Why Load Test Context Systems
Context management sits in critical AI request paths. Performance problems surface as degraded user experience or failed AI operations. Load testing reveals bottlenecks before users encounter them.
Test Design
Realistic Workloads
Model tests on actual access patterns. Analyze production traffic for read/write ratios, query complexity distribution, and concurrent user patterns. Synthetic workloads often miss real-world bottlenecks.
Ramp Patterns
Test both gradual ramp-up (normal scaling) and spike tests (viral events, batch operations). Different patterns stress different system aspects—connection pools, cache warm-up, database query planning.
Soak Tests
Extended duration tests reveal memory leaks, connection exhaustion, and gradual performance degradation. Run soak tests for hours or days mimicking production operation patterns.
Key Metrics
Track latency percentiles (p50, p95, p99), not just averages. Monitor throughput under load, error rates at various load levels, and resource utilization (CPU, memory, I/O) correlations with performance.
Bottleneck Analysis
When tests reveal problems, trace to root causes. Database query analysis, lock contention monitoring, and distributed tracing pinpoint bottlenecks. Address them systematically, starting with highest-impact issues.
