Unveiling the Architectural Brilliance of Prometheus

Extio Technology
6 min readJun 7, 2023

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Extio Prometheus Architecture

Introduction

Prometheus, a powerful open-source monitoring and alerting system, has emerged as a go-to solution for monitoring highly dynamic and distributed environments. With its flexible and scalable architecture, Prometheus has gained immense popularity among developers and DevOps teams. In this blog post, we will dive into the architectural brilliance of Prometheus, exploring its core components and their interactions, highlighting the key features that make Prometheus an exceptional tool for monitoring modern applications.

Prometheus Architecture

Prometheus follows a unique pull-based model, where it periodically scrapes metrics from target systems, allowing it to monitor a wide range of applications, services, and infrastructure components. Let’s break down the key components of the Prometheus architecture:

  1. Prometheus Server: At the heart of Prometheus lies the Prometheus server, responsible for data collection, storage, querying, and processing of metrics. It exposes a multi-dimensional data model, allowing users to organize and store time-series data efficiently. The server scrapes metrics from configured targets using HTTP-based protocols such as HTTP, HTTPS, or even specialized exporters.
  2. Data Storage: Prometheus stores collected metrics in its own time-series database (TSDB). The TSDB organizes data in blocks, with each block containing a set of compressed time-series data. The storage engine optimizes for efficient read access, enabling fast querying of historical data. Prometheus employs a powerful data retention mechanism, allowing users to define retention periods and data expiration policies.
  3. Alerting and Alert Manager: Prometheus offers a built-in alerting system that enables users to define custom rules for generating alerts based on metric thresholds or complex conditions. The Alert Manager component manages and deduplicates alerts, allowing users to define notification channels such as email, Slack, or PagerDuty. This ensures that relevant stakeholders are promptly notified of any critical issues.
  4. Exporters and Instrumentation Libraries: Prometheus boasts a vast ecosystem of exporters and instrumentation libraries that facilitate the collection of metrics from a wide range of systems and applications. Exporters, such as the Node Exporter or Blackbox Exporter, provide access to system-level and network-level metrics. Instrumentation libraries, such as client libraries for popular programming languages, allow developers to expose custom application-specific metrics.
  5. Grafana Integration: Prometheus seamlessly integrates with Grafana, a popular data visualization and exploration tool. Grafana allows users to build dynamic dashboards, visually representing Prometheus metrics and providing real-time insights into system performance. The combination of Prometheus and Grafana offers a powerful monitoring and observability solution.

Scalability and Federation

Prometheus is designed to scale horizontally to handle large-scale deployments. To achieve this, Prometheus supports federation, allowing multiple Prometheus servers to collaborate and form a federated network. This enables the collection of metrics from distributed environments, where each Prometheus server collects metrics from its own set of targets and shares aggregated data with other servers. Federation provides a unified view of metrics across multiple clusters or regions.

Examples

Let’s consider an example of Prometheus data storage and alert for a fictitious e-commerce application:

Consider a microservices-based e-commerce application composed of multiple services, including a product catalog service, order management service, and payment service. We want to monitor the performance and health of these services using Prometheus.

  1. Prometheus Server: The Prometheus server acts as the central component responsible for data collection, storage, and querying. It periodically scrapes metrics from various targets, including the microservices, using HTTP-based protocols. For example, the server can scrape the /metrics endpoint exposed by each service.
  2. Exporters and Instrumentation Libraries: To expose metrics from the microservices, we need to instrument them with the Prometheus client libraries or exporters. For our example, let’s assume we use the Prometheus client libraries available for the programming languages used in the microservices (e.g., Go, Java, Python). The client libraries enable developers to instrument the code and expose relevant metrics.

Let’s consider an example to illustrate how Prometheus stores alert data:

Scenario:
In our e-commerce application, we have set up a Prometheus alert to monitor the response time of our product catalog service. If the response time exceeds a certain threshold, an alert is triggered. Let’s assume this alert is named “HighResponseTime.”

