Prometheus
Prometheus is an open-source systems monitoring and alerting toolkit. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if certain conditions are met.
monitoring
alerting
metrics
Prometheus: Open-Source Systems Monitoring and Alerting Toolkit
A comprehensive systems monitoring and alerting toolkit with open-source capabilities, offering metrics collection, rule evaluation, result display, and alert triggering.
What is Prometheus?
Prometheus is an open source monitoring and alerting system optimized for containerized environments like Kubernetes. It provides real-time visibility into applications, infrastructure, and services running in an environment.
Prometheus works by scraping and storing time series data from various systems and services. It collects metrics from targets using either a pull model or a push model. Prometheus runs queries against this data to generate real-time graphs, alerts, and dashboards.
Some key features of Prometheus include:
- A multi-dimensional data model with time series data identified by metric name and key/value pairs
- PromQL, a flexible query language to leverage this dimensionality
- No reliance on distributed storage; single server nodes are autonomous
- Time series collection happens via a pull model over HTTP
- Pushing time series is supported via an intermediary gateway
- Targets are discovered via service discovery or static configuration
- Multiple modes of graphing and dashboarding support
- Built-in alertmanager for handling alerts and on-call rotations
Prometheus is commonly used together with Grafana for dashboarding, visualization, and alerting; as well as exporters to extract metrics from common software and hardware systems already present in an environment.
Prometheus Features
Features
- Multi-dimensional data model with time series data identified by metric name and key/value pairs
- PromQL, a flexible query language to leverage this dimensionality
- No reliance on distributed storage; single server nodes are autonomous
- Time series collection happens via a pull model over HTTP
- Pushing time series is supported via an intermediary gateway
- Targets are discovered via service discovery or static configuration
- Multiple modes of graphing and dashboarding support
Pricing
- Open Source
Pros
Highly dimensional model allows flexible and efficient queries
PromQL supports aggregation and recording rules for pre-calculation
Built-in alerting and notification routing
Highly available with simple operational model
Native support for Kubernetes
Strong ecosystem integration
Cons
Pull-based model can miss short-lived spikes between scrapes
No automatic removal of stale metrics (extra storage usage)
Limited tooling for stats analysis, forecasting, anomaly detection
No built-in federation for massive scale
Steep learning curve for PromQL and architecture
Official Links
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