上一篇文章中已经给大家整体的介绍了开源监控系统Prometheus,其中Exporter作为整个系统的Agent端,通过HTTP接口暴露需要监控的数据。那么如何将用户指标通过Exporter的形式暴露出来呢?比如说在线,请求失败数,异常请求等指标可以通过Exporter的形式暴露出来,从而基于这些指标做告警监控。
$ uname -a
Darwin 18.6.0 Darwin Kernel Version 18.6.0: Thu Apr 25 23:16:27 PDT 2019; root:xnu-4903.261.4~2/RELEASE_X86_64 x86_64
$ go version
go version go1.12.4 darwin/amd64
Prometheus定义了4种不同的指标类型:Counter(计数器),Gauge(仪表盘),Histogram(直方图),Summary(摘要)。
其中Exporter返回的样本数据中会包含数据类型的说明,例如:
# TYPE node_network_carrier_changes_total counter
node_network_carrier_changes_total{device="br-01520cb4f523"} 1
这四类指标的特征为:
Counter:只增不减(除非系统发生重启,或者用户进程有异常)的计数器。常见的监控指标如http_requests_total, node_cpu都是Counter类型的监控指标。一般推荐在定义为Counter的指标末尾加上_total作为后缀。
Gauge:可增可减的仪表盘。Gauge类型的指标侧重于反应系统当前的状态。因此此类指标的数据可增可减。常见的例如node_memory_MemAvailable_bytes(可用内存)。
HIstogram:分析数据分布的直方图。显示数据的区间分布。例如统计请求耗时在0-10ms的请求数量和10ms-20ms的请求数量分布。
Summary: 分析数据分布的摘要。显示数据的中位数,9分数等。
接下来我将用Prometheus提供的Golang SDK 编写包含上述四类指标的Exporter,示例的编写修改自SDK的example。由于example中示例比较复杂,我会精简一下,尽量让大家用最小的学习成本能够领悟到Exporter开发的精髓。第一个例子会演示Counter和Gauge的用法,第二个例子演示Histogram和Summary的用法。
package main
import (
"flag"
"log"
"net/http"
"github.com/prometheus/client_golang/prometheus/promhttp"
)
var addr = flag.String("listen-address", ":8080", "The address to listen on for HTTP requests.")
func main() {
flag.Parse()
http.Handle("/metrics", promhttp.Handler())
log.Fatal(http.ListenAndServe(*addr, nil))
}
上述代码就是一个通过0.0.0.0:8080/metrics 暴露golang信息的原始Exporter,没有包含任何的用户自定义指标信息。接下来往里面添加Counter和Gauge类型指标:
1 func recordMetrics() {
2 go func() {
3 for {
4 opsProcessed.Inc()
5 myGague.Add(11)
6 time.Sleep(2 * time.Second)
7 }
8 }()
9 }
10
11 var (
12 opsProcessed = promauto.NewCounter(prometheus.CounterOpts{
13 Name: "myapp_processed_ops_total",
14 Help: "The total number of processed events",
15 })
16 myGague = promauto.NewGauge(prometheus.GaugeOpts{
17 Name: "my_example_gauge_data",
18 Help: "my example gauge data",
19 ConstLabels:map[string]string{"error":""},
20 })
21 )
在上面的main函数中添加recordMetrics方法调用。curl 127.0.0.1:8080/metrics 能看到自定义的Counter类型指标myapp_processed_ops_total 和 Gauge 类型指标my_example_gauge_data。
# HELP my_example_gauge_data my example gauge data
# TYPE my_example_gauge_data gauge
my_example_gauge_data{error=""} 44
# HELP myapp_processed_ops_total The total number of processed events
# TYPE myapp_processed_ops_total counter
myapp_processed_ops_total 4
其中#HELP 是代码中的Help字段信息,#TYPE 说明字段的类型,例如my_example_gauge_data是gauge类型指标。my_example_gauge_data是指标名称,大括号括起来的error是该指标的维度,44是该指标的值。需要特别注意的是第12行和16行用的是promauto包的NewXXX方法,例如:
func NewCounter(opts prometheus.CounterOpts) prometheus.Counter {
c := prometheus.NewCounter(opts)
prometheus.MustRegister(c)
return c
}
可以看到该函数是会自动调用MustRegister方法,如果用的是prometheus包的NewCounter则需要再自行调用MustRegister注册收集的指标。其中Couter类型指标有以下的内置接口:
type Counter interface {
Metric
Collector
// Inc increments the counter by 1. Use Add to increment it by arbitrary
// non-negative values.
