{"id":37326,"date":"2026-04-15T12:26:04","date_gmt":"2026-04-15T16:26:04","guid":{"rendered":"https:\/\/www.lpi.org\/articles\/\/"},"modified":"2026-04-15T12:26:04","modified_gmt":"2026-04-15T16:26:04","slug":"devops-tools-introduction-13-prometheus-monitoring","status":"publish","type":"post","link":"https:\/\/www.lpi.org\/es\/blog\/2026\/04\/15\/devops-tools-introduction-13-prometheus-monitoring\/","title":{"rendered":"DevOps Tools Introduction #13: Prometheus Monitoring"},"content":{"rendered":"

While we have talked a lot about application deployment, we still need to cover how to keep these applications up and running. The DevOps Tools Engineer exam covers Prometheus Monitoring<\/a> for this task.<\/p>\n

Modern IT operations have evolved from merely keeping the systems running into a focus on ensuring that services consistently deliver value to users under varying conditions. Rather than simply maintaining infrastructure, operations teams are responsible for guaranteeing that services meet expectations related to reliability, performance, and scalability.<\/p>\n

Prometheus has emerged as a foundational tool in this landscape due to its simplicity, flexibility, and strong alignment with cloud-native architectures. It operates as a time-series database and monitoring system that collects numerical metrics over time. Each metric is associated with labels that provide contextual dimensions, enabling fine-grained filtering and aggregation. This model allows operators to analyze system behavior across multiple perspectives, such as individual instances, environments, or service components.<\/p>\n

Prometheus\u2019s documentation explains how to install<\/a> it, including using a Docker image. Once Prometheus is running, follow the Getting Started guide<\/a>, which shows how to configure Prometheus to monitor itself.<\/p>\n

The architecture of Prometheus is based on a pull model in which the server periodically retrieves metrics from configured targets. These targets expose metrics through HTTP endpoints, typically at a standard path. Once collected, the data is stored locally and can be queried using PromQL<\/a>, a powerful query language designed for time-series analysis.<\/p>\n

PromQL plays a central role in extracting meaningful insights from collected data. It allows users to retrieve metrics, filter them by labels, and perform complex aggregations. Aggregation by labels enables analysis across dimensions such as instances or services, revealing patterns such as load distribution or error concentration. Aggregation over time allows the identification of trends, smoothing out short-term fluctuations and highlighting long-term behavior. These capabilities make PromQL not only a query language but also a powerful analytical tool for understanding system dynamics.<\/p>\n

Monitoring other systems and applications requires exporters<\/em>, which collect monitoring information for Prometheus. The Node exporter<\/a> produces information on the status of a Linux system. To collect metrics that are not constantly available, the Prometheus Pushgateway<\/a> can cache information until it is collected. There are numerous other exporters for various purposes. Take a look at the Exporters documentation<\/a> and explore exporters you might find useful for your infrastructure.<\/p>\n

While collecting metrics is necessary, it is not sufficient for building a reliable monitoring solution. In addition, certain events should generate alerts so that operators can respond quickly to problems. Prometheus\u2019 Alertmanager<\/a> handles alerting and provides a flexible configuration<\/a> that defines the exact circumstances when alerts are sent.<\/p>\n

Besides alerting, reporting and visualization tools are another way to access monitoring data. Prometheus integrates with Grafana<\/a>, an advanced tool to analyze and visualize data. Once you have gathered some log data, take a look at the Grafana documentation<\/a> and build some Dashboards on your own. If you need some inspiration, search for images of other people\u2019s Dashboards to see what they get out of their monitoring data.<\/p>\n

Deciding which metrics to monitor is crucial for useful monitoring. Anita Buehrle\u2019s article RED Method for Prometheus<\/a> discusses how to handle such metrics. SmartBear\u2019s article Understanding Performance Metrics for Monitoring<\/a> goes into even more detail. Philipp Winder wrote a great Introduction to Monitoring Microservices with Prometheus<\/a> and provides some samples<\/a> about how to implement microservice monitoring in Go and Java. Even though language-specific implementations are not tested in the DevOps Tools Engineer exam, the examples are useful to understand how to make applications easy to monitor.<\/p>\n

In summary, effective IT operations depend on the ability to understand and measure both the technical and logical aspects of a service. Prometheus provides a robust framework for achieving this by combining efficient data collection, flexible querying, and strong integration with modern infrastructure.<\/p>\n

You might recall a lot of the security and infrastructure related terms from earlier postings in this series. Here we see how development and operations, how applications and infrastructure, interact. Next week we will discuss the last missing piece in this technology stack and talk about log management.<\/a><\/p>\n

<< Read the previous article of this series<\/a> | Start the series from the beginning >><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

While we have talked a lot about application deployment, we still need to cover how to keep these applications up and running. The DevOps Tools Engineer exam covers Prometheus Monitoring for this task. 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