At Mattermost, we’re on a quest to scale our application by one order of magnitude, from tens of thousands to hundreds of thousands of concurrently active users per installation. Scaling up is a complex effort involving expertise at several different levels. At its core, it’s a game of catching the next bottleneck — whether it’s application CPU usage, memory consumption, database throughput, networking, or any combination of the above (among other causes as well).

When it comes to mainframe performance, MQ tuning is often one of the most underrated aspects. We’ve seen firsthand how it can make a significant difference in system performance. In one Project, a mainframe environment was struggling to keep up with the load. Applications were lagging, users were frustrated, and the hardware, despite being robust and well within capacity, wasn’t the issue. It turned out that the MQ systems weren’t configured optimally.

Deploying Kafka on Kubernetes can feel like a game-changer—mixing the powerful message streaming capabilities of Kafka with the flexible, scalable orchestration of Kubernetes. It sounds like a match made in heaven, right? Well, not so fast. While running Kafka on Kubernetes has some fantastic benefits, it also comes with its own set of challenges. Without careful planning, it’s easy to become entangled in a web of pods, StatefulSets, and persistent volumes.

Integrating MQ monitoring into a newly modernized mainframe environment isn’t something you can just wing. We’ve worked on projects where it seemed straightforward at first—just plug in some monitoring tools and you’re good to go, right? Not quite. The reality is, if you don’t approach this with a plan, you’ll find yourself tangled in a web of configuration headaches and performance hiccups.

When it comes to securing your Kafka deployment, Access Control Lists (ACLs) are some of the most powerful tools at your disposal. But let’s be honest—ACLs can be a bit daunting if you’re not familiar with them. We’ve all been there, staring at Kafka’s ACL configurations and wondering if we’re doing it right.

If you’ve ever worked with Apache Kafka, you know that it’s a powerful tool, but it can also be a bit finicky. Things can go wrong, and when they do, it’s important to know how to troubleshoot and resolve those issues quickly. Over the years, I’ve encountered my fair share of Kafka errors—some that had me scratching my head for days and others that were relatively straightforward once I knew what to look for.

You ever get that nagging feeling that maybe, just maybe, you’ve missed something crucial in a project? When it comes to deploying Apache Kafka, that “something” often turns out to be security. I’ve been there myself, thinking everything was running smoothly, only to realize later that I’d left the door wide open for potential security issues. Kafka is powerful, but it’s easy to overlook some key security measures if you’re not careful.

In today’s age of high-profile data breaches, supply chain attacks, and aging legacy software, smart organizations understand it’s no longer a matter of whether their systems will be compromised but when it will happen next. In fact, one recent study found that 68% of organizations experienced a cyberattack within the last 12 months (experiencing an attack doesn’t necessarily mean it was successful).

Kafka is a beast when it comes to handling real-time data streams, but like any powerful tool, it needs to be fine-tuned to really shine. I’ve spent more time than I’d like to admit tweaking Kafka configurations, trying to squeeze every last drop of performance out of it. Over time, I’ve picked up some tips that can make a big difference. So, whether you’re just getting started or looking to optimize an existing setup, here are the top 10 tips for tuning Kafka performance.