Glances vs Prometheus: What are the differences?

Introduction

Glances and Prometheus are two popular monitoring tools used in the field of IT operations. While both tools serve the purpose of monitoring various aspects of an infrastructure, they differ in several key areas. In this article, we will highlight the key differences between Glances and Prometheus.

1. Data Collection: Glances mainly collects data by pulling it from the system directly. It provides real-time monitoring information by retrieving data from various sources such as system files, proc files, and external commands. On the other hand, Prometheus uses a pull-based model where it collects data by scraping metrics from the targets that expose them over HTTP. It periodically queries the targets and stores the collected data for further analysis.

2. Data Model: Glances does not have its own dedicated data model. It relies on the underlying system's data structure and presents the collected information in a simplified manner. On the contrary, Prometheus has its own internal data model called the Prometheus data model. It organizes the collected data using a time series database based on key-value pairs where metrics are identified by their metric name and a set of key-value pairs called labels. This allows for extensive querying and analysis of metrics.

3. Alerting: Glances does not have built-in alerting capabilities. It focuses mainly on monitoring and visualizing system data. In contrast, Prometheus has a powerful alerting system that allows users to define alert rules based on specific conditions and thresholds. It can send notifications to various channels such as email, PagerDuty, or other integrations, enabling proactive monitoring and issue resolution.

4. Service Discovery: Glances primarily relies on local discovery for monitoring system resources within the same environment. It does not have built-in service discovery mechanisms to automatically discover and monitor new resources. On the other hand, Prometheus provides multiple service discovery options, including file-based discovery, DNS-based discovery, and integrations with container orchestration platforms like Kubernetes. This makes it easier to monitor dynamic and large-scale infrastructures.

5. Scalability: Glances is designed to monitor a single system or a small number of systems. It may face limitations when dealing with a large number of targets or when collecting and storing a significant amount of metric data. Prometheus, on the other hand, is built to handle large-scale deployments and can easily scale horizontally by adding more Prometheus instances. It also supports federation, enabling aggregation of data from multiple Prometheus instances.

6. Query Language: Glances provides a simple command-line interface for monitoring and does not feature a rich query language. In contrast, Prometheus has a powerful query language called PromQL. PromQL allows users to perform complex queries, aggregations, and mathematical operations on the collected metric data. It provides advanced functionality for analyzing, visualizing, and alerting based on metric data.

In summary, Glances and Prometheus differ in their data collection methods, data models, alerting capabilities, service discovery options, scalability, and query languages. While Glances focuses on real-time monitoring and simplified information presentation, Prometheus offers a more comprehensive monitoring solution with advanced features such as its data model, alerting system, and powerful query language.