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// Copyright 2014 The Cockroach 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. See the AUTHORS file
// for names of contributors.
//
// Author: Tyler Neely (t@jujit.su)
// IMPORTANT: only subscribe to the metric stream
// using buffered channels that are regularly
// flushed, as reaper will NOT block while trying
// to send metrics to a subscriber, and will ignore
// a subscriber if they fail to clear their channel
// 3 times in a row!
package loghisto
import (
"errors"
"fmt"
"math"
"runtime"
"sort"
"sync"
"sync/atomic"
"time"
"github.com/golang/glog"
)
const (
// precision effects the bucketing used during histogram value compression.
precision = 100
)
// ProcessedMetricSet contains human-readable metrics that may also be
// suitable for storage in time-series databases.
type ProcessedMetricSet struct {
Time time.Time
Metrics map[string]float64
}
// RawMetricSet contains metrics in a form that supports generation of
// percentiles and other rich statistics.
type RawMetricSet struct {
Time time.Time
Counters map[string]uint64
Rates map[string]uint64
Histograms map[string]map[int16]*uint64
Gauges map[string]float64
}
// TimerToken facilitates concurrent timings of durations of the same label.
type TimerToken struct {
Name string
Start time.Time
MetricSystem *MetricSystem
}
// proportion is a compact value with a corresponding count of
// occurrences in this interval.
type proportion struct {
Value float64
Count uint64
}
// proportionArray is a sortable collection of proportion types.
type proportionArray []proportion
// MetricSystem facilitates the collection and distribution of metrics.
type MetricSystem struct {
// percentiles is a mapping from labels to desired percentiles to be
// calculated by the MetricSystem
percentiles map[string]float64
// interval is the duration between collections and broadcasts of metrics
// to subscribers.
interval time.Duration
// subscribeToRawMetrics allows subscription to a RawMetricSet generated
// by reaper at the end of each interval on a sent channel.
subscribeToRawMetrics chan chan *RawMetricSet
// unsubscribeFromRawMetrics allows subscribers to unsubscribe from
// receiving a RawMetricSet on the sent channel.
unsubscribeFromRawMetrics chan chan *RawMetricSet
// subscribeToProcessedMetrics allows subscription to a ProcessedMetricSet
// generated by reaper at the end of each interval on a sent channel.
subscribeToProcessedMetrics chan chan *ProcessedMetricSet
// unsubscribeFromProcessedMetrics allows subscribers to unsubscribe from
// receiving a ProcessedMetricSet on the sent channel.
unsubscribeFromProcessedMetrics chan chan *ProcessedMetricSet
// rawSubscribers stores current subscribers to RawMetrics
rawSubscribers map[chan *RawMetricSet]struct{}
// rawBadSubscribers tracks misbehaving subscribers who do not clear their
// subscription channels regularly.
rawBadSubscribers map[chan *RawMetricSet]int
// processedSubscribers stores current subscribers to ProcessedMetrics
processedSubscribers map[chan *ProcessedMetricSet]struct{}
// processedBadSubscribers tracks misbehaving subscribers who do not clear
// their subscription channels regularly.
processedBadSubscribers map[chan *ProcessedMetricSet]int
// subscribersMu controls access to subscription structures
subscribersMu sync.RWMutex
// counterStore maintains the total counts of counters.
counterStore map[string]*uint64
counterStoreMu sync.RWMutex
// counterCache aggregates new Counters until they are collected by reaper().
counterCache map[string]*uint64
// counterMu controls access to counterCache.
counterMu sync.RWMutex
// histogramCache aggregates Histograms until they are collected by reaper().
histogramCache map[string]map[int16]*uint64
// histogramMu controls access to histogramCache.
histogramMu sync.RWMutex
// histogramCountStore keeps track of aggregate counts and sums for aggregate
// mean calculation.
histogramCountStore map[string]*uint64
// histogramCountMu controls access to the histogramCountStore.
histogramCountMu sync.RWMutex
// gaugeFuncs maps metrics to functions used for calculating their value
gaugeFuncs map[string]func() float64
// gaugeFuncsMu controls access to the gaugeFuncs map.
gaugeFuncsMu sync.Mutex
// Has reaper() been started?
reaping bool
// Close this to bring down this MetricSystem
shutdownChan chan struct{}
}
// Metrics is the default metric system, which collects and broadcasts metrics
// to subscribers once every 60 seconds. Also includes default system stats.
