Operations | Monitoring | ITSM | DevOps | Cloud

Log analysis is a very powerful feature for an application when it comes to debugging and finding out which flow is working properly in the application and which is not. In a world of containerization and cloud computing, it is essential to understand logs generated by a Docker environment to maintain healthy performing applications. In this article, we will discuss log analysis in Docker and how logging in Docker containers is different than in other applications.

Logs are useful for troubleshooting and identifying issues in applications, as they provide a record of events and activities. However, managing log data can be challenging due to the large volume of log events generated by modern applications, as well as the need to balance the level of detail in the logs and the impact on the application's performance.

I’m Romaric, the CEO and co-founder of Qovery. With 15 years of experience managing large-scale infrastructure and a passion for computer science, I’ve embarked on a journey to build an ambitious tech product designed to revolutionize the developer experience. At Qovery, we aim to simplify the path to production for developers, ensuring they can focus on writing code without worrying about the underlying infrastructure.

Troubleshooting within Kubernetes environments can be a daunting task. If we could only have a magical artificial intelligence advisor that could gather all the data about what goes on the system, and tell me what’s wrong, and even how to solve it. Wouldn’t it be nice? K8sGPT is a young open source project that uses generative AI to give Kubernetes superpowers to everyone. It recently turned a year old, and is now part of the Cloud Native Computing Foundation (CNCF).

If you use Qovery, you probably must manage your Environment Variables. It's one of the most used features! We've recently introduced several improvements to make this process more efficient and user-friendly. Here’s what you can expect from it.

In recent years, Kubernetes has emerged as the go-to solution for container orchestration, offering flexibility and scalability for deploying and managing applications. However, organizations quickly realize that the allure of its open-source nature can be deceiving—while free to download, the costs of managing Kubernetes can stack up rapidly. Initially embraced for its agility, Kubernetes soon reveals its complexity.

Observability in IT refers to the ability to measure a system's internal functioning by studying its signals from the outside. Modern IT observability is achieved through three kinds of telemetry: metrics, traces, and logs. Metrics aggregate events to gauge a system’s current state. Tracing tracks the progress of each transaction to not only measure performance but also debug the problem. On the other hand, logs record each event, which can help during troubleshooting.