Every article tagged Observability, in one place.
Showing 6 articles
Monitoring tells you that something is wrong. Observability helps you understand why , including for problems you didn't predict. It's built from three kinds of telemetry. Metrics: numbers over time http_requests_total{route="/checkout", status="500"} 1243 htt...
console.log("user " + id + " failed") is fine on a laptop. In production you need logs that machines can search and filter: JSON with consistent fields, levels and request IDs. Basic setup import pino from 'pino'; export const logger = pino({ level: process.en...
You can measure thousands of things. These three well-known frameworks tell you which handful matter most. The four golden signals From Google's SRE book, for any user-facing system: Latency - how long requests take. Track successful and failed requests separa...
Apps can send telemetry straight to a vendor, but most production setups put an OpenTelemetry Collector in between. It receives data from many sources, processes it and exports it to one or more backends. Why use a Collector Apps only need to know one endpoint...
Plain text logs are hard to search at scale. Structured logs attach key-value fields to each message, so you can filter by user_id or status in your log platform. Go's standard library includes log/slog for this. Getting started import ( "log/slog" "os" ) func...
LLM features fail in new ways: answers get worse without any error, costs spike because a prompt grew, an agent loops through tools. Normal API monitoring doesn't catch these, so add LLM-specific telemetry. 1. Usage and cost const response = await client.messa...
