aura

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 Go to latest Published: Jun 3, 2020 License: Apache-2.0 Imports: 8 Imported by: 2

README

Aura

🔔 Aura is an SDK for the monitoring system written in Go with love.

GoDoc Go Report Card License

🎬 Overview

☁️ 在云原生时代，以 Prometheus 为中心的监控生态已经逐渐完善，社区也出现了大量的中间件，数据库以及各种基础组件的 exporter，Prometheus 官方也给出了维护了一份 exporter 列表 instrumenting/exporters。

但是 Prometheus 的缺点和它的优点一样明显，缺少高可用的集群方案。想了解 Prometheus 和监控系统的同学可阅读 Prometheus 折腾笔记 系列文章。目前开源的高可用企业级的监控方案有两个，小米的 falcon-plus 和滴滴出行的 nightingale，后者是前者的优化增强版。国内的不少公司（比如我司 🐶）的监控方案都或多或少参考了 falcon 的设计架构，falcon 的官网也维护了一份 企业用户列表。

falcon 的设计架构决定了它强悍的性能及良好的可扩展性，具体关于其相关信息可参考 官网介绍。falcon 的 slogan：

open-falcon 的目标是做最开放、最好用的互联网企业级监控产品。

目前 falcon 已经不再维护 😔，可能它已经完成了它的历史使命吧，提供一套完整监控系统的构建方案；不过 nightingale 接住了 falcon 手中的接力棒，为开源社区的监控领域又注入了新的活力 😌。虽然如此，但是 falcon/nightingale 所构建的生态也仍旧不完善，缺少像 Prometheus 生态的各类数据采集器（exporter）。以 Prometheus 为中心的采集器都是通过 暴露 HTTP 端口 来让服务端采集，是一种 Pull 模式，而 falcon 体系是采用 Push 模式，客户端主动上报。数据采集形态的不同应该是 Prometheus 和 falcon 的最大差异点。

💡 Idea

🤔 如果有一种方案，能够以比较低的开发成本，将 Prometheus 的 exporter 转为换 falcon 的 collector，那样的话 falcon 的生态就会变得丰富多彩。

  • Metric 是监控体系中的重要概念，一个 metric 代表着一个监控项。Java 有一个优秀的 metric 相关的开源库 dropwizard/metrics，同时也有开发者基于该库开发了一个 Golang 版本 rcrowley/go-metrics，关于这两个库的更多信息，可移步至项目其地址。

  • Prometheus 本身在提供服务端的同时，也开发不同语言的 SDK 客户端，如 Golang 版本 prometheus/client_golang。

当 rcrowley/go-metrics 遇上 prometheus/client_golang，Aura 就出现啦 🥺。如果你使用过 Prometheus 的 SDK，那你将会对 Aura 提供的接口非常熟悉。Aura 的目标是成为 falcon 体系的客户端 SDK。

🔰 Installation

$ go get -u github.com/chenjiandongx/aura/...

🔖 Metric

Aura 标准 Metric 结构，沿用了 falcon 的设计。

type Metric struct {
	Endpoint  string
	Metric    string
	Step      uint32
	Value     interface{}
	Type      ValueType
	Labels    map[string]string
	Timestamp int64
}
* Counter

Counter 单调递增，违反单调性时重置为 0。可以用于统计某些事件出现的次数，或者服务的 uptime。

type Counter interface {
	Collector

	Clear()
	Count() int64
	Rate() float64
	Dec(int64)
	Inc(int64)
}
* Gauge

Gauge 记录瞬时值，可以用于记录系统当下时刻的状态，比如 CPU 使用率，使用内存大小，网络 IO 情况。

type Gauge interface {
	Collector

	Update(float64)
	Value() float64
}
* Histogram

Histogram 主要用于表示一段时间范围内对数据进行采样，并能够对其指定区间以及总数进行统计，通常它采集的数据展示为直方图。

type Histogram interface {
	Collector

	Observe(int64)
}
* Timer

Timer 主要用于统计一段代码逻辑或一次事件的耗时分布。

type Timer interface {
	Collector

	Time(func())
	Update(time.Duration)
}

📝 Usage

Registry

Registry 负责注册和管理 Collectors 的生命周期。

// RegistryOpts 用于指定 Metrics 和 Desc channel 的缓存大小。
// 一般情况下不需要调整，如果采集指标量比较大的话，可以将 CapMetricChan 值设置大一点。
type RegistryOpts struct {
	CapMetricChan int // default 2500
	CapDescChan   int // default 20
}

func NewRegistry(opts *RegistryOpts) *Registry
Collector 基本用法
package main

import (
	"time"

