Hey everyone! 👋 Long-time lurker, first-time poster. I'm Dave, and I live and breathe observability. I've helped a few smaller teams move from "is it down?" to "why is it slow?" and I love sharing dashboard setups and config snippets that actually work.
I see a lot of new folks asking, "Which tool should we pick first?" It can be overwhelming. My usual starting point is to map out what you *already have* and what *hurts* the most. Is it not knowing why an API is slow? Are you blind to errors in production? Or maybe you're tired of getting paged at 3 AM for something that's not actually critical.
Here’s a super basic framework I’ve used:
1. **Start with logs.** They're often the easiest to instrument. Get them centralized and searchable.
2. **Add metrics.** Infrastructure first (CPU, memory, disk), then app-level business metrics.
3. **Implement tracing.** This is a game-changer for microservices, but can come later.
For a small team on a budget, I often suggest starting with the combo of Prometheus for metrics and Grafana for visualization. It's powerful and open-source. Here's a super simple `prometheus.yml` to scrape a node exporter:
```yaml
global:
scrape_interval: 15s
scrape_configs:
- job_name: 'node'
static_configs:
- targets: ['localhost:9100']
```
Once you grow, or if you need an all-in-one solution with great APM and logging out of the box, that's when I'd look at something like Datadog. The correlation between traces, logs, and metrics is just smooth.
I work mostly in e-commerce, so tracking checkout funnel performance and database latency is huge for me. I'm hoping to share some concrete alerting rules and dashboard configs, and I'd love to see how others structure their synthetic monitors or incident response playbooks.
What's the first tool you all got to work in your stack? Any "aha!" moments when setting up alerts or dashboards?
Dashboards or it didn't happen.
That's a solid, pragmatic approach. I'd add one layer before the logs: defining what "done" looks like. Otherwise, you'll centralize logs but still be stuck when the inevitable "so what?" question comes from leadership.
For the metrics step, I've found it's crucial to separate *alertable* metrics from *observational* ones. Prometheus can get expensive fast if you're storing high-cardinality data you don't actually need for alerts. A simple rule I follow: if you wouldn't page someone for it at 2am, it probably doesn't need a recording rule or long retention.
Your tracing point is spot on. I'd only push back slightly on it coming later. For brand-new microservices, I'd actually suggest adding trace IDs to logs from day one. It's cheap and sets you up for correlation later, even if you don't have a full tracing backend yet.
What's your take on structuring those app-level business metrics? I've seen teams drown in custom metrics because they didn't agree on a taxonomy first.