Real-Time K6 Interview Questions [2025]

K6 Core Concepts

1. What is k6 and its main purpose in performance testing?

k6 is an open-source load testing tool designed for developers to test the performance and reliability of applications. It uses JavaScript for scripting and focuses on API and browser testing. Main purposes include:

• Simulating virtual users for load scenarios.
• Measuring response times and error rates.
• Integrating with CI/CD pipelines for automation.
• Generating real-time metrics for analysis.
• Supporting stress and soak testing.
• Exporting data to Grafana for visualization.
• Handling large-scale tests efficiently.

k6 simplifies modern performance validation.

2. Why choose k6 over traditional load testing tools?

k6 stands out for its developer-friendly approach, using JavaScript for scripts and CLI for execution. It offers lightweight resource usage and cloud integration, reducing setup time by 50%. Unlike GUI-heavy tools, k6 emphasizes code-based testing, aligning with DevOps for faster feedback loops and scalable workloads.

3. When should you use k6 for load testing?

Use k6 for load testing when:

• Simulating API endpoints under traffic.
• Integrating tests into CI/CD workflows.
• Testing microservices scalability.
• Analyzing real-time metrics.
• Running distributed cloud tests.
• Versioning scripts in Git.
• Monitoring with Grafana.

This ensures efficient validation.

4. Where does k6 typically execute tests?

k6 executes tests in:

• Local machines for development.
• CI/CD environments like Jenkins.
• Cloud platforms for distributed load.
• Docker containers for isolation.
• GitHub Actions workflows.
• Monitored servers.
• Hybrid setups with Grafana.

This provides flexible deployment.

5. Who uses k6 in a DevOps team?

Developers and QA engineers use k6 in DevOps teams. They:

• Write JavaScript scripts for scenarios.
• Integrate with CI/CD pipelines.
• Analyze metrics for optimizations.
• Test browser interactions.
• Collaborate on thresholds.
• Version tests in Git.
• Troubleshoot failures.

This fosters team collaboration.

6. Which scripting language supports k6 tests?

k6 uses JavaScript for scripting, offering:

• ES6+ features for modern syntax.
• HTTP module for requests.
• Support for async functions.
• Integration with libraries.
• Versioning in Git repositories.
• Debugging with console logs.
• Scalability for complex scenarios.

JavaScript enables expressive tests.

7. How does k6 simulate virtual users?

k6 simulates virtual users (VUs) by executing JavaScript functions concurrently. Each VU runs the default function, mimicking real behavior. Configuration in options sets VU count and duration. Example: javascript export const options = { vus: 50, duration: '30s', }; export default function () { http.get('https://test-api.k6.io'); } This, with scenarios, models realistic loads.

Test Scripting

8. What is the default function in k6 scripts?

The default function defines VU behavior, executing repeatedly during tests. It contains HTTP requests, checks, and sleeps for realism. Key aspects include:

• Running for each iteration.
• Supporting async operations.
• Integrating with modules.
• Handling errors gracefully.
• Versioning in Git.
• Monitoring execution time.
• Scaling with VU count.

This drives test logic.

9. Why use modules in k6 scripting?

Modules extend k6 with HTTP, WebSocket, and browser capabilities. They promote reusable code, reduce duplication, and support custom metrics. Importing modules like 'k6/http' simplifies requests, enhancing script maintainability and performance analysis.

10. When should you use checks in k6 scripts?

Use checks when:

• Verifying response status codes.
• Validating JSON payloads.
• Ensuring thresholds pass.
• Integrating with scenarios.
• Versioning checks in Git.
• Monitoring pass rates.
• Troubleshooting failures.

This validates test outcomes.

11. Where are k6 script options defined?

k6 script options are defined in:

• The exported options object.
• CLI flags for overrides.
• JSON config files.
• CI/CD environment variables.
• Git-versioned scripts.
• Cloud test configurations.
• Monitored dashboards.

This controls test execution.

12. Who writes k6 test scripts?

Developers and performance engineers write scripts. They:

• Define scenarios and VUs.
• Implement HTTP requests.
• Add checks and thresholds.
• Test in local environments.
• Version in Git repositories.
• Integrate with CI/CD.
• Optimize for realism.

This ensures accurate testing.

13. Which module handles HTTP requests in k6?

The http module handles requests by:

• Supporting GET, POST, PUT.
• Handling headers and bodies.
• Integrating with checks.
• Supporting authentication.
• Versioning in Git.
• Monitoring response times.
• Scaling for high loads.

This enables API testing.

