Logging is a crucial part of the observability of your Java applications. Combined with metrics and traces gives full observability into the application behavior and is invaluable when troubleshooting. Logs, combined with metrics shortens the time needed to find the root cause and allows for quick and efficient resolutions of problems.
Troubleshooting issues in your applications can be a complicated task requiring visibility into various components. In the worst-case scenario, to understand what is happening and why it is happening you will need metrics, logs, and traces combined together. Having that information will give you the possibility to slice and dice the data and get to the root cause efficiently. In this article, we will focus on logs and how to configure logging for your Java applications.
When it comes to troubleshooting application performance, the more information you have the better. Logs combined with metrics and traces give you full visibility into your Java applications. Logging in your Java applications can be achieved in multiple ways – for example, you can just write data to a file, but there are far better ways on how to do that, as we explained in our Java logging tutorial.
Monitoring systems help DevOps teams detect and solve performance issues faster. With Docker and Kubernetes steadily on the rise, it’s important to get container monitoring and log management right from the start. This is no easy feat. Monitoring Docker containers is very complex. Developing a strategy and building an appropriate monitoring system is not simple at all.
Knowing how your users interact with your web application and how they experience it is crucial to provide the best possible experience. So what do you need to know? Start with metrics such as page load times, HTTP request times, and core Web Vitals – time to the first byte, first contentful paint. If you use Sematext Experience you’ll see a number of other useful metrics for your web applications and websites there. However, metrics themselves are only a part of the whole picture.
If you’re a RabbitMQ user, chances are that you’ve seen queues growing beyond their normal size. This causes messages to get consumed long after they have been published. If you’re familiar with Kafka monitoring, you’ll call it consumer lag, but in RabbitMQ-land it’s often called queue length or queue depth.
Getting visibility into your application is crucial when running your code in production. What do we mean by visibility? Primarily things like application performance via metrics, application health, and availability, its logs should you need to troubleshoot it, or its traces if you need to figure out what makes it slow and how to make it faster. Metrics give you information about the performance of each of the elements of your infrastructure.
Don’t you hate it when you see an uncaughtException error pop up and crash your Node.js app? Yeah… I feel you. Can anything be worse? Oh yeah, sorry, unhandledRejection I didn’t see you there. What a nightmare you are. 😬 I maintain all Node.js open-source repos at Sematext. A few of them can help you out with error handling, but more about that further down. Here at Sematext, we take error handling seriously! I want to share a bit of that today.
APIs have become the de-facto standard in building and running modern applications. They are an integral part of the automation workflow of any business and as more users rely on your APIs to power their applications, the need for them to be reliable is important. Any degradation in their health, availability, and performance will impact your business, so ensuring its reliability depends on proactively monitoring your APIs.
Let’s say you get an alert that one or more queries is slow. Or that your users complain, whichever comes first 🙂 We’ve all been there… How do you find the root cause for this slowness and then fix it? In this article, I’ll go through my usual thought process: first, I’d try to find which queries are slow. Then, I’d dig deeper: Let’s take a specific example and run through each step.
