#opentelemetry

Embedded telemetry stores fail slowly, not loudly: WAL growth, read cliffs, retention, and backpressure, plus the settings that decide each one.

A single Go binary writing OTLP metrics into SQLite on a 2 vCPU, 8 GB box sustained 58k metric points a second. Here is the method, and where it stops.

Running your own observability stack is a real option with a real bill. An honest framework for choosing, from a vendor that only sells the managed one.

A collector buffers, redacts and samples telemetry between your apps and your backend. Here is a minimal OTLP config that forwards to TracePath.

An embedded row store ingests telemetry surprisingly well. The dashboard is what breaks first — and which query shape breaks tells you exactly why.

An honest map of the open-source observability landscape: what each stack is shaped like, what running it actually costs, and how to pick one.

A working loop: a production error opens a GitHub issue, an agent reads the telemetry behind it over MCP, and CI turns the result into a PR or a comment.