The DAG You Forgot

P1hard7 minIncident Response

It's 11:20. Finance flagged a 5× jump on the orchestration cluster. SRE sees the scheduler CPU flatlined at 100% and DAG parse times north of 40 seconds — new tasks now schedule minutes late. Nobody remembers writing anything expensive, and the last deploy was three weeks ago. Two different things are quietly compounding at once, and you have to name both.

Cluster cost
4,200 → 820 $/day
DAG parse loop
47 → 2 s

The incident

The orchestration cluster's cost is up 5× and the scheduler is CPU-bound, adding minutes of scheduling latency. There was no recent deploy. This is not one regression — it's two independent problems (a parse-time CPU tax and a task-row explosion) that add up to one bill and one latency symptom. Fixing only one leaves the other running.

Symptoms on the table

  • Scheduler CPU pinned ~100%; dag_processing loop 40-90s (was ~2s)
  • metadata DB task_instance table +190M rows in 30 days; DB CPU high
  • Cluster cost 5× — mostly worker-hours + DB, not object storage
  • A vendor_sync DAG runs hourly, owned by a team that offboarded last month
  • No deploy in 3 weeks, no config change in the audit log

Systems on the board

The real components in play for this incident — the surface you investigate when the clock starts.

reporting_dag
top-level code
DagFileProcessor
parse loop
Scheduler
scheduling loop
vendor_sync DAG
hourly · unowned
dynamic_mapper
.expand() fan-out
metadata DB
task_instance table
workers
Celery fleet
git-sync
DAG deploy path

What you'll practice

This is a timed, hands-on incident in the Incident Response. You diagnose the symptom, trace it to a root cause across real components, and ship a fix before the clock runs out — the same loop you run on call, without the production blast radius.

Members-only challenge

Solve it in the Simulation Arcade.

The interactive workspace — live metrics, the component map, and the fix you ship — runs inside Petascale Labs. Sign in to start the clock.

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