mirror of
https://github.com/prometheus/prometheus.git
synced 2024-11-10 07:34:04 -08:00
1644 lines
52 KiB
Go
1644 lines
52 KiB
Go
// Copyright 2013 The Prometheus Authors
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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package remote
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import (
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"context"
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"errors"
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"math"
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"strconv"
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"sync"
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"time"
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"github.com/go-kit/log"
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"github.com/go-kit/log/level"
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"github.com/gogo/protobuf/proto"
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"github.com/golang/snappy"
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"github.com/prometheus/client_golang/prometheus"
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"github.com/prometheus/common/model"
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"go.opentelemetry.io/otel"
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"go.opentelemetry.io/otel/attribute"
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"go.uber.org/atomic"
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"github.com/prometheus/prometheus/config"
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"github.com/prometheus/prometheus/model/histogram"
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"github.com/prometheus/prometheus/model/labels"
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"github.com/prometheus/prometheus/model/relabel"
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"github.com/prometheus/prometheus/prompb"
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"github.com/prometheus/prometheus/scrape"
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"github.com/prometheus/prometheus/tsdb/chunks"
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"github.com/prometheus/prometheus/tsdb/record"
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"github.com/prometheus/prometheus/tsdb/wlog"
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)
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const (
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// We track samples in/out and how long pushes take using an Exponentially
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// Weighted Moving Average.
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ewmaWeight = 0.2
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shardUpdateDuration = 10 * time.Second
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// Allow 30% too many shards before scaling down.
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shardToleranceFraction = 0.3
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)
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type queueManagerMetrics struct {
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reg prometheus.Registerer
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samplesTotal prometheus.Counter
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exemplarsTotal prometheus.Counter
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histogramsTotal prometheus.Counter
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metadataTotal prometheus.Counter
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failedSamplesTotal prometheus.Counter
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failedExemplarsTotal prometheus.Counter
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failedHistogramsTotal prometheus.Counter
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failedMetadataTotal prometheus.Counter
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retriedSamplesTotal prometheus.Counter
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retriedExemplarsTotal prometheus.Counter
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retriedHistogramsTotal prometheus.Counter
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retriedMetadataTotal prometheus.Counter
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droppedSamplesTotal prometheus.Counter
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droppedExemplarsTotal prometheus.Counter
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droppedHistogramsTotal prometheus.Counter
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enqueueRetriesTotal prometheus.Counter
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sentBatchDuration prometheus.Histogram
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highestSentTimestamp *maxTimestamp
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pendingSamples prometheus.Gauge
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pendingExemplars prometheus.Gauge
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pendingHistograms prometheus.Gauge
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shardCapacity prometheus.Gauge
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numShards prometheus.Gauge
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maxNumShards prometheus.Gauge
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minNumShards prometheus.Gauge
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desiredNumShards prometheus.Gauge
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sentBytesTotal prometheus.Counter
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metadataBytesTotal prometheus.Counter
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maxSamplesPerSend prometheus.Gauge
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}
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func newQueueManagerMetrics(r prometheus.Registerer, rn, e string) *queueManagerMetrics {
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m := &queueManagerMetrics{
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reg: r,
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}
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constLabels := prometheus.Labels{
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remoteName: rn,
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endpoint: e,
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}
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m.samplesTotal = prometheus.NewCounter(prometheus.CounterOpts{
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Namespace: namespace,
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Subsystem: subsystem,
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Name: "samples_total",
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Help: "Total number of samples sent to remote storage.",
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ConstLabels: constLabels,
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})
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m.exemplarsTotal = prometheus.NewCounter(prometheus.CounterOpts{
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Namespace: namespace,
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Subsystem: subsystem,
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Name: "exemplars_total",
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Help: "Total number of exemplars sent to remote storage.",
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ConstLabels: constLabels,
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})
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m.histogramsTotal = prometheus.NewCounter(prometheus.CounterOpts{
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Namespace: namespace,
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Subsystem: subsystem,
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Name: "histograms_total",
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Help: "Total number of histograms sent to remote storage.",
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ConstLabels: constLabels,
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})
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m.metadataTotal = prometheus.NewCounter(prometheus.CounterOpts{
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Namespace: namespace,
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Subsystem: subsystem,
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Name: "metadata_total",
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Help: "Total number of metadata entries sent to remote storage.",