14. How do you parameterize data in k6 scripts?

Parameterize data by:

• Using arrays for iteration.
• Generating random values.
• Reading from CSV files.
• Integrating with environment variables.
• Testing in staging.
• Versioning data in Git.
• Ensuring realism.

This simulates diverse users.

15. What are common scripting errors in k6?

Common scripting errors include syntax issues and missing imports. Async mishandling causes race conditions. Mitigation involves debugging with logs and tests. In pipelines, linting prevents errors, ensuring reliable execution.

Scenarios and Executors

16. What are scenarios in k6?

Scenarios define test phases, controlling VU execution and timing. They model traffic patterns like ramp-up. Key elements include:

• Executor types for load models.
• Start times for sequencing.
• Duration and VU limits.
• Integration with thresholds.
• Versioning in Git.
• Monitoring scenario metrics.
• Supporting parallel runs.

Scenarios create realistic simulations.

17. Why use multiple scenarios in a test?

Multiple scenarios simulate varied traffic, like peak hours and baselines. They allow independent configuration, improving test coverage. This identifies bottlenecks under different loads, enhancing analysis accuracy.

18. When should you use ramping-vus executor?

Use ramping-vus when:

• Gradually increasing load.
• Simulating user ramp-up.
• Testing system warm-up.
• Integrating with CI/CD.
• Versioning configs in Git.
• Monitoring ramp metrics.
• Avoiding sudden spikes.

This models growth.

19. Where are executor options configured?

Executor options are configured in:

• Scenarios object in scripts.
• CLI overrides for flexibility.
• JSON config files.
• CI/CD environment variables.
• Git-versioned setups.
• Cloud test definitions.
• Monitored dashboards.

This customizes load.

20. Who defines test scenarios?

Performance engineers define scenarios. They:

• Model user behaviors.
• Set VU and duration limits.
• Integrate with thresholds.
• Test in local environments.
• Version in Git repositories.
• Analyze coverage.
• Optimize for realism.

This creates effective tests.

21. Which executor suits constant load?

Constant-vus suits constant load by:

• Maintaining fixed VU count.
• Running for specified duration.
• Supporting steady-state testing.
• Integrating with checks.
• Versioning in Git.
• Monitoring steady metrics.
• Scaling gradually.

This tests endurance.

22. How do you sequence scenarios?

Sequence scenarios by:

• Using startTime offsets.
• Defining durations to avoid overlap.
• Testing in staging runs.
• Monitoring transitions.
• Versioning in Git.
• Integrating with thresholds.
• Handling graceful stops.

This simulates phased traffic.

23. What is the per-vu-iterations executor?

The per-vu-iterations executor assigns fixed iterations per VU, ensuring even distribution. It suits controlled testing, with:

• Predefined iteration counts.
• Support for VU scaling.
• Integration with checks.
• Versioning in Git.
• Monitoring iteration progress.
• Avoiding overload.
• Customizing per VU logic.

This provides predictable loads.

24. Why use graceful stop in scenarios?

Graceful stop allows VUs to complete iterations, preventing abrupt ends. It ensures accurate metrics, reduces errors, and aligns with production-like shutdowns for reliable test results.

25. When to apply startTime in scenarios?

Apply startTime when:

• Sequencing traffic phases.
• Simulating ramp-up delays.
• Testing concurrent scenarios.
• Integrating with CI/CD.
• Versioning timings in Git.
• Monitoring phase overlaps.
• Avoiding resource spikes.

This models timed events.

26. Where are scenario tags used?

Scenario tags are used in:

• Metric filtering for analysis.
• Threshold definitions.
• Grafana dashboard queries.
• CI/CD reporting.
• Git-versioned scripts.
• Cloud test configurations.
• Observability tools.

This organizes results.

27. Who configures scenario executors?

Performance engineers configure executors. They:

• Select based on load models.
• Set VU and duration parameters.
• Test configurations locally.
• Integrate with thresholds.
• Version in Git repositories.
• Monitor execution patterns.
• Optimize for realism.

This tailors test loads.

28. Which executor models open workloads?

Ramping-arrival-rate models open workloads by:

• Controlling request rates.
• Simulating constant arrivals.
• Supporting rate increases.
• Integrating with checks.
• Versioning rates in Git.
• Monitoring arrival metrics.
• Handling bursts.

This tests throughput.

29. How do you combine executors in scenarios?

Combine executors by:

• Defining multiple scenarios.
• Setting different startTimes.
• Balancing VU allocations.
• Testing in staging runs.
• Monitoring combined metrics.
• Versioning in Git.
• Adjusting thresholds.

