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SUMMARY:Stop firefighting: practical observability for Python APIs\, worke
 rs\, and scheduled jobs - Daria Korsakova
DTSTART;TZID=Europe/Madrid:20261108T112000
DTEND;TZID=Europe/Madrid:20261108T120000
DTSTAMP:20260726T232930Z
UID:pretalx-pycones-2026-H3G9ZM@pretalx.com
DESCRIPTION:Production has a special talent for turning “seems fine” i
 nto “why is everything on fire?” — usually because we’re missing s
 ignals. A service restarts and never becomes ready\, a background worker s
 ilently stops consuming tasks\, or a database gets overloaded and latency 
 creeps up until downstream services (or customers) notice it first. These 
 situations aren’t unsolvable — they’re preventable with the right si
 gnals in place.\n\nI’ve seen how stressful this gets when a system is al
 ready in production\, but there’s no clear guidance or shared “where t
 o look first” playbook\; so every incident starts with guesswork. Over t
 ime\, we turned those lessons into a lightweight standard that replaces pa
 nic mode with a predictable investigation flow.\n\nIn this talk\, I’ll s
 hare a practical\, vendor-agnostic observability checklist for a Python se
 tup with three cooperating workloads: an HTTP API\, an event-driven worker
 \, and a scheduled daily job. Each workload fails differently\, so each re
 quires a different set of signals to stay observable.\n\nWe’ll cover wha
 t “good enough” looks like for logging\, metrics\, tracing\, and alert
 ing: what to instrument first\, what pitfalls to avoid\, and how to design
  alerts that catch problems early without creating noise. You’ll leave w
 ith a concrete checklist and a phased rollout order you can apply to your 
 own Python services — without rewriting your system or committing to a s
 pecific monitoring vendor.
LOCATION:Track 01 - Paraninfo Perk
URL:https://pretalx.com/pycones-2026/talk/H3G9ZM/
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