Prometheus

Prometheus is an open-source monitoring system and time series database originally built at SoundCloud. It is now a graduated Cloud Native Computing Foundation (CNCF) project—the second after Kubernetes. Prometheus is designed for reliability, to be the system you go to during an outage, allowing you to quickly diagnose problems.

Key features

Multi-dimensional data model

Prometheus stores time series data as metric names and key-value pairs called labels. This dimensional approach allows for flexible aggregation and slicing of data, making it powerful for complex infrastructure monitoring.

PromQL query language

PromQL (Prometheus Query Language) enables users to query, aggregate, and analyze time series data in real-time. It supports operations like aggregation, filtering, and mathematical transformations directly on the stored metrics.

Pull-based collection

Prometheus uses a pull model over HTTP to collect metrics from instrumented targets. This approach simplifies service discovery in dynamic environments like Kubernetes and makes it easier to detect down instances.

Autonomous operation

Each Prometheus server operates independently with local storage. There are no external dependencies, making deployment straightforward and eliminating complex distributed system concerns for single-node deployments.

Alerting

Prometheus includes an alerting component that evaluates rules against collected metrics and sends notifications via Alertmanager, which handles routing, grouping, and silencing of alerts.

Architecture components

• Prometheus Server: Scrapes and stores time series data
• Client Libraries: Instrumentation libraries for Go, Java, Python, Ruby, and others
• Exporters: Collect metrics from third-party systems (HAProxy, StatsD, Graphite, and hundreds more)
• Pushgateway: Accepts metrics from short-lived batch jobs
• Alertmanager: Handles alert routing, grouping, and notification

Deployment options

Prometheus is written in Go and distributed as statically linked binaries, making it easy to deploy:

• Binary: Pre-compiled binaries for all major platforms
• Docker: Official images available on Docker Hub and Quay.io
• Kubernetes: Helm charts and Prometheus Operator for native Kubernetes integration
• Package Managers: Available via apt, yum, homebrew, and others

Integrations

Prometheus integrates with hundreds of systems through exporters and has native client libraries for most programming languages. It works seamlessly with visualization tools like Grafana and Perses, and supports federation for scaling across multiple data centers.

Use cases in manufacturing

• Equipment monitoring: Track machine health metrics, temperature, vibration, and operational status
• SCADA integration: Collect metrics from industrial control systems via exporters
• Predictive maintenance: Store time-series sensor data for ML-based failure prediction
• Energy monitoring: Track power consumption across production lines
• OEE calculation: Collect availability, performance, and quality metrics

Limitations

• Not for 100% accuracy: Prometheus is designed for reliability, not absolute precision. It's not suitable for per-request billing or use cases requiring complete data accuracy.
• Local storage only: Single-node architecture means storage is limited by local disk capacity.
• No built-in long-term storage: Requires additional components like Thanos or Cortex for long-term retention.
• Pull model limitations: Services must expose HTTP endpoints or use Pushgateway for batch jobs.
• Query performance: Complex queries over large time ranges can be resource-intensive.

MING stack role

Prometheus is a core component of the MING (Mosquitto, InfluxDB, Node-RED, Grafana) monitoring stack variation, often replacing or complementing InfluxDB for metric collection and alerting scenarios.