Prometheus: 监控系统和时序数据库 |我的资源库露水湾

Prometheus 是一个用 Go 写的监控系统，Prometheus 内置一个时序数据库。Prometheus 受到 Google borgmon 监控系统启发，2012 年起源于 SoundCloud 内部，后来成为第二个加入 Cloud Native Computing Foundation 的项目。

It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if some condition is observed to be true.

Prometheus 以固定的频率从配置的目标采集监控指标信息，经过计算，显示结果，并且观察到某些条件成真时发出告警。

Prometheus 区别于其他监控系统的地方在于：

一个多维的数据模型（Dimensional data model 通过指标名字定义的时序以及键值的组合）
- 时序数据通过 metric 和 key-value 区分
- metric 可以设置任意维度标签
- 双精度浮点，Unicode 标签
灵活强大的查询语言 (PromQL)，可以轻易的利用其多维信息
Prometheus 服务是一个单独的二进制文件，可以直接在本地工作
无需依赖分布式存储；单服务器节点是自治的 (single server nodes are autonomous)
高效：每个采样点只有 3.5 bytes 占用，单一服务每秒可以处理百万级别 metrics
通过在 HTTP 上的 pull 模型实现采集
pushing timeseries 通过中间网关支持
监控目标可以通过服务发现 (service discovery) 或者静态配置 (static configuration) 实现
多种图形和仪表板支持，结合 Grafana 可以实现更丰富的展示
支持分层和水平的联合 (federation)

SoundCloud 在其官方博客 Prometheus: Monitoring at SoundCloud 中提到设计这套监控系统的目标：

<code><span class="pln">A multi</span><span class="pun">-</span><span class="pln">dimensional data model</span><span class="pun">,</span><span class="pln"> so that data can be sliced </span><span class="kwd">and</span><span class="pln"> diced at will</span><span class="pun">,</span><span class="pln"> along dimensions like instance</span><span class="pun">,</span><span class="pln"> service</span><span class="pun">,</span><span class="pln"> endpoint</span><span class="pun">,</span> <span class="kwd">and</span><span class="pln"> method</span><span class="pun">.</span>
<span class="typ">Operational</span><span class="pln"> simplicity</span><span class="pun">,</span><span class="pln"> so that you can spin up a monitoring server </span><span class="kwd">where</span> <span class="kwd">and</span> <span class="kwd">when</span><span class="pln"> you want</span><span class="pun">,</span><span class="pln"> even on your </span><span class="kwd">local</span><span class="pln"> workstation</span><span class="pun">,</span><span class="pln"> without setting up a distributed storage backend </span><span class="kwd">or</span><span class="pln"> reconfiguring the world</span><span class="pun">.</span>
<span class="typ">Scalable</span><span class="pln"> data collection </span><span class="kwd">and</span><span class="pln"> decentralized architecture</span><span class="pun">,</span><span class="pln"> so that you can reliably monitor the many instances of your services</span><span class="pun">,</span> <span class="kwd">and</span><span class="pln"> independent teams can </span><span class="kwd">set</span><span class="pln"> up independent monitoring servers</span><span class="pun">.</span>
<span class="typ">Finally</span><span class="pun">,</span><span class="pln"> a powerful query language that leverages the data model </span><span class="kwd">for</span><span class="pln"> meaningful alerting </span><span class="pun">(</span><span class="pln">including easy silencing</span><span class="pun">)</span> <span class="kwd">and</span><span class="pln"> graphing </span><span class="pun">(</span><span class="kwd">for</span><span class="pln"> dashboards </span><span class="kwd">and</span> <span class="kwd">for</span><span class="pln"> ad</span><span class="pun">-</span><span class="pln">hoc exploration</span><span class="pun">).</span></code>

