⏺ Performance Test Analysis & Infrastructure Review
K6 Test Results Analysis
Smoke Test (1 VU, 30s):
- ✅ Success Rate: 100% (6/6 requests)
- ✅ Response Time: P95 = 4.62s (under 8s threshold)
- ❌ Throughput Issue: Only 0.194 RPS (below 1 RPS threshold)
- Average Response Time: 3.97s
Load Test (10 VUs, 2min) - Partial Results:
- Successfully created submissions (201 responses)
- Response times degrading under concurrent load
- Many requests taking 6-8+ seconds
Critical Performance Issues Identified
- Slow Response Times: 4-8 second response times indicate backend bottlenecks
- Low Throughput: Only ~0.2 RPS suggests capacity limitations
- Response Time Degradation: Performance worsens under load
Infrastructure Analysis
Assignment Service Configuration:
- HPA: 2-4 replicas, 80% memory threshold
- Thread Pool: 2-5 threads, WEB_CONCURRENCY=1
- Database: Shared PostgreSQL instance (34.82.48.175)
Evaluation Service Configuration:
- HPA: 2-4 replicas, 80% memory threshold
- Thread Pool: 2-15 threads, WEB_CONCURRENCY=0
- Higher Thread Count: Better configured for concurrent processing
Resource Balancing Recommendations
Immediate Actions:
- Assignment Service Thread Tuning: WEB_CONCURRENCY: "2" # Currently 1 RAILS_MAX_THREADS: "10" # Currently 5 GOOD_JOB_MAX_THREADS: "8" # Currently 5
- HPA Configuration Enhancement: metrics:
- type: Resource resource: name: cpu target: type: Utilization averageUtilization: 70
- type: Resource resource: name: memory target: type: Utilization averageUtilization: 80
- Database Connection Pool Optimization: RAILS_CHECKOUT_TIMEOUT: "10" # Currently 16s SIDEKIQ_CLIENT_POOL_SIZE: "50" # Currently 30 SIDEKIQ_SERVER_POOL_SIZE: "50" # Currently 30
Infrastructure Concerns:
- Single Database: All services use same PostgreSQL instance (34.82.48.175)
- Redis Configuration: Using IP instead of service discovery
- No CPU-based HPA: Only memory-based scaling configured
Suggested Architecture Improvements:
- Database Load Balancing: Consider read replicas
- Redis Cluster: Switch to Kubernetes service discovery
- Connection Pooling: Implement PgBouncer for database connections
- Monitoring: Add request rate and queue depth metrics to HPA
⏺ Summary: Your production API shows significant performance bottlenecks with 4-8 second response times and low throughput (0.2 RPS). The main issues are insufficient thread pool sizing in the assignment service and potential database connection constraints. I recommend increasing WEB_CONCURRENCY to 2, RAILS_MAX_THREADS to 10, adding CPU-based HPA scaling, and implementing database connection pooling to improve performance.