Logging is essential for any type of software development, as it provides valuable insights into the behavior of an application. Moreover, it allows users to quickly troubleshoot errors or further optimize the software. Logging in a monolithic application is a relatively simple process, as all logs can be easily aggregated within the application. However, logging architectures have become increasingly complicated, with more and more software opting to move towards a more containerized microservices-based architecture. In this post, we will look at some best practices to follow when implementing logging for microservices.
Logging Complexity in Containerized Microservices
Before looking at best practices, we need to understand the reasons behind the logging complexity. Simply put, in a containerized environment, especially one managed via Kubernetes, containers that contain pods can be created and removed quickly depending on multiple scaling and availability requirements. These requirements include logging needs to account for all newly created containers and stop logging when a container is removed.
There will be multiple containers powering different aspects of the application since the application is broken down into multiple different services. These services will scale up and down independently, leading to the creation and destruction of new containers at a service level, which further complicates logging. An efficient logging architecture should be adapted since pod status changes constantly. It will enable collecting and aggregating logs from all these containers and any other resources such as DaemonSets and Kubernetes itself to provide a comprehensive logging solution.
Best Practices when Implementing Logging in Microservices based Architectures
Collect All Logs Into A Centralized Location
The most important factor when implementing logging in a microservices-based application is to centralize logging. As containers are inherently ephemeral, users will lose access to logs collected within the container when it is removed. Thus, the best approach would be to export all logs outside the container to a persistent storage location. Better yet, a dedicated service like ElasticSearch, AWS CloudWatch, and Azure Monitor will provide all the necessary tools to aggregate and manage logs from any number of services.
Centralized logging does not mean that users should just dump entire logs pertaining to the business logic of the services into a single location without any separation or filtration. Application logs must be separated by service and properly aggregated into individual log groups within the centralized logging location at a minimum.
Additionally, this centralized logging should not be limited to the application containers and include logs from all the other services and resources within the environment. For instance, it will include logs from networking services to orchestration platforms like Kubernetes itself. Users can easily troubleshoot and understand issues within the entire environment since all aspects of the application and the infrastructure are in a centralized location.
Services can generate thousands to millions of logs per day, depending on the load. Going through all these logs when an issue occurs is a near-impossible task. So, the best way to tackle this issue is to implement a method to differentiate between services and use a mechanism to identify the flow of a request.
One way to achieve this is to introduce an identifier to any request made by a client and pass that identifier to any services that are required to complete the request. It allows users to quickly track the flow of the request and drill down to issues and related services without having to go through all the collected logs. This identifier will allow users to easily locate logs in different log groups, even when logs for services are collected separately.
Configure Log Monitoring
Users should constantly monitor logs, even after configuring logging. It allows not only identifying the behavior of the environment but also understanding any issues that may arise, ensuring that the logging mechanisms themselves are working properly. Over time, there may be issues like logging agent failures, network bottlenecks, and storage limitations that can affect the log ingestion.
Application updates and updates to the underlying orchestration platform or infrastructure can also lead to interruptions or failures in log ingestion. Therefore, users must constantly monitor the overall logging across their environment to ensure that logs are collected and implement alerts to identify any issues. Some services like Azure Monitor provide built-in functionality to monitor the health of their logging services.
Configure Alerts and Visualizations
Continuing from the previous point, users need to proactively monitor the collected logs to better utilize them. The simplest method is to configure some automated alerts to notify the team when an anomaly is detected in a log. Better yet, utilize a visualization tool to monitor different types of logs. It will further simplify the log monitoring process and allow users to easily understand the collected data rather than looking at the raw text records.
Implementing the above-mentioned best practices regardless of the overall logging architecture, complexity, or tool in a microservices-based environment will enable users to create a resilient and accessible logging solution that can cater to any logging need.