prometheus/storage/local/storage.go

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// Copyright 2014 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 local contains the local time series storage used by Prometheus.
package local
import (
"container/list"
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"fmt"
"math"
"sync"
"sync/atomic"
"time"
"github.com/prometheus/client_golang/prometheus"
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"github.com/prometheus/common/log"
"github.com/prometheus/common/model"
"github.com/prometheus/prometheus/storage/metric"
)
const (
evictRequestsCap = 1024
chunkLen = 1024
// See waitForNextFP.
fpMaxSweepTime = 6 * time.Hour
fpMaxWaitDuration = 10 * time.Second
// See waitForNextFP.
maxEvictInterval = time.Minute
// Constants to control the hysteresis of entering and leaving "rushed
// mode". In rushed mode, the dirty series count is ignored for
// checkpointing, series are maintained as frequently as possible, and
// series files are not synced if the adaptive sync strategy is used.
persintenceUrgencyScoreForEnteringRushedMode = 0.8
persintenceUrgencyScoreForLeavingRushedMode = 0.7
// This factor times -storage.local.memory-chunks is the number of
// memory chunks we tolerate before throttling the storage. It is also a
// basis for calculating the persistenceUrgencyScore.
toleranceFactorMemChunks = 1.1
// This factor times -storage.local.max-chunks-to-persist is the minimum
// required number of chunks waiting for persistence before the number
// of chunks in memory may influence the persistenceUrgencyScore. (In
// other words: if there are no chunks to persist, it doesn't help chunk
// eviction if we speed up persistence.)
factorMinChunksToPersist = 0.2
)
var (
numChunksToPersistDesc = prometheus.NewDesc(
prometheus.BuildFQName(namespace, subsystem, "chunks_to_persist"),
"The current number of chunks waiting for persistence.",
nil, nil,
)
maxChunksToPersistDesc = prometheus.NewDesc(
prometheus.BuildFQName(namespace, subsystem, "max_chunks_to_persist"),
"The maximum number of chunks that can be waiting for persistence before sample ingestion will stop.",
nil, nil,
)
)
type evictRequest struct {
cd *chunkDesc
evict bool
}
// SyncStrategy is an enum to select a sync strategy for series files.
type SyncStrategy int
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// String implements flag.Value.
func (ss SyncStrategy) String() string {
switch ss {
case Adaptive:
return "adaptive"
case Always:
return "always"
case Never:
return "never"
}
return "<unknown>"
}
// Set implements flag.Value.
func (ss *SyncStrategy) Set(s string) error {
switch s {
case "adaptive":
*ss = Adaptive
case "always":
*ss = Always
case "never":
*ss = Never
default:
return fmt.Errorf("invalid sync strategy: %s", s)
}
return nil
}
// Possible values for SyncStrategy.
const (
_ SyncStrategy = iota
Never
Always
Adaptive
)
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// A syncStrategy is a function that returns whether series files should be
// synced or not. It does not need to be goroutine safe.
type syncStrategy func() bool
type memorySeriesStorage struct {
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// numChunksToPersist has to be aligned for atomic operations.
numChunksToPersist int64 // The number of chunks waiting for persistence.
maxChunksToPersist int // If numChunksToPersist reaches this threshold, ingestion will be throttled.
rushed bool // Whether the storage is in rushed mode.
rushedMtx sync.Mutex // Protects entering and exiting rushed mode.
throttled chan struct{} // This chan is sent to whenever NeedsThrottling() returns true (for logging).
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fpLocker *fingerprintLocker
fpToSeries *seriesMap
options *MemorySeriesStorageOptions
loopStopping, loopStopped chan struct{}
logThrottlingStopped chan struct{}
maxMemoryChunks int
dropAfter time.Duration
checkpointInterval time.Duration
checkpointDirtySeriesLimit int
persistence *persistence
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mapper *fpMapper
evictList *list.List
evictRequests chan evictRequest
evictStopping, evictStopped chan struct{}
persistErrors prometheus.Counter
numSeries prometheus.Gauge
seriesOps *prometheus.CounterVec
ingestedSamplesCount prometheus.Counter
outOfOrderSamplesCount prometheus.Counter
invalidPreloadRequestsCount prometheus.Counter
maintainSeriesDuration *prometheus.SummaryVec
persistenceUrgencyScore prometheus.Gauge
rushedMode prometheus.Gauge
}
// MemorySeriesStorageOptions contains options needed by
// NewMemorySeriesStorage. It is not safe to leave any of those at their zero
// values.
type MemorySeriesStorageOptions struct {
MemoryChunks int // How many chunks to keep in memory.
MaxChunksToPersist int // Max number of chunks waiting to be persisted.
PersistenceStoragePath string // Location of persistence files.
PersistenceRetentionPeriod time.Duration // Chunks at least that old are dropped.
CheckpointInterval time.Duration // How often to checkpoint the series map and head chunks.
CheckpointDirtySeriesLimit int // How many dirty series will trigger an early checkpoint.
Dirty bool // Force the storage to consider itself dirty on startup.
PedanticChecks bool // If dirty, perform crash-recovery checks on each series file.
SyncStrategy SyncStrategy // Which sync strategy to apply to series files.
MinShrinkRatio float64 // Minimum ratio a series file has to shrink during truncation.
}
// NewMemorySeriesStorage returns a newly allocated Storage. Storage.Serve still
// has to be called to start the storage.
func NewMemorySeriesStorage(o *MemorySeriesStorageOptions) Storage {
s := &memorySeriesStorage{
fpLocker: newFingerprintLocker(1024),
options: o,
loopStopping: make(chan struct{}),
loopStopped: make(chan struct{}),
logThrottlingStopped: make(chan struct{}),
throttled: make(chan struct{}, 1),
maxMemoryChunks: o.MemoryChunks,
dropAfter: o.PersistenceRetentionPeriod,
checkpointInterval: o.CheckpointInterval,
checkpointDirtySeriesLimit: o.CheckpointDirtySeriesLimit,
maxChunksToPersist: o.MaxChunksToPersist,
evictList: list.New(),
evictRequests: make(chan evictRequest, evictRequestsCap),
evictStopping: make(chan struct{}),
evictStopped: make(chan struct{}),
persistErrors: prometheus.NewCounter(prometheus.CounterOpts{
Namespace: namespace,
Subsystem: subsystem,
Name: "persist_errors_total",
Help: "The total number of errors while persisting chunks.",
}),
numSeries: prometheus.NewGauge(prometheus.GaugeOpts{
Namespace: namespace,
Subsystem: subsystem,
Name: "memory_series",
Help: "The current number of series in memory.",
}),
seriesOps: prometheus.NewCounterVec(
prometheus.CounterOpts{
Namespace: namespace,
Subsystem: subsystem,
Name: "series_ops_total",
Help: "The total number of series operations by their type.",
},
[]string{opTypeLabel},
),
ingestedSamplesCount: prometheus.NewCounter(prometheus.CounterOpts{
Namespace: namespace,
Subsystem: subsystem,
Name: "ingested_samples_total",
Help: "The total number of samples ingested.",
}),
outOfOrderSamplesCount: prometheus.NewCounter(prometheus.CounterOpts{
Namespace: namespace,
Subsystem: subsystem,
Name: "out_of_order_samples_total",
Help: "The total number of samples that were discarded because their timestamps were at or before the last received sample for a series.",
}),
invalidPreloadRequestsCount: prometheus.NewCounter(prometheus.CounterOpts{
Namespace: namespace,
Subsystem: subsystem,
Name: "invalid_preload_requests_total",
Help: "The total number of preload requests referring to a non-existent series. This is an indication of outdated label indexes.",
}),
maintainSeriesDuration: prometheus.NewSummaryVec(
prometheus.SummaryOpts{
Namespace: namespace,
Subsystem: subsystem,
Name: "maintain_series_duration_milliseconds",
Help: "The duration (in milliseconds) it took to perform maintenance on a series.",
},
[]string{seriesLocationLabel},
),
persistenceUrgencyScore: prometheus.NewGauge(prometheus.GaugeOpts{
Namespace: namespace,
Subsystem: subsystem,
Name: "persistence_urgency_score",
Help: "A score of urgency to persist chunks, 0 is least urgent, 1 most.",
}),
rushedMode: prometheus.NewGauge(prometheus.GaugeOpts{
Namespace: namespace,
Subsystem: subsystem,
Name: "rushed_mode",
Help: "1 if the storage is in rushed mode, 0 otherwise. In rushed mode, the system behaves as if the persistence_urgency_score is 1.",
}),
}
return s
}
// Start implements Storage.
