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Monitoring with Prometheus: Use Cases, Metrics, and Alternatives

  • 11 min read

What Is Prometheus?

Prometheus is an open-source monitoring system with a dimensional data model, flexible query language, and an efficient time series database. Developed originally at SoundCloud, it has gained considerable adoption for its reliability and scalability in handling large-scale service monitoring. 

Prometheus collects and stores metrics as time series data, allowing users to operate on this data with its query language, PromQL. Its main components include a time-series database, a data scraper that pulls metrics from designated targets at specified intervals, and a query engine to execute PromQL queries.

Prometheus’s architecture is designed to support multi-dimensional data collection and querying. This structure enables users to effectively monitor their systems and troubleshoot issues by analyzing real-time data.

In this article, you will learn:

Main Features of Prometheus 

Here are some of Prometheus’s most important features and capabilities.

Multi-Dimensional Data Model

Prometheus’s multi-dimensional data model allows data to be stored with multiple dimensions, called labels, making it flexible and enabling querying of complex datasets. Labels in Prometheus are key-value pairs that provide additional context for metrics, such as hostname, environment, or service name. 

This model supports detailed queries that provide more insight into system performance and behavior. It enables operators and developers to slice and dice the monitoring data from various perspectives. For example, one could query the average load of a service across all instances or drill down to specific instances in a particular environment.

PromQL

PromQL, Prometheus’s query language, is tailor-made for dealing with multi-dimensional data. It enables precise selection and aggregation of time series data based on metric name, labels, and time intervals. PromQL’s expressive power helps in crafting complex queries to derive meaningful insights from the stored data, supporting decision-making.

PromQL can be used to calculate the average response time across all instances of a specific service or to identify which versions of a service are exhibiting unusual behavior. This capacity for in-depth analysis makes PromQL an appropriate tool for system administrators and DevOps professionals.

Prometheus Operator

The Prometheus Operator caters to Kubernetes users, simplifying Prometheus’s deployment on Kubernetes clusters. It automates tasks such as configuration, upgrades, lifecycle management, and resource allocation, providing a seamless operational experience. The operator follows the Kubernetes operator pattern, defining custom resources for service monitoring configurations.

By using the Prometheus Operator, users can manage their Prometheus instances with Kubernetes-native APIs, making it easier to maintain and scale system monitoring. The operator ensures that Prometheus configurations are declarative and version-controlled, streamlining monitoring in dynamic and large-scale environments.

Monitoring Target Discovery

Prometheus can discover targets dynamically using service discovery mechanisms for environments like Kubernetes and EC2. The dynamic discovery of monitoring targets allows Prometheus to adapt to changing environments where hosts or containers frequently go up or down. This minimizes the manual upkeep required in dynamic and cloud-native deployments.

In a Kubernetes environment, Prometheus continually adjusts to the monitored components’ changes, ensuring no lapses in monitoring due to scaling events or transient states within the cluster. This automated target discovery significantly reduces the administrative burden and makes Prometheus particularly well-suited for an orchestration platform.

Visualization

Prometheus provides simple visualization tools; its built-in expression browser can execute PromQL queries and visualize the results in a rudimentary graph, useful for ad-hoc querying and quick checks. While Prometheus does not provide complex visualization tools directly, it integrates seamlessly with external visualization platforms like Grafana. 

This integration enables the creation of detailed and informative dashboards that can reflect real-time data and historical trends. Through Grafana, users can craft dashboards that cater to specific needs, displaying key metrics that provide insights into system performance and health. 

Use Cases for Prometheus Monitoring 

Prometheus is suitable for several monitoring use cases.

Infrastructure Monitoring

Prometheus is widely used for infrastructure monitoring, tracking components such as servers, databases, and network hardware. Its ability to efficiently gather, store, and analyze metrics in real time makes it useful for ensuring the performance and stability of hardware and operating systems. This enables preemptive action to address issues and maintain system health.

Infrastructure monitoring typically involves the collection of metrics like CPU usage, memory consumption, disk activity, and network traffic, which are crucial for diagnosing system problems or planning capacity expansion. The ability to deep-dive into specific segments via its label-centric data model promotes a targeted approach in operational strategies.

DevOps and CI/CD

Prometheus supports DevOps practices and continuous integration/continuous delivery (CI/CD) pipelines by providing insights into the deployments and operational health of software systems. It detects anomalies, measures performance metrics, and ensures that the deployed applications meet the desired service levels.