	"github.com/chenjiandongx/aura"
	"github.com/chenjiandongx/aura/reporter"
	"github.com/shirou/gopsutil/load"
)

const (
	namespace = "host"
	subsystem = "cpu"
	step      = 10
)

var (
	// 使用 aura.NewDesc 声明采集的指标
	// NewDesc(fqName, help string, step uint32, labelKeys []string) *Desc 
	// * fqName: 指标名称
 	// * help: 指标描述或者介绍（可为空）
	// * step: 指标步长
	// * labelkeys: 指标 label keys。
	cpuLoad1 = aura.NewDesc(
		aura.BuildFQName(namespace, subsystem, "loadavg.1"),
		"CPU load average over the last 1 minute",
		step,
		nil,
	)
	cpuLoad5 = aura.NewDesc(
		aura.BuildFQName(namespace, subsystem, "loadavg.5"),
		"load average over the last 5 minute",
		step,
		nil,
	)
	cpuLoad15 = aura.NewDesc(
		aura.BuildFQName(namespace, subsystem, "loadavg.15"),
		"load average over the last 15 minute",
		step,
		nil,
	)
)

type CPUCollector struct{}

// Interval 实现了 aura.Collector 接口。声明采集时间。
func (c *CPUCollector) Interval() time.Duration {
	return 2 * time.Second
}

// Describe 实现了 aura.Collector 接口。注册指标。
func (c *CPUCollector) Describe(ch chan<- *aura.Desc) {
	ch <- cpuLoad1
	ch <- cpuLoad5
	ch <- cpuLoad15
}

// Describe 实现了 aura.Collector 接口。指标具体采集逻辑。
func (c *CPUCollector) Collect(ch chan<- aura.Metric) {
	cpuLoad, _ := load.Avg()
	ch <- aura.MustNewConstMetric(cpuLoad1, aura.GaugeValue, cpuLoad.Load1)
	ch <- aura.MustNewConstMetric(cpuLoad5, aura.GaugeValue, cpuLoad.Load5)
	ch <- aura.MustNewConstMetric(cpuLoad15, aura.GaugeValue, cpuLoad.Load15)
}

func main() {
	// (1) 创建一个 Rigistry 对象
	registry := aura.NewRegistry(nil)
	// (2) 注册 Collector
	registry.MustRegister(&CPUCollector{})
	// (3) 注册 Reporter
	// reporter 负责将 metrics 输送到任意后端，开发者可自行为 registry 提供定制化后端
	// reporter.DefaultStreamReporter 会将采集的指标输出到 stdout
	registry.AddReporter(reporter.DefaultStreamReporter)

	// 可选项：Serve 将会启动一个 HTTP 服务用于提供 collector 本身运行的信息。
	go registry.Serve("127.0.0.1:9099")
	// (4) 开始采集指标
	registry.Run()
}

运行结果

~/project/golang/src/github.com/chenjiandongx/aura/examples/desc 🤔 go run .
{Endpoint: Metric:host.cpu.loadavg.15 Step:10 Value:2.01318359375 Type:Gauge Labels:map[] Timestamp:1590776801}
{Endpoint: Metric:host.cpu.loadavg.15 Step:10 Value:2.01318359375 Type:Gauge Labels:map[] Timestamp:1590776803}
{Endpoint: Metric:host.cpu.loadavg.15 Step:10 Value:2.01318359375 Type:Gauge Labels:map[] Timestamp:1590776805}
{Endpoint: Metric:host.cpu.loadavg.1 Step:10 Value:1.60791015625 Type:Gauge Labels:map[] Timestamp:1590776807}
{Endpoint: Metric:host.cpu.loadavg.5 Step:10 Value:2.02587890625 Type:Gauge Labels:map[] Timestamp:1590776801}
{Endpoint: Metric:host.cpu.loadavg.5 Step:10 Value:2.02587890625 Type:Gauge Labels:map[] Timestamp:1590776803}
{Endpoint: Metric:host.cpu.loadavg.1 Step:10 Value:1.748046875 Type:Gauge Labels:map[] Timestamp:1590776805}
{Endpoint: Metric:host.cpu.loadavg.15 Step:10 Value:2.0009765625 Type:Gauge Labels:map[] Timestamp:1590776807}
...

