// 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 remote import ( "context" "errors" "math" "strconv" "sync" "time" "github.com/go-kit/log" "github.com/go-kit/log/level" "github.com/gogo/protobuf/proto" "github.com/golang/snappy" "github.com/prometheus/client_golang/prometheus" "github.com/prometheus/common/model" "go.opentelemetry.io/otel" "go.opentelemetry.io/otel/attribute" semconv "go.opentelemetry.io/otel/semconv/v1.21.0" "go.uber.org/atomic" "github.com/prometheus/prometheus/config" "github.com/prometheus/prometheus/model/histogram" "github.com/prometheus/prometheus/model/labels" "github.com/prometheus/prometheus/model/metadata" "github.com/prometheus/prometheus/model/relabel" "github.com/prometheus/prometheus/model/timestamp" "github.com/prometheus/prometheus/prompb" writev2 "github.com/prometheus/prometheus/prompb/write/v2" "github.com/prometheus/prometheus/scrape" "github.com/prometheus/prometheus/tsdb/chunks" "github.com/prometheus/prometheus/tsdb/record" "github.com/prometheus/prometheus/tsdb/wlog" ) const ( // We track samples in/out and how long pushes take using an Exponentially // Weighted Moving Average. ewmaWeight = 0.2 shardUpdateDuration = 10 * time.Second // Allow 30% too many shards before scaling down. shardToleranceFraction = 0.3 reasonTooOld = "too_old" reasonDroppedSeries = "dropped_series" reasonUnintentionalDroppedSeries = "unintentionally_dropped_series" ) type queueManagerMetrics struct { reg prometheus.Registerer samplesTotal prometheus.Counter exemplarsTotal prometheus.Counter histogramsTotal prometheus.Counter metadataTotal prometheus.Counter failedSamplesTotal prometheus.Counter failedExemplarsTotal prometheus.Counter failedHistogramsTotal prometheus.Counter failedMetadataTotal prometheus.Counter retriedSamplesTotal prometheus.Counter retriedExemplarsTotal prometheus.Counter retriedHistogramsTotal prometheus.Counter retriedMetadataTotal prometheus.Counter droppedSamplesTotal *prometheus.CounterVec droppedExemplarsTotal *prometheus.CounterVec droppedHistogramsTotal *prometheus.CounterVec enqueueRetriesTotal prometheus.Counter sentBatchDuration prometheus.Histogram highestSentTimestamp *maxTimestamp pendingSamples prometheus.Gauge pendingExemplars prometheus.Gauge pendingHistograms prometheus.Gauge shardCapacity prometheus.Gauge numShards prometheus.Gauge maxNumShards prometheus.Gauge minNumShards prometheus.Gauge desiredNumShards prometheus.Gauge sentBytesTotal prometheus.Counter metadataBytesTotal prometheus.Counter maxSamplesPerSend prometheus.Gauge } func newQueueManagerMetrics(r prometheus.Registerer, rn, e string) *queueManagerMetrics { m := &queueManagerMetrics{ reg: r, } constLabels := prometheus.Labels{ remoteName: rn, endpoint: e, } m.samplesTotal = prometheus.NewCounter(prometheus.CounterOpts{ Namespace: namespace, Subsystem: subsystem, Name: "samples_total", Help: "Total number of samples sent to remote storage.", ConstLabels: constLabels, }) m.exemplarsTotal = prometheus.NewCounter(prometheus.CounterOpts{ Namespace: namespace, Subsystem: subsystem, Name: "exemplars_total", Help: "Total number of exemplars sent to remote storage.", ConstLabels: constLabels, }) m.histogramsTotal = prometheus.NewCounter(prometheus.CounterOpts{ Namespace: namespace, Subsystem: subsystem, Name: "histograms_total", Help: "Total number of histograms sent to remote storage.", ConstLabels: constLabels, }) m.metadataTotal = prometheus.NewCounter(prometheus.CounterOpts{ Namespace: namespace, Subsystem: subsystem, Name: "metadata_total", Help: "Total number of metadata entries sent to remote storage.", ConstLabels: constLabels, }) m.failedSamplesTotal = prometheus.NewCounter(prometheus.CounterOpts{ Namespace: namespace, Subsystem: subsystem, Name: "samples_failed_total", Help: "Total number of samples which failed on send to remote storage, non-recoverable errors.", ConstLabels: constLabels, }) m.failedExemplarsTotal = prometheus.NewCounter(prometheus.CounterOpts{ Namespace: namespace, Subsystem: subsystem, Name: "exemplars_failed_total", Help: "Total number of exemplars which failed on send to remote storage, non-recoverable errors.", ConstLabels: constLabels, }) m.failedHistogramsTotal = prometheus.NewCounter(prometheus.CounterOpts{ Namespace: namespace, Subsystem: subsystem, Name: "histograms_failed_total", Help: "Total number of histograms which failed on send to remote storage, non-recoverable errors.", ConstLabels: constLabels, }) m.failedMetadataTotal = prometheus.NewCounter(prometheus.CounterOpts{ Namespace: namespace, Subsystem: subsystem, Name: "metadata_failed_total", Help: "Total number of metadata entries which failed on send to remote storage, non-recoverable errors.", ConstLabels: constLabels, }) m.retriedSamplesTotal = prometheus.NewCounter(prometheus.CounterOpts{ Namespace: namespace, Subsystem: subsystem, Name: "samples_retried_total", Help: "Total number of samples which failed on send to remote storage but were retried because the send error was recoverable.", ConstLabels: constLabels, }) m.retriedExemplarsTotal = prometheus.NewCounter(prometheus.CounterOpts{ Namespace: namespace, Subsystem: subsystem, Name: "exemplars_retried_total", Help: "Total number of exemplars which failed on send to remote storage but were retried because the send error was recoverable.", ConstLabels: constLabels, }) m.retriedHistogramsTotal = prometheus.NewCounter(prometheus.CounterOpts{ Namespace: namespace, Subsystem: subsystem, Name: "histograms_retried_total", Help: "Total number of histograms which failed on send to remote storage but were retried because the send error was recoverable.", ConstLabels: constLabels, }) m.retriedMetadataTotal = prometheus.NewCounter(prometheus.CounterOpts{ Namespace: namespace, Subsystem: subsystem, Name: "metadata_retried_total", Help: "Total number of metadata entries which failed on send to remote storage but were retried because the send error was recoverable.", ConstLabels: constLabels, }) m.droppedSamplesTotal = prometheus.NewCounterVec(prometheus.CounterOpts{ Namespace: namespace, Subsystem: subsystem, Name: "samples_dropped_total", Help: "Total number of samples which were dropped after being read from the WAL before being sent via remote write, either via relabelling, due to being too old or unintentionally because of an unknown reference ID.", ConstLabels: constLabels, }, []string{"reason"}) m.droppedExemplarsTotal = prometheus.NewCounterVec(prometheus.CounterOpts{ Namespace: namespace, Subsystem: subsystem, Name: "exemplars_dropped_total", Help: "Total number of exemplars which were dropped after being read from the WAL before being sent via remote write, either via relabelling, due to being too old or unintentionally because of an unknown reference ID.", ConstLabels: constLabels, }, []string{"reason"}) m.droppedHistogramsTotal = prometheus.NewCounterVec(prometheus.CounterOpts{ Namespace: namespace, Subsystem: subsystem, Name: "histograms_dropped_total", Help: "Total number of histograms which were dropped after being read from the WAL before being sent via remote write, either via relabelling, due to being too old or unintentionally because of an unknown reference ID.", ConstLabels: constLabels, }, []string{"reason"}) m.enqueueRetriesTotal = prometheus.NewCounter(prometheus.CounterOpts{ Namespace: namespace, Subsystem: subsystem, Name: "enqueue_retries_total", Help: "Total number of times enqueue has failed because a shards queue was full.", ConstLabels: constLabels, }) m.sentBatchDuration = prometheus.NewHistogram(prometheus.HistogramOpts{ Namespace: namespace, Subsystem: subsystem, Name: "sent_batch_duration_seconds", Help: "Duration of send calls to the remote storage.", Buckets: append(prometheus.DefBuckets, 25, 60, 120, 300), ConstLabels: constLabels, }) m.highestSentTimestamp = &maxTimestamp{ Gauge: prometheus.NewGauge(prometheus.GaugeOpts{ Namespace: namespace, Subsystem: subsystem, Name: "queue_highest_sent_timestamp_seconds", Help: "Timestamp from a WAL sample, the highest timestamp successfully sent by this queue, in seconds since epoch.", ConstLabels: constLabels, }), } m.pendingSamples = prometheus.NewGauge(prometheus.GaugeOpts{ Namespace: namespace, Subsystem: subsystem, Name: "samples_pending", Help: "The number of samples pending in the queues shards to be sent to the remote storage.", ConstLabels: constLabels, }) m.pendingExemplars = prometheus.NewGauge(prometheus.GaugeOpts{ Namespace: namespace, Subsystem: subsystem, Name: "exemplars_pending", Help: "The number of exemplars pending in the queues shards to be sent to the remote