性能监控中常见的五个陷阱及其避免方法如下:过度频繁地收集指标会增加系统负载,从而影响应用程序性能。應減少指標收集頻率。不恰当的指标命名会 затруднить理解和分析性能数据。應使用有意义且描述性的名称。指标分布不均会导致某些指标被忽略,从而使监控无效。應使用分桶来确保指标分布均匀。忽略上下文指标可提供有关请求和响应的附加信息,从而有助于诊断性能问题。应在指标中包含有关请求和响应的信息。不使用监控工具会 затруднить收集、存储和可视化指标。应使用监控工具来简化监控过程。

过于频繁的指标收集会增加系统的负载,从而影响应用程序的性能。避免此陷阱,应:
import (
"time"
"github.com/go-kit/metrics/prometheus"
)
func newHistogram(name string) prometheus.Histogram {
return prometheus.NewHistogramFrom(
prometheus.HistogramOpts{
Name: name,
Buckets: []float64{0.001, 0.005, 0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 1.0},
},
time.Second,
)
}不恰当的指标命名会 затруднить理解和分析性能数据。避免此陷阱,应:
metric := prometheus.NewCounter(
prometheus.CounterOpts{
Name: "request_duration_seconds",
Help: "The duration of API requests in seconds.",
},
)指标分布不均会导致某些指标被忽略,从而使监控无效。避免此陷阱,应:
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import (
"time"
"github.com/go-kit/metrics/prometheus"
"github.com/prometheus/client_golang/prometheus/promauto"
)
func newHistogramWithBuckets(name string, buckets []float64) prometheus.Histogram {
return promauto.NewHistogramVec(
prometheus.HistogramOpts{
Name: name,
Buckets: buckets,
},
[]string{"method", "status_code"},
)
}上下文指标可提供有关请求和响应的附加信息,从而有助于诊断性能问题。避免此陷阱,应:
import (
"net/http"
"strings"
"github.com/go-kit/kit/metrics"
"github.com/go-kit/kit/transport"
)
func newHttpServerMiddleware(counter metrics.Counter) transport.ServerOption {
return transport.ServerOptionFunc(func(handler transport.Handler) transport.Handler {
return transport.Chain(
handler,
transport.ServerBefore(func(ctx context.Context, request *http.Request) context.Context {
method := request.Method
statusCode := "200"
if request.Method == "HEAD" {
method = "GET"
}
counter.With("method", method, "status_code", statusCode).Add(1)
return ctx
}),
)
})
}监控工具可简化指标的收集、存储和可视化。避免此陷阱,应:
import (
"net/http"
"github.com/prometheus/client_golang/prometheus/promhttp"
)
func startMetricsServer() error {
return http.ListenAndServe(":8080", promhttp.Handler())
}以上就是Golang 框架性能监控的常见陷阱和如何避免的详细内容,更多请关注php中文网其它相关文章!
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