Beyond Outro Main selling points Highly dynamic, built-in service discovery No hierarchical model, n-dimensional label set PromQL: for processing, graphing, alerting, and export Simple operation Highly it’s built with highly dynamic environments in mind It’s the second project to ever join CNCF and the de facto standard in cloud-native monitoring Kubelets, sidecars, microservices, ALL the cloud-native

Introduction Quick intro OpenMetrics Outro Problem statement After Prometheus Prometheus has become a de-facto standard in cloud-native metric monitoring Ease of exposing data has lead to an explosion in

是针对标签做过滤，支持四类操作符：  =：完全匹配，比如 app="clickhouse"  !=：完全不匹配，比如 app!="clickhouse"  =~：正则匹配，比如 app=~"n9e-.*"  !~：正则不匹配，比如 app!~"n9e-.*" 指标名称，通常放到大括号之外，但实际上，指标名称也是一个标签，其标签Key是 __name__，所以之前查询的例子，可以这么写： {__name__ http_request_duration_seconds_bucket{job="n9e-proxy", le="0.1"} 500 http_request_duration_seconds_bucket{job="n9e-proxy", le="1"} 700 http_request_duration_seconds_bucket{job="n9e-proxy", le="10"} 850 http_re http_request_duration_seconds_bucket{job="n9e-proxy", le="20"} 1000 http_request_duration_seconds_bucket{job="n9e-proxy", le="+Inf"} 1000 根据这个数据，我们可以计算出落在各个延迟区间的请求数量，如下： 0 ~ 0.1 : 500 0.1 ~ 1 : 200 1

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com/prometheus-vs-victoriametrics-benchmark-on-node-exporter-metrics-4ca29c75590f 总体架构 总体架构 T H A N K S !

observability Outro Main selling points Highly dynamic, built-in service discovery No hierarchical model, n-dimensional label set PromQL: for processing, graphing, alerting, and export Simple operation Highly