This blog of the observability series focuses on Prometheus, exploring its architecture and practical applications. The session covers how Prometheus scrapes metrics from node exporters and Cube State metrics, introduces PromQL for querying, and demonstrates the integration with Grafana for enhanced visualization. The importance of custom metrics and service monitoring is also highlighted.

What is the role of Prometheus in monitoring?

Prometheus plays a crucial role in monitoring by serving as a powerful tool for collecting, storing, and querying metrics from various sources within a Kubernetes environment. Its primary responsibilities include:

1. Metrics Scraping: Prometheus continuously scrapes metrics from various exporters, such as Node Exporter and Kube State Metrics. Node Exporter collects infrastructure-level metrics (like CPU and memory usage) from Kubernetes nodes, while Kube State Metrics provides information about the state of Kubernetes objects (like pods, deployments, and services).

2. Time Series Database: Prometheus stores the scraped metrics in a time series database, which records metrics along with their timestamps. This allows for historical analysis and monitoring of trends over time.

3. Querying with PromQL: Prometheus uses its own query language, PromQL, which enables users to write queries to retrieve and aggregate metrics data. This functionality allows users to analyze performance, detect anomalies, and visualize data in various formats.

4. Alerting: Prometheus can be integrated with Alertmanager to set up alerts based on specific thresholds or conditions. This feature helps notify users when certain metrics exceed predefined limits, enabling proactive monitoring and response to issues.

5. Visualization: While Prometheus provides basic visualization capabilities, it is often used in conjunction with Grafana, a more advanced visualization tool. Grafana allows users to create dashboards and visual representations of the metrics collected by Prometheus, making it easier to monitor system performance and health.

Overall, Prometheus is essential for effective monitoring in cloud-native environments, providing insights into system performance, resource utilization, and application behavior.

How does PromQL enhance data querying?

PromQL, or Prometheus Query Language, enhances data querying in several significant ways:

1. Powerful Querying Capabilities: PromQL allows users to write complex queries to retrieve and manipulate time series data. Users can aggregate, filter, and perform calculations on metrics, enabling detailed analysis of system performance.

2. Time Series Focus: Since Prometheus is a time series database, PromQL is designed to work with time-stamped data. Users can query metrics at specific time intervals, making it easier to analyze trends and changes over time.

3. Aggregation Functions: PromQL includes built-in functions for aggregation, such as sum, avg, min, and max. This allows users to compute metrics across different dimensions, such as calculating the average CPU usage across all pods in a namespace.

4. Label Filtering: PromQL supports filtering based on labels, which are key-value pairs associated with metrics. This feature enables users to narrow down queries to specific subsets of data, such as querying metrics for a particular application or service.

5. Graphing and Visualization: The results of PromQL queries can be visualized in various formats, including graphs and tables. This capability is often leveraged in tools like Grafana, which provides enhanced visualization options for the data retrieved through PromQL.

6. Alerting Integration: PromQL can be used to define alerting rules based on specific conditions. For example, users can set alerts for when CPU usage exceeds a certain threshold, allowing for proactive monitoring and response to potential issues.

Overall, PromQL enhances data querying by providing a flexible and powerful way to interact with time series data, enabling users to gain insights into system performance and health effectively.

What are custom metrics in Prometheus?

Custom metrics in Prometheus are user-defined metrics that developers can create to monitor specific aspects of their applications or systems beyond the default metrics provided by Prometheus and its exporters. These metrics allow for tailored monitoring suited to the unique requirements of an application. Here are some key points about custom metrics:

1. Definition and Purpose: Custom metrics are defined by the developers of an application to capture specific data points that are relevant to its performance, behavior, or health. They can measure various aspects, such as request latency, error rates, internal application states, or domain-specific metrics.

2. Types of Custom Metrics: Prometheus supports several types of custom metrics:

   • Counters: Monotonically increasing values that represent counts of events (e.g., number of HTTP requests processed).
   • Gauges: Values that can go up or down (e.g., current memory usage).
   • Histograms: Used to track the distribution of events (e.g., request durations).
   • Summaries: Similar to histograms but provide more detailed quantile information over a sliding time window.

3. Exposing Metrics: Developers implement custom metrics in their applications using client libraries provided by Prometheus for various programming languages (e.g., Go, Python, Java). These libraries facilitate the definition, updating, and exposition of custom metrics in a format that Prometheus can scrape.

4. Scraping and Storage: Once custom metrics are defined and exposed by an application, Prometheus scrapes these metrics at regular intervals, storing them in its time series database for further analysis and querying.

5. Integration with Dashboarding and Alerting: Custom metrics can be used in PromQL queries to monitor specific application behavior and can also be visualized in tools like Grafana. Additionally, they can be incorporated into alerting rules to notify when certain thresholds are exceeded, helping maintain application reliability and performance.

6. Use Cases: Common use cases for custom metrics include monitoring business-critical application metrics, tracking feature usage, measuring latency for specific endpoints, or monitoring internal states that are essential for troubleshooting.

In summary, custom metrics in Prometheus provide the flexibility to monitor application-specific performance and behaviors, enabling more comprehensive observability tailored to an organization's needs.