I'm trying to simplify the monitoring process for my website or server, and with so many metrics available, it can get overwhelming. I'm really interested in knowing which metrics are truly essential for catching real issues early on. If you had to narrow it down to just a few metrics that have genuinely helped you, what would those be? Also, I'm curious about what metrics you decided to stop tracking because they just added noise, and if there were any metrics that seemed critical but didn't actually provide much value. Ultimately, I want to focus on what matters most in a production environment without overcomplicating my monitoring setup.
5 Answers
The main metrics that helped me catch real issues were error rate, p95 latency, and basic uptime checks. I also keep an eye on sudden traffic drops or spikes. While CPU and memory are helpful, they’re more for context rather than primary alerts. I ended up ignoring overly granular metrics and most raw logs unless something was already broken because they just became noise. Simple alerts reflecting user experience proved to be the biggest help.
I keep it simple with just CPU, memory, disk space, and HTTP response codes. These are the essentials and provide a solid foundation for monitoring.
It really depends on what you’re running, but I’ve found that tracking response time (especially p95/p99, not just averages) and error rates is crucial. These metrics really show when users are facing issues. Uptime checks are basic, but they miss the ‘site is up but slow’ scenario.
I also pay attention to memory trends over time, as sudden spikes can indicate memory leaks or bad deployments. I stopped tracking individual CPU core usage and granular log metrics unless I was debugging something specific. And while ‘requests per second’ sounds impressive, it doesn't provide context by itself. Generally, a good system involves monitoring about 3-4 key dashboards along with alerting thresholds. If a metric isn’t checked regularly, it probably doesn’t need to be highlighted.
What type of application are you operating on?
I completely agree, especially with favoring p95/p99 over averages. The averages can be misleading when a segment of users is having a bad experience.
In my case, we usually start monitoring only after identifying a problem. When something is slow or malfunctioning, we check CPU usage, memory, and logs from our web servers and databases to figure out the issue.
That’s a good approach; it leans more towards reactive monitoring. Do you later turn those findings into alerts or dashboards, or is it usually a case-by-case basis?
For my projects, I monitor AWS costs and the total requests on Cloudflare. I frequently check Cloudflare's security analytics to understand traffic patterns and see how much is handled at Cloudflare versus my origin servers. For routine checks, that’s pretty much it; I look into other metrics as needed.
That’s an interesting perspective—focusing on traffic and cost rather than traditional monitoring. I can see how that would be beneficial with Cloudflare in the mix. Have you ever found that everything looked good from a traffic standpoint but users still had problems?
That makes a lot of sense, focusing on user impact is key. Do you prefer anomaly detection or fixed thresholds for those metrics?