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ConstLabels: constLabels,
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})
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m.failedSamplesTotal = prometheus.NewCounter(prometheus.CounterOpts{
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Namespace: namespace,
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Subsystem: subsystem,
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Name: "samples_failed_total",
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Help: "Total number of samples which failed on send to remote storage, non-recoverable errors.",
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ConstLabels: constLabels,
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})
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m.failedExemplarsTotal = prometheus.NewCounter(prometheus.CounterOpts{
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Namespace: namespace,
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Subsystem: subsystem,
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Name: "exemplars_failed_total",
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Help: "Total number of exemplars which failed on send to remote storage, non-recoverable errors.",
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ConstLabels: constLabels,
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})
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m.failedHistogramsTotal = prometheus.NewCounter(prometheus.CounterOpts{
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Namespace: namespace,
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Subsystem: subsystem,
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Name: "histograms_failed_total",
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Help: "Total number of histograms which failed on send to remote storage, non-recoverable errors.",
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ConstLabels: constLabels,
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})
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m.failedMetadataTotal = prometheus.NewCounter(prometheus.CounterOpts{
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Namespace: namespace,
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Subsystem: subsystem,
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Name: "metadata_failed_total",
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Help: "Total number of metadata entries which failed on send to remote storage, non-recoverable errors.",
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ConstLabels: constLabels,
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})
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m.retriedSamplesTotal = prometheus.NewCounter(prometheus.CounterOpts{
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Namespace: namespace,
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Subsystem: subsystem,
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Name: "samples_retried_total",
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Help: "Total number of samples which failed on send to remote storage but were retried because the send error was recoverable.",
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ConstLabels: constLabels,
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})
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m.retriedExemplarsTotal = prometheus.NewCounter(prometheus.CounterOpts{
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Namespace: namespace,
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Subsystem: subsystem,
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Name: "exemplars_retried_total",
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Help: "Total number of exemplars which failed on send to remote storage but were retried because the send error was recoverable.",
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ConstLabels: constLabels,
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})
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m.retriedHistogramsTotal = prometheus.NewCounter(prometheus.CounterOpts{
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Namespace: namespace,
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Subsystem: subsystem,
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Name: "histograms_retried_total",
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Help: "Total number of histograms which failed on send to remote storage but were retried because the send error was recoverable.",
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ConstLabels: constLabels,
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})
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m.retriedMetadataTotal = prometheus.NewCounter(prometheus.CounterOpts{
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Namespace: namespace,
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Subsystem: subsystem,
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Name: "metadata_retried_total",
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Help: "Total number of metadata entries which failed on send to remote storage but were retried because the send error was recoverable.",
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ConstLabels: constLabels,
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})
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m.droppedSamplesTotal = prometheus.NewCounter(prometheus.CounterOpts{
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Namespace: namespace,
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Subsystem: subsystem,
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Name: "samples_dropped_total",
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Help: "Total number of samples which were dropped after being read from the WAL before being sent via remote write, either via relabelling or unintentionally because of an unknown reference ID.",
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ConstLabels: constLabels,
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})
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m.droppedExemplarsTotal = prometheus.NewCounter(prometheus.CounterOpts{
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Namespace: namespace,
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Subsystem: subsystem,
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Name: "exemplars_dropped_total",
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Help: "Total number of exemplars which were dropped after being read from the WAL before being sent via remote write, either via relabelling or unintentionally because of an unknown reference ID.",
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ConstLabels: constLabels,
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})
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m.droppedHistogramsTotal = prometheus.NewCounter(prometheus.CounterOpts{
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Namespace: namespace,
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Subsystem: subsystem,
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Name: "histograms_dropped_total",
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Help: "Total number of histograms which were dropped after being read from the WAL before being sent via remote write, either via relabelling or unintentionally because of an unknown reference ID.",
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ConstLabels: constLabels,
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})
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m.enqueueRetriesTotal = prometheus.NewCounter(prometheus.CounterOpts{
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Namespace: namespace,
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Subsystem: subsystem,
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Name: "enqueue_retries_total",
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Help: "Total number of times enqueue has failed because a shards queue was full.",
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ConstLabels: constLabels,
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})
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m.sentBatchDuration = prometheus.NewHistogram(prometheus.HistogramOpts{
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Namespace: namespace,
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Subsystem: subsystem,
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Name: "sent_batch_duration_seconds",
