Performance testing in Serverless architecture needs to consider its unique characteristics such as cold starts, auto-scaling, and pay-as-you-go:
Performance testing focus:
1. Cold start testing
- First call latency: Measure the response time of the first function call
- Warm-up strategy validation: Verify the effectiveness of warm-up mechanisms
- Different runtime comparison: Compare cold start times of different languages
2. Concurrency performance testing
- Concurrent scaling capability: Test the function's scaling capability under high concurrency
- Resource limits: Verify account-level and function-level concurrency limits
- Performance bottlenecks: Identify performance and resource bottlenecks
3. Sustained load testing
- Long-running: Test the stability of functions under sustained load
- Memory leaks: Detect memory leak issues
- Resource recycling: Verify the recycling mechanism of function instances
Testing tools:
- Artillery: Supports HTTP load testing
- k6: Modern performance testing tool
- JMeter: Traditional performance testing tool
- Locust: Distributed load testing tool written in Python
Testing strategies:
- Benchmark testing: Establish performance baselines for comparing optimization results
- Stress testing: Test system performance under extreme loads
- Capacity planning: Perform capacity planning based on test results
- Continuous monitoring: Continuously monitor performance metrics in production environments
Key metrics:
- Response time (P50, P95, P99)
- Throughput (requests/second)
- Error rate
- Cold start time
- Resource utilization
Candidates should be able to share performance testing experiences and optimization results from actual projects.