除了上面提到的 Prometheus 特性，在后来的发展中，Prometheus 不断的新增特性，比如服务发现，外部存储，告警规则和多种通知方式。

Prometheus 目前已经支持 Kubernetes, etcd, Consul 等多种服务发现机制。

为了扩展 Prometheus 的采集能力和存储能力，Prometheus 引入了”联邦”的概念。多个 Prometheus 组成两层联邦，上层定时从下层 Prometheus 节点获取数据并汇总，部署多个联邦节点实现高可用。下层节点分别负责不同区域的数据采集，下层 Prometheus 节点可以被部署到单独的机房充当代理。

Prometheus 组成

Prometheus 有很多可选组件：

Prometheus Server: 收集存储时间序列，并对外提供 API，提供 PromQL 查询语言
Client：为需要监控的服务生成相应的 metrics 并暴露给 Prometheus Server，Server pull 时直接返回实时状态
Push Gateway：用户可以主动向其中 push 数据，用于短期 job
Exporters: 暴露已有的第三方服务 metrics，等待 server 拉取
Alertmanager: 从 Prometheus server 接收到 alerts 后，进行数据处理，发出报警，报警方式有：邮件，Slack，pagerduty, OpsGenie, webhook 等

大致工作流：

Prometheus Server 定期从配置的目标 (Target) 或者 exporters 中 pull 拉取 metrics，或者接收来自 Pushgateway 发送的 metrics
Prometheus 在本地存储 metrics 数据，并通过一定规则清洗整理数据，把得到的结果记录到时间序列
得到数据后，根据制定的报警规则，计算报警指标
Alertmanager 根据配置，对接收到的报警进行处理
在图形界面中可视化采集的数据

从上面的介绍能比较清晰的看到 Prometheus 定时从被监控的组件中获取监控数据，而任意的组件只要提供对应的 exporter (Prometheus 这里使用 HTTP 协议）就可以快速接入监控。这种模式就特别适合微服务，或者容器。而目前常见的组件，Prometheus 都提供了对应的 exporter，比如 Haproxy, Nginx, MySQL, Linux 系统信息等等。

Installation & Usage

Prometheus Server

Prometheus Server 可以有很多安装方式，Docker，Ansible，Chef，Puppet，SaltStack 等等，具体可以参考官网。

通过 Docker 安装：

<code><span class="pln">docker run </span><span class="pun">--</span><span class="pln">name <a href="https://www.lushuiwan.com/tag/prometheus" title="查看更多关于 prometheus 的文章" target="_blank">prometheus</a> </span><span class="pun">-</span><span class="pln">d \
</span><span class="pun">-</span><span class="pln">p </span><span class="lit">9090</span><span class="pun">:</span><span class="lit">9090</span><span class="pln"> \
</span><span class="pun">-</span><span class="pln">v </span><span class="pun">~</span><span class="str">/docker/</span><span class="pln"><a href="https://www.lushuiwan.com/tag/prometheus" title="查看更多关于 prometheus 的文章" target="_blank">prometheus</a></span><span class="pun">:</span><span class="str">/etc/</span><span class="pln"><a href="https://www.lushuiwan.com/tag/prometheus" title="查看更多关于 prometheus 的文章" target="_blank">prometheus</a> \
prom</span><span class="pun">/</span><span class="pln"><a href="https://www.lushuiwan.com/tag/prometheus" title="查看更多关于 prometheus 的文章" target="_blank">prometheus</a></span></code>

如果把 Prometheus 的配置映射到了本地，可以直接去 ~/docker/prometheus/ 下查看配置。

或者使用 Docker compose:

<code><span class="pln">version</span><span class="pun">:</span> <span class="str">'2'</span><span class="pln">
services</span><span class="pun">:</span><span class="pln">
<a href="https://www.lushuiwan.com/tag/prometheus" title="查看更多关于 prometheus 的文章" target="_blank">prometheus</a></span><span class="pun">:</span><span class="pln">
image</span><span class="pun">:</span><span class="pln"> prom</span><span class="pun">/</span><span class="pln">prometheus
volumes</span><span class="pun">:</span>
  <span class="pun">-</span> <span class="pun">./</span><span class="pln">prometheus</span><span class="pun">.</span><span class="pln">yml</span><span class="pun">:</span><span class="str">/etc/</span><span class="pln">prometheus</span><span class="pun">/</span><span class="pln">prometheus</span><span class="pun">.</span><span class="pln">yml
command</span><span class="pun">:</span>
  <span class="pun">-</span> <span class="str">'--config.file=/etc/prometheus/prometheus.yml'</span><span class="pln">
ports</span><span class="pun">:</span>
  <span class="pun">-</span> <span class="str">'9090:9090'</span></code>