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Help: "Duration of send calls to the remote storage.",
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Buckets: append(prometheus.DefBuckets, 25, 60, 120, 300),
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ConstLabels: constLabels,
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})
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m.highestSentTimestamp = &maxTimestamp{
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Gauge: prometheus.NewGauge(prometheus.GaugeOpts{
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Namespace: namespace,
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Subsystem: subsystem,
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Name: "queue_highest_sent_timestamp_seconds",
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Help: "Timestamp from a WAL sample, the highest timestamp successfully sent by this queue, in seconds since epoch.",
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ConstLabels: constLabels,
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}),
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}
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m.pendingSamples = prometheus.NewGauge(prometheus.GaugeOpts{
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Namespace: namespace,
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Subsystem: subsystem,
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Name: "samples_pending",
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Help: "The number of samples pending in the queues shards to be sent to the remote storage.",
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ConstLabels: constLabels,
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})
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m.pendingExemplars = prometheus.NewGauge(prometheus.GaugeOpts{
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Namespace: namespace,
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Subsystem: subsystem,
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Name: "exemplars_pending",
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Help: "The number of exemplars pending in the queues shards to be sent to the remote storage.",
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ConstLabels: constLabels,
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})
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m.pendingHistograms = prometheus.NewGauge(prometheus.GaugeOpts{
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Namespace: namespace,
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Subsystem: subsystem,
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Name: "histograms_pending",
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Help: "The number of histograms pending in the queues shards to be sent to the remote storage.",
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ConstLabels: constLabels,
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})
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m.shardCapacity = prometheus.NewGauge(prometheus.GaugeOpts{
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Namespace: namespace,
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Subsystem: subsystem,
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Name: "shard_capacity",
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Help: "The capacity of each shard of the queue used for parallel sending to the remote storage.",
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ConstLabels: constLabels,
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})
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m.numShards = prometheus.NewGauge(prometheus.GaugeOpts{
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Namespace: namespace,
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Subsystem: subsystem,
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Name: "shards",
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Help: "The number of shards used for parallel sending to the remote storage.",
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ConstLabels: constLabels,
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})
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m.maxNumShards = prometheus.NewGauge(prometheus.GaugeOpts{
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Namespace: namespace,
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Subsystem: subsystem,
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Name: "shards_max",
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Help: "The maximum number of shards that the queue is allowed to run.",
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ConstLabels: constLabels,
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})
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m.minNumShards = prometheus.NewGauge(prometheus.GaugeOpts{
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Namespace: namespace,
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Subsystem: subsystem,
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Name: "shards_min",
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Help: "The minimum number of shards that the queue is allowed to run.",
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ConstLabels: constLabels,
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})
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m.desiredNumShards = prometheus.NewGauge(prometheus.GaugeOpts{
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Namespace: namespace,
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Subsystem: subsystem,
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Name: "shards_desired",
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Help: "The number of shards that the queues shard calculation wants to run based on the rate of samples in vs. samples out.",
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ConstLabels: constLabels,
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})
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m.sentBytesTotal = prometheus.NewCounter(prometheus.CounterOpts{
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Namespace: namespace,
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Subsystem: subsystem,
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Name: "bytes_total",
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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.",
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ConstLabels: constLabels,
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})
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m.metadataBytesTotal = prometheus.NewCounter(prometheus.CounterOpts{
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Namespace: namespace,
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Subsystem: subsystem,
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Name: "metadata_bytes_total",
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Help: "The total number of bytes of metadata sent by the queue after compression.",
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ConstLabels: constLabels,
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})
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m.maxSamplesPerSend = prometheus.NewGauge(prometheus.GaugeOpts{
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Namespace: namespace,
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Subsystem: subsystem,
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Name: "max_samples_per_send",
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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.",
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ConstLabels: constLabels,
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})
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return m
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}
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func (m *queueManagerMetrics) register() {
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if m.reg != nil {
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m.reg.MustRegister(
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m.samplesTotal,
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m.exemplarsTotal,
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m.histogramsTotal,
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m.metadataTotal,
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m.failedSamplesTotal,
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m.failedExemplarsTotal,