Alert Data Storage: When the “HighResponseTime” alert is triggered, Prometheus stores relevant information about the alert in the TSDB. This includes the timestamp, alert name, labels associated with the alert, and any additional annotations specified.

For instance, when the alert fires due to high response time, Prometheus records the following data in the TSDB:

Time: 2023-06-07 14:30:00
Alert Name: HighResponseTime
Labels: service="product_catalog", severity="warning"
Annotations: summary="High response time detected", description="The product catalog service is experiencing high response times."

In the TSDB, the alert data is stored as time-series data just like regular metric data. The alert’s time-series can be queried and visualized, enabling you to analyze and track the occurrences of specific alerts over time.

Retrieving Alert Data: Prometheus provides a query language called PromQL, which allows you to query both metric data and alert data from the TSDB. You can retrieve alert-related information using PromQL queries and perform various analyses, such as aggregating the number of times an alert fired within a given time range or investigating the frequency of alerts for a specific service.

For example, to count the number of times the “HighResponseTime” alert fired within the last 24 hours, you can use the following PromQL query:

count_over_time(ALERTS{alertname="HighResponseTime"}[24h])

This query retrieves the time-series data for the “HighResponseTime” alert within the last 24 hours and counts the occurrences.

Let’s consider another example of a Prometheus alert for our fictitious e-commerce application:

Scenario:

In our e-commerce application, we have a payment service responsible for processing customer payments. We want to set up an alert to notify us if the failure rate of payment transactions exceeds a certain threshold.

Alert Definition: To create this alert, we define a Prometheus alert rule using PromQL (Prometheus Query Language). Let’s assume the failure rate threshold is 5%. If the failure rate of payment transactions exceeds this threshold, we want to be alerted.

The alert rule can be defined as follows:

groups:
- name: payment_alerts
rules:
- alert: HighPaymentFailureRate
expr: sum(rate(payment_service_transaction_failures_total[5m])) / sum(rate(payment_service_transactions_total[5m])) > 0.05
for: 5m
labels:
severity: critical
annotations:
summary: High payment failure rate detected
description: "The payment service is experiencing a high failure rate in processing transactions."

Explanation:

  • groups: Allows grouping multiple alert rules together.
  • name: Specifies the name of the group. In this case, it's "payment_alerts."
  • rules: Contains the actual alert rules.
  • alert: Assigns a name to the alert. Here, it's "HighPaymentFailureRate."
  • expr: Defines the expression used to evaluate the alert condition. The expression calculates the failure rate as the ratio of failed payment transactions to total payment transactions over a 5-minute period. If this ratio exceeds 5%, the alert condition is met.
  • for: Specifies the duration the alert condition needs to persist before triggering an alert. In this case, it's set to 5 minutes.
  • labels: Allows adding labels to the alert for further categorization. Here, we assign a severity label with a value of "critical" to indicate the alert's severity.
  • annotations: Provides additional information about the alert.
  • summary: A brief summary of the alert. In this case, it's "High payment failure rate detected."
  • description: A more detailed description of the alert. Here, it informs us that the payment service is experiencing a high failure rate in processing transactions.

When the alert condition is met (i.e., the failure rate exceeds 5% for 5 consecutive minutes), Prometheus triggers the “HighPaymentFailureRate” alert. The alert manager can then handle the alert according to its configuration, such as sending notifications to the specified channels (e.g., email, Slack) or performing custom actions.

This example demonstrates how Prometheus alerts can be defined and customized to suit specific monitoring requirements. By setting up alerts, you can proactively detect and respond to critical issues in your application or infrastructure.

Conclusion

The architecture of Prometheus reflects its ability to adapt to the complexities of modern monitoring requirements. Its pull-based model, robust data storage, alerting capabilities, extensive ecosystem of exporters, and seamless integration with Grafana make it a compelling choice for monitoring and observability. Whether you are managing a small application or a large-scale distributed system, Prometheus empowers you to gain valuable insights into your environment and proactively respond to anomalies. Embrace Prometheus, and unlock the full potential of your monitoring strategy.

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