This creates hybrid loads.

30. What are the steps to define a multi-scenario test?

Defining a multi-scenario test models complex traffic. Steps include planning phases, configuring executors, and validating results for comprehensive coverage.

Identify traffic patterns. Define scenarios with executors. Set VU counts and durations. Add startTime for sequencing. Implement checks and thresholds. Test locally. Analyze with Grafana.

Thresholds and Metrics

31. What are thresholds in k6?

Thresholds define pass/fail criteria for metrics, automating test outcomes. They use expressions like rate<0.01 for errors. Key uses include:

• Validating response times.
• Checking error rates.
• Integrating with CI/CD.
• Versioning in Git.
• Monitoring in Grafana.
• Supporting SLOs.
• Alerting on failures.

Thresholds ensure quality gates.

32. Why set thresholds for response times?

Setting thresholds for response times ensures applications meet SLAs, identifying bottlenecks early. Expressions like p(95)<500ms validate percentiles, reducing user impact and aligning with performance goals.

33. When should you use rate thresholds?

Use rate thresholds when:

• Monitoring error rates.
• Validating success percentages.
• Integrating with CI/CD.
• Versioning expressions in Git.
• Alerting on spikes.
• Testing under load.
• Analyzing trends.

This detects anomalies.

34. Where are threshold results reported?

Threshold results are reported in:

• Console output during runs.
• Grafana dashboards.
• JSON exports for analysis.
• CI/CD logs.
• Git-versioned reports.
• Cloud test summaries.
• Observability platforms.

This facilitates review.

35. Who defines k6 thresholds?

Performance engineers define thresholds. They:

• Base on SLOs and SLAs.
• Test expressions locally.
• Integrate with scenarios.
• Monitor pass rates.
• Version in Git repositories.
• Adjust for environments.
• Collaborate on goals.

This sets quality standards.

36. Which metric is key for throughput?

http_reqs measures throughput by:

• Counting completed requests.
• Tracking rates per second.
• Integrating with thresholds.
• Versioning in Git.
• Monitoring in Grafana.
• Scaling with VUs.
• Analyzing trends.

This gauges capacity.

37. How do you customize metrics in k6?

Customize metrics by:

• Using add() for custom gauges.
• Grouping with tags.
• Integrating with checks.
• Testing in staging.
• Versioning in Git.
• Exporting to InfluxDB.
• Visualizing in Grafana.

This tracks specific KPIs.

38. What is http_req_duration?

http_req_duration tracks request-response time, with:

• Percentiles like p95.
• Integration with thresholds.
• Support for breakdowns.
• Versioning in Git.
• Monitoring in real-time.
• Scaling for analysis.
• Alerting on spikes.

This measures latency.

39. Why monitor http_req_failed?

Monitoring http_req_failed detects reliability issues, ensuring high success rates. It alerts on spikes, integrates with CI/CD, and supports troubleshooting for stable performance.

40. When to use custom trends?

Use custom trends when:

• Tracking business metrics.
• Analyzing user journeys.
• Integrating with thresholds.
• Versioning in Git.
• Monitoring in Grafana.
• Scaling for scenarios.
• Troubleshooting anomalies.

This captures domain-specific data.

41. Where are k6 metrics exported?

k6 metrics are exported to:

• InfluxDB for time-series.
• Grafana for visualization.
• JSON files for analysis.
• CI/CD logs.
• Git-versioned exports.
• Cloud platforms.
• Observability tools.

This enables sharing.

42. Who analyzes k6 metrics?

Performance analysts analyze metrics. They:

• Review percentiles and rates.
• Identify bottlenecks.
• Integrate with dashboards.
• Test optimizations.
• Version reports in Git.
• Collaborate on improvements.
• Ensure SLO compliance.

This drives insights.

43. Which metric indicates system stability?

vus_active indicates stability by:

• Tracking running VUs.
• Alerting on drops.
• Integrating with scenarios.
• Versioning in Git.
• Monitoring in real-time.
• Scaling for loads.
• Analyzing trends.

This gauges endurance.

44. How do you set percentile thresholds?

Set percentile thresholds by:

• Using p(95)<500 in options.
• Defining for http_req_duration.
• Testing in staging runs.
• Monitoring pass rates.
• Versioning in Git.
• Adjusting for SLAs.
• Integrating with alerts.

This enforces SLAs.

