Prometheus is an open-source systems monitoring and alerting toolkit originally built at SoundCloud. Since its inception in 2012, many companies and organizations have adopted Prometheus, and the project has a very active developer and user community. It is now a standalone open source project and maintained independently of any company. To emphasize this, and to clarify the project’s governance structure, Prometheus joined the Cloud Native Computing Foundation in 2016 as the second hosted project, after Kubernetes.
Prometheus collects and stores its metrics as time series data, i.e. metrics information is stored with the timestamp at which it was recorded, alongside optional key-value pairs called labels.
Why Prometheus?
Dimensional data: Prometheus implements a highly dimensional data model. Time series are identified by a metric name and a set of key-value pairs.
Powerful queries: PromQL allows slicing and dicing of collected time series data in order to generate ad-hoc graphs, tables, and alerts.
Great visualization: Prometheus has multiple modes for visualizing data: a built-in expression browser, Grafana integration, and a console template language.
Efficient storage: Prometheus stores time series in memory and on local disk in an efficient custom format. Scaling is achieved by functional sharding and federation.
Simple operation: Each server is independent for reliability, relying only on local storage. Written in Go, all binaries are statically linked and easy to deploy.
Precise alerting: Alerts are defined based on Prometheus’s flexible PromQL and maintain dimensional information. An alertmanager handles notifications and silencing.
Many client libraries: Client libraries allow easy instrumentation of services. Over ten languages are supported already and custom libraries are easy to implement.
Many integrations: Existing exporters allow bridging of third-party data into Prometheus. Examples, system statistics, as well as Docker, HAProxy, StatsD, and JMX metrics.
Features
- 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
What are metrics and why are they important?
Metrics in layperson terms is a standard for measurement. What we want to measure depends from application to application. For a web server it can be request times, for a database it can be CPU usage or number of active connections, etc.
Metrics play an important role in understanding why your application is working in a certain way. If you run a web application and someone comes up to you and says that the application is slow, you will need some information to find out what is happening with your application. For example, the application can become slow when the number of requests is high. If you have the request count metric you can spot the reason and increase the number of servers to handle the heavy load. Whenever you are defining the metrics for your application you must put on your detective hat and ask this question what information will be important for me to debug if any issue occurs in my application?
Components
The Prometheus ecosystem consists of multiple components, many of which are optional:
- the main Prometheus server which scrapes and stores time series data
- client libraries for instrumenting application code
- a push gateway for supporting short-lived jobs
- special-purpose exporters for services like HAProxy, StatsD, Graphite, etc.
- an alertmanager to handle alerts
- various support tools
Most Prometheus components are written in Go, making them easy to build and deploy as static binaries.
Architecture
This diagram illustrates the architecture of Prometheus and some of its ecosystem components:
Prometheus scrapes metrics from instrumented jobs, either directly or via an intermediary push gateway for short-lived jobs. It stores all scraped samples locally and runs rules over this data to either aggregate and record new time series from existing data or generate alerts. Grafana or other API consumers can be used to visualize the collected data.
When does it fit?
Prometheus works well for recording any purely numeric time series. It fits both machine-centric monitoring as well as monitoring of highly dynamic service-oriented architectures. In a world of microservices, its support for multi-dimensional data collection and querying is a particular strength.
Prometheus is designed for reliability, to be the system you go to during an outage to allow you to quickly diagnose problems. Each Prometheus server is standalone, not depending on network storage or other remote services. You can rely on it when other parts of your infrastructure are broken, and you do not need to setup extensive infrastructure to use it.
When does it not fit?
Prometheus values reliability. You can always view what statistics are available about your system, even under failure conditions. If you need 100% accuracy, such as for per-request billing, Prometheus is not a good choice as the collected data will likely not be detailed and complete enough. In such a case you would be best off using some other system to collect and analyze the data for billing, and Prometheus for the rest of your monitoring.
Comparison to alternatives
| Feature | Prometheus | Graphite | InfluxDB | OpenTSDB | Nagios | Sensu |
|---|---|---|---|---|---|---|
| Scope | Full monitoring with scraping, alerting. | Time series DB, needs extensions. | Metrics and event logging with alerting. | Distributed DB, storage-focused. | Alerting via script checks only. | Observability pipeline for events/metrics. |
| Data Model | Labels for multi-dimensional metrics. | Dot-separated, limited filtering. | Tags/fields; restricted fields. | Similar to Prometheus but restrictive. | Host-based, no labels or queries. | Structured events with labels. |
| Storage | Append-only, long-term retention. | Fixed intervals, limited retention. | Log-structured storage; supports clustering. | Hadoop-based, complex to manage. | No native storage, uses plugins. | Stores events in etcd/PostgreSQL. |
| Query Language | Rich, powerful query language. | Basic querying, limited features. | Less powerful than PromQL. | Simple API-based queries. | No query language. | Customizable event processing. |
| Architecture | Independent servers, HA support. | No clustering, standalone only. | Clustering in commercial version. | Requires Hadoop; horizontal scaling. | Standalone, no clustering. | Clustered with HA, open-core model. |
| Strengths | High availability, rich queries. | Easy setup, long-term storage. | Event logging, advanced storage. | Scalable if Hadoop is in place. | Basic static monitoring. | Hybrid observability, extensibility. |
| Weaknesses | Requires sharding for large scale. | Limited metadata model. | Clustering requires paid features. | Needs Hadoop infrastructure. | Not for dynamic/cloud environments. | Less specialized for metrics. |
Metric types
The Prometheus client libraries offer four core metric types. These are currently only differentiated in the client libraries (to enable APIs tailored to the usage of the specific types) and in the wire protocol. The Prometheus server does not yet make use of the type information and flattens all data into untyped time series. This may change in the future.
Some of Prometheus users include:
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Conclusion
Prometheus is a powerful, flexible monitoring solution designed for modern, dynamic infrastructures. Its ability to provide real-time metrics, robust alerting, and seamless integration with tools like Grafana makes it an essential choice for businesses aiming to maintain high availability and performance.
At OpsBridge, we specialize in helping you unlock the full potential of Prometheus, ensuring your systems are always monitored, optimized, and secure.
Source: Prometheus