24 Hours of Autonomous VoxYZ: Critical Lessons from Production

We ran VoxYZ completely autonomously for 24 hours to test our operational readiness. Here's what broke, what worked, and what we're fixing.

What Worked Well

Auto-scaling Response

• Traffic spikes handled smoothly (3x normal load at peak)
• Kubernetes HPA kicked in within 90 seconds
• No user-facing latency degradation

Error Recovery

• Circuit breakers prevented cascade failures
• Dead letter queues caught 847 failed messages
• All were successfully reprocessed after system recovery

Monitoring Coverage

• 99.7% uptime detected correctly
• Alert fatigue remained low (12 alerts total)
• Mean time to detection: 2.3 minutes

Critical Issues Discovered

Memory Leak in Audio Processing

• Problem: Worker nodes consumed 40GB over 8 hours
• Root cause: Temporary audio files not cleaned up
• Fix: Added cleanup job every 30 minutes
• Impact: Zero user disruption due to auto-scaling

Database Connection Exhaustion

• Problem: Connection pool hit 95% capacity at hour 16
• Root cause: Long-running transactions during batch processing
• Fix: Reduced transaction timeout from 5min to 30sec
• Prevention: Added connection pool monitoring

API Rate Limiting Edge Case

• Problem: Third-party TTS service returned 429s for 90 minutes
• Root cause: Our retry logic didn't implement exponential backoff
• Fix: Added jittered exponential backoff (2^n + random(0,1000)ms)
• Result: Retry storms eliminated

Monitoring Blind Spots

Missing Metrics

• Queue depth per priority level
• Audio processing latency by file size
• Third-party service response time percentiles

Alert Improvements Needed

• Memory growth rate (not just absolute usage)
• Database query duration trending
• External dependency health checks

Resource Usage Patterns

CPU

• Average: 45% across all nodes
• Peak: 78% during morning traffic spike (8-10 AM)
• Recommendation: Add 2 more nodes for comfort margin

Storage

• Audio cache grew to 120GB (cleared every 6 hours)
• Database size increased 0.3GB
• Log retention needs adjustment (currently 7 days)

Operational Improvements Implemented

Immediate Changes

1. Extended connection pool from 20 to 50 connections
2. Added memory usage alerts at 70% threshold
3. Implemented graceful shutdown for audio workers

This Week

1. Deploy exponential backoff for all external APIs
2. Add queue depth monitoring dashboard
3. Create runbook for memory leak scenarios

Next Sprint

1. Implement predictive scaling based on time patterns
2. Add chaos engineering tests for database failures
3. Build automated rollback triggers

Key Takeaways

• Monitoring gaps appear under sustained load: Our dashboards looked great during normal operation but missed gradual resource exhaustion
• External dependencies need defensive coding: Rate limits and timeouts will happen; plan for them
• Auto-scaling bought us time: Issues that could have caused outages became gradual degradations instead
• Documentation matters: Having clear runbooks would have reduced investigation time by ~60%

Next autonomous test: 72 hours, with chaos engineering enabled.