Inc()
// Add adds the given value to the counter. It panics if the value is <
// 0.
Add(float64)
}
可以通过Inc()接口给指标直接进行+1操作,也可以通过Add(float64)给指标加上某个值。还有继承自Metric和Collector的一些描述接口,这里不做展开。
Gauge类型的内置接口有:
type Gauge interface {
Metric
Collector
// Set sets the Gauge to an arbitrary value.
Set(float64)
// Inc increments the Gauge by 1. Use Add to increment it by arbitrary
// values.
Inc()
// Dec decrements the Gauge by 1. Use Sub to decrement it by arbitrary
// values.
Dec()
// Add adds the given value to the Gauge. (The value can be negative,
// resulting in a decrease of the Gauge.)
Add(float64)
// Sub subtracts the given value from the Gauge. (The value can be
// negative, resulting in an increase of the Gauge.)
Sub(float64)
// SetToCurrentTime sets the Gauge to the current Unix time in seconds.
SetToCurrentTime()
}
需要注意的是Gauge提供了Sub(float64)的减操作接口,因为Gauge是可增可减的指标。Counter因为是只增不减的指标,所以只有加的接口。
1 package main
2
3 import (
4 "flag"
5 "fmt"
6 "log"
7 "math"
8 "math/rand"
9 "net/http"
10 "time"
11
12 "github.com/prometheus/client_golang/prometheus"
13 "github.com/prometheus/client_golang/prometheus/promhttp"
14 )
15
16 var (
17 addr = flag.String("listen-address", ":8080", "The address to listen on for HTTP requests.")
18 uniformDomain = flag.Float64("uniform.domain", 0.0002, "The domain for the uniform distribution.")
19 normDomain = flag.Float64("normal.domain", 0.0002, "The domain for the normal distribution.")
20 normMean = flag.Float64("normal.mean", 0.00001, "The mean for the normal distribution.")
21 oscillationPeriod = flag.Duration("oscillation-period", 10*time.Minute, "The duration of the rate oscillation period.")
22 )
23
24 var (
25 rpcDurations = prometheus.NewSummaryVec(
26 prometheus.SummaryOpts{
27 Name: "rpc_durations_seconds",
28 Help: "RPC latency distributions.",
29 Objectives: map[float64]float64{0.5: 0.05, 0.9: 0.01, 0.99: 0.001},
30 },
31 []string{"service","error_code"},
32 )
33 rpcDurationsHistogram = prometheus.NewHistogram(prometheus.HistogramOpts{
34 Name: "rpc_durations_histogram_seconds",
35 Help: "RPC latency distributions.",
36 Buckets: prometheus.LinearBuckets(0, 5, 20),
37 })
38 )
39
40 func init() {
41 // Register the summary and the histogram with Prometheus's default registry.
42 prometheus.MustRegister(rpcDurations)
43 prometheus.MustRegister(rpcDurationsHistogram)
44 // Add Go module build info.
45 prometheus.MustRegister(prometheus.NewBuildInfoCollector())
46 }
47
48 func main() {
49 flag.Parse()
50
51 start := time.Now()
52
53 oscillationFactor := func() float64 {
54 return 2 + math.Sin(math.Sin(2*math.Pi*float64(time.Since(start))/float64(*oscillationPeriod)))
55 }
56
57 go func() {
58 i := 1
59 for {
60 time.Sleep(time.Duration(75*oscillationFactor()) * time.Millisecond)
61 if (i*3) > 100 {
62 break
63 }
64 rpcDurations.WithLabelValues("normal","400").Observe(float64((i*3)%100))
65 rpcDurationsHistogram.Observe(float64((i*3)%100))
66 fmt.Println(float64((i*3)%100), " i=", i)
67 i++
68 }
69 }()
70
71 go func() {
72 for {
73 v := rand.ExpFloat64() / 1e6
74 rpcDurations.WithLabelValues("exponential", "303").Observe(v)
75 time.Sleep(time.Duration(50*oscillationFactor()) * time.Millisecond)
76 }
77 }()
78
79 // Expose the registered metrics via HTTP.