Node exporter

Prometheus 主要用于监控 Web 服务，如果要监控物理机，则需要在机器上安装 node exporter, exporter 会暴露 metrics 给 Prometheus，包括 cpu 负载，内存使用，磁盘 IO，网络等等。[^exporter]

[^exporter](https://github.com/prometheus/node_exporter#enabled-by-default)

node exporter 安装步骤：³

在官网获取最新的 node exporter 地址：

<code><span class="pln">curl </span><span class="pun">-</span><span class="pln">LO https</span><span class="pun">:</span><span class="com">//github.com/prometheus/node_exporter/releases/download/v0.18.1/node_exporter-0.18.1.linux-amd64.tar.gz</span></code>

解压

<code><span class="pln">tar </span><span class="pun">-</span><span class="pln">xzvf node_exporter</span><span class="pun">-</span><span class="lit">0.18</span><span class="pun">.</span><span class="lit">1.linux</span><span class="pun">-</span><span class="pln">amd64</span><span class="pun">.</span><span class="pln">tar</span><span class="pun">.</span><span class="pln">gz</span></code>

移动到 /usr/local/bin 目录

<code><span class="pln">sudo mv node_exporter</span><span class="pun">-</span><span class="lit">0.18</span><span class="pun">.</span><span class="lit">1.linux</span><span class="pun">-</span><span class="pln">amd64</span><span class="pun">/</span><span class="pln">node_exporter </span><span class="pun">/</span><span class="pln">usr</span><span class="pun">/</span><span class="kwd">local</span><span class="pun">/</span><span class="pln">bin</span><span class="pun">/</span></code>

创建 node exporter service，首先创建用户，然后添加服务

<code><span class="pln">sudo useradd </span><span class="pun">-</span><span class="pln">rs </span><span class="pun">/</span><span class="pln">bin</span><span class="pun">/</span><span class="kwd">false</span><span class="pln"> node_exporter
sudo vi </span><span class="pun">/</span><span class="pln">etc</span><span class="pun">/</span><span class="pln">systemd</span><span class="pun">/</span><span class="pln">system</span><span class="pun">/</span><span class="pln">node_exporter</span><span class="pun">.</span><span class="pln">service</span></code>

保存如下内容：

<code><span class="pun">[</span><span class="typ">Unit</span><span class="pun">]</span>
<span class="typ">Description</span><span class="pun">=</span><span class="typ">Node</span> <span class="typ">Exporter</span>
<span class="typ">After</span><span class="pun">=</span><span class="pln">network</span><span class="pun">.</span><span class="pln">target

</span><span class="pun">[</span><span class="typ">Service</span><span class="pun">]</span>
<span class="typ">User</span><span class="pun">=</span><span class="pln">node_exporter
</span><span class="typ">Group</span><span class="pun">=</span><span class="pln">node_exporter
</span><span class="typ">Type</span><span class="pun">=</span><span class="pln">simple
</span><span class="typ">ExecStart</span><span class="pun">=</span><span class="str">/usr/</span><span class="kwd">local</span><span class="pun">/</span><span class="pln">bin</span><span class="pun">/</span><span class="pln">node_exporter

</span><span class="pun">[</span><span class="typ">Install</span><span class="pun">]</span>
<span class="typ">WantedBy</span><span class="pun">=</span><span class="pln">multi</span><span class="pun">-</span><span class="pln">user</span><span class="pun">.</span><span class="pln">target</span></code>

