Performance Tuning at Scale

As your traffic grows, small inefficiencies compound. Here's how to identify and fix performance bottlenecks on LykCloud.

Measure First ​

Before changing anything, establish a baseline:

lykcloud bench --duration 300s --concurrency 100 --output baseline.json

Focus on P95 and P99 latencies, not averages. Averages hide tail latency problems.

Database Queries ​

Slow queries are the most common bottleneck. Enable query logging:

lykcloud db enable-query-log --threshold 100ms

Common fixes:

• Add indexes on frequently queried columns
• Use connection pooling to reduce handshake overhead
• Consider read replicas for read-heavy workloads

Caching Strategy ​

Implement a layered caching approach:

• L1: In-memory cache (process-level)
• L2: Edge cache (CDN, 1-hour TTL)
• L3: Database query cache

Invalidate caches explicitly when data changes:

lykcloud cache invalidate --pattern "user:*"

Async Processing ​

Offload non-critical work to background queues:

lykcloud queues create email-queue --max-concurrency 10

This prevents request latency from being dominated by email sending, image processing, or report generation.

Conclusion ​

Performance tuning is iterative. Measure, change, measure again. The LykCloud observability stack gives you everything you need to keep latency low as you scale.