storage.", ConstLabels: constLabels, }) m.pendingHistograms = prometheus.NewGauge(prometheus.GaugeOpts{ Namespace: namespace, Subsystem: subsystem, Name: "histograms_pending", Help: "The number of histograms pending in the queues shards to be sent to the remote storage.", ConstLabels: constLabels, }) m.shardCapacity = prometheus.NewGauge(prometheus.GaugeOpts{ Namespace: namespace, Subsystem: subsystem, Name: "shard_capacity", Help: "The capacity of each shard of the queue used for parallel sending to the remote storage.", ConstLabels: constLabels, }) m.numShards = prometheus.NewGauge(prometheus.GaugeOpts{ Namespace: namespace, Subsystem: subsystem, Name: "shards", Help: "The number of shards used for parallel sending to the remote storage.", ConstLabels: constLabels, }) m.maxNumShards = prometheus.NewGauge(prometheus.GaugeOpts{ Namespace: namespace, Subsystem: subsystem, Name: "shards_max", Help: "The maximum number of shards that the queue is allowed to run.", ConstLabels: constLabels, }) m.minNumShards = prometheus.NewGauge(prometheus.GaugeOpts{ Namespace: namespace, Subsystem: subsystem, Name: "shards_min", Help: "The minimum number of shards that the queue is allowed to run.", ConstLabels: constLabels, }) m.desiredNumShards = prometheus.NewGauge(prometheus.GaugeOpts{ Namespace: namespace, Subsystem: subsystem, Name: "shards_desired", Help: "The number of shards that the queues shard calculation wants to run based on the rate of samples in vs. samples out.", ConstLabels: constLabels, }) m.sentBytesTotal = prometheus.NewCounter(prometheus.CounterOpts{ Namespace: namespace, Subsystem: subsystem, Name: "bytes_total", Help: "The total number of bytes of data (not metadata) sent by the queue after compression. Note that when exemplars over remote write is enabled the exemplars included in a remote write request count towards this metric.", ConstLabels: constLabels, }) m.metadataBytesTotal = prometheus.NewCounter(prometheus.CounterOpts{ Namespace: namespace, Subsystem: subsystem, Name: "metadata_bytes_total", Help: "The total number of bytes of metadata sent by the queue after compression.", ConstLabels: constLabels, }) m.maxSamplesPerSend = prometheus.NewGauge(prometheus.GaugeOpts{ Namespace: namespace, Subsystem: subsystem, Name: "max_samples_per_send", Help: "The maximum number of samples to be sent, in a single request, to the remote storage. Note that, when sending of exemplars over remote write is enabled, exemplars count towards this limt.", ConstLabels: constLabels, }) return m } func (m *queueManagerMetrics) register() { if m.reg != nil { m.reg.MustRegister( m.samplesTotal, m.exemplarsTotal, m.histogramsTotal, m.metadataTotal, m.failedSamplesTotal, m.failedExemplarsTotal, m.failedHistogramsTotal, m.failedMetadataTotal, m.retriedSamplesTotal, m.retriedExemplarsTotal, m.retriedHistogramsTotal, m.retriedMetadataTotal, m.droppedSamplesTotal, m.droppedExemplarsTotal, m.droppedHistogramsTotal, m.enqueueRetriesTotal, m.sentBatchDuration, m.highestSentTimestamp, m.pendingSamples, m.pendingExemplars, m.pendingHistograms, m.shardCapacity, m.numShards, m.maxNumShards, m.minNumShards, m.desiredNumShards, m.sentBytesTotal, m.metadataBytesTotal, m.maxSamplesPerSend, ) } } func (m *queueManagerMetrics) unregister() { if m.reg != nil { m.reg.Unregister(m.samplesTotal) m.reg.Unregister(m.exemplarsTotal) m.reg.Unregister(m.histogramsTotal) m.reg.Unregister(m.metadataTotal) m.reg.Unregister(m.failedSamplesTotal) m.reg.Unregister(m.failedExemplarsTotal) m.reg.Unregister(m.failedHistogramsTotal) m.reg.Unregister(m.failedMetadataTotal) m.reg.Unregister(m.retriedSamplesTotal) m.reg.Unregister(m.retriedExemplarsTotal) m.reg.Unregister(m.retriedHistogramsTotal) m.reg.Unregister(m.retriedMetadataTotal) m.reg.Unregister(m.droppedSamplesTotal) m.reg.Unregister(m.droppedExemplarsTotal) m.reg.Unregister(m.droppedHistogramsTotal) m.reg.Unregister(m.enqueueRetriesTotal) m.reg.Unregister(m.sentBatchDuration) m.reg.Unregister(m.highestSentTimestamp) m.reg.Unregister(m.pendingSamples) m.reg.Unregister(m.pendingExemplars) m.reg.Unregister(m.pendingHistograms) m.reg.Unregister(m.shardCapacity) m.reg.Unregister(m.numShards) m.reg.Unregister(m.maxNumShards) m.reg.Unregister(m.minNumShards) m.reg.Unregister(m.desiredNumShards) m.reg.Unregister(m.sentBytesTotal) m.reg.Unregister(m.metadataBytesTotal) m.reg.Unregister(m.maxSamplesPerSend) } } // WriteClient defines an interface for sending a batch of samples to an // external timeseries database. type WriteClient interface { // Store stores the given samples in the remote storage. Store(ctx context.Context, req []byte, retryAttempt int) error // Name uniquely identifies the remote storage. Name() string // Endpoint is the remote read or write endpoint for the storage client. Endpoint() string } const ( Version1 config.RemoteWriteFormat = iota // 1.0, 0.1, etc. Version2 // symbols are indices into an array of strings ) // QueueManager manages a queue of samples to be sent to the Storage // indicated by the provided WriteClient. Implements writeTo interface // used by WAL Watcher. type QueueManager struct { lastSendTimestamp atomic.Int64 buildRequestLimitTimestamp atomic.Int64 logger log.Logger flushDeadline time.Duration cfg config.QueueConfig mcfg config.MetadataConfig externalLabels []labels.Label relabelConfigs []*relabel.Config sendExemplars bool sendNativeHistograms bool watcher *wlog.Watcher metadataWatcher *MetadataWatcher // experimental feature, new remote write proto format rwFormat config.RemoteWriteFormat clientMtx sync.RWMutex storeClient WriteClient seriesMtx sync.Mutex // Covers seriesLabels, seriesMetadata and droppedSeries. seriesLabels map[chunks.HeadSeriesRef]labels.Labels seriesMetadata map[chunks.HeadSeriesRef]*metadata.Metadata droppedSeries map[chunks.HeadSeriesRef]struct{} seriesSegmentMtx sync.Mutex // Covers seriesSegmentIndexes - if you also lock seriesMtx, take seriesMtx first. seriesSegmentIndexes map[chunks.HeadSeriesRef]int shards *shards numShards int reshardChan chan int quit chan struct{} wg sync.WaitGroup dataIn, dataDropped, dataOut, dataOutDuration *ewmaRate metrics *queueManagerMetrics interner *pool highestRecvTimestamp *maxTimestamp } // NewQueueManager builds a new QueueManager and starts a new // WAL watcher with queue manager as the WriteTo destination. // The WAL watcher takes the dir parameter as the base directory // for where the WAL shall be located. Note that the full path to // the WAL directory will be constructed as /wal. func NewQueueManager( metrics *queueManagerMetrics, watcherMetrics *wlog.WatcherMetrics, readerMetrics *wlog.LiveReaderMetrics, logger log.Logger, dir string, samplesIn *ewmaRate, cfg config.QueueConfig, mCfg config.MetadataConfig, externalLabels labels.Labels, relabelConfigs []*relabel.Config, client WriteClient, flushDeadline time.Duration, interner *pool, highestRecvTimestamp *maxTimestamp, sm ReadyScrapeManager, enableExemplarRemoteWrite bool, enableNativeHistogramRemoteWrite bool, rwFormat config.RemoteWriteFormat, ) *QueueManager { if logger == nil { logger = log.NewNopLogger() } // Copy externalLabels into a slice, which we need for processExternalLabels. extLabelsSlice := make([]labels.Label, 0, externalLabels.Len()) externalLabels.Range(func(l labels.Label) { extLabelsSlice = append(extLabelsSlice, l) }) logger = log.With(logger, remoteName, client.Name(), endpoint, client.Endpoint()) t := &QueueManager{ logger: logger, flushDeadline: flushDeadline, cfg: cfg, mcfg: mCfg, externalLabels: extLabelsSlice, relabelConfigs: relabelConfigs, storeClient: client, sendExemplars: enableExemplarRemoteWrite, sendNativeHistograms: enableNativeHistogramRemoteWrite, // TODO: we should eventually set the format via content negotiation, // so this field would be the desired format, maybe with a fallback? rwFormat: rwFormat, seriesLabels: make(map[chunks.HeadSeriesRef]labels.Labels), seriesMetadata: make(map[chunks.HeadSeriesRef]*metadata.Metadata), seriesSegmentIndexes: make(map[chunks.HeadSeriesRef]int), droppedSeries: make(map[chunks.HeadSeriesRef]struct{}), numShards: cfg.MinShards, reshardChan: make(chan int), quit: make(chan struct{}), dataIn: samplesIn, dataDropped: newEWMARate(ewmaWeight, shardUpdateDuration), dataOut: newEWMARate(ewmaWeight, shardUpdateDuration), dataOutDuration: newEWMARate(ewmaWeight, shardUpdateDuration), metrics: metrics, interner: interner, highestRecvTimestamp: highestRecvTimestamp, } walMetadata := false if t.rwFormat > Version1 { walMetadata = true } t.watcher = wlog.NewWatcher(watcherMetrics, readerMetrics, logger, client.Name(), t, dir, enableExemplarRemoteWrite, enableNativeHistogramRemoteWrite, walMetadata) // The current MetadataWatcher implementation is mutually exclusive // with the new approach, which stores metadata as WAL records and // ships them alongside series. If both mechanisms are set, the new one // takes precedence by implicitly disabling the older one. if t.mcfg.Send && t.rwFormat > Version1 { level.Warn(logger).Log("msg", "usage of 'metadata_config.send' is redundant when using remote write v2 (or higher) as metadata will always be gathered from the WAL and included for every series within each write request") t.mcfg.Send = false } if t.mcfg.Send { t.metadataWatcher = NewMetadataWatcher(logger, sm, client.Name(), t, t.mcfg.SendInterval, flushDeadline) } t.shards = t.newShards() return t } // AppendWatcherMetadata sends metadata to the remote storage. Metadata is sent in batches, but is not parallelized. // This is only used for the metadata_config.send setting and 1.x Remote Write. func (t *QueueManager) AppendWatcherMetadata(ctx context.Context, metadata []scrape.MetricMetadata) { // no op for any newer proto format, which will cache metadata sent to it from the WAL watcher. if t.rwFormat > Version2 { return } // 1.X will still get metadata in batches. mm := make([]prompb.MetricMetadata, 0, len(metadata)) for _, entry := range metadata { mm = append(mm, prompb.MetricMetadata{ MetricFamilyName: entry.Metric, Help: entry.Help, Type: metricTypeToMetricTypeProto(entry.Type), Unit: entry.Unit, }) } pBuf := proto.NewBuffer(nil) numSends := int(math.Ceil(float64(len(metadata)) / float64(t.mcfg.MaxSamplesPerSend))) for i := 0; i < numSends; i++ { last := (i + 1) * t.mcfg.MaxSamplesPerSend if last > len(metadata) { last = len(metadata) } err := t.sendMetadataWithBackoff(ctx, mm[i*t.mcfg.MaxSamplesPerSend:last], pBuf) if err != nil { t.metrics.failedMetadataTotal.Add(float64(last - (i * t.mcfg.MaxSamplesPerSend))) level.Error(t.logger).Log("msg", "non-recoverable error while sending metadata", "count", last-(i*t.mcfg.MaxSamplesPerSend), "err", err) } } } func (t *QueueManager) sendMetadataWithBackoff(ctx context.Context, metadata []prompb.MetricMetadata, pBuf *proto.Buffer) error { // Build the WriteRequest with no samples. req, _, _, err := buildWriteRequest(t.logger, nil, metadata, pBuf, nil, nil) if err != nil { return err } metadataCount := len(metadata) attemptStore := func(try int) error { ctx, span := otel.Tracer("").Start(ctx, "Remote Metadata Send Batch") defer span.End() span.SetAttributes( attribute.Int("metadata", metadataCount), attribute.Int("try", try), attribute.String("remote_name", t.storeClient.Name()), attribute.String("remote_url", t.storeClient.Endpoint()), ) // Attributes defined by OpenTelemetry semantic conventions. if try > 0 { span.SetAttributes(semconv.HTTPResendCount(try)) } begin := time.Now() err := t.storeClient.Store(ctx, req, try) t.metrics.sentBatchDuration.Observe(time.Since(begin).Seconds()) if err != nil { span.RecordError(err) return err } return nil } retry := func() { t.metrics.retriedMetadataTotal.Add(float64(len(metadata))) } err = sendWriteRequestWithBackoff(ctx, t.cfg, t.logger, attemptStore, retry) if err != nil { return err } t.metrics.metadataTotal.Add(float64(len(metadata))) t.metrics.metadataBytesTotal.Add(float64(len(req))) return nil } func isSampleOld(baseTime time.Time, sampleAgeLimit time.Duration, ts int64) bool { if sampleAgeLimit == 0 { // If sampleAgeLimit is unset, then we never skip samples due to their age. return false } limitTs := baseTime.Add(-sampleAgeLimit) sampleTs := timestamp.Time(ts) return sampleTs.Before(limitTs) } func isTimeSeriesOldFilter(metrics *queueManagerMetrics, baseTime time.Time, sampleAgeLimit time.Duration) func(ts prompb.TimeSeries) bool { return func(ts prompb.TimeSeries) bool { if sampleAgeLimit == 0 { // If sampleAgeLimit is unset, then we never skip samples due to their age. return false } switch { // Only the first element should be set in the series, therefore we only check the first element. case len(ts.Samples) > 0: if isSampleOld(baseTime, sampleAgeLimit, ts.Samples[0].Timestamp) { metrics.droppedSamplesTotal.WithLabelValues(reasonTooOld).Inc() return true } case len(ts.Histograms) > 0: if isSampleOld(baseTime, sampleAgeLimit, ts.Histograms[0].Timestamp) { metrics.droppedHistogramsTotal.WithLabelValues(reasonTooOld).Inc() return true } case len(ts.Exemplars) > 0: if isSampleOld(baseTime, sampleAgeLimit, ts.Exemplars[0].Timestamp) { metrics.droppedExemplarsTotal.WithLabelValues(reasonTooOld).Inc() return true } default: return false } return false } } func isV2TimeSeriesOldFilter(metrics *queueManagerMetrics, baseTime time.Time, sampleAgeLimit time.Duration) func(ts writev2.TimeSeries) bool { return func(ts writev2.TimeSeries) bool { if sampleAgeLimit == 0 { // If sampleAgeLimit is unset, then we never skip samples due to their age. return false } switch { // Only the first element should be set in the series, therefore we only check the first element. case len(ts.Samples) > 0: if isSampleOld(baseTime, sampleAgeLimit, ts.Samples[0].Timestamp) { metrics.droppedSamplesTotal.WithLabelValues(reasonTooOld).Inc() return true } case len(ts.Histograms) > 0: if isSampleOld(baseTime, sampleAgeLimit, ts.Histograms[0].Timestamp) { metrics.droppedHistogramsTotal.WithLabelValues(reasonTooOld).Inc() return true } case len(ts.Exemplars) > 0: if isSampleOld(baseTime, sampleAgeLimit, ts.Exemplars[0].Timestamp) { metrics.droppedExemplarsTotal.WithLabelValues(reasonTooOld).Inc() return true } default: return false } return false } } // Append queues a sample to be sent to the remote storage. Blocks until all samples are // enqueued on their shards or a shutdown signal is received. func (t *QueueManager) Append(samples []record.RefSample) bool { currentTime := time.Now() outer: for _, s := range samples { if isSampleOld(currentTime, time.Duration(t.cfg.SampleAgeLimit), s.T) { t.metrics.droppedSamplesTotal.WithLabelValues(reasonTooOld).Inc() continue } t.seriesMtx.Lock() lbls, ok := t.seriesLabels[s.Ref] if !ok { t.dataDropped.incr(1) if _, ok := t.droppedSeries[s.Ref]; !ok { level.Info(t.logger).Log("msg", "Dropped sample for series that was not explicitly dropped via relabelling", "ref", s.Ref) t.metrics.droppedSamplesTotal.WithLabelValues(reasonUnintentionalDroppedSeries).Inc() } else { t.metrics.droppedSamplesTotal.WithLabelValues(reasonDroppedSeries).Inc() } t.seriesMtx.Unlock() continue } // todo: handle or at least log an error if no metadata is found meta := t.seriesMetadata[s.Ref] t.seriesMtx.Unlock() // Start with a very small backoff. This should not be t.cfg.MinBackoff // as it can happen without errors, and we want to pickup work after // filling a queue/resharding as quickly as possible. // TODO: Consider using the average duration of a request as the backoff. backoff := model.Duration(5 * time.Millisecond) for { select { case <-t.quit: return false default: } if t.shards.enqueue(s.Ref, timeSeries{ seriesLabels: lbls, metadata: meta, timestamp: s.T, value: s.V, sType: tSample, }) { continue outer } t.metrics.enqueueRetriesTotal.Inc() time.Sleep(time.Duration(backoff)) backoff *= 2 // It is reasonable to use t.cfg.MaxBackoff here, as if we have hit // the full backoff we are likely waiting for external resources. if backoff > t.cfg.MaxBackoff { backoff = t.cfg.MaxBackoff } } } return true } func (t *QueueManager) AppendExemplars(exemplars []record.RefExemplar) bool { if !t.sendExemplars { return true } currentTime := time.Now() outer: for _, e := range exemplars { if isSampleOld(currentTime, time.Duration(t.cfg.SampleAgeLimit), e.T) { t.metrics.droppedExemplarsTotal.WithLabelValues(reasonTooOld).Inc() continue } t.seriesMtx.Lock() lbls, ok := t.seriesLabels[e.Ref] if !ok { // Track dropped exemplars in the same EWMA for sharding calc. t.dataDropped.incr(1) if _, ok := t.droppedSeries[e.Ref]; !ok { level.Info(t.logger).Log("msg", "Dropped exemplar for series that was not explicitly dropped via relabelling", "ref", e.Ref) t.metrics.droppedExemplarsTotal.WithLabelValues(reasonUnintentionalDroppedSeries).Inc() } else { t.metrics.droppedExemplarsTotal.WithLabelValues(reasonDroppedSeries).Inc() } t.seriesMtx.Unlock() continue } meta := t.seriesMetadata[e.Ref] t.seriesMtx.Unlock() // This will only loop if the queues are being resharded. backoff := t.cfg.MinBackoff for { select { case <-t.quit: return false default: } if t.shards.enqueue(e.Ref, timeSeries{ seriesLabels: lbls, metadata: meta, timestamp: e.T, value: e.V, exemplarLabels: e.Labels, sType: tExemplar, }) { continue outer } t.metrics.enqueueRetriesTotal.Inc() time.Sleep(time.Duration(backoff)) backoff *= 2 if backoff > t.cfg.MaxBackoff { backoff = t.cfg.MaxBackoff } } } return true } func (t *QueueManager) AppendHistograms(histograms []record.RefHistogramSample) bool { if !t.sendNativeHistograms { return true } currentTime := time.Now() outer: for _, h := range histograms { if isSampleOld(currentTime, time.Duration(t.cfg.SampleAgeLimit), h.T) { t.metrics.droppedHistogramsTotal.WithLabelValues(reasonTooOld).Inc() continue } t.seriesMtx.Lock() lbls, ok := t.seriesLabels[h.Ref] if !ok { t.dataDropped.incr(1) if _, ok := t.droppedSeries[h.Ref]; !ok { level.Info(t.logger).Log("msg", "Dropped histogram for series that was not explicitly dropped via relabelling", "ref", h.Ref) t.metrics.droppedHistogramsTotal.WithLabelValues(reasonUnintentionalDroppedSeries).Inc() } else { t.metrics.droppedHistogramsTotal.WithLabelValues(reasonDroppedSeries).Inc() } t.seriesMtx.Unlock() continue } meta := t.seriesMetadata[h.Ref] t.seriesMtx.Unlock() backoff := model.Duration(5 * time.Millisecond) for { select { case <-t.quit: return false default: } if t.shards.enqueue(h.Ref, timeSeries{ seriesLabels: lbls, metadata: meta, timestamp: h.T, histogram: h.H, sType: tHistogram, }) { continue outer } t.metrics.enqueueRetriesTotal.Inc() time.Sleep(time.Duration(backoff)) backoff *= 2 if backoff > t.cfg.MaxBackoff { backoff = t.cfg.MaxBackoff } } } return true } func (t *QueueManager) AppendFloatHistograms(floatHistograms []record.RefFloatHistogramSample) bool { if !t.sendNativeHistograms { return true } currentTime := time.Now() outer: for _, h := range floatHistograms { if isSampleOld(currentTime, time.Duration(t.cfg.SampleAgeLimit), h.T) { t.metrics.droppedHistogramsTotal.WithLabelValues(reasonTooOld).Inc() continue } t.seriesMtx.Lock() lbls, ok := t.seriesLabels[h.Ref] if !ok { t.dataDropped.incr(1) if _, ok := t.droppedSeries[h.Ref]; !ok { level.Info(t.logger).Log("msg", "Dropped histogram for series that was not explicitly dropped via relabelling", "ref", h.Ref) t.metrics.droppedHistogramsTotal.WithLabelValues(reasonUnintentionalDroppedSeries).Inc() } else { t.metrics.droppedHistogramsTotal.WithLabelValues(reasonDroppedSeries).Inc() } t.seriesMtx.Unlock() continue } meta := t.seriesMetadata[h.Ref] t.seriesMtx.Unlock() backoff := model.Duration(5 * time.Millisecond) for { select { case <-t.quit: return false default: } if t.shards.enqueue(h.Ref, timeSeries{ seriesLabels: lbls, metadata: meta, timestamp: h.T, floatHistogram: h.FH, sType: tFloatHistogram, }) { continue outer } t.metrics.enqueueRetriesTotal.Inc() time.Sleep(time.Duration(backoff)) backoff *= 2 if backoff > t.cfg.MaxBackoff { backoff = t.cfg.MaxBackoff } } } return true } // Start the queue manager sending samples to the remote storage. // Does not block. func (t *QueueManager) Start() { // Register and initialise some metrics. t.metrics.register() t.metrics.shardCapacity.Set(float64(t.cfg.Capacity)) t.metrics.maxNumShards.Set(float64(t.cfg.MaxShards)) t.metrics.minNumShards.Set(float64(t.cfg.MinShards)) t.metrics.desiredNumShards.Set(float64(t.cfg.MinShards)) t.metrics.maxSamplesPerSend.Set(float64(t.cfg.MaxSamplesPerSend)) t.shards.start(t.numShards) t.watcher.Start() if t.mcfg.Send { t.metadataWatcher.Start() } t.wg.Add(2) go t.updateShardsLoop() go t.reshardLoop() } // Stop stops sending samples to the remote storage and waits for pending // sends to complete. func (t *QueueManager) Stop() { level.Info(t.logger).Log("msg", "Stopping remote storage...") defer level.Info(t.logger).Log("msg", "Remote storage stopped.") close(t.quit) t.wg.Wait() // Wait for all QueueManager routines to end before stopping shards, metadata watcher, and WAL watcher. This // is to ensure we don't end up executing a reshard and shards.stop() at the same time, which // causes a closed channel panic. t.shards.stop() t.watcher.Stop() if t.mcfg.Send { t.metadataWatcher.Stop() } // On shutdown, release the strings in the labels from the intern pool. t.seriesMtx.Lock() for _, labels := range t.seriesLabels { t.releaseLabels(labels) } t.seriesMtx.Unlock() t.metrics.unregister() } // StoreSeries keeps track of which series we know about for lookups when sending samples to remote. func (t *QueueManager) StoreSeries(series []record.RefSeries, index int) { t.seriesMtx.Lock() defer t.seriesMtx.Unlock() t.seriesSegmentMtx.Lock() defer t.seriesSegmentMtx.Unlock() for _, s := range series { // Just make sure all the Refs of Series will insert into seriesSegmentIndexes map for tracking. t.seriesSegmentIndexes[s.Ref] = index ls := processExternalLabels(s.Labels, t.externalLabels) lbls, keep := relabel.Process(ls, t.relabelConfigs...) if !keep || lbls.IsEmpty() { t.droppedSeries[s.Ref] = struct{}{} continue } t.internLabels(lbls) // We should not ever be replacing a series labels in the map, but just // in case we do we need to ensure we do not leak the replaced interned // strings. if orig, ok := t.seriesLabels[s.Ref]; ok { t.releaseLabels(orig) } t.seriesLabels[s.Ref] = lbls } } // StoreMetadata keeps track of known series' metadata for lookups when sending samples to remote. func (t *QueueManager) StoreMetadata(meta []record.RefMetadata) { if t.rwFormat < Version2 { return } t.seriesMtx.Lock() defer t.seriesMtx.Unlock() for _, m := range meta { t.seriesMetadata[m.Ref] = &metadata.Metadata{ Type: record.ToMetricType(m.Type), Unit: m.Unit, Help: m.Help, } } } // UpdateSeriesSegment updates the segment number held against the series, // so we can trim older ones in SeriesReset. func (t *QueueManager) UpdateSeriesSegment(series []record.RefSeries, index int) { t.seriesSegmentMtx.Lock() defer t.seriesSegmentMtx.Unlock() for _, s := range series { t.seriesSegmentIndexes[s.Ref] = index } } // SeriesReset is used when reading a checkpoint. WAL Watcher should have // stored series records with the checkpoints index number, so we can now // delete any ref ID's lower than that # from the two maps. func (t *QueueManager) SeriesReset(index int) { t.seriesMtx.Lock() defer t.seriesMtx.Unlock() t.seriesSegmentMtx.Lock() defer t.seriesSegmentMtx.Unlock() // Check for series that are in segments older than the checkpoint // that were not also present in the checkpoint. for k, v := range t.seriesSegmentIndexes { if v < index { delete(t.seriesSegmentIndexes, k) t.releaseLabels(t.seriesLabels[k]) delete(t.seriesLabels, k) delete(t.seriesMetadata, k) delete(t.droppedSeries, k) } } } // SetClient updates the client used by a queue. Used when only client specific // fields are updated to avoid restarting the queue. func (t *QueueManager) SetClient(c WriteClient) { t.clientMtx.Lock() t.storeClient = c t.clientMtx.Unlock() } func (t *QueueManager) client() WriteClient { t.clientMtx.RLock() defer t.clientMtx.RUnlock() return t.storeClient } func (t *QueueManager) internLabels(lbls labels.Labels) { lbls.InternStrings(t.interner.intern) } func (t *QueueManager) releaseLabels(ls labels.Labels) { ls.ReleaseStrings(t.interner.release) } // processExternalLabels merges externalLabels into ls. If ls contains // a label in externalLabels, the value in ls wins. func processExternalLabels(ls labels.Labels, externalLabels []labels.Label) labels.Labels { if len(externalLabels) == 0 { return ls } b := labels.NewScratchBuilder(ls.Len() + len(externalLabels)) j := 0 ls.Range(func(l labels.Label) { for j < len(externalLabels) && l.Name > externalLabels[j].Name { b.Add(externalLabels[j].Name, externalLabels[j].Value) j++ } if j < len(externalLabels) && l.Name == externalLabels[j].Name { j++ } b.Add(l.Name, l.Value) }) for ; j < len(externalLabels); j++ { b.Add(externalLabels[j].Name, externalLabels[j].Value) } return b.Labels() } func (t *QueueManager) updateShardsLoop() { defer t.wg.Done() ticker := time.NewTicker(shardUpdateDuration) defer ticker.Stop() for { select { case <-ticker.C: desiredShards := t.calculateDesiredShards() if !t.shouldReshard(desiredShards) { continue } // Resharding can take some time, and we want this loop // to stay close to shardUpdateDuration. select { case t.reshardChan <- desiredShards: level.Info(t.logger).Log("msg", "Remote storage resharding", "from", t.numShards, "to", desiredShards) t.numShards = desiredShards default: level.Info(t.logger).Log("msg", "Currently resharding, skipping.") } case <-t.quit: return } } } // shouldReshard returns whether resharding should occur. func (t *QueueManager) shouldReshard(desiredShards int) bool { if desiredShards == t.numShards { return false } // We shouldn't reshard if Prometheus hasn't been able to send to the // remote endpoint successfully within some period of time. minSendTimestamp := time.Now().Add(-2 * time.Duration(t.cfg.BatchSendDeadline)).Unix() lsts := t.lastSendTimestamp.Load() if lsts < minSendTimestamp { level.Warn(t.logger).Log("msg", "Skipping resharding, last successful send was beyond threshold", "lastSendTimestamp", lsts, "minSendTimestamp", minSendTimestamp) return false } return true } // calculateDesiredShards returns the number of desired shards, which will be // the current QueueManager.numShards if resharding should not occur for reasons // outlined in this functions implementation. It is up to the caller to reshard, or not, // based on the return value. func (t *QueueManager) calculateDesiredShards() int { t.dataOut.tick() t.dataDropped.tick() t.dataOutDuration.tick() // We use the number of incoming samples as a prediction of how much work we // will need to do next iteration. We add to this any pending samples // (received - send) so we can catch up with any backlog. We use the average // outgoing batch latency to work out how many shards we need. var ( dataInRate = t.dataIn.rate() dataOutRate = t.dataOut.rate() dataKeptRatio = dataOutRate / (t.dataDropped.rate() + dataOutRate) dataOutDuration = t.dataOutDuration.rate() / float64(time.Second) dataPendingRate = dataInRate*dataKeptRatio - dataOutRate highestSent = t.metrics.highestSentTimestamp.Get() highestRecv = t.highestRecvTimestamp.Get() delay = highestRecv - highestSent dataPending = delay * dataInRate * dataKeptRatio ) if dataOutRate <= 0 { return t.numShards } var ( // When behind we will try to catch up on 5% of samples per second. backlogCatchup = 0.05 * dataPending // Calculate Time to send one sample, averaged across all sends done this tick. timePerSample = dataOutDuration / dataOutRate desiredShards = timePerSample * (dataInRate*dataKeptRatio + backlogCatchup) ) t.metrics.desiredNumShards.Set(desiredShards) level.Debug(t.logger).Log("msg", "QueueManager.calculateDesiredShards", "dataInRate", dataInRate, "dataOutRate", dataOutRate, "dataKeptRatio", dataKeptRatio, "dataPendingRate", dataPendingRate, "dataPending", dataPending, "dataOutDuration", dataOutDuration, "timePerSample", timePerSample, "desiredShards", desiredShards, "highestSent", highestSent, "highestRecv", highestRecv, ) // Changes in the number of shards must be greater than shardToleranceFraction. var ( lowerBound = float64(t.numShards) * (1. - shardToleranceFraction) upperBound = float64(t.numShards) * (1. + shardToleranceFraction) ) level.Debug(t.logger).Log("msg", "QueueManager.updateShardsLoop", "lowerBound", lowerBound, "desiredShards", desiredShards, "upperBound", upperBound) desiredShards = math.Ceil(desiredShards) // Round up to be on the safe side. if lowerBound <= desiredShards && desiredShards <= upperBound { return t.numShards } numShards := int(desiredShards) // Do not downshard if we are more than ten seconds back. if numShards < t.numShards && delay > 10.0 { level.Debug(t.logger).Log("msg", "Not downsharding due to being too far behind") return t.numShards } switch { case numShards > t.cfg.MaxShards: numShards = t.cfg.MaxShards case numShards < t.cfg.MinShards: numShards = t.cfg.MinShards } return numShards } func (t *QueueManager) reshardLoop() { defer t.wg.Done() for { select { case numShards := <-t.reshardChan: // We start the newShards after we have stopped (the therefore completely // flushed) the oldShards, to guarantee we only every deliver samples in // order. t.shards.stop() t.shards.start(numShards) case <-t.quit: return } } } func (t *QueueManager) newShards() *shards { s := &shards{ qm: t, done: make(chan struct{}), } return s } type shards struct { mtx sync.RWMutex // With the WAL, this is never actually contended. qm *QueueManager queues []*queue // So we can accurately track how many of each are lost during shard shutdowns. enqueuedSamples atomic.Int64 enqueuedExemplars atomic.Int64 enqueuedHistograms atomic.Int64 // Emulate a wait group with a channel and an atomic int, as you // cannot select on a wait group. done chan struct{} running atomic.Int32 // Soft shutdown context will prevent new enqueues and deadlocks. softShutdown chan struct{} // Hard shutdown context is used to terminate outgoing HTTP connections // after giving them a chance to terminate. hardShutdown context.CancelFunc samplesDroppedOnHardShutdown atomic.Uint32 exemplarsDroppedOnHardShutdown atomic.Uint32 histogramsDroppedOnHardShutdown atomic.Uint32 metadataDroppedOnHardShutdown atomic.Uint32 } // start the shards; must be called before any call to enqueue. func (s *shards) start(n int) { s.mtx.Lock() defer s.mtx.Unlock() s.qm.metrics.pendingSamples.Set(0) s.qm.metrics.numShards.Set(float64(n)) newQueues := make([]*queue, n) for i := 0; i < n; i++ { newQueues[i] = newQueue(s.qm.cfg.MaxSamplesPerSend, s.qm.cfg.Capacity) } s.queues = newQueues var hardShutdownCtx context.Context hardShutdownCtx, s.hardShutdown = context.WithCancel(context.Background()) s.softShutdown = make(chan struct{}) s.running.Store(int32(n)) s.done = make(chan struct{}) s.enqueuedSamples.Store(0) s.enqueuedExemplars.Store(0) s.enqueuedHistograms.Store(0) s.samplesDroppedOnHardShutdown.Store(0) s.exemplarsDroppedOnHardShutdown.Store(0) s.histogramsDroppedOnHardShutdown.Store(0) s.metadataDroppedOnHardShutdown.Store(0) for i := 0; i < n; i++ { go s.runShard(hardShutdownCtx, i, newQueues[i]) } } // stop the shards; subsequent call to enqueue will return false. func (s *shards) stop() { // Attempt a clean shutdown, but only wait flushDeadline for all the shards // to cleanly exit. As we're doing RPCs, enqueue can block indefinitely. // We must be able so call stop concurrently, hence we can only take the // RLock here. s.mtx.RLock() close(s.softShutdown) s.mtx.RUnlock() // Enqueue should now be unblocked, so we can take the write lock. This // also ensures we don't race with writes to the queues, and get a panic: // send on closed channel. s.mtx.Lock() defer s.mtx.Unlock() for _, queue := range s.queues { go queue.FlushAndShutdown(s.done) } select { case <-s.done: return case <-time.After(s.qm.flushDeadline): } // Force an unclean shutdown. s.hardShutdown() <-s.done if dropped := s.samplesDroppedOnHardShutdown.Load(); dropped > 0 { level.Error(s.qm.logger).Log("msg", "Failed to flush all samples on shutdown", "count", dropped) } if dropped := s.exemplarsDroppedOnHardShutdown.Load(); dropped > 0 { level.Error(s.qm.logger).Log("msg", "Failed to flush all exemplars on shutdown", "count", dropped) } } // enqueue data (sample or exemplar). If the shard is full, shutting down, or // resharding, it will return false; in this case, you should back off and // retry. A shard is full when its configured capacity has been reached, // specifically, when s.queues[shard] has filled its batchQueue channel and the // partial batch has also been filled. func (s *shards) enqueue(ref chunks.HeadSeriesRef, data timeSeries) bool { s.mtx.RLock() defer s.mtx.RUnlock() shard := uint64(ref) % uint64(len(s.queues)) select { case <-s.softShutdown: return false default: appended := s.queues[shard].Append(data) if !appended { return false } switch data.sType { case tSample: s.qm.metrics.pendingSamples.Inc() s.enqueuedSamples.Inc() case tExemplar: s.qm.metrics.pendingExemplars.Inc() s.enqueuedExemplars.Inc() case tHistogram, tFloatHistogram: s.qm.metrics.pendingHistograms.Inc() s.enqueuedHistograms.Inc() } return true } } type queue struct { // batchMtx covers operations appending to or publishing the partial batch. batchMtx sync.Mutex batch []timeSeries batchQueue chan []timeSeries // Since we know there are a limited number of batches out, using a stack // is easy and safe so a sync.Pool is not necessary. // poolMtx covers adding and removing batches from the batchPool. poolMtx sync.Mutex batchPool [][]timeSeries } type timeSeries struct { seriesLabels labels.Labels value float64 histogram *histogram.Histogram floatHistogram *histogram.FloatHistogram metadata *metadata.Metadata timestamp int64 exemplarLabels labels.Labels // The type of series: sample, exemplar, or histogram. sType seriesType } type seriesType int const ( tSample seriesType = iota tExemplar tHistogram tFloatHistogram tMetadata ) func newQueue(batchSize, capacity int) *queue { batches := capacity / batchSize // Always create an unbuffered channel even if capacity is configured to be // less than max_samples_per_send. if batches == 0 { batches = 1 } return &queue{ batch: make([]timeSeries, 