Monitoring a monolith was easy, but now we have 20 services and no idea where requests are failing. When a user reports a 500 error, it's like finding a needle in a haystack. What tools are you using to track a single request as it travels through multiple services? We need something that combines logs, metrics, and traces into a single view.
3 answers
OpenTelemetry (OTel) is the absolute standard you should be looking at in 2024. It provides a vendor-neutral way to collect traces, metrics, and logs. For visualization, the combination of Grafana, Prometheus (for metrics), and Jaeger (for tracing) is incredibly powerful. The key is "Trace Propagation"—every service must pass a Trace ID in the HTTP headers. When you search for that ID in Jaeger, you see a waterfall chart of exactly how long each service took and where the error occurred. It completely removes the guesswork from debugging distributed systems.
Does implementing full distributed tracing add a lot of overhead to the request payload? I'm worried about the performance hit if we are attaching metadata to every single internal API call.
We recently moved to an ELK stack (Elasticsearch, Logstash, Kibana) for centralized logging, and it’s been a game changer for searching through millions of logs across our Kubernetes clusters.
ELK is great, Melissa, but make sure you integrate it with a tracing tool. Logs tell you what happened, but traces tell you where it happened in the chain of services.
Thomas, the header overhead is negligible (just a few bytes). The real cost is the data storage and processing. Most teams use "Sampling"—you don't trace 100% of requests. You might trace 1% of successful calls and 100% of errors. This gives you the visibility you need without blowing up your storage costs or impacting performance.