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m.failedHistogramsTotal,
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m.failedMetadataTotal,
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m.retriedSamplesTotal,
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m.retriedExemplarsTotal,
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m.retriedHistogramsTotal,
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m.retriedMetadataTotal,
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m.droppedSamplesTotal,
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m.droppedExemplarsTotal,
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m.droppedHistogramsTotal,
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m.enqueueRetriesTotal,
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m.sentBatchDuration,
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m.highestSentTimestamp,
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m.pendingSamples,
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m.pendingExemplars,
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m.pendingHistograms,
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m.shardCapacity,
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m.numShards,
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m.maxNumShards,
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m.minNumShards,
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m.desiredNumShards,
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m.sentBytesTotal,
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m.metadataBytesTotal,
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m.maxSamplesPerSend,
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)
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}
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}
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func (m *queueManagerMetrics) unregister() {
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if m.reg != nil {
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m.reg.Unregister(m.samplesTotal)
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m.reg.Unregister(m.exemplarsTotal)
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m.reg.Unregister(m.histogramsTotal)
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m.reg.Unregister(m.metadataTotal)
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m.reg.Unregister(m.failedSamplesTotal)
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m.reg.Unregister(m.failedExemplarsTotal)
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m.reg.Unregister(m.failedHistogramsTotal)
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m.reg.Unregister(m.failedMetadataTotal)
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m.reg.Unregister(m.retriedSamplesTotal)
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m.reg.Unregister(m.retriedExemplarsTotal)
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m.reg.Unregister(m.retriedHistogramsTotal)
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m.reg.Unregister(m.retriedMetadataTotal)
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m.reg.Unregister(m.droppedSamplesTotal)
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m.reg.Unregister(m.droppedExemplarsTotal)
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m.reg.Unregister(m.droppedHistogramsTotal)
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m.reg.Unregister(m.enqueueRetriesTotal)
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m.reg.Unregister(m.sentBatchDuration)
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m.reg.Unregister(m.highestSentTimestamp)
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m.reg.Unregister(m.pendingSamples)
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m.reg.Unregister(m.pendingExemplars)
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m.reg.Unregister(m.pendingHistograms)
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m.reg.Unregister(m.shardCapacity)
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m.reg.Unregister(m.numShards)
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m.reg.Unregister(m.maxNumShards)
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m.reg.Unregister(m.minNumShards)
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m.reg.Unregister(m.desiredNumShards)
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m.reg.Unregister(m.sentBytesTotal)
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m.reg.Unregister(m.metadataBytesTotal)
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m.reg.Unregister(m.maxSamplesPerSend)
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}
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}
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// WriteClient defines an interface for sending a batch of samples to an
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// external timeseries database.
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type WriteClient interface {
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// Store stores the given samples in the remote storage.
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Store(context.Context, []byte) error
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// Name uniquely identifies the remote storage.
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Name() string
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// Endpoint is the remote read or write endpoint for the storage client.
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Endpoint() string
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}
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// QueueManager manages a queue of samples to be sent to the Storage
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// indicated by the provided WriteClient. Implements writeTo interface
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// used by WAL Watcher.
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type QueueManager struct {
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lastSendTimestamp atomic.Int64
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logger log.Logger
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flushDeadline time.Duration
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cfg config.QueueConfig
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mcfg config.MetadataConfig
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externalLabels []labels.Label
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relabelConfigs []*relabel.Config
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sendExemplars bool
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sendNativeHistograms bool
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watcher *wlog.Watcher
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metadataWatcher *MetadataWatcher
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clientMtx sync.RWMutex
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storeClient WriteClient
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seriesMtx sync.Mutex // Covers seriesLabels and droppedSeries.
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seriesLabels map[chunks.HeadSeriesRef]labels.Labels
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droppedSeries map[chunks.HeadSeriesRef]struct{}
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seriesSegmentMtx sync.Mutex // Covers seriesSegmentIndexes - if you also lock seriesMtx, take seriesMtx first.
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seriesSegmentIndexes map[chunks.HeadSeriesRef]int
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shards *shards
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numShards int
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reshardChan chan int
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quit chan struct{}
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wg sync.WaitGroup
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dataIn, dataDropped, dataOut, dataOutDuration *ewmaRate
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metrics *queueManagerMetrics
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interner *pool
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highestRecvTimestamp *maxTimestamp
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}
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|
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// NewQueueManager builds a new QueueManager and starts a new
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// WAL watcher with queue manager as the WriteTo destination.
