Centralized logging & log analytics in a busy, distributed environment

Logs play a crucial role in any system, providing valuable insights into your application’s behavior, system activities, and the causes of errors. It is essential to efficiently monitor these logs to ensure your system is functioning as expected. In case something goes wrong, you need to quickly identify the issue, understand what happened, and take preventive measures to avoid its recurrence.

If you’re operating in a distributed and scalable infrastructure like Kubernetes and haven’t given much attention to log management, it’s vital to address it as early as possible. Why, you ask?

Consider the lifecycle of Kubernetes pods. These pods are managed by the Kubernetes cluster and can be restarted or rescheduled at any time. Relying on ephemeral pod volumes to capture logs is not a reliable approach, as these volumes can be lost, and recovery becomes impossible.

Horizontal scaling is undoubtedly advantageous. However, monitoring each individual server separately becomes an anti-pattern. To effectively monitor logs regardless of scale and volume, it’s crucial to have a centralized location where all logs can be consolidated.

In an event-based architecture, logs play a pivotal role in understanding the state of your system. Without a mechanism to correlate these events, you may find yourself lost in a sea of data with no way to navigate.

Centralized logging addresses these concerns and more. However, designing a robust centralized logging system can be complex, requiring a deep understanding of all the moving parts. This article sheds light on the key aspects involved in centralizing logs in a distributed environment.

Numerous tools are available to assist in achieving this goal. In this article, we focus on the fluent ecosystem for log tailing, log analytics using Loki, and visualization with Grafana. Moreover, we highlight some common pitfalls that can save you hours of debugging and frustration.