0, batchSize), batchQueue: make(chan []timeSeries, batches), // batchPool should have capacity for everything in the channel + 1 for // the batch being processed. batchPool: make([][]timeSeries, 0, batches+1), } } // Append the timeSeries to the buffered batch. Returns false if it // cannot be added and must be retried. func (q *queue) Append(datum timeSeries) bool { q.batchMtx.Lock() defer q.batchMtx.Unlock() // TODO: check if metadata now means we've reduced the total # of samples // we can batch together here, and if so find a way to not include metadata // in the batch size calculation. q.batch = append(q.batch, datum) if len(q.batch) == cap(q.batch) { select { case q.batchQueue <- q.batch: q.batch = q.newBatch(cap(q.batch)) return true default: // Remove the sample we just appended. It will get retried. q.batch = q.batch[:len(q.batch)-1] return false } } return true } func (q *queue) Chan() <-chan []timeSeries { return q.batchQueue } // Batch returns the current batch and allocates a new batch. func (q *queue) Batch() []timeSeries { q.batchMtx.Lock() defer q.batchMtx.Unlock() select { case batch := <-q.batchQueue: return batch default: batch := q.batch q.batch = q.newBatch(cap(batch)) return batch } } // ReturnForReuse adds the batch buffer back to the internal pool. func (q *queue) ReturnForReuse(batch []timeSeries) { q.poolMtx.Lock() defer q.poolMtx.Unlock() if len(q.batchPool) < cap(q.batchPool) { q.batchPool = append(q.batchPool, batch[:0]) } } // FlushAndShutdown stops the queue and flushes any samples. No appends can be // made after this is called. func (q *queue) FlushAndShutdown(done <-chan struct{}) { for q.tryEnqueueingBatch(done) { time.Sleep(time.Second) } q.batch = nil close(q.batchQueue) } // tryEnqueueingBatch tries to send a batch if necessary. If sending needs to // be retried it will return true. func (q *queue) tryEnqueueingBatch(done <-chan struct{}) bool { q.batchMtx.Lock() defer q.batchMtx.Unlock() if len(q.batch) == 0 { return false } select { case q.batchQueue <- q.batch: return false case <-done: // The shard has been hard shut down, so no more samples can be sent. // No need to try again as we will drop everything left in the queue. return false default: // The batchQueue is full, so we need to try again later. return true } } func (q *queue) newBatch(capacity int) []timeSeries { q.poolMtx.Lock() defer q.poolMtx.Unlock() batches := len(q.batchPool) if batches > 0 { batch := q.batchPool[batches-1] q.batchPool = q.batchPool[:batches-1] return batch } return make([]timeSeries, 0, capacity) } func (s *shards) runShard(ctx context.Context, shardID int, queue *queue) { defer func() { if s.running.Dec() == 0 { close(s.done) } }() shardNum := strconv.Itoa(shardID) symbolTable := newRwSymbolTable() // Send batches of at most MaxSamplesPerSend samples to the remote storage. // If we have fewer samples than that, flush them out after a deadline anyways. var ( max = s.qm.cfg.MaxSamplesPerSend pBuf = proto.NewBuffer(nil) pBufRaw []byte buf []byte ) // TODO(@tpaschalis) Should we also raise the max if we have WAL metadata? if s.qm.sendExemplars { max += int(float64(max) * 0.1) } // TODO we should make an interface for the timeseries type batchQueue := queue.Chan() pendingData := make([]prompb.TimeSeries, max) for i := range pendingData { pendingData[i].Samples = []prompb.Sample{{}} if s.qm.sendExemplars { pendingData[i].Exemplars = []prompb.Exemplar{{}} } } pendingMinStrData := make([]writev2.TimeSeries, max) for i := range pendingMinStrData { pendingMinStrData[i].Samples = []writev2.Sample{{}} } timer := time.NewTimer(time.Duration(s.qm.cfg.BatchSendDeadline)) stop := func() { if !timer.Stop() { select { case <-timer.C: default: } } } defer stop() for { select { case <-ctx.Done(): // In this case we drop all samples in the buffer and the queue. // Remove them from pending and mark them as failed. droppedSamples := int(s.enqueuedSamples.Load()) droppedExemplars := int(s.enqueuedExemplars.Load()) droppedHistograms := int(s.enqueuedHistograms.Load()) s.qm.metrics.pendingSamples.Sub(float64(droppedSamples)) s.qm.metrics.pendingExemplars.Sub(float64(droppedExemplars)) s.qm.metrics.pendingHistograms.Sub(float64(droppedHistograms)) s.qm.metrics.failedSamplesTotal.Add(float64(droppedSamples)) s.qm.metrics.failedExemplarsTotal.Add(float64(droppedExemplars)) s.qm.metrics.failedHistogramsTotal.Add(float64(droppedHistograms)) s.samplesDroppedOnHardShutdown.Add(uint32(droppedSamples)) s.exemplarsDroppedOnHardShutdown.Add(uint32(droppedExemplars)) s.histogramsDroppedOnHardShutdown.Add(uint32(droppedHistograms)) return case batch, ok := <-batchQueue: if !ok { return } switch s.qm.rwFormat { case Version1: nPendingSamples, nPendingExemplars, nPendingHistograms := populateTimeSeries(batch, pendingData, s.qm.sendExemplars, s.qm.sendNativeHistograms) n := nPendingSamples + nPendingExemplars + nPendingHistograms s.sendSamples(ctx, pendingData[:n], nPendingSamples, nPendingExemplars, nPendingHistograms, pBuf, &buf) case Version2: nPendingSamples, nPendingExemplars, nPendingHistograms, nPendingMetadata := populateV2TimeSeries(&symbolTable, batch, pendingMinStrData, s.qm.sendExemplars, s.qm.sendNativeHistograms) n := nPendingSamples + nPendingExemplars + nPendingHistograms s.sendV2Samples(ctx, pendingMinStrData[:n], symbolTable.LabelsStrings(), nPendingSamples, nPendingExemplars, nPendingHistograms, nPendingMetadata, &pBufRaw, &buf) symbolTable.clear() } queue.ReturnForReuse(batch) stop() timer.Reset(time.Duration(s.qm.cfg.BatchSendDeadline)) case <-timer.C: batch := queue.Batch() if len(batch) > 0 { switch s.qm.rwFormat { case Version1: nPendingSamples, nPendingExemplars, nPendingHistograms := populateTimeSeries(batch, pendingData, s.qm.sendExemplars, s.qm.sendNativeHistograms) n := nPendingSamples + nPendingExemplars + nPendingHistograms s.sendSamples(ctx, pendingData[:n], nPendingSamples, nPendingExemplars, nPendingHistograms, pBuf, &buf) level.Debug(s.qm.logger).Log("msg", "runShard timer ticked, sending buffered data", "samples", nPendingSamples, "exemplars", nPendingExemplars, "shard", shardNum, "histograms", nPendingHistograms) case Version2: nPendingSamples, nPendingExemplars, nPendingHistograms, nPendingMetadata := populateV2TimeSeries(&symbolTable, batch, pendingMinStrData, s.qm.sendExemplars, s.qm.sendNativeHistograms) n := nPendingSamples + nPendingExemplars + nPendingHistograms s.sendV2Samples(ctx, pendingMinStrData[:n], symbolTable.LabelsStrings(), nPendingSamples, nPendingExemplars, nPendingHistograms, nPendingMetadata, &pBufRaw, &buf) symbolTable.clear() } } queue.ReturnForReuse(batch) timer.Reset(time.Duration(s.qm.cfg.BatchSendDeadline)) } } } func populateTimeSeries(batch []timeSeries, pendingData []prompb.TimeSeries, sendExemplars, sendNativeHistograms bool) (int, int, int) { var nPendingSamples, nPendingExemplars, nPendingHistograms int for nPending, d := range batch { pendingData[nPending].Samples = pendingData[nPending].Samples[:0] if sendExemplars { pendingData[nPending].Exemplars = pendingData[nPending].Exemplars[:0] } if sendNativeHistograms { pendingData[nPending].Histograms = pendingData[nPending].Histograms[:0] } // Number of pending samples is limited by the fact that sendSamples (via sendSamplesWithBackoff) // retries endlessly, so once we reach max samples, if we can never send to the endpoint we'll // stop reading from the queue. This makes it safe to reference pendingSamples by index. pendingData[nPending].Labels = labelsToLabelsProto(d.seriesLabels, pendingData[nPending].Labels) switch d.sType { case tSample: pendingData[nPending].Samples = append(pendingData[nPending].Samples, prompb.Sample{ Value: d.value, Timestamp: d.timestamp, }) nPendingSamples++ case tExemplar: pendingData[nPending].Exemplars = append(pendingData[nPending].Exemplars, prompb.Exemplar{ Labels: labelsToLabelsProto(d.exemplarLabels, nil), Value: d.value, Timestamp: d.timestamp, }) nPendingExemplars++ case tHistogram: pendingData[nPending].Histograms = append(pendingData[nPending].Histograms, HistogramToHistogramProto(d.timestamp, d.histogram)) nPendingHistograms++ case tFloatHistogram: pendingData[nPending].Histograms = append(pendingData[nPending].Histograms, FloatHistogramToHistogramProto(d.timestamp, d.floatHistogram)) nPendingHistograms++ } } return nPendingSamples, nPendingExemplars, nPendingHistograms } func (s *shards) sendSamples(ctx context.Context, samples []prompb.TimeSeries, sampleCount, exemplarCount, histogramCount int, pBuf *proto.Buffer, buf *[]byte) { begin := time.Now() err := s.sendSamplesWithBackoff(ctx, samples, sampleCount, exemplarCount, histogramCount, 0, pBuf, buf) s.updateMetrics(ctx, err, sampleCount, exemplarCount, histogramCount, 0, time.Since(begin)) } func (s *shards) sendV2Samples(ctx context.Context, samples []writev2.TimeSeries, labels []string, sampleCount, exemplarCount, histogramCount, metadataCount int, pBuf, buf *[]byte) { begin := time.Now() err := s.sendV2SamplesWithBackoff(ctx, samples, labels, sampleCount, exemplarCount, histogramCount, metadataCount, pBuf, buf) s.updateMetrics(ctx, err, sampleCount, exemplarCount, histogramCount, metadataCount, time.Since(begin)) } func (s *shards) updateMetrics(ctx context.Context, err error, sampleCount, exemplarCount, histogramCount, metadataCount int, duration time.Duration) { if err != nil { level.Error(s.qm.logger).Log("msg", "non-recoverable error", "count", sampleCount, "exemplarCount", exemplarCount, "err", err) s.qm.metrics.failedSamplesTotal.Add(float64(sampleCount)) s.qm.metrics.failedExemplarsTotal.Add(float64(exemplarCount)) s.qm.metrics.failedHistogramsTotal.Add(float64(histogramCount)) } // These counters are used to calculate the dynamic sharding, and as such // should be maintained irrespective of success or failure. s.qm.dataOut.incr(int64(sampleCount + exemplarCount + histogramCount + metadataCount)) s.qm.dataOutDuration.incr(int64(duration)) s.qm.lastSendTimestamp.Store(time.Now().Unix()) // Pending samples/exemplars/histograms also should be subtracted, as an error means // they will not be retried. s.qm.metrics.pendingSamples.Sub(float64(sampleCount)) s.qm.metrics.pendingExemplars.Sub(float64(exemplarCount)) s.qm.metrics.pendingHistograms.Sub(float64(histogramCount)) s.enqueuedSamples.Sub(int64(sampleCount)) s.enqueuedExemplars.Sub(int64(exemplarCount)) s.enqueuedHistograms.Sub(int64(histogramCount)) } // sendSamples to the remote storage with backoff for recoverable errors. func (s *shards) sendSamplesWithBackoff(ctx context.Context, samples []prompb.TimeSeries, sampleCount, exemplarCount, histogramCount, metadataCount int, pBuf *proto.Buffer, buf *[]byte) error { // Build the WriteRequest with no metadata. req, highest, lowest, err := buildWriteRequest(s.qm.logger, samples, nil, pBuf, buf, nil) s.qm.buildRequestLimitTimestamp.Store(lowest) if err != nil { // Failing to build the write request is non-recoverable, since it will // only error if marshaling the proto to bytes fails. return err } reqSize := len(req) *buf = req // An anonymous function allows us to defer the completion of our per-try spans // without causing a memory leak, and it has the nice effect of not propagating any // parameters for sendSamplesWithBackoff/3. attemptStore := func(try int) error { currentTime := time.Now() lowest := s.qm.buildRequestLimitTimestamp.Load() if isSampleOld(currentTime, time.Duration(s.qm.cfg.SampleAgeLimit), lowest) { // This will filter out old samples during retries. req, _, lowest, err := buildWriteRequest( s.qm.logger, samples, nil, pBuf, buf, isTimeSeriesOldFilter(s.qm.metrics, currentTime, time.Duration(s.qm.cfg.SampleAgeLimit)), ) s.qm.buildRequestLimitTimestamp.Store(lowest) if err != nil { return err } *buf = req } ctx, span := otel.Tracer("").Start(ctx, "Remote Send Batch") defer span.End() span.SetAttributes( attribute.Int("request_size", reqSize), attribute.Int("samples", sampleCount), attribute.Int("try", try), attribute.String("remote_name", s.qm.storeClient.Name()), attribute.String("remote_url", s.qm.storeClient.Endpoint()), ) if exemplarCount > 0 { span.SetAttributes(attribute.Int("exemplars", exemplarCount)) } if histogramCount > 0 { span.SetAttributes(attribute.Int("histograms", histogramCount)) } begin := time.Now() s.qm.metrics.samplesTotal.Add(float64(sampleCount)) s.qm.metrics.exemplarsTotal.Add(float64(exemplarCount)) s.qm.metrics.histogramsTotal.Add(float64(histogramCount)) s.qm.metrics.metadataTotal.Add(float64(metadataCount)) err := s.qm.client().Store(ctx, *buf, try) s.qm.metrics.sentBatchDuration.Observe(time.Since(begin).Seconds()) if err != nil { span.RecordError(err) return err } return nil } onRetry := func() { s.qm.metrics.retriedSamplesTotal.Add(float64(sampleCount)) s.qm.metrics.retriedExemplarsTotal.Add(float64(exemplarCount)) s.qm.metrics.retriedHistogramsTotal.Add(float64(histogramCount)) } err = sendWriteRequestWithBackoff(ctx, s.qm.cfg, s.qm.logger, attemptStore, onRetry) if errors.Is(err, context.Canceled) { // When there is resharding, we cancel the context for this queue, which means the data is not sent. // So we exit early to not update the metrics. return err } s.qm.metrics.sentBytesTotal.Add(float64(reqSize)) s.qm.metrics.highestSentTimestamp.Set(float64(highest / 1000)) return err } // sendV2Samples to the remote storage with backoff for recoverable errors. func (s *shards) sendV2SamplesWithBackoff(ctx context.Context, samples []writev2.TimeSeries, labels []string, sampleCount, exemplarCount, histogramCount, metadataCount int, pBuf, buf *[]byte) error { // Build the WriteRequest with no metadata. req, highest, lowest, err := buildV2WriteRequest(s.qm.logger, samples, labels, pBuf, buf, nil) s.qm.buildRequestLimitTimestamp.Store(lowest) if err != nil { // Failing to build the write request is non-recoverable, since it will // only error if marshaling the proto to bytes fails. return err } reqSize := len(req) *buf = req // An anonymous function allows us to defer the completion of our per-try spans // without causing a memory leak, and it has the nice effect of not propagating any // parameters for sendSamplesWithBackoff/3. attemptStore := func(try int) error { currentTime := time.Now() lowest := s.qm.buildRequestLimitTimestamp.Load() if isSampleOld(currentTime, time.Duration(s.qm.cfg.SampleAgeLimit), lowest) { // This will filter out old samples during retries. req, _, lowest, err := buildV2WriteRequest( s.qm.logger, samples, labels, pBuf, buf, isV2TimeSeriesOldFilter(s.qm.metrics, currentTime, time.Duration(s.qm.cfg.SampleAgeLimit)), ) s.qm.buildRequestLimitTimestamp.Store(lowest) if err != nil { return err } *buf = req } ctx, span := otel.Tracer("").Start(ctx, "Remote Send Batch") defer span.End() span.SetAttributes( attribute.Int("request_size", reqSize), attribute.Int("samples", sampleCount), attribute.Int("try", try), attribute.String("remote_name", s.qm.storeClient.Name()), attribute.String("remote_url", s.qm.storeClient.Endpoint()), ) if exemplarCount > 0 { span.SetAttributes(attribute.Int("exemplars", exemplarCount)) } if histogramCount > 0 { span.SetAttributes(attribute.Int("histograms", histogramCount)) } begin := time.Now() s.qm.metrics.samplesTotal.Add(float64(sampleCount)) s.qm.metrics.exemplarsTotal.Add(float64(exemplarCount)) s.qm.metrics.histogramsTotal.Add(float64(histogramCount)) s.qm.metrics.metadataTotal.Add(float64(metadataCount)) err := s.qm.client().Store(ctx, *buf, try) s.qm.metrics.sentBatchDuration.Observe(time.Since(begin).Seconds()) if err != nil { span.RecordError(err) return err } return nil } onRetry := func() { s.qm.metrics.retriedSamplesTotal.Add(float64(sampleCount)) s.qm.metrics.retriedExemplarsTotal.Add(float64(exemplarCount)) s.qm.metrics.retriedHistogramsTotal.Add(float64(histogramCount)) } err = sendWriteRequestWithBackoff(ctx, s.qm.cfg, s.qm.logger, attemptStore, onRetry) if errors.Is(err, context.Canceled) { // When there is resharding, we cancel the context for this queue, which means the data is not sent. // So we exit early to not update the metrics. return err } s.qm.metrics.sentBytesTotal.Add(float64(reqSize)) s.qm.metrics.highestSentTimestamp.Set(float64(highest / 1000)) return err } func populateV2TimeSeries(symbolTable *rwSymbolTable, batch []timeSeries, pendingData []writev2.TimeSeries, sendExemplars, sendNativeHistograms bool) (int, int, int, int) { var nPendingSamples, nPendingExemplars, nPendingHistograms, nPendingMetadata int for nPending, d := range batch { pendingData[nPending].Samples = pendingData[nPending].Samples[:0] // todo: should we also safeguard against empty metadata here? if d.metadata != nil { pendingData[nPending].Metadata.Type = metricTypeToMetricTypeProtoV2(d.metadata.Type) pendingData[nPending].Metadata.HelpRef = symbolTable.RefStr(d.metadata.Help) pendingData[nPending].Metadata.HelpRef = symbolTable.RefStr(d.metadata.Unit) nPendingMetadata++ } if sendExemplars { pendingData[nPending].Exemplars = pendingData[nPending].Exemplars[:0] } if sendNativeHistograms { pendingData[nPending].Histograms = pendingData[nPending].Histograms[:0] } // Number of pending samples is limited by the fact that