Operations | Monitoring | ITSM | DevOps | Cloud

Owning your server stack shouldn't be a source of anxiety. Unfortunately, it often is, especially if you only pay attention to the problems you can feel in your gut: Is the app running? Is it throwing exceptions? Does it seem fast enough? These are great intuitive measurements, but just as a doctor uses diagnostics to catch high blood pressure before it becomes a crisis, you need deeper visibility to detect memory leaks, CPU spikes, and disk consumption before they bring your project to a halt.

When my Sidekiq job starts failing or slowing down, I often feel frustrated, especially if I don’t know how to fix it. If you’re using Sidekiq to run your background jobs, you know what I’m talking about. It’s a vital element of your stack, handling everything from data exports to password reset requests. It runs silently in the background, and most of the time, you’re not even giving it a second thought.

Documentation search is often the last thing devs think about, until someone posts publicly that they couldn't find a basic answer, or your support queue fills up with things that are genuinely in the docs. We decided to get ahead of that. This is the story of how we went from a minimal keyword-only search on our docs to a conversational Ask AI experience.

In the wake of 2010s, PaaS felt like magic. You focused on the code, and the platform did the rest. You could ship a production app without knowing anything about networking or, heck, even what a load balancer is. Heroku in particular made deployment a lost thought, especially for early-stage companies. That era is somewhat over, not because platforms got worse overnight, but because the assumptions underneath them quietly stopped being true.

Most of us run multiple virtual private servers (VPS) at a time. That’s why it’s important to keep an eye on the CPU usage and memory. However, since this step often slips our minds, there is room for automated monitoring. Open-source tools tend to be a default choice, and for a good reason. The problem is that they don't provide everything you need for monitoring in a single place. As a result, you may find yourself writing custom shell scripts for automation.

Affordable hosting has always been a puzzle. Heroku made deploying Rails apps simple, but with Salesforce at the helm, active development has stalled. Many developers are left wondering what comes next, locked into a platform that is no longer moving forward. Chris, the founder of GoRails, felt that same frustration. That is why he built Hatchbox. Hatchbox handles your deployments, runs on servers you own, and keeps costs predictable. No dyno management, no add-on sprawl.

N+1 query problems are one of the most common, and quietly damaging, performance issues in production applications. One extra query per record feels harmless in development. At scale, it becomes the reason your response times degrade and your database buckles under load. Today, AppSignal adds N+1 detection to its OpenTelemetry support. When we identify the pattern in a trace, we collapse the repetitive spans directly in the timeline, making the problem immediately visible in the trace itself.

Slow endpoints are difficult to detect because they don’t fail. They simply get slower and slower. Average latency may look fine, but that can be misleading. That’s why we need to look at other values, like p90 and p95, which often reflect what’s really going on. For example, p90 represents the slowest 10% of requests, and p95 represents the slowest 5%. When these values increase, users start experiencing delays.

Monitoring in Kubernetes can seem like opening an airplane's black box. Everything happens silently, behind the scenes, hidden away. This can be a lot of trouble, as you don’t really want to dig through a bunch of logs at 3 a.m. after a call letting you know that a certain feature is broken. You want something direct, concise, and helpful.

You could have the most detailed metrics displayed on your dashboard, but if no one gets notified when things break, you’re just collecting data. Alerts help turn this passive monitoring into an active response. It’s like they tell you, “Hey, your error rate just spiked!” or “Your memory usage is through the roof,” even before your users start filing support tickets, or worse, give up on your tool entirely.