Collector 指标及运行状态

~/project/golang/src/github.com/chenjiandongx/aura 🤔 curl -s http://localhost:9099/-/metadata | jq
[
  {
    "metric": "host.cpu.loadavg.1",
    "help": "CPU load average over the last 1 minute",
    "step": 10
  },
  {
    "metric": "host.cpu.loadavg.5",
    "help": "load average over the last 5 minute",
    "step": 10
  },
  {
    "metric": "host.cpu.loadavg.15",
    "help": "load average over the last 15 minute",
    "step": 10
  }
]
~/project/golang/src/github.com/chenjiandongx/aura 🤔 curl -s http://localhost:9099/-/stats | jq
{
  "metricsChanCap": 2500,
  "metricsChanLen": 0
}
客户端埋点形式
package main

import (
	"math/rand"
	"time"

	"github.com/chenjiandongx/aura"
	"github.com/chenjiandongx/aura/reporter"
)

const (
	step = 15
)

var (
	// 声明采集指标
	echo = aura.NewHistogramVec(
		"http.service",
		"simple echo service",
		step,
		15*time.Second,
		[]string{"endpoint", "uri", "status"},
		// 直方图上报数据如果指定了 HVTypes/Percentiles 那上报就是计算后的指标
		// 计算后的指标形式
		// http.service.min
		// http.service.max
		// http.service.mean
		// http.service.count
		// http.service.0.50
		// http.service.0.75
		// http.service.0.90
		// http.service.0.99
		&aura.HistogramOpts{
			HVTypes: []aura.HistogramVType{
				aura.HistogramVTMin, aura.HistogramVTMax, aura.HistogramVTMean, aura.HistogramVTCount,
			},
			Percentiles: []float64{0.5, 0.75, 0.9, 0.99},
		},
	)
)

func main() {
	registry := aura.NewRegistry(nil)
	registry.MustRegister(echo)

	go func() {
		for range time.Tick(200 * time.Millisecond) {
			echo.WithLabelValues("echo", "/api/index", "200").Observe(rand.Int63() % 600)
			echo.With(map[string]string{
				"endpoint": "echo",
				"uri":      "/api/noexists",
				"status":   "404",
			}).Observe(rand.Int63() % 600)
		}
	}()

	registry.AddReporter(reporter.DefaultStreamReporter)

	go registry.Serve("localhost:9099")
	registry.Run()
}

运行结果

~/project/golang/src/github.com/chenjiandongx/aura/examples/histogram 🤔 go run .
{Endpoint:echo Metric:http.service.max Step:15 Value:590 Type:Gauge Labels:map[endpoint:echo status:200 uri:/api/index] Timestamp:1590778743}
{Endpoint:echo Metric:http.service.0.75 Step:15 Value:460.5 Type:Gauge Labels:map[endpoint:echo status:200 uri:/api/index] Timestamp:1590778743}
{Endpoint:echo Metric:http.service.0.99 Step:15 Value:590 Type:Gauge Labels:map[endpoint:echo status:200 uri:/api/index] Timestamp:1590778743}
{Endpoint:echo Metric:http.service.count Step:15 Value:20 Type:Gauge Labels:map[endpoint:echo status:404 uri:/api/noexists] Timestamp:1590778743}
{Endpoint:echo Metric:http.service.0.50 Step:15 Value:325.5 Type:Gauge Labels:map[endpoint:echo status:404 uri:/api/noexists] Timestamp:1590778743}
{Endpoint:echo Metric:http.service.0.75 Step:15 Value:460.5 Type:Gauge Labels:map[endpoint:echo status:404 uri:/api/noexists] Timestamp:1590778743}
{Endpoint:echo Metric:http.service.0.90 Step:15 Value:572.4000000000001 Type:Gauge Labels:map[endpoint:echo status:404 uri:/api/noexists] Timestamp:1590778743}
{Endpoint:echo Metric:http.service.0.99 Step:15 Value:590 Type:Gauge Labels:map[endpoint:echo status:404 uri:/api/noexists] Timestamp:1590778743}
...
自定义 Reporter
package main

import (
	"os"
	"time"