sendSamples (via sendSamplesWithBackoff) // retries endlessly, so once we reach max samples, if we can never send to the endpoint we'll // stop reading from the queue. This makes it safe to reference pendingSamples by index. // pendingData[nPending].Labels = labelsToLabelsProto(d.seriesLabels, pendingData[nPending].Labels) pendingData[nPending].LabelsRefs = labelsToLabelsProtoV2Refs(d.seriesLabels, symbolTable, pendingData[nPending].LabelsRefs) switch d.sType { case tSample: pendingData[nPending].Samples = append(pendingData[nPending].Samples, writev2.Sample{ Value: d.value, Timestamp: d.timestamp, }) nPendingSamples++ case tExemplar: pendingData[nPending].Exemplars = append(pendingData[nPending].Exemplars, writev2.Exemplar{ LabelsRefs: labelsToLabelsProtoV2Refs(d.exemplarLabels, symbolTable, nil), // TODO: optimize, reuse slice Value: d.value, Timestamp: d.timestamp, }) nPendingExemplars++ case tHistogram: pendingData[nPending].Histograms = append(pendingData[nPending].Histograms, HistogramToMinHistogramProto(d.timestamp, d.histogram)) nPendingHistograms++ case tFloatHistogram: pendingData[nPending].Histograms = append(pendingData[nPending].Histograms, FloatHistogramToMinHistogramProto(d.timestamp, d.floatHistogram)) nPendingHistograms++ case tMetadata: // TODO: log or return an error? // we shouldn't receive metadata type data here, it should already be inserted into the timeSeries } } return nPendingSamples, nPendingExemplars, nPendingHistograms, nPendingMetadata } func sendWriteRequestWithBackoff(ctx context.Context, cfg config.QueueConfig, l log.Logger, attempt func(int) error, onRetry func()) error { backoff := cfg.MinBackoff sleepDuration := model.Duration(0) try := 0 for { select { case <-ctx.Done(): return ctx.Err() default: } err := attempt(try) if err == nil { return nil } // If the error is unrecoverable, we should not retry. var backoffErr RecoverableError if !errors.As(err, &backoffErr) { return err } sleepDuration = backoff switch { case backoffErr.retryAfter > 0: sleepDuration = backoffErr.retryAfter level.Info(l).Log("msg", "Retrying after duration specified by Retry-After header", "duration", sleepDuration) case backoffErr.retryAfter < 0: level.Debug(l).Log("msg", "retry-after cannot be in past, retrying using default backoff mechanism") } select { case <-ctx.Done(): case <-time.After(time.Duration(sleepDuration)): } // If we make it this far, we've encountered a recoverable error and will retry. onRetry() level.Warn(l).Log("msg", "Failed to send batch, retrying", "err", err) backoff = sleepDuration * 2 if backoff > cfg.MaxBackoff { backoff = cfg.MaxBackoff } try++ } } func buildTimeSeries(timeSeries []prompb.TimeSeries, filter func(prompb.TimeSeries) bool) (int64, int64, []prompb.TimeSeries, int, int, int) { var highest int64 var lowest int64 var droppedSamples, droppedExemplars, droppedHistograms int keepIdx := 0 lowest = math.MaxInt64 for i, ts := range timeSeries { if filter != nil && filter(ts) { if len(ts.Samples) > 0 { droppedSamples++ } if len(ts.Exemplars) > 0 { droppedExemplars++ } if len(ts.Histograms) > 0 { droppedHistograms++ } continue } // At the moment we only ever append a TimeSeries with a single sample or exemplar in it. if len(ts.Samples) > 0 && ts.Samples[0].Timestamp > highest { highest = ts.Samples[0].Timestamp } if len(ts.Exemplars) > 0 && ts.Exemplars[0].Timestamp > highest { highest = ts.Exemplars[0].Timestamp } if len(ts.Histograms) > 0 && ts.Histograms[0].Timestamp > highest { highest = ts.Histograms[0].Timestamp } // Get lowest timestamp if len(ts.Samples) > 0 && ts.Samples[0].Timestamp < lowest { lowest = ts.Samples[0].Timestamp } if len(ts.Exemplars) > 0 && ts.Exemplars[0].Timestamp < lowest { lowest = ts.Exemplars[0].Timestamp } if len(ts.Histograms) > 0 && ts.Histograms[0].Timestamp < lowest { lowest = ts.Histograms[0].Timestamp } // Move the current element to the write position and increment the write pointer timeSeries[keepIdx] = timeSeries[i] keepIdx++ } timeSeries = timeSeries[:keepIdx] return highest, lowest, timeSeries, droppedSamples, droppedExemplars, droppedHistograms } func buildWriteRequest(logger log.Logger, timeSeries []prompb.TimeSeries, metadata []prompb.MetricMetadata, pBuf *proto.Buffer, buf *[]byte, filter func(prompb.TimeSeries) bool) ([]byte, int64, int64, error) { highest, lowest, timeSeries, droppedSamples, droppedExemplars, droppedHistograms := buildTimeSeries(timeSeries, filter) if droppedSamples > 0 || droppedExemplars > 0 || droppedHistograms > 0 { level.Debug(logger).Log("msg", "dropped data due to their age", "droppedSamples", droppedSamples, "droppedExemplars", droppedExemplars, "droppedHistograms", droppedHistograms) } req := &prompb.WriteRequest{ Timeseries: timeSeries, Metadata: metadata, } if pBuf == nil { pBuf = proto.NewBuffer(nil) // For convenience in tests. Not efficient. } else { pBuf.Reset() } err := pBuf.Marshal(req) if err != nil { return nil, highest, lowest, err } // snappy uses len() to see if it needs to allocate a new slice. Make the // buffer as long as possible. if buf != nil { *buf = (*buf)[0:cap(*buf)] } else { buf = &[]byte{} } compressed := snappy.Encode(*buf, pBuf.Bytes()) if n := snappy.MaxEncodedLen(len(pBuf.Bytes())); n > len(*buf) { // grow the buffer for the next time *buf = make([]byte, n) } return compressed, highest, lowest, nil } type rwSymbolTable struct { strings []string symbolsMap map[string]uint32 } func newRwSymbolTable() rwSymbolTable { return rwSymbolTable{ symbolsMap: make(map[string]uint32), } } func (r *rwSymbolTable) RefStr(str string) uint32 { if ref, ok := r.symbolsMap[str]; ok { return ref } ref := uint32(len(r.strings)) r.strings = append(r.strings, str) r.symbolsMap[str] = ref return ref } func (r *rwSymbolTable) LabelsStrings() []string { return r.strings } func (r *rwSymbolTable) clear() { r.strings = r.strings[:0] for k := range r.symbolsMap { delete(r.symbolsMap, k) } } func buildV2WriteRequest(logger log.Logger, samples []writev2.TimeSeries, labels []string, pBuf, buf *[]byte, filter func(writev2.TimeSeries) bool) ([]byte, int64, int64, error) { highest, lowest, timeSeries, droppedSamples, droppedExemplars, droppedHistograms := buildV2TimeSeries(samples, filter) if droppedSamples > 0 || droppedExemplars > 0 || droppedHistograms > 0 { level.Debug(logger).Log("msg", "dropped data due to their age", "droppedSamples", droppedSamples, "droppedExemplars", droppedExemplars, "droppedHistograms", droppedHistograms) } req := &writev2.WriteRequest{ Symbols: labels, Timeseries: timeSeries, } if pBuf == nil { pBuf = &[]byte{} // For convenience in tests. Not efficient. } data, err := req.OptimizedMarshal(*pBuf) if err != nil { return nil, highest, lowest, err } *pBuf = data // snappy uses len() to see if it needs to allocate a new slice. Make the // buffer as long as possible. if buf != nil { *buf = (*buf)[0:cap(*buf)] } else { buf = &[]byte{} } compressed := snappy.Encode(*buf, data) if n := snappy.MaxEncodedLen(len(data)); n > len(*buf) { // grow the buffer for the next time *buf = make([]byte, n) } return compressed, highest, lowest, nil } func buildV2TimeSeries(timeSeries []writev2.TimeSeries, filter func(writev2.TimeSeries) bool) (int64, int64, []writev2.TimeSeries, int, int, int) { var highest int64 var lowest int64 var droppedSamples, droppedExemplars, droppedHistograms int keepIdx := 0 lowest = math.MaxInt64 for i, ts := range timeSeries { if filter != nil && filter(ts) { if len(ts.Samples) > 0 { droppedSamples++ } if len(ts.Exemplars) > 0 { droppedExemplars++ } if len(ts.Histograms) > 0 { droppedHistograms++ } continue } // At the moment we only ever append a TimeSeries with a single sample or exemplar in it. if len(ts.Samples) > 0 && ts.Samples[0].Timestamp > highest { highest = ts.Samples[0].Timestamp } if len(ts.Exemplars) > 0 && ts.Exemplars[0].Timestamp > highest { highest = ts.Exemplars[0].Timestamp } if len(ts.Histograms) > 0 && ts.Histograms[0].Timestamp > highest { highest = ts.Histograms[0].Timestamp } // Get lowest timestamp if len(ts.Samples) > 0 && ts.Samples[0].Timestamp < lowest { lowest = ts.Samples[0].Timestamp } if len(ts.Exemplars) > 0 && ts.Exemplars[0].Timestamp < lowest { lowest = ts.Exemplars[0].Timestamp } if len(ts.Histograms) > 0 && ts.Histograms[0].Timestamp < lowest { lowest = ts.Histograms[0].Timestamp } // Move the current element to the write position and increment the write pointer timeSeries[keepIdx] = timeSeries[i] keepIdx++ } timeSeries = timeSeries[:keepIdx] return highest, lowest, timeSeries, droppedSamples, droppedExemplars, droppedHistograms }