func (s *memorySeriesStorage) Start() (err error) {
var syncStrategy syncStrategy
switch s.options.SyncStrategy {
case Never:
syncStrategy = func() bool { return false }
case Always:
syncStrategy = func() bool { return true }
case Adaptive:
syncStrategy = func() bool { return s.calculatePersistenceUrgencyScore() < 1 }
default:
panic("unknown sync strategy")
}
var p *persistence
p, err = newPersistence(
s.options.PersistenceStoragePath,
s.options.Dirty, s.options.PedanticChecks,
syncStrategy,
s.options.MinShrinkRatio,
)
if err != nil {
return err
}
s.persistence = p
// Persistence must start running before loadSeriesMapAndHeads() is called.
go s.persistence.run()
defer func() {
if err != nil {
if e := p.close(); e != nil {
log.Errorln("Error closing persistence:", e)
}
}
}()
log.Info("Loading series map and head chunks...")
s.fpToSeries, s.numChunksToPersist, err = p.loadSeriesMapAndHeads()
if err != nil {
return err
}
log.Infof("%d series loaded.", s.fpToSeries.length())
s.numSeries.Set(float64(s.fpToSeries.length()))
s.mapper, err = newFPMapper(s.fpToSeries, p)
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if err != nil {
return err
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}
go s.handleEvictList()
go s.logThrottling()
go s.loop()
return nil
}
// Stop implements Storage.
func (s *memorySeriesStorage) Stop() error {
log.Info("Stopping local storage...")
log.Info("Stopping maintenance loop...")
close(s.loopStopping)
<-s.loopStopped
log.Info("Stopping chunk eviction...")
close(s.evictStopping)
<-s.evictStopped
// One final checkpoint of the series map and the head chunks.
if err := s.persistence.checkpointSeriesMapAndHeads(s.fpToSeries, s.fpLocker); err != nil {
return err
}
if err := s.persistence.close(); err != nil {
return err
}
log.Info("Local storage stopped.")
return nil
}
// WaitForIndexing implements Storage.
func (s *memorySeriesStorage) WaitForIndexing() {
s.persistence.waitForIndexing()
}
// LastSampleForFingerprint implements Storage.
func (s *memorySeriesStorage) LastSamplePairForFingerprint(fp model.Fingerprint) model.SamplePair {
s.fpLocker.Lock(fp)
defer s.fpLocker.Unlock(fp)
series, ok := s.fpToSeries.get(fp)
if !ok {
return ZeroSamplePair
}
return series.lastSamplePair()
}
// boundedIterator wraps a SeriesIterator and does not allow fetching
// data from earlier than the configured start time.
type boundedIterator struct {
it SeriesIterator
start model.Time
}
Streamline series iterator creation This will fix issue #1035 and will also help to make issue #1264 less bad. The fundamental problem in the current code: In the preload phase, we quite accurately determine which chunks will be used for the query being executed. However, in the subsequent step of creating series iterators, the created iterators are referencing _all_ in-memory chunks in their series, even the un-pinned ones. In iterator creation, we copy a pointer to each in-memory chunk of a series into the iterator. While this creates a certain amount of allocation churn, the worst thing about it is that copying the chunk pointer out of the chunkDesc requires a mutex acquisition. (Remember that the iterator will also reference un-pinned chunks, so we need to acquire the mutex to protect against concurrent eviction.) The worst case happens if a series doesn't even contain any relevant samples for the query time range. We notice that during preloading but then we will still create a series iterator for it. But even for series that do contain relevant samples, the overhead is quite bad for instant queries that retrieve a single sample from each series, but still go through all the effort of series iterator creation. All of that is particularly bad if a series has many in-memory chunks. This commit addresses the problem from two sides: First, it merges preloading and iterator creation into one step, i.e. the preload call returns an iterator for exactly the preloaded chunks. Second, the required mutex acquisition in chunkDesc has been greatly reduced. That was enabled by a side effect of the first step, which is that the iterator is only referencing pinned chunks, so there is no risk of concurrent eviction anymore, and chunks can be accessed without mutex acquisition. To simplify the code changes for the above, the long-planned change of ValueAtTime to ValueAtOrBefore time was performed at the same time. (It should have been done first, but it kind of accidentally happened while I was in the middle of writing the series iterator changes. Sorry for that.) So far, we actively filtered the up to two values that were returned by ValueAtTime, i.e. we invested work to retrieve up to two values, and then we invested more work to throw one of them away. The SeriesIterator.BoundaryValues method can be removed once #1401 is fixed. But I really didn't want to load even more changes into this PR. Benchmarks: The BenchmarkFuzz.* benchmarks run 83% faster (i.e. about six times faster) and allocate 95% fewer bytes. The reason for that is that the benchmark reads one sample after another from the time series and creates a new series iterator for each sample read. To find out how much these improvements matter in practice, I have mirrored a beefy Prometheus server at SoundCloud that suffers from both issues #1035 and #1264. To reach steady state that would be comparable, the server needs to run for 15d. So far, it has run for 1d. The test server currently has only half as many memory time series and 60% of the memory chunks the main server has. The 90th percentile rule evaluation cycle time is ~11s on the main server and only ~3s on the test server. However, these numbers might get much closer over time. In addition to performance improvements, this commit removes about 150 LOC.
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// ValueAtOrBeforeTime implements the SeriesIterator interface.
func (bit *boundedIterator) ValueAtOrBeforeTime(ts model.Time) model.SamplePair {
if ts < bit.start {
return ZeroSamplePair
}
Streamline series iterator creation This will fix issue #1035 and will also help to make issue #1264 less bad. The fundamental problem in the current code: In the preload phase, we quite accurately determine which chunks will be used for the query being executed. However, in the subsequent step of creating series iterators, the created iterators are referencing _all_ in-memory chunks in their series, even the un-pinned ones. In iterator creation, we copy a pointer to each in-memory chunk of a series into the iterator. While this creates a certain amount of allocation churn, the worst thing about it is that copying the chunk pointer out of the chunkDesc requires a mutex acquisition. (Remember that the iterator will also reference un-pinned chunks, so we need to acquire the mutex to protect against concurrent eviction.) The worst case happens if a series doesn't even contain any relevant samples for the query time range. We notice that during preloading but then we will still create a series iterator for it. But even for series that do contain relevant samples, the overhead is quite bad for instant queries that retrieve a single sample from each series, but still go through all the effort of series iterator creation. All of that is particularly bad if a series has many in-memory chunks. This commit addresses the problem from two sides: First, it merges preloading and iterator creation into one step, i.e. the preload call returns an iterator for exactly the preloaded chunks. Second, the required mutex acquisition in chunkDesc has been greatly reduced. That was enabled by a side effect of the first step, which is that the iterator is only referencing pinned chunks, so there is no risk of concurrent eviction anymore, and chunks can be accessed without mutex acquisition. To simplify the code changes for the above, the long-planned change of ValueAtTime to ValueAtOrBefore time was performed at the same time. (It should have been done first, but it kind of accidentally happened while I was in the middle of writing the series iterator changes. Sorry for that.) So far, we actively filtered the up to two values that were returned by ValueAtTime, i.e. we invested work to retrieve up to two values, and then we invested more work to throw one of them away. The SeriesIterator.BoundaryValues method can be removed once #1401 is fixed. But I really didn't want to load even more changes into this PR. Benchmarks: The BenchmarkFuzz.* benchmarks run 83% faster (i.e. about six times faster) and allocate 95% fewer bytes. The reason for that is that the benchmark reads one sample after another from the time series and creates a new series iterator for each sample read. To find out how much these improvements matter in practice, I have mirrored a beefy Prometheus server at SoundCloud that suffers from both issues #1035 and #1264. To reach steady state that would be comparable, the server needs to run for 15d. So far, it has run for 1d. The test server currently has only half as many memory time series and 60% of the memory chunks the main server has. The 90th percentile rule evaluation cycle time is ~11s on the main server and only ~3s on the test server. However, these numbers might get much closer over time. In addition to performance improvements, this commit removes about 150 LOC.
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return bit.it.ValueAtOrBeforeTime(ts)
}
// RangeValues implements the SeriesIterator interface.
func (bit *boundedIterator) RangeValues(interval metric.Interval) []model.SamplePair {
if interval.NewestInclusive < bit.start {
return []model.SamplePair{}
}
if interval.OldestInclusive < bit.start {
interval.OldestInclusive = bit.start
}
return bit.it.RangeValues(interval)
}
// NewPreloader implements Storage.
func (s *memorySeriesStorage) NewPreloader() Preloader {
return &memorySeriesPreloader{
storage: s,
}
}
// fingerprintsForLabelPairs returns the set of fingerprints that have the given labels.