	"github.com/chenjiandongx/aura"
)

var (
	// declare metrics
	uptime = aura.NewCounter(
		"service.uptime",
		"service uptime in seconds",
		5,
		5*time.Second,
	)
)

type MyReporter struct{}

// Custom reporter which will writes data the local file.
func (r MyReporter) Report(ch chan aura.Metric) {
	filename := "metrics.log"

	f, err := os.Create(filename)
	if err != nil {
		panic(err)
	}

	for m := range ch {
		if _, err := f.WriteString(m.String() + "\n"); err != nil {
			panic(err)
		}
	}
}

func main() {
	registry := aura.NewRegistry(nil)
	registry.MustRegister(uptime)

	go func() {
		for range time.Tick(1 * time.Second) {
			uptime.Inc(1)
		}
	}()

	registry.AddReporter(MyReporter{})
	registry.Run()
}

运行结果

~/project/golang/src/github.com/chenjiandongx/aura/examples/reporter 🤔 tail -f metrics.log
<Metadata Endpoint:, Metric:service.uptime, Type:Counter Timestamp:1590945775, Step:5, Value:1, Tags:map[]>
<Metadata Endpoint:, Metric:service.uptime, Type:Counter Timestamp:1590945778, Step:5, Value:5, Tags:map[]>
<Metadata Endpoint:, Metric:service.uptime, Type:Counter Timestamp:1590945783, Step:5, Value:10, Tags:map[]>
<Metadata Endpoint:, Metric:service.uptime, Type:Counter Timestamp:1590945788, Step:5, Value:15, Tags:map[]>
<Metadata Endpoint:, Metric:service.uptime, Type:Counter Timestamp:1590945793, Step:5, Value:19, Tags:map[]>
<Metadata Endpoint:, Metric:service.uptime, Type:Counter Timestamp:1590945798, Step:5, Value:25, Tags:map[]>
<Metadata Endpoint:, Metric:service.uptime, Type:Counter Timestamp:1590945803, Step:5, Value:29, Tags:map[]>
<Metadata Endpoint:, Metric:service.uptime, Type:Counter Timestamp:1590945808, Step:5, Value:34, Tags:map[]>
...

Aura 提供了一些示例位于 examples 文件夹。同时也基于 prometheus/memcached_exporter 开发了 memcached-collector，作为一个标准 collector 写法供使用的同学参考。

📃 License

Apache License v2

Documentation

Index

Constants

This section is empty.

Variables

View Source 
var (
	DefaultHistogramOpts = &HistogramOpts{
		HVTypes:     []HistogramVType{HistogramVTMin, HistogramVTMax, HistogramVTMean},
		Percentiles: nil,
	}
)
View Source 
var DefaultRegistryOpts = &RegistryOpts{
	CapMetricChan: defaultCapMetricChan,
	CapDescChan:   defaultCapDescChan,
}

DefaultRegistryOpts holds the RegistryOpts by default case.

View Source 
var (
	DefaultTimerOpts = &TimerOpts{
		HVTypes:     []TimerVType{TimerVTMin, TimerVTMax, TimerVTMean},
		Percentiles: nil,
	}
)

Functions

func BuildFQName 

func BuildFQName(namespace, subsystem, name string) string

BuildFQName joins the given three name components by ".". Empty name components are ignored.

