Monitoring and Tracing in Microservice Architecture

Introduction

Microservice architecture offers many advantages, such as modularity, scalability, and technological diversity. However, this architecture also increases system complexity and complicates its monitoring, as each service operates independently and maintains its own state. Therefore, creating an effective monitoring and tracking system is essential to ensure the stability and reliability of microservices. In this article, we will discuss the main tools and practices that will help us build a flexible and scalable monitoring system.

Prometheus: Collecting and Monitoring Metrics

Prometheus is an open-source monitoring system designed for microservices and containerized environments. It stands out for its simplicity, high scalability, and integration with popular technologies. Prometheus collects metrics using a "pull" model, which means it periodically requests data from each service via HTTP endpoints.

Prometheus is configured through YAML files, which describe the sources of metrics (targets) and their scrape intervals. For example, the following configuration defines two services running on different ports:

In this example, Prometheus will poll every 15 seconds and collect metrics from the specified services. The collected metrics will be stored in Prometheus' database, allowing for further analysis and visualization.

Each microservice exports metrics in a format compatible with Prometheus. For this, we can use Prometheus client libraries, which are available for all popular programming languages. For example, in a .NET application, we can use the Prometheus .NET Client library to register and export metrics:

Grafana: Data Visualization and Analytics

Grafana is often used to visualize and analyze the metrics collected by Prometheus. It is an open-source platform that offers interactive and flexible dashboards from various data sources.

In Grafana, we create dashboards consisting of individual panels. Each panel displays certain metrics or sets of metrics in various visual forms: graphs, gauges, tables, etc. For example, we can create a panel that shows the number of HTTP requests over time for each service or the 95th percentile request duration.

Grafana integrates with Prometheus using its built-in Data Source mechanism. We simply specify the Prometheus server address, and then we can use the Prometheus query language (PromQL) to request and visualize data.

Additionally, in Grafana, we can define "alerts" that send notifications when certain conditions are met, such as if the service response time exceeds a permissible threshold or if the number of errors suddenly increases.

Centralized Logging with the ELK Stack

In microservices, it is important to collect and analyze not only metrics but also logs. Each microservice logs its own data, but often it is necessary to centralize these records to get a complete picture of the system's state.

The ELK stack (Elasticsearch, Logstash, Kibana) is a popular choice for log management. In this architecture, Logstash collects log records from various sources, normalizes them, and then sends them to Elasticsearch for indexing and storage. Elasticsearch is a scalable search and analytics engine that allows us to quickly search and filter logs using various criteria. Finally, Kibana is a visualization tool that enables us to create dashboards and perform interactive log analysis.

Implementing logging in microservices is relatively simple using appropriate logging libraries. In .NET, a popular choice is Serilog, which supports various logging destinations (Sinks), including Elasticsearch. We configure Serilog to send logs to Logstash for further processing:

Incident Management and Escalation with PagerDuty

An important component of a monitoring system is generating alerts and responding appropriately to incidents. PagerDuty is a platform that automates the incident management process.

PagerDuty receives alerts from various sources (e.g., Grafana, Prometheus Alert Manager) and automatically creates incidents. It then escalates incidents according to a predefined scheme, sending notifications to the relevant team members and tracking the incident status throughout its lifecycle.

For example, if we have an alert in Prometheus that checks service availability and the service suddenly becomes unavailable, Prometheus will send an alert to PagerDuty. PagerDuty will create a new incident and send a notification to the on-call engineer. At the same time, it will create an incident page with the incident details and current status.

Conclusion

Monitoring and tracking are critically important for the stable and reliable operation of a microservice architecture. In this article, we discussed several key components for building an effective monitoring system: Prometheus for collecting metrics, Grafana for data visualization, centralized logging with the ELK stack, and PagerDuty for incident management.