// This does not work with empty label values.
func (s *memorySeriesStorage) fingerprintsForLabelPairs(pairs ...model.LabelPair) map[model.Fingerprint]struct{} {
var result map[model.Fingerprint]struct{}
for _, pair := range pairs {
intersection := map[model.Fingerprint]struct{}{}
fps, err := s.persistence.fingerprintsForLabelPair(pair)
if err != nil {
log.Error("Error getting fingerprints for label pair: ", err)
}
if len(fps) == 0 {
return nil
}
for _, fp := range fps {
if _, ok := result[fp]; ok || result == nil {
intersection[fp] = struct{}{}
}
}
if len(intersection) == 0 {
return nil
}
result = intersection
}
return result
}
// MetricsForLabelMatchers implements Storage.
func (s *memorySeriesStorage) MetricsForLabelMatchers(matchers ...*metric.LabelMatcher) map[model.Fingerprint]metric.Metric {
var (
equals []model.LabelPair
filters []*metric.LabelMatcher
)
for _, lm := range matchers {
if lm.Type == metric.Equal && lm.Value != "" {
equals = append(equals, model.LabelPair{
Name: lm.Name,
Value: lm.Value,
})
} else {
filters = append(filters, lm)
}
}
var resFPs map[model.Fingerprint]struct{}
if len(equals) > 0 {
resFPs = s.fingerprintsForLabelPairs(equals...)
} else {
// If we cannot make a preselection based on equality matchers, expanding the other matchers to labels
// and intersecting their fingerprints is still likely to be the best choice.
var remaining metric.LabelMatchers
for _, matcher := range filters {
// Equal matches are all empty values.
if matcher.Match("") {
remaining = append(remaining, matcher)
continue
}
intersection := map[model.Fingerprint]struct{}{}
matches := matcher.Filter(s.LabelValuesForLabelName(matcher.Name))
if len(matches) == 0 {
return nil
}
for _, v := range matches {
fps := s.fingerprintsForLabelPairs(model.LabelPair{
Name: matcher.Name,
Value: v,
})
for fp := range fps {
if _, ok := resFPs[fp]; ok || resFPs == nil {
intersection[fp] = struct{}{}
}
}
}
resFPs = intersection
}
// The intersected matchers no longer need to be compared against the actual metrics.
filters = remaining
}
result := make(map[model.Fingerprint]metric.Metric, len(resFPs))
for fp := range resFPs {
result[fp] = s.MetricForFingerprint(fp)
}
for _, matcher := range filters {
for fp, met := range result {
if !matcher.Match(met.Metric[matcher.Name]) {
delete(result, fp)
}
}
}
return result
}
// LabelValuesForLabelName implements Storage.
func (s *memorySeriesStorage) LabelValuesForLabelName(labelName model.LabelName) model.LabelValues {
lvs, err := s.persistence.labelValuesForLabelName(labelName)
if err != nil {
log.Errorf("Error getting label values for label name %q: %v", labelName, err)
}
return lvs
}
// MetricForFingerprint implements Storage.
func (s *memorySeriesStorage) MetricForFingerprint(fp model.Fingerprint) metric.Metric {
s.fpLocker.Lock(fp)
defer s.fpLocker.Unlock(fp)
series, ok := s.fpToSeries.get(fp)
if ok {
// Wrap the returned metric in a copy-on-write (COW) metric here because
// the caller might mutate it.
return metric.Metric{
Metric: series.metric,
}
}
met, err := s.persistence.archivedMetric(fp)
if err != nil {
log.Errorf("Error retrieving archived metric for fingerprint %v: %v", fp, err)
}
return metric.Metric{
Metric: met,
Copied: false,
}
}
// DropMetric implements Storage.
func (s *memorySeriesStorage) DropMetricsForFingerprints(fps ...model.Fingerprint) {
for _, fp := range fps {
s.fpLocker.Lock(fp)
if series, ok := s.fpToSeries.get(fp); ok {
s.fpToSeries.del(fp)
s.numSeries.Dec()
s.persistence.unindexMetric(fp, series.metric)
} else if err := s.persistence.purgeArchivedMetric(fp); err != nil {
log.Errorf("Error purging metric with fingerprint %v: %v", fp, err)
}
// Attempt to delete series file in any case.
if _, err := s.persistence.deleteSeriesFile(fp); err != nil {
log.Errorf("Error deleting series file for %v: %v", fp, err)
}
s.fpLocker.Unlock(fp)
s.seriesOps.WithLabelValues(requestedPurge).Inc()
}
}
Streamline series iterator creation This will fix issue #1035 and will also help to make issue #1264 less bad. The fundamental problem in the current code: In the preload phase, we quite accurately determine which chunks will be used for the query being executed. However, in the subsequent step of creating series iterators, the created iterators are referencing _all_ in-memory chunks in their series, even the un-pinned ones. In iterator creation, we copy a pointer to each in-memory chunk of a series into the iterator. While this creates a certain amount of allocation churn, the worst thing about it is that copying the chunk pointer out of the chunkDesc requires a mutex acquisition. (Remember that the iterator will also reference un-pinned chunks, so we need to acquire the mutex to protect against concurrent eviction.) The worst case happens if a series doesn't even contain any relevant samples for the query time range. We notice that during preloading but then we will still create a series iterator for it. But even for series that do contain relevant samples, the overhead is quite bad for instant queries that retrieve a single sample from each series, but still go through all the effort of series iterator creation. All of that is particularly bad if a series has many in-memory chunks. This commit addresses the problem from two sides: First, it merges preloading and iterator creation into one step, i.e. the preload call returns an iterator for exactly the preloaded chunks. Second, the required mutex acquisition in chunkDesc has been greatly reduced. That was enabled by a side effect of the first step, which is that the iterator is only referencing pinned chunks, so there is no risk of concurrent eviction anymore, and chunks can be accessed without mutex acquisition. To simplify the code changes for the above, the long-planned change of ValueAtTime to ValueAtOrBefore time was performed at the same time. (It should have been done first, but it kind of accidentally happened while I was in the middle of writing the series iterator changes. Sorry for that.) So far, we actively filtered the up to two values that were returned by ValueAtTime, i.e. we invested work to retrieve up to two values, and then we invested more work to throw one of them away. The SeriesIterator.BoundaryValues method can be removed once #1401 is fixed. But I really didn't want to load even more changes into this PR. Benchmarks: The BenchmarkFuzz.* benchmarks run 83% faster (i.e. about six times faster) and allocate 95% fewer bytes. The reason for that is that the benchmark reads one sample after another from the time series and creates a new series iterator for each sample read. To find out how much these improvements matter in practice, I have mirrored a beefy Prometheus server at SoundCloud that suffers from both issues #1035 and #1264. To reach steady state that would be comparable, the server needs to run for 15d. So far, it has run for 1d. The test server currently has only half as many memory time series and 60% of the memory chunks the main server has. The 90th percentile rule evaluation cycle time is ~11s on the main server and only ~3s on the test server. However, these numbers might get much closer over time. In addition to performance improvements, this commit removes about 150 LOC.
2016-02-16 09:47:50 -08:00
// ErrOutOfOrderSample is returned if a sample has a timestamp before the latest
// timestamp in the series it is appended to.
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var ErrOutOfOrderSample = fmt.Errorf("sample timestamp out of order")
// Append implements Storage.