80 http.Handle("/metrics", promhttp.Handler())
81 log.Fatal(http.ListenAndServe(*addr, nil))
82 }
第25-32行定义了一个Summary类型指标,其中有service和errro_code两个维度。第33-37行定义了一个Histogram类型指标,从0开始,5为宽度,有20个直方。也就是0-5,6-10,11-15 .... 等20个范围统计。
其中直方图HIstogram指标的相关结果为:
1 # HELP rpc_durations_histogram_seconds RPC latency distributions.
2 # TYPE rpc_durations_histogram_seconds histogram
3 rpc_durations_histogram_seconds_bucket{le="0"} 0
4 rpc_durations_histogram_seconds_bucket{le="5"} 1
5 rpc_durations_histogram_seconds_bucket{le="10"} 3
6 rpc_durations_histogram_seconds_bucket{le="15"} 5
7 rpc_durations_histogram_seconds_bucket{le="20"} 6
8 rpc_durations_histogram_seconds_bucket{le="25"} 8
9 rpc_durations_histogram_seconds_bucket{le="30"} 10
10 rpc_durations_histogram_seconds_bucket{le="35"} 11
11 rpc_durations_histogram_seconds_bucket{le="40"} 13
12 rpc_durations_histogram_seconds_bucket{le="45"} 15
13 rpc_durations_histogram_seconds_bucket{le="50"} 16
14 rpc_durations_histogram_seconds_bucket{le="55"} 18
15 rpc_durations_histogram_seconds_bucket{le="60"} 20
16 rpc_durations_histogram_seconds_bucket{le="65"} 21
17 rpc_durations_histogram_seconds_bucket{le="70"} 23
18 rpc_durations_histogram_seconds_bucket{le="75"} 25
19 rpc_durations_histogram_seconds_bucket{le="80"} 26
20 rpc_durations_histogram_seconds_bucket{le="85"} 28
21 rpc_durations_histogram_seconds_bucket{le="90"} 30
22 rpc_durations_histogram_seconds_bucket{le="95"} 31
23 rpc_durations_histogram_seconds_bucket{le="+Inf"} 33
24 rpc_durations_histogram_seconds_sum 1683
25 rpc_durations_histogram_seconds_count 33
xxx_count反应当前指标的记录总数,xxx_sum表示当前指标的总数。不同的le表示不同的区间,后面的数字是从开始到这个区间的总数。例如le="30"后面的10表示有10个样本落在0-30区间,那么26-30这个区间一共有多少个样本呢,只需要用len="30" - len="25",即2个。也就是27和30这两个点。
Summary相关的结果如下:
1 # HELP rpc_durations_seconds RPC latency distributions.
2 # TYPE rpc_durations_seconds summary
3 rpc_durations_seconds{error_code="303",service="exponential",quantile="0.5"} 7.176288428497417e-07
4 rpc_durations_seconds{error_code="303",service="exponential",quantile="0.9"} 2.6582266087185467e-06
5 rpc_durations_seconds{error_code="303",service="exponential",quantile="0.99"} 4.013935374172691e-06
6 rpc_durations_seconds_sum{error_code="303",service="exponential"} 0.00015065426336339398
7 rpc_durations_seconds_count{error_code="303",service="exponential"} 146
8 rpc_durations_seconds{error_code="400",service="normal",quantile="0.5"} 51
9 rpc_durations_seconds{error_code="400",service="normal",quantile="0.9"} 90
10 rpc_durations_seconds{error_code="400",service="normal",quantile="0.99"} 99
11 rpc_durations_seconds_sum{error_code="400",service="normal"} 1683
12 rpc_durations_seconds_count{error_code="400",service="normal"} 33
其中sum和count指标的含义和上面Histogram一致。拿第8-10行指标来说明,第8行的quantile 0.5 表示这里指标的中位数是51,9分数是90。
如果上面Counter,Gauge,Histogram,Summary四种内置指标都不能满足我们要求时,我们还可以自定义类型。只要实现了Collect接口的方法,然后调用MustRegister即可:
func MustRegister(cs ...Collector) {
DefaultRegisterer.MustRegister(cs...)
}
type Collector interface {
Describe(chan<- *Desc)
Collect(chan<- Metric)
}
文章通过Prometheus内置的Counter(计数器),Gauge(仪表盘),Histogram(直方图),Summary(摘要)演示了Exporter的开发,最后提供了自定义类型的实现方法。
https://prometheus.io/docs/guides/go-application/
https://yunlzheng.gitbook.io/prometheus-book/parti-prometheus-ji-chu/promql/prometheus-metrics-types
https://songjiayang.gitbooks.io/prometheus/content/concepts/metric-types.html
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