var Metrics = NewMetricSystem(60*time.Second, true)
// NewMetricSystem returns a new metric system that collects and broadcasts
// metrics after each interval.
func NewMetricSystem(interval time.Duration, sysStats bool) *MetricSystem {
ms := &MetricSystem{
percentiles: map[string]float64{
"%s_min": 0,
"%s_50": .5,
"%s_75": .75,
"%s_90": .9,
"%s_95": .95,
"%s_99": .99,
"%s_99.9": .999,
"%s_99.99": .9999,
"%s_max": 1,
},
interval: interval,
subscribeToRawMetrics: make(chan chan *RawMetricSet, 64),
unsubscribeFromRawMetrics: make(chan chan *RawMetricSet, 64),
subscribeToProcessedMetrics: make(chan chan *ProcessedMetricSet, 64),
unsubscribeFromProcessedMetrics: make(chan chan *ProcessedMetricSet, 64),
rawSubscribers: make(map[chan *RawMetricSet]struct{}),
rawBadSubscribers: make(map[chan *RawMetricSet]int),
processedSubscribers: make(map[chan *ProcessedMetricSet]struct{}),
processedBadSubscribers: make(map[chan *ProcessedMetricSet]int),
counterStore: make(map[string]*uint64),
counterCache: make(map[string]*uint64),
histogramCache: make(map[string]map[int16]*uint64),
histogramCountStore: make(map[string]*uint64),
gaugeFuncs: make(map[string]func() float64),
shutdownChan: make(chan struct{}),
}
if sysStats {
ms.gaugeFuncsMu.Lock()
ms.gaugeFuncs["sys.Alloc"] = func() float64 {
memStats := new(runtime.MemStats)
runtime.ReadMemStats(memStats)
return float64(memStats.Alloc)
}
ms.gaugeFuncs["sys.NumGC"] = func() float64 {
memStats := new(runtime.MemStats)
runtime.ReadMemStats(memStats)
return float64(memStats.NumGC)
}
ms.gaugeFuncs["sys.PauseTotalNs"] = func() float64 {
memStats := new(runtime.MemStats)
runtime.ReadMemStats(memStats)
return float64(memStats.PauseTotalNs)
}
ms.gaugeFuncs["sys.NumGoroutine"] = func() float64 {
return float64(runtime.NumGoroutine())
}
ms.gaugeFuncsMu.Unlock()
}
return ms
}
// SpecifyPercentiles allows users to override the default collected
// and reported percentiles.
func (ms *MetricSystem) SpecifyPercentiles(percentiles map[string]float64) {
ms.percentiles = percentiles
}
// SubscribeToRawMetrics registers a channel to receive RawMetricSets
// periodically generated by reaper at each interval.
func (ms *MetricSystem) SubscribeToRawMetrics(metricStream chan *RawMetricSet) {
ms.subscribeToRawMetrics <- metricStream
}
// UnsubscribeFromRawMetrics registers a channel to receive RawMetricSets
// periodically generated by reaper at each interval.
func (ms *MetricSystem) UnsubscribeFromRawMetrics(
metricStream chan *RawMetricSet) {
ms.unsubscribeFromRawMetrics <- metricStream
}
// SubscribeToProcessedMetrics registers a channel to receive
// ProcessedMetricSets periodically generated by reaper at each interval.
func (ms *MetricSystem) SubscribeToProcessedMetrics(
metricStream chan *ProcessedMetricSet) {
ms.subscribeToProcessedMetrics <- metricStream
}
// UnsubscribeFromProcessedMetrics registers a channel to receive
// ProcessedMetricSets periodically generated by reaper at each interval.
func (ms *MetricSystem) UnsubscribeFromProcessedMetrics(
metricStream chan *ProcessedMetricSet) {
ms.unsubscribeFromProcessedMetrics <- metricStream
}
// StartTimer begins a timer and returns a token which is required for halting
// the timer. This allows for concurrent timings under the same name.
func (ms *MetricSystem) StartTimer(name string) TimerToken {
return TimerToken{
Name: name,
Start: time.Now(),
MetricSystem: ms,
}
}
// Stop stops a timer given by StartTimer, submits a Histogram of its duration
// in nanoseconds, and returns its duration in nanoseconds.
func (tt *TimerToken) Stop() time.Duration {
duration := time.Since(tt.Start)
tt.MetricSystem.Histogram(tt.Name, float64(duration.Nanoseconds()))
return duration
}
// Counter is used for recording a running count of the total occurrences of
// a particular event. A rate is also exported for the amount that a counter
// has increased during an interval of this MetricSystem.
