feat: Performance Optimization & Monitoring (Sprint 10)

[fregataa]

Overview

Optimize API performance, add caching, and implement application observability for the config-server.

Goals

• Optimize database queries and API performance
• Implement caching layer with Redis
• Add comprehensive application metrics
• Set up distributed tracing
• Establish performance benchmarks

Tasks

1. Performance Analysis

• Profile API endpoints with pprof
• Identify slow database queries
• Analyze memory usage
• Benchmark critical paths
• Document performance bottlenecks

2. Database Optimization

• Add missing database indexes
• Optimize N+1 query problems
• Implement query result caching
• Configure connection pooling
• Add query timeout settings

3. Caching Layer

• Set up Redis for caching
• Implement cache-aside pattern
• Cache frequently accessed data (groups, namespaces)
• Add cache invalidation logic
• Configure cache TTLs

4. API Optimization

• Implement response compression (gzip)
• Add rate limiting middleware
• Implement request timeout
• Add pagination for list endpoints
• Optimize JSON serialization

5. Application Metrics

• Implement Prometheus metrics endpoint
• Add HTTP request metrics (latency, status codes)
• Add database metrics (query time, connection pool)
• Add business metrics (targets, groups, tokens)
• Create Grafana dashboard for app metrics

6. Distributed Tracing

• Set up OpenTelemetry
• Add tracing to handlers
• Add tracing to services
• Add tracing to repository
• Configure Jaeger exporter

7. Structured Logging

• Replace fmt.Println with structured logger (zerolog/zap)
• Add request ID to all logs
• Add context to log entries
• Configure log levels

8. Load Testing

• Create load test scenarios (k6/vegeta)
• Test critical endpoints
• Measure performance improvements
• Document performance benchmarks
• Set performance SLOs

Deliverables

• Optimized database queries
• Redis caching layer
• Application metrics in Prometheus format
• Distributed tracing with Jaeger
• Structured logging
• Load testing results
• Performance benchmarks

Success Criteria

• API response time <100ms (p99)
• Database query time <50ms (p99)
• Cache hit rate >80%
• Load tests pass at 100 req/s
• Zero memory leaks

Performance Targets

Endpoint	Target
List targets	<50ms
Get single resource	<20ms
Create resource	<100ms
Service Discovery	<200ms

Notes

• Monitor production metrics after deployment
• Iterate on optimization based on real usage
• Balance performance with code simplicity