In CI/CD workflows, Prometheus can help in monitoring the impact of new releases instantly. By setting up specific alerts or thresholds, teams can quickly identify if a deployment leads to unexpected system behavior or degradation of performance, enabling rapid intervention.

Database Monitoring

Prometheus offers insights into database performance, resource utilization, and operational issues in real time. By leveraging exporters that convert database metrics to Prometheus-friendly formats, users can track key indicators such as query execution times, lock waits, or connection errors. These metrics help in managing databases, avoiding bottlenecks and outages.

Prometheus’s ability to handle high-precision metrics becomes critical in environments where database performance directly impacts the user experience. This kind of monitoring supports immediate reactive measures and aids in strategic planning like indexing or schema adjustments based on trends.

Kubernetes Monitoring

Kubernetes environments benefit significantly from Prometheus monitoring due to Prometheus’s inherent support for dynamic, container-based architectures. Monitoring Kubernetes with Prometheus involves tracking the performance and health of nodes, pods, and services. 

This provides administrators and developers with visibility into operational aspects, helping in efficient scaling and management of containerized applications. Prometheus’s service discovery mechanisms are naturally compatible with Kubernetes, allowing seamless monitoring as the environment scales or evolves. 

What Are Prometheus Metrics? 

Prometheus metrics are a set of measurements that denote quantitative data points related to software and hardware performance across a network. These metrics are primarily time-series data used to detect patterns and anomalies in the system being monitored. They are stored in a time-series database, allowing for efficient retrieval and real-time analytics.

Configured correctly, these metrics cover a range of indicators, from system load and query response times to more specific ones like the number of active threads or cache hits and misses.

What Can You Monitor with Prometheus? 

There are several types of metrics that can be monitored with Prometheus.

Service Metrics

Service metrics help monitor the health and performance of microservices and applications within an infrastructure. This includes tracking request counts, error rates, response times, and system throughput. Service metrics are critical for maintaining operations and ensuring service level agreements are met.

These measurements allow teams to identify underperforming services and potential bottlenecks within their applications, facilitating timely optimization and adjustments. Additionally, the granular insight provided by Prometheus’s label-based queries enables developers to drill down into specific service issues with precision.

Host Metrics

Host metrics provide data on the physical and virtual machines’ health in a network. Prometheus can track host metrics such as CPU utilization, memory usage, disk I/O operations, and network traffic. Monitoring these metrics is useful for understanding resource allocation and usage patterns, which assists in capacity planning and performance tuning.

These metrics help system administrators to prevent resource exhaustion and ensure that the hosts run efficiently under varying loads. By alerting on thresholds and abnormalities in host metrics, Prometheus acts as a first line of defense against system instability or failures.

Application Uptime

Monitoring application uptime and status helps ensure business continuity and user satisfaction. Prometheus allows tracking of application availability and response times, providing real-time alerts when performance degrades or sites become inaccessible. The data gleaned from these metrics aids in quick troubleshooting and resolution of service disruptions.

Cronjobs

Cronjobs or scheduled tasks are common in managing routine operations on servers, such as backups, reports, and maintenance activities. Monitoring cronjobs with Prometheus ensures they run at their scheduled times and alert if they fail or overrun their expected duration. 

This oversight helps maintain system hygiene and guarantees that time-sensitive tasks are completed as planned. Tracking metrics from cronjobs provides insights into script performance and potential areas for optimization, contributing to smoother system operations and better allocation of resources.

Notable Prometheus Alternatives 

1. Coralogix

Coralogix sets itself apart in observability with its modern architecture, enabling real-time insights into logs, metrics, and traces with built-in cost optimization. Coralogix’s straightforward pricing covers all its platform offerings including APM, RUM, SIEM, infrastructure monitoring and much more. With unparalleled support that features less than 1 minute response times and 1 hour resolution times, Coralogix is a leading choice for thousands of organizations across the globe.

Learn more about Coralogix APM with Prometheus integration

2. Nagios

Nagios is a monitoring and alerting system designed for applications, servers, and network infrastructure. It provides monitoring capabilities that help ensure the availability and performance of critical IT infrastructure components.

  • Flexibility in monitoring: Nagios can monitor nearly any system, application, protocol, or service across a range of operating systems.
  • Plugin system: The community-driven Nagios Exchange has thousands of plugins available, allowing users to extend its monitoring capabilities.
  • Alerting and remediation: Offers customizable alerting thresholds and can automatically initiate remediation processes when issues are detected.
  • Visualization and reporting: Features a dashboard for visualization and reporting, which provides a central view of IT infrastructure health.

