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How to Detect Memory Leaks in Java

One of the most important features of Java is the built-in garbage collector (GC), which automates memory management. The GC is capable of handling the majority of memory leak issues because it implicitly handles memory allocation and freeing. While the GC is capable of handling a significant amount of memory, it does not provide a guaranteed solution to memory leaks. The GC is intelligent, but not without flaws. Even with the most attentive developer's applications, memory leaks can occur.

Getting Started With Docker Compose and Speedscale CLI

Observability, introspection, logging, and dependency mapping are critical when building APIs. With the advent of microservice architecture, understanding what happens inside your container is vital during development. Speedscale CLI is a container-centric tool that allows you to monitor inbound and outbound traffic. With Speedscale CLI, you can monitor raw requests, latency, encoding, and detected technologies.

How to Combat Python Memory Leaks

Memory leaks are one of developers’ worst nightmares. They can easily take down a healthy running application within hours if not minutes. It can be difficult to detect some of such leaks since they slowly grow and take over your app’s available memory. On top of it, each programming language manages memory in its own unique ways and hence can leak memory in different ways. Hence proactive measures to identify and prevent such leaks from happening is crucial.

7 Tools that Can Help Automate Repetitive Tasks

How often have you found been buried among piles of emails, expense reports, meeting invites, and other organizational tasks where a little part of you wished that this never-ending to-do list could organize itself on its own or even better, magically vanish? McKinsey reported that in an average workweek, 28% of an employee’s time goes into just responding to emails. Additionally, another 19% is spent on gathering information, while 14% of time is spent on communicative and collaborative tasks.

Deployment-time testing with Grafana k6 and Flagger

When it comes to building and deploying applications, one increasingly popular approach these days is to use microservices in Kubernetes. It provides an easy way to collaborate across organizational boundaries and is a great way to scale. However, it comes with many operational challenges. One big issue is that it’s difficult to test the microservices in real-life scenarios before letting production traffic reach them. But there are ways to get around it.