func (ms *MetricSystem) Counter(name string, amount uint64) {
ms.counterMu.RLock()
_, exists := ms.counterCache[name]
// perform lock promotion when we need more control
if exists {
atomic.AddUint64(ms.counterCache[name], amount)
ms.counterMu.RUnlock()
} else {
ms.counterMu.RUnlock()
ms.counterMu.Lock()
_, syncExists := ms.counterCache[name]
if !syncExists {
var z uint64
ms.counterCache[name] = &z
}
atomic.AddUint64(ms.counterCache[name], amount)
ms.counterMu.Unlock()
}
}
// Histogram is used for generating rich metrics, such as percentiles, from
// periodically occurring continuous values.
func (ms *MetricSystem) Histogram(name string, value float64) {
compressedValue := compress(value)
ms.histogramMu.RLock()
_, present := ms.histogramCache[name][compressedValue]
if present {
atomic.AddUint64(ms.histogramCache[name][compressedValue], 1)
ms.histogramMu.RUnlock()
} else {
ms.histogramMu.RUnlock()
ms.histogramMu.Lock()
_, syncPresent := ms.histogramCache[name][compressedValue]
if !syncPresent {
var z uint64
_, mapPresent := ms.histogramCache[name]
if !mapPresent {
ms.histogramCache[name] = make(map[int16]*uint64)
}
ms.histogramCache[name][compressedValue] = &z
}
atomic.AddUint64(ms.histogramCache[name][compressedValue], 1)
ms.histogramMu.Unlock()
}
}
// RegisterGaugeFunc registers a function to be called at each interval
// whose return value will be used to populate the <name> metric.
func (ms *MetricSystem) RegisterGaugeFunc(name string, f func() float64) {
ms.gaugeFuncsMu.Lock()
ms.gaugeFuncs[name] = f
ms.gaugeFuncsMu.Unlock()
}
// DeregisterGaugeFunc deregisters a function for the <name> metric.
func (ms *MetricSystem) DeregisterGaugeFunc(name string) {
ms.gaugeFuncsMu.Lock()
delete(ms.gaugeFuncs, name)
ms.gaugeFuncsMu.Unlock()
}
// compress takes a float64 and lossily shrinks it to an int16 to facilitate
// bucketing of histogram values, staying within 1% of the true value. This
// fails for large values of 1e142 and above, and is inaccurate for values
// closer to 0 than +/- 0.51 or +/- math.Inf.
func compress(value float64) int16 {
i := int16(precision*math.Log(1.0+math.Abs(value)) + 0.5)
if value < 0 {
return -1 * i
}
return i
}
// decompress takes a lossily shrunk int16 and returns a float64 within 1% of
// the original float64 passed to compress.
func decompress(compressedValue int16) float64 {
f := math.Exp(math.Abs(float64(compressedValue))/precision) - 1.0
if compressedValue < 0 {
return -1.0 * f
}
return f
}
// processHistograms derives rich metrics from histograms, currently
// percentiles, sum, count, and mean.
func (ms *MetricSystem) processHistograms(name string,
valuesToCounts map[int16]*uint64) map[string]float64 {
output := make(map[string]float64)
totalSum := float64(0)
totalCount := uint64(0)
proportions := make([]proportion, 0, len(valuesToCounts))
for compressedValue, count := range valuesToCounts {
value := decompress(compressedValue)
totalSum += value * float64(*count)
totalCount += *count
proportions = append(proportions, proportion{Value: value, Count: *count})
}
sumName := fmt.Sprintf("%s_sum", name)
countName := fmt.Sprintf("%s_count", name)
avgName := fmt.Sprintf("%s_avg", name)
// increment interval sum and count
output[countName] = float64(totalCount)
output[sumName] = totalSum
output[avgName] = totalSum / float64(totalCount)
// increment aggregate sum and count
ms.histogramCountMu.RLock()
_, present := ms.histogramCountStore[sumName]
if !present {
ms.histogramCountMu.RUnlock()
ms.histogramCountMu.Lock()
_, syncPresent := ms.histogramCountStore[sumName]
if !syncPresent {
var x uint64
ms.histogramCountStore[sumName] = &x
var z uint64
ms.histogramCountStore[countName] = &z
}
ms.histogramCountMu.Unlock()
ms.histogramCountMu.RLock()
}
atomic.AddUint64(ms.histogramCountStore[sumName], uint64(totalSum))
atomic.AddUint64(ms.histogramCountStore[countName], totalCount)
ms.histogramCountMu.RUnlock()
for label, p := range ms.percentiles {
value, err := percentile(totalCount, proportions, p)
if err != nil {
glog.Errorf("unable to calculate percentile: %s", err)
} else {
output[fmt.Sprintf(label, name)] = value
}
}
return output
}
// These next 3 methods are for the implementation of sort.Interface
func (s proportionArray) Len() int {
return len(s)
}
func (s proportionArray) Less(i, j int) bool {
return s[i].Value < s[j].Value
}
func (s proportionArray) Swap(i, j int) {
s[i], s[j] = s[j], s[i]
}
// percentile calculates a percentile represented as a float64 between 0 and 1
// inclusive from a proportionArray. totalCount is the sum of all counts of
// elements in the proportionArray.