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// The WAL watcher takes the dir parameter as the base directory
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// for where the WAL shall be located. Note that the full path to
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// the WAL directory will be constructed as <dir>/wal.
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func NewQueueManager(
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metrics *queueManagerMetrics,
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watcherMetrics *wlog.WatcherMetrics,
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readerMetrics *wlog.LiveReaderMetrics,
|
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logger log.Logger,
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dir string,
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samplesIn *ewmaRate,
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cfg config.QueueConfig,
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mCfg config.MetadataConfig,
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externalLabels labels.Labels,
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relabelConfigs []*relabel.Config,
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client WriteClient,
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flushDeadline time.Duration,
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interner *pool,
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highestRecvTimestamp *maxTimestamp,
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sm ReadyScrapeManager,
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enableExemplarRemoteWrite bool,
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enableNativeHistogramRemoteWrite bool,
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) *QueueManager {
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if logger == nil {
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logger = log.NewNopLogger()
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}
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|
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// Copy externalLabels into a slice, which we need for processExternalLabels.
|
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extLabelsSlice := make([]labels.Label, 0, externalLabels.Len())
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externalLabels.Range(func(l labels.Label) {
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extLabelsSlice = append(extLabelsSlice, l)
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})
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|
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logger = log.With(logger, remoteName, client.Name(), endpoint, client.Endpoint())
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t := &QueueManager{
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logger: logger,
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flushDeadline: flushDeadline,
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cfg: cfg,
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mcfg: mCfg,
|
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externalLabels: extLabelsSlice,
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relabelConfigs: relabelConfigs,
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storeClient: client,
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sendExemplars: enableExemplarRemoteWrite,
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sendNativeHistograms: enableNativeHistogramRemoteWrite,
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|
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seriesLabels: make(map[chunks.HeadSeriesRef]labels.Labels),
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seriesSegmentIndexes: make(map[chunks.HeadSeriesRef]int),
|
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droppedSeries: make(map[chunks.HeadSeriesRef]struct{}),
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|
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numShards: cfg.MinShards,
|
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reshardChan: make(chan int),
|
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quit: make(chan struct{}),
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|
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dataIn: samplesIn,
|
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dataDropped: newEWMARate(ewmaWeight, shardUpdateDuration),
|
|
dataOut: newEWMARate(ewmaWeight, shardUpdateDuration),
|
|
dataOutDuration: newEWMARate(ewmaWeight, shardUpdateDuration),
|
|
|
|
metrics: metrics,
|
|
interner: interner,
|
|
highestRecvTimestamp: highestRecvTimestamp,
|
|
}
|
|
|
|
t.watcher = wlog.NewWatcher(watcherMetrics, readerMetrics, logger, client.Name(), t, dir, enableExemplarRemoteWrite, enableNativeHistogramRemoteWrite)
|
|
if t.mcfg.Send {
|
|
t.metadataWatcher = NewMetadataWatcher(logger, sm, client.Name(), t, t.mcfg.SendInterval, flushDeadline)
|
|
}
|
|
t.shards = t.newShards()
|
|
|
|
return t
|
|
}
|
|
|
|
// AppendMetadata sends metadata to the remote storage. Metadata is sent in batches, but is not parallelized.
