{"id":2123,"date":"2025-05-06T22:15:00","date_gmt":"2025-05-06T22:15:00","guid":{"rendered":"https:\/\/serverhub.com\/kb\/?p=2123"},"modified":"2025-05-07T03:07:33","modified_gmt":"2025-05-07T03:07:33","slug":"prometheus-powerful-open-source-monitoring-alerting-toolkit","status":"publish","type":"post","link":"https:\/\/serverhub.com\/kb\/prometheus-powerful-open-source-monitoring-alerting-toolkit\/","title":{"rendered":"Prometheus: Powerful Open-Source Monitoring & Alerting Toolkit"},"content":{"rendered":"\n

Welcome to the wild world of Prometheus monitoring<\/strong>! If you’ve ever wondered how to make sense of thousands of metrics, query your data like a pro, or configure Prometheus on Windows machines, you’re in the right place.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

What is Prometheus and What Does It Do?<\/strong><\/h2>\n\n\n\n

Prometheus <\/strong>is an open-source monitoring<\/strong> and alerting toolkit<\/strong> originally developed at SoundCloud. It helps developers and DevOps engineers to efficiently collect, store, and query<\/strong> metrics. Think of it as a digital watchdog\u2014but instead of barking, it sends alerts when things go haywire.<\/p>\n\n\n\n

Prometheus excels at time-series data collection<\/strong>, meaning it continuously scrapes and stores data points indexed by time and labels. Unlike traditional logging solutions, Prometheus is optimized for high-cardinality data, which means you can tag your metrics with as much detail as needed without breaking performance.<\/p>\n\n\n\n

How Does Prometheus Work?<\/strong><\/h2>\n\n\n\n

Prometheus works on a pull-based model<\/strong> where it scrapes metrics from configured targets at regular intervals. The high-level breakdown on how Prometheus works:<\/p>\n\n\n\n

    \n
  1. Data Collection:<\/strong> Prometheus scrapes metrics from applications, nodes, or external services using exporters.<\/li>\n\n\n\n
  2. Storage:<\/strong> The data is stored in a time-series database optimized for high-volume, real-time querying.<\/li>\n\n\n\n
  3. Querying:<\/strong> You can use PromQL<\/strong> (Prometheus Query Language) to extract and analyze metrics.<\/li>\n\n\n\n
  4. Alerting:<\/strong> Integrated with Alertmanager<\/strong>, it sends notifications when anomalies occur.<\/li>\n\n\n\n
  5. Visualization:<\/strong> Prometheus integrates with Grafana for dashboards and visualizations.<\/li>\n<\/ol>\n\n\n\n

    Prometheus vs. Grafana: What\u2019s the Difference?<\/strong><\/h2>\n\n\n\n

    Prometheus is a monitoring system that collects data, while Grafana is a visualization tool that uses that data to create dashboards and graphs. Think of Prometheus<\/strong> as your detective gathering clues (metrics), and Grafana<\/strong> as the artist painting a picture with those clues. Together, they can be a powerful combination for data-driven insights and decision-making.<\/p>\n\n\n\n


    They collaborate when Grafana retrieves data from Prometheus via PromQL when it is integrated by the DevOps teams on top of Prometheus. Both Grafana and Prometheus work with a variety of data sources. Prometheus and Grafana are useful tools for creating dashboards that show system data, experimenting with metrics, and troubleshooting metrics collection-related difficulties.<\/p>\n\n\n\n

    Passing Multiple Query Parameters in Prometheus Queries<\/strong><\/h2>\n\n\n\n

    You can pass multiple query parameters in PromQL<\/strong> using curly braces and logical operators. An example is the following command:<\/p>\n\n\n\n

    node_cpu_seconds_total{mode=”idle”, instance=”localhost:9090″}<\/mark><\/p>\n\n\n\n

    Want to query multiple labels at once? Try this command:<\/p>\n\n\n\n

    node_cpu_seconds_total{mode=~”idle|user”, instance=~”localhost.*”}<\/mark><\/p>\n\n\n\n

    \ud83d\ude80Pro tip:<\/strong> Use regex carefully\u2014there\u2019s a Prometheus regex character limit<\/strong> to keep in mind!<\/p>\n\n\n\n

    Prometheus Metrics for Kubernetes Cronjobs<\/strong><\/h2>\n\n\n\n

    To send metrics from Kubernetes CronJobs<\/strong> to Prometheus, do these steps:<\/p>\n\n\n\n

      \n
    1. Expose metrics<\/strong> in your CronJob container using an HTTP endpoint.<\/li>\n\n\n\n
    2. Configure a ServiceMonitor<\/strong> to scrape those metrics.<\/li>\n\n\n\n
    3. Use PromQL queries<\/strong> to analyze scheduled job executions.<\/li>\n<\/ol>\n\n\n\n

      Example K8s Prometheus alert rules<\/strong>:<\/p>\n\n\n\n

      alert: CronJobFailures
      expr: kube_job_status_failed > 0
      for: 5m
      labels:
      severity: critical
      annotations:
      summary: “CronJob failed”<\/mark><\/p>\n\n\n\n

      Prometheus and AWS: AlertManager, IAM, and CLI<\/strong><\/h2>\n\n\n\n

      When using AWS Prometheus<\/strong> (AMP – Amazon Managed Prometheus), you need proper IAM permissions<\/strong> to interact with it.<\/p>\n\n\n\n

      Setting Up AWS Prometheus AlertManager Role<\/strong><\/h2>\n\n\n\n

      When using AWS Prometheus (AMP – Amazon Managed Prometheus), you need proper IAM permissions to interact with it.<\/p>\n\n\n\n

      Setting Up AWS Prometheus AlertManager Role<\/strong><\/h2>\n\n\n\n

      {
      “Version”: “2012-10-17”,
      “Statement”: [
      {
      “Effect”: “Allow”,
      “Action”: [
      “aps:ListAlertManagers”,
      “aps:PutAlertManagerDefinition”
      ],
      “Resource”: “*”
      }
      ]
      }<\/p>\n\n\n\n

      Using the AWS Prometheus CLI<\/strong><\/h2>\n\n\n\n

      Use the following commands:<\/p>\n\n\n\n

      aws aps list-workspaces
      aws aps get-metrics –workspace-id=xyz<\/mark><\/p>\n\n\n\n

      Prometheus Metrics for Everything!<\/strong><\/h2>\n\n\n\n

      From pods to databases, Prometheus captures it all. Some essential metric categories:<\/p>\n\n\n\n