func percentile(totalCount uint64, proportions proportionArray,
percentile float64) (float64, error) {
//TODO(tyler) handle multiple percentiles at once for efficiency
sort.Sort(proportions)
sofar := uint64(0)
for _, proportion := range proportions {
sofar += proportion.Count
if float64(sofar)/float64(totalCount) >= percentile {
return proportion.Value, nil
}
}
return 0, errors.New("Invalid percentile. Should be between 0 and 1.")
}
func (ms *MetricSystem) collectRawMetrics() *RawMetricSet {
normalizedInterval := time.Unix(0, time.Now().UnixNano()/
ms.interval.Nanoseconds()*
ms.interval.Nanoseconds())
ms.counterMu.Lock()
freshCounters := ms.counterCache
ms.counterCache = make(map[string]*uint64)
ms.counterMu.Unlock()
rates := make(map[string]uint64)
for name, count := range freshCounters {
rates[name] = *count
}
counters := make(map[string]uint64)
ms.counterStoreMu.RLock()
// update counters
for name, count := range freshCounters {
_, exists := ms.counterStore[name]
// only take a write lock when it's a totally new counter
if !exists {
ms.counterStoreMu.RUnlock()
ms.counterStoreMu.Lock()
_, syncExists := ms.counterStore[name]
if !syncExists {
var z uint64
ms.counterStore[name] = &z
}
ms.counterStoreMu.Unlock()
ms.counterStoreMu.RLock()
}
atomic.AddUint64(ms.counterStore[name], *count)
}
// copy counters for export
for name, count := range ms.counterStore {
counters[name] = *count
}
ms.counterStoreMu.RUnlock()
ms.histogramMu.Lock()
histograms := ms.histogramCache
ms.histogramCache = make(map[string]map[int16]*uint64)
ms.histogramMu.Unlock()
ms.gaugeFuncsMu.Lock()
gauges := make(map[string]float64)
for name, f := range ms.gaugeFuncs {
gauges[name] = f()
}
ms.gaugeFuncsMu.Unlock()
return &RawMetricSet{
Time: normalizedInterval,
Counters: counters,
Rates: rates,
Histograms: histograms,
Gauges: gauges,
}
}
// processMetrics (potentially slowly) creates human consumable metrics from a
// RawMetricSet, deriving rich statistics from histograms such as percentiles.
func (ms *MetricSystem) processMetrics(
rawMetrics *RawMetricSet) *ProcessedMetricSet {
metrics := make(map[string]float64)
for name, count := range rawMetrics.Counters {
metrics[name] = float64(count)
}
for name, count := range rawMetrics.Rates {
metrics[fmt.Sprintf("%s_rate", name)] = float64(count)
}
for name, valuesToCounts := range rawMetrics.Histograms {
for histoName, histoValue := range ms.processHistograms(name, valuesToCounts) {
metrics[histoName] = histoValue
}
}
for name, value := range rawMetrics.Gauges {
metrics[name] = value
}
return &ProcessedMetricSet{Time: rawMetrics.Time, Metrics: metrics}
}
func (ms *MetricSystem) updateSubscribers() {
ms.subscribersMu.Lock()
defer ms.subscribersMu.Unlock()
for {
select {
case subscriber := <-ms.subscribeToRawMetrics:
ms.rawSubscribers[subscriber] = struct{}{}
case unsubscriber := <-ms.unsubscribeFromRawMetrics:
delete(ms.rawSubscribers, unsubscriber)
case subscriber := <-ms.subscribeToProcessedMetrics:
ms.processedSubscribers[subscriber] = struct{}{}
case unsubscriber := <-ms.unsubscribeFromProcessedMetrics:
delete(ms.processedSubscribers, unsubscriber)
default: // no changes in subscribers
return
}
}
}
// reaper wakes up every <interval> seconds,
// collects and processes metrics, and pushes
// them to the corresponding subscribing channels.
