Interview Questions
Key factors include: 1) Number and complexity of tests, 2) Async operation handling, 3) Test setup/teardown overhead, 4) File I/O operations, 5) Database interactions, 6) Network requests, 7) Resource cleanup efficiency.
Measuring methods: 1) Use --reporter spec for timing info, 2) Implement custom reporters for timing, 3) Use console.time/timeEnd, 4) Track slow tests with --slow flag, 5) Monitor hook execution time.
Setup optimization: 1) Use beforeAll for one-time setup, 2) Minimize per-test setup, 3) Share setup when possible, 4) Cache test resources, 5) Use efficient data creation methods.
Identification methods: 1) Use --slow flag to mark slow tests, 2) Implement timing reporters, 3) Monitor test duration, 4) Profile test execution, 5) Track resource usage. Example: mocha --slow 75.
Hook impacts: 1) Setup/teardown overhead, 2) Resource allocation costs, 3) Database operation time, 4) File system operations, 5) Network request delays. Optimize hooks for better performance.
Parallelization benefits: 1) Reduced total execution time, 2) Better resource utilization, 3) Concurrent test execution, 4) Improved CI/CD pipeline speed, 5) Efficient test distribution.
Timeout considerations: 1) Default timeout settings, 2) Per-test timeouts, 3) Hook timeouts, 4) Async operation timing, 5) Timeout impact on test speed. Balance between reliability and speed.
Async optimization: 1) Use proper async patterns, 2) Avoid unnecessary waiting, 3) Implement efficient promises, 4) Handle concurrent operations, 5) Optimize async cleanup.
Mocking impacts: 1) Mock creation overhead, 2) Stub implementation efficiency, 3) Mock cleanup costs, 4) Memory usage, 5) Mock verification time. Balance between isolation and performance.
Data management impacts: 1) Data creation time, 2) Cleanup overhead, 3) Database operations, 4) Memory usage, 5) I/O operations. Optimize data handling for better performance.
Suite optimization: 1) Group related tests, 2) Implement efficient setup, 3) Optimize resource usage, 4) Use proper test isolation, 5) Implement caching strategies.
Database optimization: 1) Use transactions, 2) Batch operations, 3) Implement connection pooling, 4) Cache query results, 5) Minimize database calls.
I/O optimization: 1) Minimize file operations, 2) Use buffers efficiently, 3) Implement caching, 4) Batch file operations, 5) Use streams when appropriate.
Memory optimization: 1) Proper resource cleanup, 2) Minimize object creation, 3) Handle large datasets efficiently, 4) Implement garbage collection, 5) Monitor memory leaks.
Network optimization: 1) Mock network calls, 2) Cache responses, 3) Batch requests, 4) Implement request pooling, 5) Use efficient protocols.
Reporter optimization: 1) Use efficient output formats, 2) Minimize logging, 3) Implement async reporting, 4) Optimize data collection, 5) Handle large test suites.
Fixture optimization: 1) Implement fixture caching, 2) Minimize setup costs, 3) Share fixtures when possible, 4) Efficient cleanup, 5) Optimize data generation.
Hook optimization: 1) Minimize hook operations, 2) Share setup when possible, 3) Implement efficient cleanup, 4) Use appropriate hook types, 5) Optimize async hooks.
Assertion optimization: 1) Use efficient matchers, 2) Minimize assertion count, 3) Implement custom matchers, 4) Optimize async assertions, 5) Handle complex comparisons.
Profiling approaches: 1) Use Node.js profiler, 2) Implement custom profiling, 3) Monitor execution times, 4) Track resource usage, 5) Analyze bottlenecks.
Advanced parallelization: 1) Custom worker pools, 2) Load balancing, 3) Resource coordination, 4) State management, 5) Result aggregation.
Distributed optimization: 1) Service coordination, 2) Resource allocation, 3) Network optimization, 4) State synchronization, 5) Result collection.
Large suite optimization: 1) Test segmentation, 2) Resource management, 3) Execution planning, 4) Cache strategies, 5) Performance monitoring.
Custom monitoring: 1) Metric collection, 2) Performance analysis, 3) Resource tracking, 4) Alert systems, 5) Reporting tools.
Factory optimization: 1) Efficient data generation, 2) Caching strategies, 3) Resource management, 4) Memory optimization, 5) Cleanup procedures.
CI/CD optimization: 1) Pipeline optimization, 2) Resource allocation, 3) Cache utilization, 4) Parallel execution, 5) Result handling.
Runner optimization: 1) Custom runner implementation, 2) Execution optimization, 3) Resource management, 4) Result collection, 5) Performance tuning.
Benchmarking implementation: 1) Metric definition, 2) Measurement tools, 3) Analysis methods, 4) Comparison strategies, 5) Reporting systems.
Framework optimization: 1) Architecture improvements, 2) Resource efficiency, 3) Execution optimization, 4) Plugin management, 5) Performance tuning.