We’ve completed comprehensive load testing of our aviation data API to validate performance under high-traffic scenarios. Load testing is essential for understanding system limits and ensuring reliability when demand spikes.
What Is Load Testing?
Load testing simulates high traffic volumes to identify performance bottlenecks, capacity limits, and failure points before they impact real users. It’s a critical component of production readiness for any API service.
Types of Load Tests
- Baseline testing: Normal traffic patterns to establish performance benchmarks
- Stress testing: Pushing beyond expected limits to find breaking points
- Spike testing: Sudden traffic surges to test elasticity and recovery
- Endurance testing: Sustained high load to identify memory leaks and degradation
Our Test Methodology
We simulated various traffic scenarios:
- Normal load: 1,000 requests per second (typical usage)
- Peak load: 5,000 requests per second (anticipated growth)
- Extreme load: 10,000 requests per second (stress test)
- Sustained load: 24-hour test at 2x normal capacity
Each test measured:
- Response times (average, median, 95th percentile, 99th percentile)
- Error rates (both server errors and timeouts)
- Throughput (successful requests per second)
- Resource utilization (CPU, memory, GPU, database connections)
Results and Findings
Good News
- Scales well: Performance remains stable up to 7,000 req/s
- Fast response times: 95th percentile under 150ms at normal load
- Low error rates: Less than 0.1% errors under expected traffic
- Auto-scaling works: Infrastructure scales up/down appropriately
Areas for Improvement
- Database connections: Connection pool needs tuning for extreme loads
- Cache warming: Initial requests after scaling show higher latency
- GPU queuing: ML inference requests queue up beyond 8,000 req/s
Action Items
Based on test results, we’re implementing:
- Increased database connection pool size
- Improved cache pre-warming during scale-up events
- Additional GPU resources for inference workloads
- Enhanced monitoring to detect approaching capacity limits
Load testing gives us confidence that our API can handle growth while maintaining performance. We’ll continue periodic testing as our infrastructure evolves.
