Performance Testing Interview Questions

Performance testing involves: 1) Measuring test execution speed, 2) Monitoring resource usage, 3) Identifying bottlenecks, 4) Optimizing test runs, 5) Tracking performance metrics. Important for maintaining efficient test suites.

Execution time measurement: 1) Use built-in reporters, 2) Implement custom timing, 3) Track individual test durations, 4) Monitor suite execution, 5) Use performance APIs. Example: console.time() or process.hrtime().

Common bottlenecks: 1) Slow test setup/teardown, 2) Inefficient assertions, 3) Synchronous operations, 4) Resource leaks, 5) Poor test isolation. Understanding helps optimization.

Slow test identification: 1) Use --slow flag, 2) Monitor execution times, 3) Implement timing reporters, 4) Track test duration, 5) Profile test execution. Example: mocha --slow 75.

Hook impact: 1) Setup/teardown overhead, 2) Resource allocation, 3) Asynchronous operations, 4) Database operations, 5) File system access. Optimize hooks for better performance.

Setup/teardown optimization: 1) Minimize operations, 2) Use efficient methods, 3) Share setup when possible, 4) Implement proper cleanup, 5) Cache resources. Reduces overhead.

Async/await impact: 1) Efficient async handling, 2) Reduced callback complexity, 3) Better error handling, 4) Improved readability, 5) Sequential execution control. Important for async operations.

Memory management: 1) Monitor memory usage, 2) Clean up resources, 3) Prevent memory leaks, 4) Optimize object creation, 5) Handle large datasets. Important for stability.

Parallelization strategies: 1) Use multiple processes, 2) Split test suites, 3) Balance test distribution, 4) Handle shared resources, 5) Manage concurrency. Improves execution speed.

Performance monitoring: 1) Track execution metrics, 2) Use profiling tools, 3) Monitor resource usage, 4) Collect timing data, 5) Analyze bottlenecks. Important for optimization.

Assertion optimization: 1) Use efficient matchers, 2) Minimize assertions, 3) Optimize complex checks, 4) Handle async assertions, 5) Implement custom matchers. Improves test speed.

Database optimization: 1) Use transactions, 2) Implement connection pooling, 3) Optimize queries, 4) Handle cleanup efficiently, 5) Cache database operations. Reduces database overhead.

I/O optimization: 1) Minimize file operations, 2) Use streams efficiently, 3) Implement caching, 4) Handle cleanup properly, 5) Optimize read/write operations. Reduces I/O overhead.

Network optimization: 1) Mock network calls, 2) Cache responses, 3) Minimize requests, 4) Handle timeouts efficiently, 5) Implement request pooling. Reduces network overhead.

Resource management: 1) Proper allocation, 2) Efficient cleanup, 3) Resource pooling, 4) Cache utilization, 5) Memory optimization. Important for test efficiency.

Benchmark implementation: 1) Define metrics, 2) Create baseline tests, 3) Measure performance, 4) Compare results, 5) Track trends. Important for monitoring improvements.

Concurrency testing: 1) Handle parallel execution, 2) Test race conditions, 3) Manage shared resources, 4) Verify thread safety, 5) Test synchronization. Important for parallel code.

Data optimization: 1) Efficient data creation, 2) Data reuse strategies, 3) Cleanup optimization, 4) Data caching, 5) Memory-efficient structures. Reduces data overhead.

Cache optimization: 1) Implement caching layers, 2) Optimize cache hits, 3) Handle cache invalidation, 4) Manage cache size, 5) Monitor cache performance. Improves test speed.

Performance profiling: 1) Use profiling tools, 2) Analyze bottlenecks, 3) Monitor resource usage, 4) Track execution paths, 5) Identify optimization opportunities. Guides improvements.

Advanced patterns: 1) Complex benchmarking, 2) Distributed testing, 3) Load simulation, 4) Performance analysis, 5) Advanced metrics. Enables comprehensive testing.

Distributed testing: 1) Coordinate test execution, 2) Aggregate results, 3) Handle network latency, 4) Manage resources, 5) Monitor system performance. Tests scalability.

Limit testing: 1) Test resource boundaries, 2) Verify system capacity, 3) Check performance degradation, 4) Monitor system stability, 5) Test recovery behavior. Tests system limits.

Load testing: 1) Simulate user load, 2) Monitor system response, 3) Test scalability, 4) Measure performance impact, 5) Analyze system behavior. Tests under load.

Stress testing: 1) Push system limits, 2) Test failure modes, 3) Verify recovery, 4) Monitor resource exhaustion, 5) Test system stability. Tests system resilience.

Endurance testing: 1) Long-running tests, 2) Monitor resource usage, 3) Check memory leaks, 4) Verify system stability, 5) Test sustained performance. Tests long-term behavior.

Spike testing: 1) Test sudden load increases, 2) Verify system response, 3) Check recovery time, 4) Monitor resource usage, 5) Test system stability. Tests burst handling.

Scalability testing: 1) Test system scaling, 2) Verify performance consistency, 3) Check resource utilization, 4) Monitor bottlenecks, 5) Test scaling limits. Tests growth capacity.

Volume testing: 1) Test data volume handling, 2) Verify system performance, 3) Check storage capacity, 4) Monitor processing speed, 5) Test data limits. Tests data handling.