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func (s *memorySeriesStorage) Append(sample *model.Sample) error {
for ln, lv := range sample.Metric {
if len(lv) == 0 {
delete(sample.Metric, ln)
}
}
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rawFP := sample.Metric.FastFingerprint()
s.fpLocker.Lock(rawFP)
fp, err := s.mapper.mapFP(rawFP, sample.Metric)
if err != nil {
log.Errorf("Error while mapping fingerprint %v: %v", rawFP, err)
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s.persistence.setDirty(true)
}
if fp != rawFP {
// Switch locks.
s.fpLocker.Unlock(rawFP)
s.fpLocker.Lock(fp)
}
series := s.getOrCreateSeries(fp, sample.Metric)
if sample.Timestamp <= series.lastTime {
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s.fpLocker.Unlock(fp)
// Don't report "no-op appends", i.e. where timestamp and sample
// value are the same as for the last append, as they are a
// common occurrence when using client-side timestamps
// (e.g. Pushgateway or federation).
if sample.Timestamp == series.lastTime &&
series.lastSampleValueSet &&
sample.Value == series.lastSampleValue {
return nil
}
s.outOfOrderSamplesCount.Inc()
return ErrOutOfOrderSample
}
completedChunksCount := series.add(&model.SamplePair{
Value: sample.Value,
Timestamp: sample.Timestamp,
})
s.fpLocker.Unlock(fp)
s.ingestedSamplesCount.Inc()
s.incNumChunksToPersist(completedChunksCount)
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return nil
}
// NeedsThrottling implements Storage.
func (s *memorySeriesStorage) NeedsThrottling() bool {
if s.getNumChunksToPersist() > s.maxChunksToPersist ||
float64(atomic.LoadInt64(&numMemChunks)) > float64(s.maxMemoryChunks)*toleranceFactorMemChunks {
select {
case s.throttled <- struct{}{}:
default: // Do nothing, signal already pending.
}
return true
}
return false
}
// logThrottling handles logging of throttled events and has to be started as a
// goroutine. It stops once s.loopStopping is closed.
//
// Logging strategy: Whenever Throttle() is called and returns true, an signal
// is sent to s.throttled. If that happens for the first time, an Error is
// logged that the storage is now throttled. As long as signals continues to be
// sent via s.throttled at least once per minute, nothing else is logged. Once
// no signal has arrived for a minute, an Info is logged that the storage is not
// throttled anymore. This resets things to the initial state, i.e. once a
// signal arrives again, the Error will be logged again.
func (s *memorySeriesStorage) logThrottling() {
timer := time.NewTimer(time.Minute)
timer.Stop()
// Signal exit of the goroutine. Currently only needed by test code.
defer close(s.logThrottlingStopped)
for {
select {
case <-s.throttled:
if !timer.Reset(time.Minute) {
log.
With("chunksToPersist", s.getNumChunksToPersist()).
With("maxChunksToPersist", s.maxChunksToPersist).
With("memoryChunks", atomic.LoadInt64(&numMemChunks)).
With("maxToleratedMemChunks", int(float64(s.maxMemoryChunks)*toleranceFactorMemChunks)).
Error("Storage needs throttling. Scrapes and rule evaluations will be skipped.")
}
case <-timer.C:
log.
With("chunksToPersist", s.getNumChunksToPersist()).
With("maxChunksToPersist", s.maxChunksToPersist).
With("memoryChunks", atomic.LoadInt64(&numMemChunks)).
With("maxToleratedMemChunks", int(float64(s.maxMemoryChunks)*toleranceFactorMemChunks)).
Info("Storage does not need throttling anymore.")
case <-s.loopStopping:
return
}
}
}
func (s *memorySeriesStorage) getOrCreateSeries(fp model.Fingerprint, m model.Metric) *memorySeries {
series, ok := s.fpToSeries.get(fp)
if !ok {
var cds []*chunkDesc
var modTime time.Time
unarchived, err := s.persistence.unarchiveMetric(fp)
if err != nil {
log.Errorf("Error unarchiving fingerprint %v (metric %v): %v", fp, m, err)
}
if unarchived {
s.seriesOps.WithLabelValues(unarchive).Inc()
// We have to load chunkDescs anyway to do anything with
// the series, so let's do it right now so that we don't
// end up with a series without any chunkDescs for a
// while (which is confusing as it makes the series
// appear as archived or purged).
cds, err = s.loadChunkDescs(fp, 0)
if err != nil {
log.Errorf("Error loading chunk descs for fingerprint %v (metric %v): %v", fp, m, err)
}
modTime = s.persistence.seriesFileModTime(fp)
} else {
// This was a genuinely new series, so index the metric.
s.persistence.indexMetric(fp, m)
s.seriesOps.WithLabelValues(create).Inc()
}
series = newMemorySeries(m, cds, modTime)
s.fpToSeries.put(fp, series)
s.numSeries.Inc()
}
return series
}
func (s *memorySeriesStorage) preloadChunksForRange(
fp model.Fingerprint,
from model.Time, through model.Time,
lastSampleOnly bool,
Streamline series iterator creation This will fix issue #1035 and will also help to make issue #1264 less bad. The fundamental problem in the current code: In the preload phase, we quite accurately determine which chunks will be used for the query being executed. However, in the subsequent step of creating series iterators, the created iterators are referencing _all_ in-memory chunks in their series, even the un-pinned ones. In iterator creation, we copy a pointer to each in-memory chunk of a series into the iterator. While this creates a certain amount of allocation churn, the worst thing about it is that copying the chunk pointer out of the chunkDesc requires a mutex acquisition. (Remember that the iterator will also reference un-pinned chunks, so we need to acquire the mutex to protect against concurrent eviction.) The worst case happens if a series doesn't even contain any relevant samples for the query time range. We notice that during preloading but then we will still create a series iterator for it. But even for series that do contain relevant samples, the overhead is quite bad for instant queries that retrieve a single sample from each series, but still go through all the effort of series iterator creation. All of that is particularly bad if a series has many in-memory chunks. This commit addresses the problem from two sides: First, it merges preloading and iterator creation into one step, i.e. the preload call returns an iterator for exactly the preloaded chunks. Second, the required mutex acquisition in chunkDesc has been greatly reduced. That was enabled by a side effect of the first step, which is that the iterator is only referencing pinned chunks, so there is no risk of concurrent eviction anymore, and chunks can be accessed without mutex acquisition. To simplify the code changes for the above, the long-planned change of ValueAtTime to ValueAtOrBefore time was performed at the same time. (It should have been done first, but it kind of accidentally happened while I was in the middle of writing the series iterator changes. Sorry for that.) So far, we actively filtered the up to two values that were returned by ValueAtTime, i.e. we invested work to retrieve up to two values, and then we invested more work to throw one of them away. The SeriesIterator.BoundaryValues method can be removed once #1401 is fixed. But I really didn't want to load even more changes into this PR. Benchmarks: The BenchmarkFuzz.* benchmarks run 83% faster (i.e. about six times faster) and allocate 95% fewer bytes. The reason for that is that the benchmark reads one sample after another from the time series and creates a new series iterator for each sample read. To find out how much these improvements matter in practice, I have mirrored a beefy Prometheus server at SoundCloud that suffers from both issues #1035 and #1264. To reach steady state that would be comparable, the server needs to run for 15d. So far, it has run for 1d. The test server currently has only half as many memory time series and 60% of the memory chunks the main server has. The 90th percentile rule evaluation cycle time is ~11s on the main server and only ~3s on the test server. However, these numbers might get much closer over time. In addition to performance improvements, this commit removes about 150 LOC.
2016-02-16 09:47:50 -08:00
) ([]*chunkDesc, SeriesIterator, error) {
s.fpLocker.Lock(fp)
defer s.fpLocker.Unlock(fp)
series, ok := s.fpToSeries.get(fp)
if !ok {
has, first, last, err := s.persistence.hasArchivedMetric(fp)
if err != nil {
Streamline series iterator creation This will fix issue #1035 and will also help to make issue #1264 less bad. The fundamental problem in the current code: In the preload phase, we quite accurately determine which chunks will be used for the query being executed. However, in the subsequent step of creating series iterators, the created iterators are referencing _all_ in-memory chunks in their series, even the un-pinned ones. In iterator creation, we copy a pointer to each in-memory chunk of a series into the iterator. While this creates a certain amount of allocation churn, the worst thing about it is that copying the chunk pointer out of the chunkDesc requires a mutex acquisition. (Remember that the iterator will also reference un-pinned chunks, so we need to acquire the mutex to protect against concurrent eviction.) The worst case happens if a series doesn't even contain any relevant samples for the query time range. We notice that during preloading but then we will still create a series iterator for it. But even for series that do contain relevant samples, the overhead is quite bad for instant queries that retrieve a single sample from each series, but still go through all the effort of series iterator creation. All of that is particularly bad if a series has many in-memory chunks. This commit addresses the problem from two sides: First, it merges preloading and iterator creation into one step, i.e. the preload call returns an iterator for exactly the preloaded chunks. Second, the required mutex acquisition in chunkDesc has been greatly reduced. That was enabled by a side effect of the first step, which is that the iterator is only referencing pinned chunks, so there is no risk of concurrent eviction anymore, and chunks can be accessed without mutex acquisition. To simplify the code changes for the above, the long-planned change of ValueAtTime to ValueAtOrBefore time was performed at the same time. (It should have been done first, but it kind of accidentally happened while I was in the middle of writing the series iterator changes. Sorry for that.) So far, we actively filtered the up to two values that were returned by ValueAtTime, i.e. we invested work to retrieve up to two values, and then we invested more work to throw one of them away. The SeriesIterator.BoundaryValues method can be removed once #1401 is fixed. But I really didn't want to load even more changes into this PR. Benchmarks: The BenchmarkFuzz.* benchmarks run 83% faster (i.e. about six times faster) and allocate 95% fewer bytes. The reason for that is that the benchmark reads one sample after another from the time series and creates a new series iterator for each sample read. To find out how much these improvements matter in practice, I have mirrored a beefy Prometheus server at SoundCloud that suffers from both issues #1035 and #1264. To reach steady state that would be comparable, the server needs to run for 15d. So far, it has run for 1d. The test server currently has only half as many memory time series and 60% of the memory chunks the main server has. The 90th percentile rule evaluation cycle time is ~11s on the main server and only ~3s on the test server. However, these numbers might get much closer over time. In addition to performance improvements, this commit removes about 150 LOC.