Types

type Collector 

type Collector interface {
	// Interval sets the interval of a metric collecting period.
	Interval() time.Duration

	// Describe sends the super-set of all possible descriptors of metrics
	// collected by this Collector to the provided channel.
	Describe(chan<- *Desc)

	// Collect do the collecting stuffs and the implementation sends each
	// collected metric via the provided channel.
	Collect(ch chan<- Metric)
}

Collector is the interface implemented by anything that can be used by Reporter to report metrics. A Collector has to be registered for collection.

type Counter 

type Counter interface {
	Collector

	Clear()
	Count() int64
	Rate() float64
	Dec(int64)
	Inc(int64)
}

Counter is just a gauge for an AtomicLong instance. You can increment or decrement its value.

func NewCounter 

func NewCounter(fqName, help string, step uint32, interval time.Duration) Counter

type CounterVec 

type CounterVec struct {
	*Desc
	// contains filtered or unexported fields
}

func NewCounterVec 

func NewCounterVec(fqName, help string, step uint32, interval time.Duration, labelKeys []string) *CounterVec

func (*CounterVec) Collect 

func (cv *CounterVec) Collect(ch chan<- Metric)

Collect implements aura.Collector.

func (*CounterVec) Describe 

func (cv *CounterVec) Describe(ch chan<- *Desc)

Describe implements aura.Collector.

func (*CounterVec) Interval 

func (cv *CounterVec) Interval() time.Duration

Interval implements aura.Collector.

func (*CounterVec) With 

func (cv *CounterVec) With(labels map[string]string) Counter

func (*CounterVec) WithLabelValues 

func (cv *CounterVec) WithLabelValues(lvs ...string) Counter

type Desc 

type Desc struct {
	// contains filtered or unexported fields
}

Desc is the descriptor used by every Metric.

func NewDesc 

func NewDesc(fqName, help string, step uint32, labelKeys []string) *Desc

NewDesc allocates and initializes a new Desc. Errors are recorded in the Desc and will be reported on registration time.

func (*Desc) IsKeyIn 

func (d *Desc) IsKeyIn(k string) bool

IsKeyIn returns true if the key is in the labels.

type Gauge 

type Gauge interface {
	Collector

	Update(float64)
	Value() float64
}

Gauge is an instantaneous measurement of a value. For example, we may want to measure the number of pending jobs in a queue.

func NewGauge 

func NewGauge(fqName, help string, step uint32, interval time.Duration) Gauge

type GaugeVec 

type GaugeVec struct {
	*Desc
	// contains filtered or unexported fields
}

func NewGaugeVec 

func NewGaugeVec(fqName, help string, step uint32, interval time.Duration, labelKeys []string) *GaugeVec

func (*GaugeVec) Collect 

func (gv *GaugeVec) Collect(ch chan<- Metric)

func (*GaugeVec) Describe 

func (gv *GaugeVec) Describe(ch chan<- *Desc)

func (*GaugeVec) Interval 

func (gv *GaugeVec) Interval() time.Duration

func (*GaugeVec) With 

func (gv *GaugeVec) With(labels map[string]string) Gauge

func (*GaugeVec) WithLabelValues 

func (gv *GaugeVec) WithLabelValues(lvs ...string) Gauge

type Histogram 

type Histogram interface {
	Collector

	Observe(int64)
}

Histogram measures the statistical distribution of values in a stream of data. In addition to minimum, maximum, mean, etc. it also measures median, 75th, 90th, 95th, 98th, 99th, and 99.9th percentiles.

func NewHistogram 

func NewHistogram(fqName, help string, step uint32, interval time.Duration, opts *HistogramOpts) Histogram

type HistogramOpts 

type HistogramOpts struct {
	HVTypes     []HistogramVType
	Percentiles []float64
}

type HistogramVType 

type HistogramVType string
const (
	HistogramVTMin      HistogramVType = "min"
	HistogramVTMax      HistogramVType = "max"
	HistogramVTMean     HistogramVType = "mean"
	HistogramVTCount    HistogramVType = "count"
	HistogramVTStdDev   HistogramVType = "stdDev"
	HistogramVTSum      HistogramVType = "sum"
	HistogramVTVariance HistogramVType = "variance"
)

type HistogramVec 

type HistogramVec struct {
	*Desc
	// contains filtered or unexported fields
}

func NewHistogramVec 

func NewHistogramVec(fqName, help string, step uint32, interval time.Duration, labelKeys []string, opts *HistogramOpts) *HistogramVec

func (*HistogramVec) Collect 

func (hv *HistogramVec) Collect(ch chan<- Metric)

func (*HistogramVec) Describe 

func (hv *HistogramVec) Describe(ch chan<- *Desc)

func (*HistogramVec) Interval 

func (hv *HistogramVec) Interval() time.Duration

func (*HistogramVec) With 

func (hv *HistogramVec) With(labels map[string]string) Histogram

func (*HistogramVec) WithLabelValues 

func (hv *HistogramVec) WithLabelValues(lvs ...string) Histogram

type MetaData 

type MetaData struct {
	Metric string `json:"metric"`
	Help   string `json:"help"`
	Step   uint32 `json:"step"`
}