func (ms *MetricSystem) reaper() {
ms.reaping = true
// create goroutine pool to handle multiple processing tasks at once
processChan := make(chan func(), 16)
for i := 0; i < int(math.Max(float64(runtime.NumCPU()/4), 4)); i++ {
go func() {
for {
c, ok := <-processChan
if !ok {
return
}
c()
}
}()
}
// begin reaper main loop
for {
// sleep until the next interval, or die if shutdownChan is closed
tts := ms.interval.Nanoseconds() -
(time.Now().UnixNano() % ms.interval.Nanoseconds())
select {
case <-time.After(time.Duration(tts)):
case <-ms.shutdownChan:
ms.reaping = false
close(processChan)
return
}
rawMetrics := ms.collectRawMetrics()
ms.updateSubscribers()
// broadcast raw metrics
for subscriber := range ms.rawSubscribers {
// new subscribers get all counters, otherwise just the new diffs
select {
case subscriber <- rawMetrics:
delete(ms.rawBadSubscribers, subscriber)
default:
ms.rawBadSubscribers[subscriber]++
glog.Error("a raw subscriber has allowed their channel to fill up. ",
"dropping their metrics on the floor rather than blocking.")
if ms.rawBadSubscribers[subscriber] >= 2 {
glog.Error("this raw subscriber has caused dropped metrics at ",
"least 3 times in a row. closing the channel.")
delete(ms.rawSubscribers, subscriber)
close(subscriber)
}
}
}
// Perform the rest in another goroutine since processing is not
// gauranteed to complete before the interval is up.
sendProcessed := func() {
// this is potentially expensive if there is a massive number of metrics
processedMetrics := ms.processMetrics(rawMetrics)
// add aggregate mean
for name := range rawMetrics.Histograms {
ms.histogramCountMu.RLock()
aggCountPtr, countPresent :=
ms.histogramCountStore[fmt.Sprintf("%s_count", name)]
aggCount := atomic.LoadUint64(aggCountPtr)
aggSumPtr, sumPresent :=
ms.histogramCountStore[fmt.Sprintf("%s_sum", name)]
aggSum := atomic.LoadUint64(aggSumPtr)
ms.histogramCountMu.RUnlock()
if countPresent && sumPresent && aggCount > 0 {
processedMetrics.Metrics[fmt.Sprintf("%s_agg_avg", name)] =
float64(aggSum / aggCount)
processedMetrics.Metrics[fmt.Sprintf("%s_agg_count", name)] =
float64(aggCount)
processedMetrics.Metrics[fmt.Sprintf("%s_agg_sum", name)] =
float64(aggSum)
}
}
// broadcast processed metrics
ms.subscribersMu.Lock()
for subscriber := range ms.processedSubscribers {
select {
case subscriber <- processedMetrics:
delete(ms.processedBadSubscribers, subscriber)
default:
ms.processedBadSubscribers[subscriber]++
glog.Error("a subscriber has allowed their channel to fill up. ",
"dropping their metrics on the floor rather than blocking.")
if ms.processedBadSubscribers[subscriber] >= 2 {
glog.Error("this subscriber has caused dropped metrics at ",
"least 3 times in a row. closing the channel.")
delete(ms.processedSubscribers, subscriber)
close(subscriber)
}
}
}
ms.subscribersMu.Unlock()
}
select {
case processChan <- sendProcessed:
default:
// processChan has filled up, this metric load is not sustainable
glog.Errorf("processing of metrics is taking longer than this node can "+
"handle. dropping this entire interval of %s metrics on the "+
"floor rather than blocking the reaper.", rawMetrics.Time)
}
} // end main reaper loop
}
// Start spawns a goroutine for merging metrics into caches from
// metric submitters, and a reaper goroutine that harvests metrics at the
// default interval of every 60 seconds.
func (ms *MetricSystem) Start() {
if !ms.reaping {
go ms.reaper()
}
}
// Stop shuts down a MetricSystem
func (ms *MetricSystem) Stop() {
close(ms.shutdownChan)
}
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