重新加载 system daemon，启动服务：

<code><span class="pln">sudo systemctl daemon</span><span class="pun">-</span><span class="pln">reload
sudo systemctl start node_exporter

sudo systemctl status node_exporter</span></code>

开机启动：

<code><span class="pln">sudo systemctl enable node_exporter</span></code>

服务启动后会监听 9100 端口，使用如下命令验证：

<code><span class="pln">curl http</span><span class="pun">:</span><span class="com">//localhost:9100/metrics</span></code>

或者查看 9100 端口是否起来 netstat -anp | grep 9100.

配置这台服务器作为 Prometheus Server 的 Target 监控目标。

<code><span class="pln">sudo vi </span><span class="pun">/</span><span class="pln">etc</span><span class="pun">/</span><span class="pln">prometheus</span><span class="pun">/</span><span class="pln">prometheus</span><span class="pun">.</span><span class="pln">yml</span></code>

在 scrape 配置下，记得 IP 换成自己的

<code><span class="pun">-</span><span class="pln"> job_name</span><span class="pun">:</span> <span class="str">'node_exporter_metrics'</span><span class="pln">
  scrape_interval</span><span class="pun">:</span> <span class="lit">5s</span><span class="pln">
  static_configs</span><span class="pun">:</span>
    <span class="pun">-</span><span class="pln"> targets</span><span class="pun">:</span> <span class="pun">[</span><span class="str">'10.10.0.1:9100'</span><span class="pun">]</span></code>

重启 Prometheus 服务，如果是 Docker 起的，则需要重启容器

<code><span class="pln">sudo systemctl restart prometheus</span></code>

到 Prometheus 后台 Targes 下验证是否添加成功。

简单查询验证：

<code><span class="pln">node_memory_MemFree_bytes
node_cpu_seconds_total
node_filesystem_avail_bytes
rate</span><span class="pun">(</span><span class="pln">node_cpu_seconds_total</span><span class="pun">{</span><span class="pln">mode</span><span class="pun">=</span><span class="str">"system"</span><span class="pun">}[</span><span class="lit">1m</span><span class="pun">])</span><span class="pln">
rate</span><span class="pun">(</span><span class="pln">node_network_receive_bytes_total</span><span class="pun">[</span><span class="lit">1m</span><span class="pun">])</span></code>

Alertmanger

Alertmanger 也可以通过 Docker 来安装使用：

<code><span class="pln">docker run </span><span class="pun">-</span><span class="pln">d </span><span class="pun">-</span><span class="pln">p </span><span class="lit">9093</span><span class="pun">:</span><span class="lit">9093</span><span class="pln"> \
    </span><span class="pun">-</span><span class="pln">v </span><span class="pun">/</span><span class="pln">path</span><span class="pun">/</span><span class="pln">to</span><span class="pun">/</span><span class="pln">alertmanager</span><span class="pun">/</span><span class="pln">config</span><span class="pun">.</span><span class="pln">yml</span><span class="pun">:</span><span class="str">/etc/</span><span class="pln">alertmanager</span><span class="pun">/</span><span class="pln">config</span><span class="pun">.</span><span class="pln">yml \
	</span><span class="pun">--</span><span class="pln">name alertmanager \
	prom</span><span class="pun">/</span><span class="pln">alertmanager</span></code>

push gateway

Prometheus 默认的数据采集方式是通过 pull 模型，在配置中能看到 5 秒的配置，但是如果有些数据不适合使用这样的方式来监控，那么就需要使用 Push Gateway 将数据 Push 给 Prometheus 。

<code><span class="pln">docker run </span><span class="pun">-</span><span class="pln">d </span><span class="pun">-</span><span class="pln">p </span><span class="lit">9091</span><span class="pun">:</span><span class="lit">9091</span> <span class="pun">--</span><span class="pln">name pushgateway prom</span><span class="pun">/</span><span class="pln">pushgateway</span></code>