2016-02-16 09:47:50 -08:00
return nil, nopIter, err
}
if !has {
s.invalidPreloadRequestsCount.Inc()
Streamline series iterator creation This will fix issue #1035 and will also help to make issue #1264 less bad. The fundamental problem in the current code: In the preload phase, we quite accurately determine which chunks will be used for the query being executed. However, in the subsequent step of creating series iterators, the created iterators are referencing _all_ in-memory chunks in their series, even the un-pinned ones. In iterator creation, we copy a pointer to each in-memory chunk of a series into the iterator. While this creates a certain amount of allocation churn, the worst thing about it is that copying the chunk pointer out of the chunkDesc requires a mutex acquisition. (Remember that the iterator will also reference un-pinned chunks, so we need to acquire the mutex to protect against concurrent eviction.) The worst case happens if a series doesn't even contain any relevant samples for the query time range. We notice that during preloading but then we will still create a series iterator for it. But even for series that do contain relevant samples, the overhead is quite bad for instant queries that retrieve a single sample from each series, but still go through all the effort of series iterator creation. All of that is particularly bad if a series has many in-memory chunks. This commit addresses the problem from two sides: First, it merges preloading and iterator creation into one step, i.e. the preload call returns an iterator for exactly the preloaded chunks. Second, the required mutex acquisition in chunkDesc has been greatly reduced. That was enabled by a side effect of the first step, which is that the iterator is only referencing pinned chunks, so there is no risk of concurrent eviction anymore, and chunks can be accessed without mutex acquisition. To simplify the code changes for the above, the long-planned change of ValueAtTime to ValueAtOrBefore time was performed at the same time. (It should have been done first, but it kind of accidentally happened while I was in the middle of writing the series iterator changes. Sorry for that.) So far, we actively filtered the up to two values that were returned by ValueAtTime, i.e. we invested work to retrieve up to two values, and then we invested more work to throw one of them away. The SeriesIterator.BoundaryValues method can be removed once #1401 is fixed. But I really didn't want to load even more changes into this PR. Benchmarks: The BenchmarkFuzz.* benchmarks run 83% faster (i.e. about six times faster) and allocate 95% fewer bytes. The reason for that is that the benchmark reads one sample after another from the time series and creates a new series iterator for each sample read. To find out how much these improvements matter in practice, I have mirrored a beefy Prometheus server at SoundCloud that suffers from both issues #1035 and #1264. To reach steady state that would be comparable, the server needs to run for 15d. So far, it has run for 1d. The test server currently has only half as many memory time series and 60% of the memory chunks the main server has. The 90th percentile rule evaluation cycle time is ~11s on the main server and only ~3s on the test server. However, these numbers might get much closer over time. In addition to performance improvements, this commit removes about 150 LOC.
2016-02-16 09:47:50 -08:00
return nil, nopIter, nil
}
Streamline series iterator creation This will fix issue #1035 and will also help to make issue #1264 less bad. The fundamental problem in the current code: In the preload phase, we quite accurately determine which chunks will be used for the query being executed. However, in the subsequent step of creating series iterators, the created iterators are referencing _all_ in-memory chunks in their series, even the un-pinned ones. In iterator creation, we copy a pointer to each in-memory chunk of a series into the iterator. While this creates a certain amount of allocation churn, the worst thing about it is that copying the chunk pointer out of the chunkDesc requires a mutex acquisition. (Remember that the iterator will also reference un-pinned chunks, so we need to acquire the mutex to protect against concurrent eviction.) The worst case happens if a series doesn't even contain any relevant samples for the query time range. We notice that during preloading but then we will still create a series iterator for it. But even for series that do contain relevant samples, the overhead is quite bad for instant queries that retrieve a single sample from each series, but still go through all the effort of series iterator creation. All of that is particularly bad if a series has many in-memory chunks. This commit addresses the problem from two sides: First, it merges preloading and iterator creation into one step, i.e. the preload call returns an iterator for exactly the preloaded chunks. Second, the required mutex acquisition in chunkDesc has been greatly reduced. That was enabled by a side effect of the first step, which is that the iterator is only referencing pinned chunks, so there is no risk of concurrent eviction anymore, and chunks can be accessed without mutex acquisition. To simplify the code changes for the above, the long-planned change of ValueAtTime to ValueAtOrBefore time was performed at the same time. (It should have been done first, but it kind of accidentally happened while I was in the middle of writing the series iterator changes. Sorry for that.) So far, we actively filtered the up to two values that were returned by ValueAtTime, i.e. we invested work to retrieve up to two values, and then we invested more work to throw one of them away. The SeriesIterator.BoundaryValues method can be removed once #1401 is fixed. But I really didn't want to load even more changes into this PR. Benchmarks: The BenchmarkFuzz.* benchmarks run 83% faster (i.e. about six times faster) and allocate 95% fewer bytes. The reason for that is that the benchmark reads one sample after another from the time series and creates a new series iterator for each sample read. To find out how much these improvements matter in practice, I have mirrored a beefy Prometheus server at SoundCloud that suffers from both issues #1035 and #1264. To reach steady state that would be comparable, the server needs to run for 15d. So far, it has run for 1d. The test server currently has only half as many memory time series and 60% of the memory chunks the main server has. The 90th percentile rule evaluation cycle time is ~11s on the main server and only ~3s on the test server. However, these numbers might get much closer over time. In addition to performance improvements, this commit removes about 150 LOC.
2016-02-16 09:47:50 -08:00
if from.Before(last) && through.After(first) {
metric, err := s.persistence.archivedMetric(fp)
if err != nil {
Streamline series iterator creation This will fix issue #1035 and will also help to make issue #1264 less bad. The fundamental problem in the current code: In the preload phase, we quite accurately determine which chunks will be used for the query being executed. However, in the subsequent step of creating series iterators, the created iterators are referencing _all_ in-memory chunks in their series, even the un-pinned ones. In iterator creation, we copy a pointer to each in-memory chunk of a series into the iterator. While this creates a certain amount of allocation churn, the worst thing about it is that copying the chunk pointer out of the chunkDesc requires a mutex acquisition. (Remember that the iterator will also reference un-pinned chunks, so we need to acquire the mutex to protect against concurrent eviction.) The worst case happens if a series doesn't even contain any relevant samples for the query time range. We notice that during preloading but then we will still create a series iterator for it. But even for series that do contain relevant samples, the overhead is quite bad for instant queries that retrieve a single sample from each series, but still go through all the effort of series iterator creation. All of that is particularly bad if a series has many in-memory chunks. This commit addresses the problem from two sides: First, it merges preloading and iterator creation into one step, i.e. the preload call returns an iterator for exactly the preloaded chunks. Second, the required mutex acquisition in chunkDesc has been greatly reduced. That was enabled by a side effect of the first step, which is that the iterator is only referencing pinned chunks, so there is no risk of concurrent eviction anymore, and chunks can be accessed without mutex acquisition. To simplify the code changes for the above, the long-planned change of ValueAtTime to ValueAtOrBefore time was performed at the same time. (It should have been done first, but it kind of accidentally happened while I was in the middle of writing the series iterator changes. Sorry for that.) So far, we actively filtered the up to two values that were returned by ValueAtTime, i.e. we invested work to retrieve up to two values, and then we invested more work to throw one of them away. The SeriesIterator.BoundaryValues method can be removed once #1401 is fixed. But I really didn't want to load even more changes into this PR. Benchmarks: The BenchmarkFuzz.* benchmarks run 83% faster (i.e. about six times faster) and allocate 95% fewer bytes. The reason for that is that the benchmark reads one sample after another from the time series and creates a new series iterator for each sample read. To find out how much these improvements matter in practice, I have mirrored a beefy Prometheus server at SoundCloud that suffers from both issues #1035 and #1264. To reach steady state that would be comparable, the server needs to run for 15d. So far, it has run for 1d. The test server currently has only half as many memory time series and 60% of the memory chunks the main server has. The 90th percentile rule evaluation cycle time is ~11s on the main server and only ~3s on the test server. However, these numbers might get much closer over time. In addition to performance improvements, this commit removes about 150 LOC.
