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

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