ChrisCruz.ai Backend Reliability
Backend Reliability March 16, 2025 • 7 min read

Diagnosing the Cuculi Backend Slowdown After the 1:30 PM Deployment

Two minutes after the 1:30 PM push, Cuculi's response times tripled and socket listeners started to lag. This is the full breakdown of where the regression most likely lives, what to inspect first, and how to get the Hapi stack breathing again.

CC
Christopher Manuel Cruz-Guzman

Deployments are supposed to be non-events. When the 1:30 PM drop lit up PagerDuty with latency warnings, we knew something slipped past review. The good news: the architecture already tells us where the weak spots are. Here's the play-by-play of how to triage it.

"If everything slows down at once, hunt for anything synchronous pretending to be asynchronous."

The Five Most Likely Culprits

1. Synchronous Background Tasks Freezing the Event Loop

Cuculi's background workers still run in-process with the Hapi server. Any loop that hits a blocking API, waits on file I/O, or crunches analytics synchronously will stall every request handler. Review new cron jobs, schedulers, or analytics runs introduced in the deployment and confirm they yield control with await or get pushed to a worker queue.

2. MongoDB Connection Pool Exhaustion

Atlas is capped at five concurrent connections right now. Any action that fans out reads, opens transactions, or streams aggregations without closing cursors will choke the pool. Check for new repository calls or unbounded list queries, then watch the connection chart in Atlas to confirm if you're pegged at five.

3. EventAggregator Overload

The EventListenersService can only absorb so many slow consumers before the queue backs up. If the deployment added cascading publishes or heavy listeners, you'll see CPU spikes and delayed socket broadcasts. Audit new subscriptions and watch for long-running callbacks that should be moved to background processors.

4. Inversify Container Misconfiguration

Incomplete DI bindings show up as retry storms and memory churn when the container tries to resolve dependencies on every request. Double-check any new services registered in the container for circular references, transient bindings that should be singletons, or missing @injectable() decorators.

5. Socket.io Connection Buildup

Unlimited socket sessions are great until idle clients never disconnect. If new handlers broadcast more frequently or leak references, memory and CPU will climb steadily. Inspect connection lifecycle hooks and confirm you're clearing intervals, listeners, and rooms on disconnect.

Where to Look in the Codebase

Start with the files that changed in the 1:30 PM deploy. The goal is to isolate anything that introduces synchronous work, expensive DB queries, or new DI registrations.

  • CronService & Scheduler: Confirm timers offload work to async tasks and clear their intervals cleanly.
  • EventListenersService: Review new listeners for loops, nested publishes, or blocking operations.
  • Repository Updates: Guard new queries with pagination, projections, and indexes.
  • StartupRunner.executeStartupActions(): Measure the boot path to ensure no startup hook is looping forever.
  • New Actions & Services: Verify every async function actually awaits its promises.

Immediate Debugging Checklist

1

Pull Live Metrics

Tail the server logs for event loop delays, memory spikes, and DB errors. Pair that with Atlas metrics to see connection counts and slow query logs in real time.

2

Diff the Deployment

Compare the 1:30 PM bundle against the previous release. Flag anything that added background jobs, heavy queries, or new event publishers.

3

Load Test Locally

Spin up the branch in staging, apply synthetic load, and watch for CPU pegging or connection exhaustion. If it reproduces, instrument the exact handler with profiling.

4

Throttle Event Volume

Temporarily mute non-critical event publishers or reduce socket broadcast intervals to keep the system breathable while you isolate the offender.

5

Validate Connection Hygiene

Run a script that opens and closes multiple Mongo connections, ensuring pool clients are released. Watch socket disconnect handlers to confirm they free every listener.

Stabilization Plan

Once you identify the root cause, lock in a fix that prevents regressions. Here’s the order of operations I recommend for hardening Cuculi after this incident:

  1. Move blocking background work into isolated worker processes or queues so Hapi stays dedicated to request handling.
  2. Raise the Mongo pool beyond five connections and add query-level timeouts to protect from runaway operations.
  3. Instrument EventAggregator with timing metrics and guardrails that flag slow listeners automatically.
  4. Audit DI registrations to ensure every service has explicit lifecycle scope and no circular dependencies.
  5. Set socket limits with heartbeat-based disconnects and memory leak detection in dev.

Ship the Postmortem

Document the timeline, root cause, and corrective actions within 24 hours. Share it with the team so future deploys get the guardrails they need. Incidents are tuition—capture the lesson while it’s fresh.

The 1:30 PM slowdown is a symptom, not the disease. With disciplined tracing, you’ll surface the offending task, query, or listener quickly. More importantly, you’ll reinforce Cuculi’s architecture so the next deploy is boring—in the best way possible.