer traverses an expression and determines which data has to be requested
// from the storage. It is bound to a context that allows cancellation and timing out.
type Analyzer struct {
// The storage from which to query data.
Storage local.Storage
// The expression being analyzed.
Expr Expr
// The time range for evaluation of Expr.
Start, End model.Time
// The preload times for different query time offsets.
offsetPreloadTimes map[time.Duration]preloadTimes
}
// preloadTimes tracks which instants or ranges to preload for a set of
// fingerprints. One of these structs is collected for each offset by the query
// analyzer.
type preloadTimes struct {
// Ranges require loading a range of samples. They can be triggered by
// two type of expressions: First a range expression AKA matrix
// selector, where the Duration in the ranges map is the length of the
// range in the range expression. Second an instant expression AKA
// vector selector, where the Duration in the ranges map is the
// StalenessDelta. In preloading, both types of expressions result in
// the same effect: Preload everything between the specified start time
// minus the Duration in the ranges map up to the specified end time.
ranges map[model.Fingerprint]time.Duration
// Instants require a single sample to be loaded. This only happens for
// instant expressions AKA vector selectors iff the specified start ond
// end time are the same, Thus, instants is only populated if start and
// end time are the same.
instants map[model.Fingerprint]struct{}
}
// Analyze the provided expression and attach metrics and fingerprints to data-selecting
// AST nodes that are later used to preload the data from the storage.
func (a *Analyzer) Analyze(ctx context.Context) error {
a.offsetPreloadTimes = map[time.Duration]preloadTimes{}
getPreloadTimes := func(offset time.Duration) preloadTimes {
if pt, ok := a.offsetPreloadTimes[offset]; ok {
return pt
}
pt := preloadTimes{
instants: map[model.Fingerprint]struct{}{},
ranges: map[model.Fingerprint]time.Duration{},
}
a.offsetPreloadTimes[offset] = pt
return pt
}
// Retrieve fingerprints and metrics for the required time range for
// each metric or matrix selector node.
Inspect(a.Expr, func(node Node) bool {
switch n := node.(type) {
case *VectorSelector:
n.metrics = a.Storage.MetricsForLabelMatchers(
a.Start.Add(-n.Offset-StalenessDelta), a.End.Add(-n.Offset),
n.LabelMatchers...,
)
n.iterators = make(map[model.Fingerprint]local.SeriesIterator, len(n.metrics))
pt := getPreloadTimes(n.Offset)
for fp := range n.metrics {
r, alreadyInRanges := pt.ranges[fp]
if a.Start.Equal(a.End) && !alreadyInRanges {
// A true instant, we only need one value.
pt.instants[fp] = struct{}{}
continue
}
if r < StalenessDelta {
pt.ranges[fp] = StalenessDelta
}
}
case *MatrixSelector:
n.metrics = a.Storage.MetricsForLabelMatchers(
a.Start.Add(-n.Offset-n.Range), a.End.Add(-n.Offset),
n.LabelMatchers...,
)
n.iterators = make(map[model.Fingerprint]local.SeriesIterator, len(n.metrics))
pt := getPreloadTimes(n.Offset)
for fp := range n.metrics {
if pt.ranges[fp] < n.Range {
pt.ranges[fp] = n.Range
// Delete the fingerprint from the instants. Ranges always contain more
// points and span more time than instants, so we don't need to track
// an instant for the same fingerprint, should we have one.
delete(pt.instants, fp)
}
}
}
return true
})
// Currently we do not return an error but we might place a context check in here
// or extend the stage in some other way.
return nil
}
// Prepare the expression evaluation by preloading all required chunks from the storage
// and setting the respective storage iterators in the AST nodes.
func (a *Analyzer) Prepare(ctx context.Context) (local.Preloader, error) {
const env = "query preparation"
if a.offsetPreloadTimes == nil {
return nil, errors.New("analysis must be performed before preparing query")
}
var err error
// The preloader must not be closed unless an error occured as closing
// unpins the preloaded chunks.
p := a.Storage.NewPreloader()
defer func() {
if err != nil {
p.Close()
}
}()
// Preload all analyzed ranges.
iters := map[time.Duration]map[model.Fingerprint]local.SeriesIterator{}
for offset, pt := range a.offsetPreloadTimes {
itersForDuration := map[model.Fingerprint]local.SeriesIterator{}
iters[offset] = itersForDuration
start := a.Start.Add(-offset)
end := a.End.Add(-offset)
for fp, rangeDuration := range pt.ranges {
if err = contextDone(ctx, env); err != nil {
return nil, err
}
itersForDuration[fp] = p.PreloadRange(fp, start.Add(-rangeDuration), end)
}
for fp := range pt.instants {
if err = contextDone(ctx, env); err != nil {
return nil, err
}
itersForDuration[fp] = p.PreloadInstant(fp, start, StalenessDelta)
}
}
// Attach storage iterators to AST nodes.
Inspect(a.Expr, func(node Node) bool {
switch n := node.(type) {
case *VectorSelector:
for fp := range n.metrics {
n.iterators[fp] = iters[n.Offset][fp]
}
case *MatrixSelector:
for fp := range n.metrics {
n.iterators[fp] = iters[n.Offset][fp]
}
}
return true
})
return p, nil
}