MetaData represents the metrics metadata for the `/-/metadata` API

type Metric 

type Metric struct {
	Endpoint  string
	Metric    string
	Step      uint32
	Value     interface{}
	Type      ValueType
	Labels    map[string]string
	Timestamp int64
}

func MustNewConstMetric 

func MustNewConstMetric(desc *Desc, valueType ValueType, value interface{}, lvs ...string) Metric

func NewConstMetric 

func NewConstMetric(desc *Desc, valueType ValueType, value interface{}, lvs ...string) (Metric, error)

func (Metric) String 

func (m Metric) String() string

type Registry 

type Registry struct {
	// contains filtered or unexported fields
}

Registry registers aura collectors, collects their metrics.

func NewRegistry 

func NewRegistry(opts *RegistryOpts) *Registry

NewRegistry returns a Registry instance for managing the collecting jobs.

func (*Registry) AddReporter 

func (r *Registry) AddReporter(reporter Reporter)

AddReporter adds the reporter to decide where metrics go forward.

func (*Registry) MustRegister 

func (r *Registry) MustRegister(cs ...Collector)

func (*Registry) Register 

func (r *Registry) Register(c Collector) error

Register register a collector and handler all its `metrics desc`.

func (*Registry) Run 

func (r *Registry) Run()

func (*Registry) Serve 

func (r *Registry) Serve(address string)

Serve run the HTTP server which will exports the collectors infos to the user.

func (*Registry) Stop 

func (r *Registry) Stop()

type RegistryOpts 

type RegistryOpts struct {
	CapMetricChan int
	CapDescChan   int
}

RegistryOpts specifies the buffer size of metric channel and desc channel.

type Reporter 

type Reporter interface {
	Report(ch chan Metric)
}

Reporter is in charge of sending metrics collected to the backend you used.

type Timer 

type Timer interface {
	Collector

	Time(func())
	Update(time.Duration)
}

Timer measures both the rate that a particular piece of code is called and the distribution of its duration.

func NewTimer 

func NewTimer(fqName, help string, step uint32, interval time.Duration, opts *TimerOpts) Timer

type TimerOpts 

type TimerOpts struct {
	HVTypes     []TimerVType
	Percentiles []float64
}

type TimerVType 

type TimerVType string
const (
	TimerVTMin      TimerVType = "min"
	TimerVTMax      TimerVType = "max"
	TimerVTMean     TimerVType = "mean"
	TimerVTCount    TimerVType = "count"
	TimerVTStdDev   TimerVType = "stdDev"
	TimerVTSum      TimerVType = "sum"
	TimerVTVariance TimerVType = "variance"
	TimerVTRate1    TimerVType = "rate1"
	TimerVTRate5    TimerVType = "rate5"
	TimerVTRate15   TimerVType = "rate15"
	TimerVTRateMean TimerVType = "rateMean"
)

type TimerVec 

type TimerVec struct {
	*Desc
	// contains filtered or unexported fields
}

func NewTimerVec 

func NewTimerVec(fqName, help string, step uint32, interval time.Duration, labelKeys []string, opts *TimerOpts) *TimerVec

func (*TimerVec) Collect 

func (tv *TimerVec) Collect(ch chan<- Metric)

func (*TimerVec) Describe 

func (tv *TimerVec) Describe(ch chan<- *Desc)

func (*TimerVec) Interval 

func (tv *TimerVec) Interval() time.Duration

func (*TimerVec) With 

func (tv *TimerVec) With(labels map[string]string) Timer

func (*TimerVec) WithLabelValues 

func (tv *TimerVec) WithLabelValues(lvs ...string) Timer

type ValueType 

type ValueType string
const (
	CounterValue ValueType = "Counter"
	GaugeValue   ValueType = "Gauge"
)

Source Files

View all Source files

Directories

Path Synopsis
examples
counter command
desc command
gauge command
histogram command
reporter command
timer command

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