Prometheus is an open-source monitoring solution that collects and aggregates metrics in the form of time series data. To put it another way, each item in a Prometheus store is a metric event with a timestamp.

Prometheus was created at Soundcloud, but it is now a community project supported by the Cloud Native Computing Foundation (CNCF). Its combination of querying features and cloud-native architecture has quickly propelled it to prominence over the last decade, making it the ideal monitoring stack for modern applications.

In this article, we'll discuss Prometheus' role, architecture of prometheus, how it stores and exposes data, and where Prometheus' responsibility ends. The software's interoperability with other platforms, which can surface data in more convenient formats, contributes to its popularity.

What Is Prometheus' Role?

Prometheus captures and stores events in real time. Memory consumption, network utilization, or individual incoming requests are examples of events that may be relevant to your application.

A "metric" is the fundamental data unit. Each metric is given a name that can be used to refer to it as well as a set of labels. Labels are arbitrary key-value data pairs that can be used to filter your database's metrics.

Metrics are always based on one of four main types of instruments:

Counter - A counter is a value that steadily increases rather than decreasing or resetting.

Gauge - A gauge is a value that can change at any time and in any direction.

Histogram - A multiple-value sampling that provides a sum of all stored values as well as the number of recorded events.

Summary - Similar to a histogram, a summary supports configurable quantiles for aggregate monitoring over sliding time periods.

Prometheus uses a pull-based data fetching mechanism to determine the current value of your metrics. It will poll the data source that supports each metric on a regular basis and save the results as a new event in the time-series database. The monitored application is responsible for implementing the data source endpoint; such data providers are commonly referred to as exporters.

The pull-based model makes it easier to integrate Prometheus into your applications. All you have to do is provide a compatible endpoint that displays the current value of the metric you want to collect. Everything else is handled by Prometheus. Although this may result in inefficiencies (for example, if Prometheus polls the endpoint again before its data has changed), it does mean that your code does not need to handle metric transportation.

Architecture of Prometheus

Prometheus is an open-source monitoring and alerting system for collecting and processing time-series data. Its architecture is made up of several components, each of which serves a specific purpose in the system.

Prometheus Server: The Prometheus server is the heart of the architecture, collecting metrics data from various sources, storing it, and serving it to the Prometheus UI for querying and visualisation. It collects data from targets using a variety of mechanisms such as HTTP, SNMP, or custom collectors and stores it in a local time-series database.

Exporters: Prometheus exporters are software agents that collect metrics data from various systems and applications, convert it to the Prometheus data format, and expose it over HTTP. The data is then scraped at regular intervals by the Prometheus server, stored in its time-series database, and made queryable.

Alertmanager: The Alertmanager is in charge of processing Prometheus alerts and sending notifications to various notification channels such as email, Slack, PagerDuty, and so on. It receives Prometheus server alerts via a webhook API, groups and deduplicates them, and routes them to the appropriate notification channels.

Grafana: Grafana is a popular open-source platform for data visualization and analytics. It can be used in conjunction with Prometheus to create dashboards and visualize time-series data collected by Prometheus.

Pushgateway: The Pushgateway is an optional component that enables applications to push metrics data to Prometheus rather than having Prometheus scrape the data. This is useful when the application cannot expose metrics data via HTTP or when scraping is inefficient due to a large number of metrics.

Prometheus is intended to be highly scalable, fault-tolerant, and adaptable. Because of its architecture, it is a popular choice for monitoring and alerting in modern distributed systems.

More Information on Exporters

Exporters are in charge of exposing your application's metrics so that Prometheus can collect them. Many users will start with a simple installation of the Node Exporter, which collects basic system metrics from the Linux host on which it is installed.

There are numerous exporters available, many of which are provided by Prometheus or official community vendors. There's a good chance an exporter already exists if you're monitoring a popular database engine like MySQL, PostgreSQL, or MongoDB, or if you're monitoring an HTTP server, logging engine, or messaging bus.

By writing your own exporter, you can track your application's own metrics. This approach has virtually no limitations; you could track time spent on a landing page, sales volume, user registrations, or anything else that is important to your system.

Exporters are straightforward HTTP API endpoints that can be written in any programming language. Prometheus provides official client libraries for Go, Java/Scala, Python, and Ruby to make instrumenting your code easier.

What is it that Prometheus cannot do?

While Prometheus is a comprehensive monitoring solution, it is not appropriate for all roles. Prometheus is designed with dependability and performance as central tenets. This results in trade-offs in metric accuracy.

Prometheus does not guarantee that the data collected will be completely accurate. It's designed for high-volume scenarios where a few dropped events have no impact on the overall picture. If you're tracking sensitive statistics that must be accurate, you should use a different platform. You could still use Prometheus for your system's less critical values.

Furthermore, Prometheus may not be the only component in your monitoring stack that you require. It is primarily concerned with storing and querying your events via HTTP APIs. The built-in web UI can support basic graphing but not advanced custom dashboards. In most cases, data visualization scenarios are handled by deploying a Grafana instance alongside; this provides dashboarding and metric analysis capabilities as well as built-in Prometheus integration.

Conclusion

Prometheus is a time series data store that can be used to manage any sequential time-based data. It's typically used to track the metrics of other applications in your stack. While Prometheus is a powerful system for storing and querying metrics, it is typically used in conjunction with other solutions to power graphical dashboards and advanced visualizations. Its popularity stems from its ability to work with custom metrics, support rich queries, and collaborate with other cloud-native ecosystem members.

Prometheus strives for maximum dependability. It's intended to be your go-to tool during an incident, assisting you in determining why other components are failing. Individual Prometheus nodes in a clustered deployment are completely self-contained, with no reliance on remote storage. Because of its dependability, Prometheus cannot guarantee data accuracy. When planning a new deployment, you should prioritize this disadvantage.

While we haven't gone over the specifics of installing Prometheus in this article, the official documentation includes a comprehensive quick-start guide if you want to give it a shot. Prometheus is most commonly used as a Docker container, but it is also available as source code or as pre-compiled binaries for popular Linux distributions. The Dockerized approach is the most user-friendly because it includes all core components in a ready-to-run state.