2016-02-16 09:47:50 -08:00
return nil, nopIter, err
}
series = s.getOrCreateSeries(fp, metric)
} else {
Streamline series iterator creation This will fix issue #1035 and will also help to make issue #1264 less bad. The fundamental problem in the current code: In the preload phase, we quite accurately determine which chunks will be used for the query being executed. However, in the subsequent step of creating series iterators, the created iterators are referencing _all_ in-memory chunks in their series, even the un-pinned ones. In iterator creation, we copy a pointer to each in-memory chunk of a series into the iterator. While this creates a certain amount of allocation churn, the worst thing about it is that copying the chunk pointer out of the chunkDesc requires a mutex acquisition. (Remember that the iterator will also reference un-pinned chunks, so we need to acquire the mutex to protect against concurrent eviction.) The worst case happens if a series doesn't even contain any relevant samples for the query time range. We notice that during preloading but then we will still create a series iterator for it. But even for series that do contain relevant samples, the overhead is quite bad for instant queries that retrieve a single sample from each series, but still go through all the effort of series iterator creation. All of that is particularly bad if a series has many in-memory chunks. This commit addresses the problem from two sides: First, it merges preloading and iterator creation into one step, i.e. the preload call returns an iterator for exactly the preloaded chunks. Second, the required mutex acquisition in chunkDesc has been greatly reduced. That was enabled by a side effect of the first step, which is that the iterator is only referencing pinned chunks, so there is no risk of concurrent eviction anymore, and chunks can be accessed without mutex acquisition. To simplify the code changes for the above, the long-planned change of ValueAtTime to ValueAtOrBefore time was performed at the same time. (It should have been done first, but it kind of accidentally happened while I was in the middle of writing the series iterator changes. Sorry for that.) So far, we actively filtered the up to two values that were returned by ValueAtTime, i.e. we invested work to retrieve up to two values, and then we invested more work to throw one of them away. The SeriesIterator.BoundaryValues method can be removed once #1401 is fixed. But I really didn't want to load even more changes into this PR. Benchmarks: The BenchmarkFuzz.* benchmarks run 83% faster (i.e. about six times faster) and allocate 95% fewer bytes. The reason for that is that the benchmark reads one sample after another from the time series and creates a new series iterator for each sample read. To find out how much these improvements matter in practice, I have mirrored a beefy Prometheus server at SoundCloud that suffers from both issues #1035 and #1264. To reach steady state that would be comparable, the server needs to run for 15d. So far, it has run for 1d. The test server currently has only half as many memory time series and 60% of the memory chunks the main server has. The 90th percentile rule evaluation cycle time is ~11s on the main server and only ~3s on the test server. However, these numbers might get much closer over time. In addition to performance improvements, this commit removes about 150 LOC.
2016-02-16 09:47:50 -08:00
return nil, nopIter, nil
}
}
return series.preloadChunksForRange(fp, from, through, lastSampleOnly, s)
}
func (s *memorySeriesStorage) handleEvictList() {
ticker := time.NewTicker(maxEvictInterval)
count := 0
2015-01-07 10:02:38 -08:00
for {
// To batch up evictions a bit, this tries evictions at least
// once per evict interval, but earlier if the number of evict
// requests with evict==true that have happened since the last
// evict run is more than maxMemoryChunks/1000.
select {
case req := <-s.evictRequests:
if req.evict {
req.cd.evictListElement = s.evictList.PushBack(req.cd)
count++
if count > s.maxMemoryChunks/1000 {
s.maybeEvict()
count = 0
}
} else {
if req.cd.evictListElement != nil {
s.evictList.Remove(req.cd.evictListElement)
req.cd.evictListElement = nil
}
}
case <-ticker.C:
if s.evictList.Len() > 0 {
s.maybeEvict()
}
case <-s.evictStopping:
// Drain evictRequests forever in a goroutine to not let
// requesters hang.
go func() {
for {
<-s.evictRequests
2015-01-07 10:02:38 -08:00
}
}()
ticker.Stop()
log.Info("Chunk eviction stopped.")
close(s.evictStopped)
return
}
}
}
// maybeEvict is a local helper method. Must only be called by handleEvictList.
func (s *memorySeriesStorage) maybeEvict() {
numChunksToEvict := int(atomic.LoadInt64(&numMemChunks)) - s.maxMemoryChunks
if numChunksToEvict <= 0 {
return
}
chunkDescsToEvict := make([]*chunkDesc, numChunksToEvict)
for i := range chunkDescsToEvict {
e := s.evictList.Front()
if e == nil {
break
}
cd := e.Value.(*chunkDesc)
cd.evictListElement = nil
chunkDescsToEvict[i] = cd
s.evictList.Remove(e)
}
// Do the actual eviction in a goroutine as we might otherwise deadlock,
// in the following way: A chunk was unpinned completely and therefore
// scheduled for eviction. At the time we actually try to evict it,
// another goroutine is pinning the chunk. The pinning goroutine has
// currently locked the chunk and tries to send the evict request (to
// remove the chunk from the evict list) to the evictRequests
// channel. The send blocks because evictRequests is full. However, the
// goroutine that is supposed to empty the channel is waiting for the
// chunkDesc lock to try to evict the chunk.
go func() {
for _, cd := range chunkDescsToEvict {
if cd == nil {
break
}
cd.maybeEvict()
// We don't care if the eviction succeeds. If the chunk
// was pinned in the meantime, it will be added to the
// evict list once it gets unpinned again.
}
}()
}
// waitForNextFP waits an estimated duration, after which we want to process
// another fingerprint so that we will process all fingerprints in a tenth of
// s.dropAfter assuming that the system is doing nothing else, e.g. if we want
// to drop chunks after 40h, we want to cycle through all fingerprints within
// 4h. The estimation is based on the total number of fingerprints as passed
// in. However, the maximum sweep time is capped at fpMaxSweepTime. Also, the
// method will never wait for longer than fpMaxWaitDuration.
//
// The maxWaitDurationFactor can be used to reduce the waiting time if a faster
// processing is required (for example because unpersisted chunks pile up too
// much).
//
// Normally, the method returns true once the wait duration has passed. However,
// if s.loopStopped is closed, it will return false immediately.
func (s *memorySeriesStorage) waitForNextFP(numberOfFPs int, maxWaitDurationFactor float64) bool {
d := fpMaxWaitDuration
if numberOfFPs != 0 {
sweepTime := s.dropAfter / 10
if sweepTime > fpMaxSweepTime {
sweepTime = fpMaxSweepTime
}
calculatedWait := time.Duration(float64(sweepTime) / float64(numberOfFPs) * maxWaitDurationFactor)
if calculatedWait < d {
d = calculatedWait
}
}
if d == 0 {
return true
}
t := time.NewTimer(d)
select {
case <-t.C:
return true
case <-s.loopStopping:
return false
}
}
// cycleThroughMemoryFingerprints returns a channel that emits fingerprints for
// series in memory in a throttled fashion. It continues to cycle through all
// fingerprints in memory until s.loopStopping is closed.
func (s *memorySeriesStorage) cycleThroughMemoryFingerprints() chan model.Fingerprint {
memoryFingerprints := make(chan model.Fingerprint)
go func() {
var fpIter <-chan model.Fingerprint
defer func() {
if fpIter != nil {
for range fpIter {
// Consume the iterator.