Source: Nagios

Related content: Read our guide to Prometheus vs Nagios (coming soon)

3. Zabbix

Zabbix offers a scalable, high-performance monitoring solution for networks and applications. Known for its real-time monitoring capabilities, Zabbix is particularly effective for large-scale environments due to its native support for polling and trapping mechanisms.

  • Scalability: Capable of monitoring millions of metrics, making it suitable for large-scale environments.
  • Real-time monitoring: Provides real-time monitoring of servers, virtual machines, and network devices with immediate problem detection.
  • Auto-discovery: Automatically discovers network devices and configuration changes in dynamic environments.
  • Problem detection: Utilizes flexible threshold definitions and complex event processing for advanced problem detection and resolution.

Source: Zabbix

Related content: Read our guide to Prometheus vs Zabbix (coming soon)

4. Graphite

Graphite is an enterprise-scale monitoring tool that focuses on storing and visualizing time series data. It is designed to handle large amounts of numerical data generated by applications, services, and systems.

  • Data storage: Uses Whisper, a fixed-size database similar to RRD (round-robin database), which makes data storage highly efficient.
  • Rich graphing: Offers powerful graphing capabilities that can render graphs of any metric with ease, allowing for complex queries and visualization.
  • Scalable architecture: Its component-based architecture is inherently scalable and can handle vast amounts of data.
  • Integration friendly: Graphite can integrate with a range of other monitoring tools and supports various data ingestion methods, making it flexible for complex environments.

Source: Graphite

Learn more in our detailed guide to Prometheus alternatives (coming soon)

Best Practices for Prometheus Monitoring 

Here are some best practices to make the most of Prometheus for monitoring.

Set Actionable Alerts

In Prometheus, setting actionable alerts means defining clear, meaningful conditions under which alerts will be triggered. It is important that these alerts correlate directly with significant issues that require immediate attention, preventing alert fatigue among teams and focusing resources on genuine problems. 

Each alert should guide towards a problem’s resolution or escalate it appropriately. It should be precise and include contextual data that helps in diagnosing issues quickly. Alerts thresholds can be set based on historical data and real usage patterns, ensuring that they are triggered by true anomalies, not predictable fluctuations.

Use Labels Effectively

Labels allow for the slicing and dicing of metrics for detailed analysis. It is important to maintain a consistent labeling scheme across all metrics and use labels thoughtfully to keep queries efficient and manageable. Overusing labels can lead to increased query complexity and storage usage, which might degrade performance.

Use a minimal set of highly relevant labels that serve actual use cases, such as distinguishing between environments, services, or geographical locations. This helps in keeping data manageable and improves the system’s responsiveness.

Choose the Best Exporter

Exporters bridge the gap between Prometheus and the applications or hardware it needs to monitor. The choice of exporter affects the granularity and usefulness of the data collected. Always opt for official or widely-recognized exporters where available, as they offer better security, updates, and compatibility.

When an official exporter is not available, consider developing custom exporters that precisely capture the metrics relevant to your business needs. Ensure that these custom exporters are maintained actively and adhere to Prometheus standards for reliability and efficiency.

Learn more in our detailed guide to Prometheus exporter (coming soon)

Properly Instrument Applications and Infrastructure Components

Instrumentation involves adding code to applications and configuring infrastructure components to expose metrics. It helps achieve comprehensive monitoring coverage and obtain accurate insights into system performance and health. When instrumenting applications, focus on exporting metrics that reflect the application’s operational status, behavior under load, and functional events.

For infrastructure components, embed metrics collection in the system architecture to ensure that monitoring keeps pace with changes and scales as the infrastructure grows. This allows teams to rectify issues promptly and improve system design based on empirical data.

Conclusion

In conclusion, Prometheus caters to the dynamic and scalable monitoring needs of modern cloud native infrastructure. With its robust data handling capabilities and precise alerting mechanisms, it ensures that performance metrics are not only gathered but also meaningfully analyzed to maintain system integrity and efficiency. 

As organizations continue to transition to the cloud, Prometheus provides the necessary insights to ensure that their operational landscapes are resilient. This facilitates a proactive approach to system management and enhances the ability to make informed decisions and troubleshoot operational issues.

Learn more about Coralogix APM with Prometheus integration

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