通过浏览器访问 9091 端口，Prometheus 提供了多个语言的 SDK 用来想 Push Gateway 发送数据，为了测试可以使用 shell 命令：

<code><span class="pln">echo </span><span class="str">"cqh_metric 100"</span> <span class="pun">|</span><span class="pln"> curl </span><span class="pun">--</span><span class="pln">data</span><span class="pun">-</span><span class="pln">binary </span><span class="pun">@-</span><span class="pln"> http</span><span class="pun">:</span><span class="com">//ip:9091/metrics/job/cqh</span></code>

推送多个指标：

<code><span class="pln">cat </span><span class="pun"><<</span><span class="pln">EOF </span><span class="pun">|</span><span class="pln"> curl </span><span class="pun">--</span><span class="pln">data</span><span class="pun">-</span><span class="pln">binary </span><span class="pun">@-</span><span class="pln"> http</span><span class="pun">:</span><span class="com">//ip:9091/metrics/job/cqh/instance/test</span><span class="pln">
muscle_metric</span><span class="pun">{</span><span class="pln">label</span><span class="pun">=</span><span class="str">"gym"</span><span class="pun">}</span> <span class="lit">8800</span><span class="pln">
bench_press </span><span class="lit">100</span><span class="pln">
dead_lift </span><span class="lit">160</span><span class="pln">
deep_squal </span><span class="lit">160</span><span class="pln">
EOF</span></code>

Grafana

通过 Docker compose 安装：

<code><span class="pln">grafana</span><span class="pun">:</span><span class="pln">
	image</span><span class="pun">:</span><span class="pln"> grafana</span><span class="pun">/</span><span class="pln">grafana
	volumes</span><span class="pun">:</span>
		<span class="pun">-</span><span class="pln"> grafana_data</span><span class="pun">:</span><span class="str">/var/</span><span class="pln">lib</span><span class="pun">/</span><span class="pln">grafana
	environment</span><span class="pun">:</span>
	  <span class="pun">-</span><span class="pln"> GF_SECURITY_ADMIN_PASSWORD</span><span class="pun">=</span><span class="kwd">pass</span><span class="pln">
	depends_on</span><span class="pun">:</span>
	  <span class="pun">-</span><span class="pln"> prometheus
	ports</span><span class="pun">:</span>
	  <span class="pun">-</span> <span class="str">'3000:3000'</span></code>

Prometheus config

在安装好 Prometheus 会有 yaml 格式的配置文件。主要分为这几个部分：

global: 全局配置
scrape_configs: 定义 Prometheus 需要 pull 的目标
alerting: Alertmanager 配置
rule_files: 告警规则

更多参数的解释可以参考官网.

alert rules

告警配置样例。

<code><span class="com"># Alert for any instance that is unreachable for >5 minutes.</span><span class="pln">
ALERT </span><span class="typ">InstanceDown</span>   <span class="com"># alert 名字</span><span class="pln">
  IF up </span><span class="pun">==</span> <span class="lit">0</span>           <span class="com"># 判断条件</span><span class="pln">
  FOR </span><span class="lit">5m</span>             <span class="com"># 条件保持 5m 才会发出 alert</span><span class="pln">
  LABELS </span><span class="pun">{</span><span class="pln"> severity </span><span class="pun">=</span> <span class="str">"critical"</span> <span class="pun">}</span>  <span class="com"># 设置 alert 的标签</span><span class="pln">
  ANNOTATIONS </span><span class="pun">{</span>             <span class="com"># alert 的其他标签，但不用于标识 alert</span><span class="pln">
    summary </span><span class="pun">=</span> <span class="str">"Instance  down"</span><span class="pun">,</span><span class="pln">
	description </span><span class="pun">=</span> <span class="str">" of job  has been down for more than 5 minutes."</span><span class="pun">,</span>
  <span class="pun">}</span></code>

