Prometheus Architecture

Prometheus is a powerful open-source monitoring and alerting system, widely used in cloud-native environments to track the performance of applications and infrastructure. Its flexible data model and powerful querying language make it a go-to choice for DevOps teams. In this blog, we'll dive deep into Prometheus architecture, breaking down each component with the help of the reference image to give you a comprehensive understanding.

Overview

Prometheus follows a pull-based model where it periodically scrapes metrics from monitored targets (applications or services). It stores these metrics as time-series data in its database, enabling robust querying and alerting capabilities. Let's explore each component shown in the architecture diagram.

Key Components of Prometheus

Exporters

Exporters are responsible for collecting metrics from applications and making them available in a format that Prometheus understands. They serve as intermediaries, gathering data from various sources like databases, operating systems, or custom applications.

Example Command:

  prometheus_exporter --web.listen-address=:9187

Common Exporters:
- Node Exporter: For hardware and OS metrics.
- Blackbox Exporter: For probing endpoints over HTTP, HTTPS, DNS, TCP, etc.
- Custom Exporters: Developers can write custom exporters for unique applications.

Push Gateway

Push Gateway is used to temporarily store metrics from short-lived jobs, which do not exist long enough to be scraped by Prometheus. These jobs push their metrics to the Push Gateway, which in turn exposes them to Prometheus.

Use Case: Batch jobs or CI/CD pipelines that start and stop quickly.

Example Command:

  echo "sample_metric 42" | curl --data-binary @- http://localhost:9091/metrics/job/sample_job

Service Discovery

Service Discovery is essential for dynamically discovering targets in cloud-native environments like Kubernetes. Prometheus can automatically discover targets based on service annotations, reducing manual configuration.

Supported Platforms: Kubernetes, Consul, EC2, Azure, and more.

Example Configuration:

  scrape_configs:
    - job_name: 'kubernetes-nodes'
      kubernetes_sd_configs:
        - role: node

Retrieval

Prometheus continuously scrapes metrics from the exporters and Push Gateway using HTTP requests. This is achieved by the retrieval component, ensuring up-to-date data is collected at specified intervals.

Example:
```
  scrape_interval: 15s
```

Time-Series Database

All the scraped metrics are stored in a time-series database. Prometheus uses an efficient storage format that compresses the data to optimize resource utilization.

Data Retention:
```
  --storage.tsdb.retention.time=30d
```
Persistent Storage:
- Prometheus supports persistent storage using external solutions like EBS, Ceph, or NFS for data durability and recovery.

HTTP Server and PromQL

The HTTP Server exposes the Prometheus UI and API endpoints, enabling users to query metrics using PromQL (Prometheus Query Language). PromQL provides a powerful way to analyze and visualize time-series data.

Example Query:
```
  node_cpu_seconds_total{mode="idle"}
```

Alert Manager

The Alert Manager handles alerting, sending notifications to various channels such as Email, Slack, or PagerDuty. Alerts are defined based on conditions written in PromQL.

Example Alert Rule:

  groups:
    - name: example
      rules:
        - alert: HighCPUUsage
          expr: node_cpu_seconds_total > 80
          for: 5m
          labels:
            severity: critical
          annotations:
            summary: "High CPU Usage"

Visualization: Prometheus UI and Grafana

Prometheus offers a basic web UI for querying metrics, but most users prefer Grafana for advanced dashboards and visualizations. Grafana seamlessly integrates with Prometheus as a data source.

Example Configuration in Grafana:

  data_sources:
    - name: Prometheus
      type: prometheus
      url: http://prometheus-server:9090

How Prometheus Architecture Works (Illustration)

Exporters collect metrics from the application or infrastructure.
Push Gateway temporarily stores metrics from short-lived jobs.
Service Discovery dynamically discovers targets for scraping.
Retrieval pulls metrics from exporters and stores them in the Time-Series Database.
HTTP Server exposes the data to users for querying using PromQL.
Alert Manager evaluates rules and sends alerts when conditions are met.
Prometheus UI and Grafana provide visualization and dashboard capabilities.

Benefits of Prometheus Architecture

Scalable and Reliable: Prometheus efficiently handles large-scale deployments.
Flexible Data Model: Store multi-dimensional data with labels.
Powerful Querying: PromQL enables complex queries for detailed insights.
Cloud-Native Integration: Seamlessly integrates with Kubernetes and other cloud platforms.
Community Support: Large open-source community and extensive integrations.

Use Cases

Infrastructure Monitoring: Track CPU, memory, and disk usage.
Application Performance Monitoring (APM): Measure latency, request rate, and error rate.
Alerting and Incident Management: Trigger alerts on threshold breaches.
Business Metrics Monitoring: Custom metrics like user signups, purchases, etc.

Prometheus architecture is robust and flexible, designed to cater to modern cloud-native environments. Its pull-based model, along with dynamic service discovery, makes it an ideal choice for monitoring dynamic infrastructures like Kubernetes. By leveraging Prometheus along with Grafana for visualization and Alert Manager for notifications, you can achieve end-to-end monitoring and observability.

Hands-on Task for Readers

To get started with Prometheus, try setting up a monitoring stack for a Kubernetes cluster. Here are the steps:

Deploy Prometheus using Helm:

 helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
 helm install prometheus prometheus-community/prometheus

Expose Prometheus Dashboard:

 kubectl port-forward svc/prometheus-server 9090:80

Install Grafana for Visualization:
```
 helm install grafana grafana/grafana
```
Configure Prometheus as a data source in Grafana and create a custom dashboard.

Prometheus is more than just a monitoring tool; it's a comprehensive observability platform. Its flexible architecture and powerful querying language enable organizations to gain deep insights into their systems. Whether you're managing microservices, cloud-native applications, or traditional infrastructure, Prometheus can help you achieve operational excellence.

With this guide and the provided commands, you're well on your way to mastering Prometheus Architecture. Start exploring today and take your monitoring strategy to the next level.