|
|
func (t *QueueManager) AppendMetadata(ctx context.Context, metadata []scrape.MetricMetadata) {
|
|
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(nil, metadata, pBuf, 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()),
|
|
)
|
|
|
|
begin := time.Now()
|
|
err := t.storeClient.Store(ctx, req)
|
|
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
|
|
}
|
|
|
|
// 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 {
|
|
outer:
|
|
for _, s := range samples {
|
|
t.seriesMtx.Lock()
|
|
lbls, ok := t.seriesLabels[s.Ref]
|
|
if !ok {
|
|
t.metrics.droppedSamplesTotal.Inc()
|
|
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.seriesMtx.Unlock()
|
|
continue
|
|
}
|
|
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,
|
|
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
|
|
}
|
|
|
|
outer:
|
|
for _, e := range exemplars {
|
|
t.seriesMtx.Lock()
|
|
lbls, ok := t.seriesLabels[e.Ref]
|
|
if !ok {
|
|
t.metrics.droppedExemplarsTotal.Inc()
|
|
// 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.seriesMtx.Unlock()
|
|
continue
|
|
}
|
|
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,
|
|
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
|
|
}
|
|
|
|
outer:
|
|
for _, h := range histograms {
|
|
t.seriesMtx.Lock()
|
|
lbls, ok := t.seriesLabels[h.Ref]
|
|
if !ok {
|
|
t.metrics.droppedHistogramsTotal.Inc()
|
|
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.seriesMtx.Unlock()
|
|
continue
|
|
}
|
|
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,
|
|
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
|
|
}
|
|
|
|
outer:
|
|
for _, h := range floatHistograms {
|
|
t.seriesMtx.Lock()
|
|
lbls, ok := t.seriesLabels[h.Ref]
|
|
if !ok {
|
|
t.metrics.droppedHistogramsTotal.Inc()
|
|
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.seriesMtx.Unlock()
|
|
continue
|
|
}
|
|
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,
|
|
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
|
|
}
|
|
}
|
|
|
|
// 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.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
|
|
}
|
|
|
|
// 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)
|
|
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
|
|
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
|
|
)
|
|
|
|
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()
|
|
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)
|
|
|
|
// 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)
|
|
buf []byte
|
|
)
|
|
if s.qm.sendExemplars {
|
|
max += int(float64(max) * 0.1)
|
|
}
|
|
|
|
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{{}}
|
|
}
|
|
}
|
|
|
|
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
|
|
}
|
|
nPendingSamples, nPendingExemplars, nPendingHistograms := s.populateTimeSeries(batch, pendingData)
|
|
queue.ReturnForReuse(batch)
|
|
n := nPendingSamples + nPendingExemplars + nPendingHistograms
|
|
s.sendSamples(ctx, pendingData[:n], nPendingSamples, nPendingExemplars, nPendingHistograms, pBuf, &buf)
|
|
|
|
stop()
|
|
timer.Reset(time.Duration(s.qm.cfg.BatchSendDeadline))
|
|
|
|
case <-timer.C:
|
|
batch := queue.Batch()
|
|
if len(batch) > 0 {
|
|
nPendingSamples, nPendingExemplars, nPendingHistograms := s.populateTimeSeries(batch, pendingData)
|
|
n := nPendingSamples + nPendingExemplars + nPendingHistograms
|
|
level.Debug(s.qm.logger).Log("msg", "runShard timer ticked, sending buffered data", "samples", nPendingSamples,
|
|
"exemplars", nPendingExemplars, "shard", shardNum, "histograms", nPendingHistograms)
|
|
s.sendSamples(ctx, pendingData[:n], nPendingSamples, nPendingExemplars, nPendingHistograms, pBuf, &buf)
|
|
}
|
|
queue.ReturnForReuse(batch)
|
|
timer.Reset(time.Duration(s.qm.cfg.BatchSendDeadline))
|
|
}
|
|
}
|
|
}
|
|
|
|
func (s *shards) populateTimeSeries(batch []timeSeries, pendingData []prompb.TimeSeries) (int, int, int) {
|
|
var nPendingSamples, nPendingExemplars, nPendingHistograms int
|
|
for nPending, d := range batch {
|
|
pendingData[nPending].Samples = pendingData[nPending].Samples[:0]
|
|
if s.qm.sendExemplars {
|
|
pendingData[nPending].Exemplars = pendingData[nPending].Exemplars[:0]
|
|
}
|
|
if s.qm.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, pBuf, buf)
|
|
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(len(samples)))
|
|
s.qm.dataOutDuration.incr(int64(time.Since(begin)))
|
|
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 int, pBuf *proto.Buffer, buf *[]byte) error {
|
|
// Build the WriteRequest with no metadata.
|
|
req, highest, err := buildWriteRequest(samples, nil, pBuf, *buf)
|
|
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 {
|
|
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))
|
|
err := s.qm.client().Store(ctx, *buf)
|
|
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 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 buildWriteRequest(samples []prompb.TimeSeries, metadata []prompb.MetricMetadata, pBuf *proto.Buffer, buf []byte) ([]byte, int64, error) {
|
|
var highest int64
|
|
for _, ts := range samples {
|
|
// 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
|
|
}
|
|
}
|
|
|
|
req := &prompb.WriteRequest{
|
|
Timeseries: samples,
|
|
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, 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)]
|
|
}
|
|
compressed := snappy.Encode(buf, pBuf.Bytes())
|
|
return compressed, highest, nil
|
|
}
|