使用 Prometheus 监控 Flask 应用

我在最初寻找监控系统的时候就是为了给 Flask 应用使用。而 Prometheus 在各个方面都超出了我的预期，不过再回到原始的初衷。

Flask 中使用 Prometheus 需要引入 prometheus_client , Prometheus 的 Python 客户端。

<code><span class="kwd">import</span><span class="pln"> prometheus_client
</span><span class="kwd">from</span><span class="pln"> prometheus_client </span><span class="kwd">import</span> <span class="typ">Counter</span>
<span class="kwd">from</span><span class="pln"> flask </span><span class="kwd">import</span> <span class="typ">Response</span><span class="pun">,</span> <span class="typ">Flask</span><span class="pun">,</span><span class="pln"> jsonify

app </span><span class="pun">=</span> <span class="typ">Flask</span><span class="pun">(</span><span class="pln">__name__</span><span class="pun">)</span><span class="pln">

total_requests </span><span class="pun">=</span> <span class="typ">Counter</span><span class="pun">(</span><span class="str">'request_count'</span><span class="pun">,</span> <span class="str">'Total webapp request count'</span><span class="pun">)</span>

<span class="lit">@app</span><span class="pun">.</span><span class="pln">route</span><span class="pun">(</span><span class="str">'/metrics'</span><span class="pun">)</span>
<span class="kwd">def</span><span class="pln"> requests_count</span><span class="pun">():</span><span class="pln">
	total_requests</span><span class="pun">.</span><span class="pln">inc</span><span class="pun">()</span>
	<span class="kwd">return</span> <span class="typ">Response</span><span class="pun">(</span><span class="pln">prometheus_client</span><span class="pun">.</span><span class="pln">generate_latest</span><span class="pun">(</span><span class="pln">total_requests</span><span class="pun">),</span><span class="pln"> mimetype</span><span class="pun">=</span><span class="str">'text/plain'</span><span class="pun">)</span>

<span class="lit">@app</span><span class="pun">.</span><span class="pln">route</span><span class="pun">(</span><span class="str">'/'</span><span class="pun">)</span>
<span class="kwd">def</span><span class="pln"> index</span><span class="pun">():</span><span class="pln">
	total_requests</span><span class="pun">.</span><span class="pln">inc</span><span class="pun">()</span>
	<span class="kwd">return</span><span class="pln"> jsonify</span><span class="pun">({</span>
	    <span class="str">'status'</span><span class="pun">:</span> <span class="str">'ok'</span>
	<span class="pun">})</span>

<span class="kwd">if</span><span class="pln"> __name__ </span><span class="pun">==</span> <span class="str">'__main__'</span><span class="pun">:</span><span class="pln">
	app</span><span class="pun">.</span><span class="pln">run</span><span class="pun">(</span><span class="pln">host</span><span class="pun">=</span><span class="str">'0.0.0.0'</span><span class="pun">,</span><span class="pln"> port</span><span class="pun">=</span><span class="lit">5000</span><span class="pun">)</span></code>

修改 prometheus.yml 配置文件。

<code><span class="pln">scrape_configs</span><span class="pun">:</span>
  <span class="pun">-</span><span class="pln"> job_name</span><span class="pun">:</span> <span class="str">'prometheus'</span><span class="pln">
  static_configs</span><span class="pun">:</span>
    <span class="pun">-</span><span class="pln"> targets</span><span class="pun">:</span> <span class="pun">[</span><span class="str">'localhost:9090'</span><span class="pun">]</span>
  <span class="pun">-</span><span class="pln"> job_name</span><span class="pun">:</span> <span class="str">'api_monitor'</span><span class="pln">
  scrape_interval</span><span class="pun">:</span> <span class="lit">5s</span><span class="pln">
  static_configs</span><span class="pun">:</span>
   <span class="pun">-</span><span class="pln"> targets</span><span class="pun">:</span> <span class="pun">[</span><span class="str">'web:5000'</span><span class="pun">]</span></code>