}
}
close(memoryFingerprints)
}()
for {
// Initial wait, also important if there are no FPs yet.
if !s.waitForNextFP(s.fpToSeries.length(), 1) {
return
}
begin := time.Now()
fpIter = s.fpToSeries.fpIter()
count := 0
for fp := range fpIter {
select {
case memoryFingerprints <- fp:
case <-s.loopStopping:
return
}
// Reduce the wait time according to the urgency score.
s.waitForNextFP(s.fpToSeries.length(), 1-s.calculatePersistenceUrgencyScore())
count++
}
if count > 0 {
log.Infof(
"Completed maintenance sweep through %d in-memory fingerprints in %v.",
count, time.Since(begin),
)
}
}
}()
return memoryFingerprints
}
// cycleThroughArchivedFingerprints returns a channel that emits fingerprints
// for archived series in a throttled fashion. It continues to cycle through all
// archived fingerprints until s.loopStopping is closed.
func (s *memorySeriesStorage) cycleThroughArchivedFingerprints() chan model.Fingerprint {
archivedFingerprints := make(chan model.Fingerprint)
go func() {
defer close(archivedFingerprints)
for {
archivedFPs, err := s.persistence.fingerprintsModifiedBefore(
model.Now().Add(-s.dropAfter),
)
if err != nil {
log.Error("Failed to lookup archived fingerprint ranges: ", err)
s.waitForNextFP(0, 1)
continue
}
// Initial wait, also important if there are no FPs yet.
if !s.waitForNextFP(len(archivedFPs), 1) {
return
}
begin := time.Now()
for _, fp := range archivedFPs {
select {
case archivedFingerprints <- fp:
case <-s.loopStopping:
return
}
// Never speed up maintenance of archived FPs.
s.waitForNextFP(len(archivedFPs), 1)
}
if len(archivedFPs) > 0 {
log.Infof(
"Completed maintenance sweep through %d archived fingerprints in %v.",
len(archivedFPs), time.Since(begin),
)
}
}
}()
return archivedFingerprints
}
func (s *memorySeriesStorage) loop() {
checkpointTimer := time.NewTimer(s.checkpointInterval)
dirtySeriesCount := 0
defer func() {
checkpointTimer.Stop()
log.Info("Maintenance loop stopped.")
close(s.loopStopped)
}()
memoryFingerprints := s.cycleThroughMemoryFingerprints()
archivedFingerprints := s.cycleThroughArchivedFingerprints()
loop:
for {
select {
case <-s.loopStopping:
break loop
case <-checkpointTimer.C:
err := s.persistence.checkpointSeriesMapAndHeads(s.fpToSeries, s.fpLocker)
if err != nil {
log.Errorln("Error while checkpointing:", err)
} else {
dirtySeriesCount = 0
}
// If a checkpoint takes longer than checkpointInterval, unluckily timed
// combination with the Reset(0) call below can lead to a case where a
// time is lurking in C leading to repeated checkpointing without break.
select {
case <-checkpointTimer.C: // Get rid of the lurking time.
default:
}
checkpointTimer.Reset(s.checkpointInterval)
case fp := <-memoryFingerprints:
if s.maintainMemorySeries(fp, model.Now().Add(-s.dropAfter)) {
dirtySeriesCount++
// Check if we have enough "dirty" series so that we need an early checkpoint.
// However, if we are already behind persisting chunks, creating a checkpoint
// would be counterproductive, as it would slow down chunk persisting even more,
// while in a situation like that, where we are clearly lacking speed of disk
// maintenance, the best we can do for crash recovery is to persist chunks as
// quickly as possible. So only checkpoint if the urgency score is < 1.
if dirtySeriesCount >= s.checkpointDirtySeriesLimit &&
s.calculatePersistenceUrgencyScore() < 1 {
checkpointTimer.Reset(0)
}
}
case fp := <-archivedFingerprints:
s.maintainArchivedSeries(fp, model.Now().Add(-s.dropAfter))
}
}
// Wait until both channels are closed.
for range memoryFingerprints {
}
for range archivedFingerprints {
}
}
// maintainMemorySeries maintains a series that is in memory (i.e. not
// archived). It returns true if the method has changed from clean to dirty
// (i.e. it is inconsistent with the latest checkpoint now so that in case of a
// crash a recovery operation that requires a disk seek needed to be applied).
//
// The method first closes the head chunk if it was not touched for the duration
// of headChunkTimeout.
//
// Then it determines the chunks that need to be purged and the chunks that need
// to be persisted. Depending on the result, it does the following:
//
// - If all chunks of a series need to be purged, the whole series is deleted
// for good and the method returns false. (Detecting non-existence of a series
// file does not require a disk seek.)
//
// - If any chunks need to be purged (but not all of them), it purges those
// chunks from memory and rewrites the series file on disk, leaving out the
// purged chunks and appending all chunks not yet persisted (with the exception
// of a still open head chunk).
//
// - If no chunks on disk need to be purged, but chunks need to be persisted,
// those chunks are simply appended to the existing series file (or the file is
// created if it does not exist yet).
//
// - If no chunks need to be purged and no chunks need to be persisted, nothing
// happens in this step.
//
// Next, the method checks if all chunks in the series are evicted. In that
// case, it archives the series and returns true.
//
// Finally, it evicts chunkDescs if there are too many.
func (s *memorySeriesStorage) maintainMemorySeries(
fp model.Fingerprint, beforeTime model.Time,
) (becameDirty bool) {
defer func(begin time.Time) {
s.maintainSeriesDuration.WithLabelValues(maintainInMemory).Observe(
float64(time.Since(begin)) / float64(time.Millisecond),
)
}(time.Now())
s.fpLocker.Lock(fp)
defer s.fpLocker.Unlock(fp)
series, ok := s.fpToSeries.get(fp)
if !ok {
// Series is actually not in memory, perhaps archived or dropped in the meantime.
return false
}
defer s.seriesOps.WithLabelValues(memoryMaintenance).Inc()
if series.maybeCloseHeadChunk() {
s.incNumChunksToPersist(1)
}
seriesWasDirty := series.dirty
if s.writeMemorySeries(fp, series, beforeTime) {
// Series is gone now, we are done.
return false
}
iOldestNotEvicted := -1
for i, cd := range series.chunkDescs {
if !cd.isEvicted() {
iOldestNotEvicted = i
break
}
}
// Archive if all chunks are evicted.
if iOldestNotEvicted == -1 {
s.fpToSeries.del(fp)
s.numSeries.Dec()
if err := s.persistence.archiveMetric(
fp, series.metric, series.firstTime(), series.lastTime,
); err != nil {
log.Errorf("Error archiving metric %v: %v", series.metric, err)
return
}
s.seriesOps.WithLabelValues(archive).Inc()
return
}
// If we are here, the series is not archived, so check for chunkDesc
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// eviction next.
series.evictChunkDescs(iOldestNotEvicted)
return series.dirty && !seriesWasDirty
}
// writeMemorySeries (re-)writes a memory series file. While doing so, it drops
2015-03-18 11:09:07 -07:00
// chunks older than beforeTime from both the series file (if it exists) as well
// as from memory. The provided chunksToPersist are appended to the newly
// written series file. If no chunks need to be purged, but chunksToPersist is
// not empty, those chunks are simply appended to the series file. If the series
// contains no chunks after dropping old chunks, it is purged entirely. In that
// case, the method returns true.
//
// The caller must have locked the fp.
func (s *memorySeriesStorage) writeMemorySeries(
fp model.Fingerprint, series *memorySeries, beforeTime model.Time,
) bool {
cds := series.chunksToPersist()
defer func() {
for _, cd := range cds {
cd.unpin(s.evictRequests)
}
s.incNumChunksToPersist(-len(cds))
chunkOps.WithLabelValues(persistAndUnpin).Add(float64(len(cds)))
series.modTime = s.persistence.seriesFileModTime(fp)
}()
// Get the actual chunks from underneath the chunkDescs.
// No lock required as chunks still to persist cannot be evicted.
chunks := make([]chunk, len(cds))
for i, cd := range cds {
chunks[i] = cd.c
}
if !series.firstTime().Before(beforeTime) {
// Oldest sample not old enough, just append chunks, if any.
if len(cds) == 0 {
return false
}
offset, err := s.persistence.persistChunks(fp, chunks)
if err != nil {
s.persistErrors.Inc()
return false
}
if series.chunkDescsOffset == -1 {
// This is the first chunk persisted for a newly created
// series that had prior chunks on disk. Finally, we can
// set the chunkDescsOffset.
series.chunkDescsOffset = offset
}
return false
}
newFirstTime, offset, numDroppedFromPersistence, allDroppedFromPersistence, err :=
s.persistence.dropAndPersistChunks(fp, beforeTime, chunks)
if err != nil {
s.persistErrors.Inc()
return false
}
series.dropChunks(beforeTime)
if len(series.chunkDescs) == 0 && allDroppedFromPersistence {
// All chunks dropped from both memory and persistence. Delete the series for good.
s.fpToSeries.del(fp)
s.numSeries.Dec()
s.seriesOps.WithLabelValues(memoryPurge).Inc()
s.persistence.unindexMetric(fp, series.metric)
return true
}
series.savedFirstTime = newFirstTime
if series.chunkDescsOffset == -1 {
series.chunkDescsOffset = offset
} else {
series.chunkDescsOffset -= numDroppedFromPersistence
if series.chunkDescsOffset < 0 {
log.Errorf("Dropped more chunks from persistence than from memory for fingerprint %v, series %v.", fp, series)
s.persistence.setDirty(true)
series.chunkDescsOffset = -1 // Makes sure it will be looked at during crash recovery.
