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Prometheus 指南

Posted on Edited on In Disqus:

Prometheus 作为业务级监控告警工具，再配合上可视化工具 Grafana，运维人员能方便的监控所需的指标。本文记录了小白入门学习的过程

学习资料

  1. Prometheus 中文文档
  2. 官方文档地址

入门体验区

快速安装

快速安装应用首选 docker 方式，不需要配置复杂的环境。当我们已经非常熟悉如何使用 prometheus 的时候，再返回来使用普通安装。

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# 只监听在本地
docker run --name prometheus -d -p 127.0.0.1:9090:9090 prom/prometheus

# 将配置文件挂载到容器中，方便修改
docker run -p 9090:9090 -v /tmp/prometheus.yml:/etc/prometheus/prometheus.yml prom/prometheus

# 使用额外的数据卷挂载配置文件：
docker run -p 9090:9090 -v /prometheus-data prom/prometheus --config.file=/prometheus-data/prometheus.yml

安装完成后，即可访问 http://localhost:9090 可以在 Graph 查询监控项，在 Status 查看监控了哪些机器，配置文件等。

修改配置文件

[配置介绍官方文档]https://prometheus.io/docs/prometheus/latest/configuration/configuration/)
我是在 Mac 电脑上 docker 安装 prometheus。由于我没有把配置文件挂载出来，只能进入容器去修改。

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docker exec -u root -it 51ae3954e880 sh

vi /etc/prometheus/prometheus.yml

配置文件如下所示

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# my global config
global:
  scrape_interval:     15s # Set the scrape interval to every 15 seconds. Default is every 1 minute.
  evaluation_interval: 15s # Evaluate rules every 15 seconds. The default is every 1 minute.
  # scrape_timeout is set to the global default (10s).

# Alertmanager configuration
alerting:
  alertmanagers:
  - static_configs:
    - targets:
      # - alertmanager:9093

# Load rules once and periodically evaluate them according to the global 'evaluation_interval'.
rule_files:
  # - "first_rules.yml"
  # - "second_rules.yml"

# A scrape configuration containing exactly one endpoint to scrape:
# Here it's Prometheus itself.
scrape_configs:
  # The job name is added as a label `job=<job_name>` to any timeseries scraped from this config.
  - job_name: 'prometheus'

    # metrics_path defaults to '/metrics'
    # scheme defaults to 'http'.

    static_configs:
    - targets: ['localhost:9090']

  - job_name: 'feiy'
    scrape_interval: 5s
    static_configs:
      - targets: ['host.docker.internal:8000']
        labels:
          group: 'production'

完整的配置例子，请点击这里

检查结果

打开 http://localhost:9090/targets 我们可以看到如下的结果

图中两个 target，第一个 feiy 是我自己写的一个 django 小程序，暴露出来了我关心的指标，用了Prometheus Python Client。第二个则是 Prometheus 服务器自带的监控数据。

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# feiy 暴露出来的数据

# HELP python_gc_objects_collected_total Objects collected during gc
# TYPE python_gc_objects_collected_total counter
python_gc_objects_collected_total{generation="0"} 17999.0
python_gc_objects_collected_total{generation="1"} 2384.0
python_gc_objects_collected_total{generation="2"} 833.0
# HELP python_gc_objects_uncollectable_total Uncollectable object found during GC
# TYPE python_gc_objects_uncollectable_total counter
python_gc_objects_uncollectable_total{generation="0"} 0.0
python_gc_objects_uncollectable_total{generation="1"} 0.0
python_gc_objects_uncollectable_total{generation="2"} 0.0
# HELP python_gc_collections_total Number of times this generation was collected
# TYPE python_gc_collections_total counter
python_gc_collections_total{generation="0"} 255.0
python_gc_collections_total{generation="1"} 23.0
python_gc_collections_total{generation="2"} 2.0
# HELP python_info Python platform information
# TYPE python_info gauge
python_info{implementation="CPython",major="3",minor="7",patchlevel="1",version="3.7.1"} 1.0
# HELP request_processing_seconds Time spent processing request
# TYPE request_processing_seconds summary
request_processing_seconds_count{endpoint="/metrics/",method="GET",status_code="200"} 3.0
request_processing_seconds_sum{endpoint="/metrics/",method="GET",status_code="200"} 0.0
# TYPE request_processing_seconds_created gauge
request_processing_seconds_created{endpoint="/metrics/",method="GET",status_code="200"} 1.570089497964517e+09
# HELP request_byte_sum_total Total request byte sum
# TYPE request_byte_sum_total counter
request_byte_sum_total{endpoint="/metrics/",method="GET",status_code="200"} 0.0
# TYPE request_byte_sum_created gauge
request_byte_sum_created{endpoint="/metrics/",method="GET",status_code="200"} 1.570089497964633e+09
# HELP response_byte_sum_total Total response byte sum
# TYPE response_byte_sum_total counter
response_byte_sum_total{endpoint="/metrics/",method="GET",status_code="200"} 5533.0
# TYPE response_byte_sum_created gauge
response_byte_sum_created{endpoint="/metrics/",method="GET",status_code="200"} 1.5700894979645782e+09

我们可以在 Graph 查询结果，比如查询 python_info{implementation=”CPython”,major=”3”,minor=”7”,patchlevel=”1”,version=”3.7.1”} 1.0
得到了如下的结果

进阶区

暴露数据 node exporter

  1. 监控 Linux 机器
  2. 监控容器

metric

  1. 官方文档
  2. 详细解读 Prometheus 的指标类型
  3. 一文搞懂 Prometheus 的直方图

rules

  1. 官方文档
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    # 如果有错的话，则那一条规则不会更新。
    go get github.com/prometheus/prometheus/cmd/promtool
    promtool check rules /path/to/example.rules.yml

采坑区