Amazon Timestream
Serverless time-series database for IoT, metrics, and operational telemetry
Open-Source Projects
Discover top open-source software, updated regularly with real-world adoption signals.
Discover top open-source software, updated regularly with real-world adoption signals.
Flexible monitoring system with powerful queries and autonomous servers
Prometheus collects metrics via pull or push, stores them in a multi‑dimensional time series database, offers PromQL for analysis, and supports alerting, federation, and diverse service discovery.
Prometheus is designed for developers, SREs, and operations teams that need a cloud‑native monitoring solution for dynamic environments such as Kubernetes, micro‑service architectures, and virtual machines. It runs as a single‑server process that scrapes metrics from configured targets, stores them locally, and makes them available through the expressive PromQL query language.
The system uses a pull‑based HTTP model, with optional push via an intermediary gateway for batch jobs. Service discovery plugins automatically locate targets across cloud providers, container orchestrators, and static configurations. Metrics are stored in a multi‑dimensional time‑series database, enabling powerful aggregation and alerting rules. For larger installations, horizontal federation lets multiple Prometheus servers share data while each node remains autonomous. Deployment options include pre‑compiled binaries, Docker images, or building from source with Go and NodeJS toolchains. The built‑in web UI provides basic graphing, while integrations with Grafana deliver richer dashboards.
When teams consider Prometheus, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Cluster health monitoring
Continuously scrapes node and pod metrics, alerts on resource exhaustion, and visualizes trends via Grafana.
Service latency alerting
Uses PromQL to define latency thresholds, triggering alerts when SLA limits are breached.
Capacity planning
Analyzes historical time‑series data to forecast resource needs and guide scaling decisions.
Federated multi‑region monitoring
Aggregates metrics from regional Prometheus instances into a central server for unified dashboards.
It stores metrics as time‑series identified by a metric name and a set of key/value labels, enabling multi‑dimensional queries.
Primarily via an HTTP pull model; targets expose /metrics endpoints that Prometheus scrapes at configured intervals. A push gateway can be used for batch jobs.
A single server is autonomous, but large deployments use federation to combine data from multiple servers while keeping each node independent.
PromQL is Prometheus’s query language that lets you select and aggregate time‑series data using the metric’s labels and functions.
It includes a basic web UI for graphing, and integrates with Grafana for advanced dashboards and alerting.
Project at a glance
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