}
}
return false
}
// maintainArchivedSeries drops chunks older than beforeTime from an archived
// series. If the series contains no chunks after that, it is purged entirely.
func (s *memorySeriesStorage) maintainArchivedSeries(fp model.Fingerprint, beforeTime model.Time) {
defer func(begin time.Time) {
s.maintainSeriesDuration.WithLabelValues(maintainArchived).Observe(
float64(time.Since(begin)) / float64(time.Millisecond),
)
}(time.Now())
s.fpLocker.Lock(fp)
defer s.fpLocker.Unlock(fp)
has, firstTime, lastTime, err := s.persistence.hasArchivedMetric(fp)
if err != nil {
log.Error("Error looking up archived time range: ", err)
return
}
if !has || !firstTime.Before(beforeTime) {
// Oldest sample not old enough, or metric purged or unarchived in the meantime.
return
}
defer s.seriesOps.WithLabelValues(archiveMaintenance).Inc()
newFirstTime, _, _, allDropped, err := s.persistence.dropAndPersistChunks(fp, beforeTime, nil)
if err != nil {
log.Error("Error dropping persisted chunks: ", err)
}
if allDropped {
if err := s.persistence.purgeArchivedMetric(fp); err != nil {
log.Errorf("Error purging archived metric for fingerprint %v: %v", fp, err)
return
}
s.seriesOps.WithLabelValues(archivePurge).Inc()
return
}
if err := s.persistence.updateArchivedTimeRange(fp, newFirstTime, lastTime); err != nil {
log.Errorf("Error updating archived time range for fingerprint %v: %s", fp, err)
}
}
// See persistence.loadChunks for detailed explanation.
func (s *memorySeriesStorage) loadChunks(fp model.Fingerprint, indexes []int, indexOffset int) ([]chunk, error) {
return s.persistence.loadChunks(fp, indexes, indexOffset)
}
// See persistence.loadChunkDescs for detailed explanation.
func (s *memorySeriesStorage) loadChunkDescs(fp model.Fingerprint, offsetFromEnd int) ([]*chunkDesc, error) {
return s.persistence.loadChunkDescs(fp, offsetFromEnd)
}
// getNumChunksToPersist returns numChunksToPersist in a goroutine-safe way.
func (s *memorySeriesStorage) getNumChunksToPersist() int {
return int(atomic.LoadInt64(&s.numChunksToPersist))
}
// incNumChunksToPersist increments numChunksToPersist in a goroutine-safe way. Use a
// negative 'by' to decrement.
func (s *memorySeriesStorage) incNumChunksToPersist(by int) {
atomic.AddInt64(&s.numChunksToPersist, int64(by))
}
// calculatePersistenceUrgencyScore calculates and returns an urgency score for
// the speed of persisting chunks. The score is between 0 and 1, where 0 means
// no urgency at all and 1 means highest urgency.
//
// The score is the maximum of the two following sub-scores:
//
// (1) The first sub-score is the number of chunks waiting for persistence
// divided by the maximum number of chunks allowed to be waiting for
// persistence.
//
// (2) If there are more chunks in memory than allowed AND there are more chunks
// waiting for persistence than factorMinChunksToPersist times
// -storage.local.max-chunks-to-persist, then the second sub-score is the
// fraction the number of memory chunks has reached between
// -storage.local.memory-chunks and toleranceFactorForMemChunks times
// -storage.local.memory-chunks.
//
// Should the score ever hit persintenceUrgencyScoreForEnteringRushedMode, the
// storage locks into "rushed mode", in which the returned score is always
// bumped up to 1 until the non-bumped score is below
// persintenceUrgencyScoreForLeavingRushedMode.
//
// This method is not goroutine-safe, but it is only ever called by the single
// goroutine that is in charge of series maintenance. According to the returned
// score, series maintenance should be sped up. If a score of 1 is returned,
// checkpointing based on dirty-series count should be disabled, and series
// files should not by synced anymore provided the user has specified the
// adaptive sync strategy.
func (s *memorySeriesStorage) calculatePersistenceUrgencyScore() float64 {
s.rushedMtx.Lock()
defer s.rushedMtx.Unlock()
var (
chunksToPersist = float64(s.getNumChunksToPersist())
maxChunksToPersist = float64(s.maxChunksToPersist)
memChunks = float64(atomic.LoadInt64(&numMemChunks))
maxMemChunks = float64(s.maxMemoryChunks)
)
score := chunksToPersist / maxChunksToPersist
if chunksToPersist > maxChunksToPersist*factorMinChunksToPersist {
score = math.Max(
score,
(memChunks/maxMemChunks-1)/(toleranceFactorMemChunks-1),
)
}
if score > 1 {
score = 1
}
s.persistenceUrgencyScore.Set(score)
if s.rushed {
// We are already in rushed mode. If the score is still above
// persintenceUrgencyScoreForLeavingRushedMode, return 1 and
// leave things as they are.
if score > persintenceUrgencyScoreForLeavingRushedMode {
return 1
}
// We are out of rushed mode!
s.rushed = false
s.rushedMode.Set(0)
log.
With("urgencyScore", score).
With("chunksToPersist", int(chunksToPersist)).
With("maxChunksToPersist", int(maxChunksToPersist)).
With("memoryChunks", int(memChunks)).
With("maxMemoryChunks", int(maxMemChunks)).
Info("Storage has left rushed mode.")
return score
}
if score > persintenceUrgencyScoreForEnteringRushedMode {
// Enter rushed mode.
s.rushed = true
s.rushedMode.Set(1)
log.
With("urgencyScore", score).
With("chunksToPersist", int(chunksToPersist)).
With("maxChunksToPersist", int(maxChunksToPersist)).
With("memoryChunks", int(memChunks)).
With("maxMemoryChunks", int(maxMemChunks)).
Warn("Storage has entered rushed mode.")
return 1
}
return score
}
// Describe implements prometheus.Collector.
func (s *memorySeriesStorage) Describe(ch chan<- *prometheus.Desc) {
s.persistence.Describe(ch)
s.mapper.Describe(ch)
ch <- s.persistErrors.Desc()
ch <- maxChunksToPersistDesc
ch <- numChunksToPersistDesc
ch <- s.numSeries.Desc()
s.seriesOps.Describe(ch)
ch <- s.ingestedSamplesCount.Desc()
ch <- s.outOfOrderSamplesCount.Desc()
ch <- s.invalidPreloadRequestsCount.Desc()
ch <- numMemChunksDesc
s.maintainSeriesDuration.Describe(ch)
ch <- s.persistenceUrgencyScore.Desc()
ch <- s.rushedMode.Desc()
}
// Collect implements prometheus.Collector.
func (s *memorySeriesStorage) Collect(ch chan<- prometheus.Metric) {
s.persistence.Collect(ch)
s.mapper.Collect(ch)
ch <- s.persistErrors
ch <- prometheus.MustNewConstMetric(
maxChunksToPersistDesc,
prometheus.GaugeValue,
float64(s.maxChunksToPersist),
)
ch <- prometheus.MustNewConstMetric(
numChunksToPersistDesc,
prometheus.GaugeValue,
float64(s.getNumChunksToPersist()),
)
ch <- s.numSeries
s.seriesOps.Collect(ch)
ch <- s.ingestedSamplesCount
ch <- s.outOfOrderSamplesCount
ch <- s.invalidPreloadRequestsCount
ch <- prometheus.MustNewConstMetric(
numMemChunksDesc,
prometheus.GaugeValue,
float64(atomic.LoadInt64(&numMemChunks)),
)
s.maintainSeriesDuration.Collect(ch)
ch <- s.persistenceUrgencyScore
ch <- s.rushedMode
}