PromQL

Prometheus 内置了数据查询语言 PromQL，它提供对时间序列数据丰富的查询，聚合以及逻辑运算的能力。同时也可以利用 PromQL 做告警和数据可视化。利用 Prometheus 可以轻易的回答这些问题：⁴

访问量前 10 的 HTTP 地址 topk(10, http_requests_total)
计算 CPU 温度在两个小时内的差异 delta(cpu_temp_celsius{host="zeus"}[2h])
预测系统磁盘空间在 4 个小时之后的剩余情况 predict_linear(node_filesystem_free{job="node"}[1h], 4 * 3600)
过去 5 分钟占用 CPU 最高的应用服务

PromQL 是 Prometheus 中非常重要的概念。最简单的使用方式就是输入指标名称，比如

<code><span class="pln">up</span></code>

指定 label 查询：

<code><span class="pln">up</span><span class="pun">{</span><span class="pln">job</span><span class="pun">=</span><span class="str">"prometheus"</span><span class="pun">}</span></code>

或者使用 Instant vector selectors:

<code><span class="pln">up</span><span class="pun">{</span><span class="pln">job</span><span class="pun">!=</span><span class="str">"prometheus"</span><span class="pun">}</span><span class="pln">
up</span><span class="pun">{</span><span class="pln">job</span><span class="pun">=~</span><span class="str">"server|mysql"</span><span class="pun">}</span><span class="pln">
up</span><span class="pun">{</span><span class="pln">job</span><span class="pun">=~</span><span class="str">"10\.10\.0\.1.+"</span><span class="pun">}</span></code>

=~ 正则匹配，使用 RE2 语法

选择一段时间内所有数据，Range vector selectors，比如查询 5 分钟内所有的 HTTP 请求数：

<code><span class="pln">http_requests_total</span><span class="pun">[</span><span class="lit">5m</span><span class="pun">]</span></code>

Range vector selectors 返回的数据类型是 Range vector，一般需要和 rate() 或 irate() 函数一起使用。

<code><span class="com"># 计算的是每秒的平均值，适用于变化很慢的 counter</span>
<span class="com"># per-second average rate of increase, for slow-moving counters</span><span class="pln">
rate</span><span class="pun">(</span><span class="pln">http_requests_total</span><span class="pun">[</span><span class="lit">5m</span><span class="pun">])</span>

<span class="com"># 计算的是每秒瞬时增加速率，适用于变化很快的 counter</span>
<span class="com"># per-second instant rate of increase, for volatile and fast-moving counters</span><span class="pln">
irate</span><span class="pun">(</span><span class="pln">http_requests_total</span><span class="pun">[</span><span class="lit">5m</span><span class="pun">])</span></code>

PromQL 还支持 count, sum, min, max, avg, stddev（标准差）,topk（前 k 条）, quantile（分布统计）, 等聚合操作，支持 rate, abs, ceil, floor 等内置函数，更多例子见官网.

和其他监控系统对比

Prometheus 值得注意的点

上面这么多文字可以看到 Prometheus 是一个很强大的监控系统，同时部署也非常方便，但 Prometheus 也并非 Silver Bullet，它并不能用来解决一切问题。可以注意到的是 Prometheus 非常适合微服务架构，利用服务发现可以轻松的将监控目标扩展到成千成万。

数据非 100% 可靠

Prometheus 采集的数据可能有丢失，不适用于对采集数据要求 100% 精确的场景。Prometheus 只针对可用性及性能进行监控，不具备日志监控等功能。

存储有限

Prometheus 只认为最近的监控数据有查询需求，Prometheus 在设计之初将数据保存在本地就并非为大量数据存储。如果需要对历史数据进行分析，可以使用 Prometheus 提供的远端存储 OpenTSDB, M3db 等等。

无权限系统

Prometheus 没有任何权限管理的功能，它只专注于做好一件事情，那就是监控及告警，Prometheus 认为权限管理应该属于上层管理系统去维护，所以 Prometheus 在设计时没有考虑任何权限管理问题。

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Prometheus: 监控系统和时序数据库