45. What is iteration_duration?

iteration_duration measures VU iteration time, with:

• Support for custom thresholds.
• Integration with scenarios.
• Versioning in Git.
• Monitoring for bottlenecks.
• Scaling for analysis.
• Alerting on spikes.
• Visualizing in Grafana.

This tracks efficiency.

46. Why use groups in k6?

Groups organize script sections for:

• Logical metric grouping.
• Easier result filtering.
• Integration with thresholds.
• Versioning in Git.
• Monitoring nested behaviors.
• Supporting complex scenarios.
• Analyzing user flows.

This improves readability.

47. When to define custom metrics?

Define custom metrics when:

• Tracking business KPIs.
• Analyzing custom logic.
• Integrating with checks.
• Versioning in Git.
• Monitoring in Grafana.
• Scaling for scenarios.
• Troubleshooting issues.

This captures unique data.

48. Where are k6 thresholds evaluated?

k6 thresholds are evaluated:

• At test end for pass/fail.
• During runs for real-time alerts.
• In CI/CD for automation.
• Git-versioned configs.
• Cloud test summaries.
• Observability platforms.
• Dashboard visualizations.

This provides outcomes.

49. Who sets up k6 metric exports?

DevOps engineers set up exports. They:

• Configure InfluxDB connections.
• Integrate with Grafana.
• Test exports in staging.
• Monitor data flow.
• Version configs in Git.
• Ensure compliance.
• Troubleshoot issues.

This enables analysis.

50. Which threshold expression checks error rates?

http_req_failed['rate']<0.01 checks error rates by:

• Setting acceptable failure thresholds.
• Integrating with scenarios.
• Versioning in Git.
• Monitoring in real-time.
• Alerting on breaches.
• Scaling for loads.
• Analyzing trends.

This ensures reliability.

Integration and CI/CD

51. Why integrate k6 with CI/CD?

Integrating k6 with CI/CD automates performance checks, preventing regressions. It runs tests on commits, enforces thresholds, and reports to dashboards, aligning with DevOps for continuous validation and faster releases.

52. When should k6 tests run in pipelines?

Run k6 tests in pipelines when:

• Validating pull requests.
• Checking nightly builds.
• Enforcing pre-deploy gates.
• Integrating with GitHub Actions.
• Versioning tests in Git.
• Monitoring pipeline metrics.
• Troubleshooting failures.

This catches issues early.

53. Where are k6 tests stored in CI/CD?

k6 tests are stored in:

• Git repositories for versioning.
• Pipeline YAML configs.
• Container images.
• Cloud storage for results.
• Monitored dashboards.
• Artifact repositories.
• Integrated tools.

This ensures traceability.

54. Who configures k6 in CI/CD?

DevOps engineers configure k6. They:

• Set up pipeline stages.
• Define thresholds for gates.
• Test integrations locally.
• Monitor run times.
• Version configs in Git.
• Handle failures.
• Optimize for speed.

This automates validation.

55. Which CI tool pairs well with k6?

GitHub Actions pairs well with k6 by:

• Running tests on workflows.
• Supporting matrix strategies.
• Integrating with artifacts.
• Versioning in Git.
• Monitoring with badges.
• Scaling for loads.
• Alerting on failures.

This simplifies automation.

56. How do you run k6 in GitHub Actions?

Run k6 in GitHub Actions by:

• Using actions/setup-k6.
• Defining steps for scripts.
• Setting up thresholds.
• Publishing results.
• Testing in forks.
• Versioning workflows in Git.
• Handling secrets.

This enables CI testing.

57. What are the steps to integrate k6 with Jenkins?

Integrating k6 with Jenkins automates performance gates. Steps include installing plugins, defining pipelines, and reporting results for continuous validation.

Install k6 binary on agents. Create Jenkinsfile with k6 run stage. Set thresholds for pass/fail. Publish JUnit reports. Monitor with plugins. Version pipelines in Git.

58. Why use k6 cloud execution?

k6 cloud execution scales tests globally, distributing load without local resources. It provides dashboards, collaboration, and integrations, reducing setup by 40%. For compliance, it ensures auditable runs.

Browser and Advanced Testing

59. What is k6 browser testing?

k6 browser testing simulates real browsers using Chromium, capturing frontend metrics. It tests interactions like clicks and navigation, with:

• Support for Playwright-like APIs.
• Real-time screenshots.
• Integration with load tests.
• Versioning in Git.
• Monitoring Web Vitals.
• Scaling for hybrid scenarios.
• Debugging timelines.

This complements API testing.

60. Why combine browser and API testing?

Combining browser and API testing provides end-to-end visibility, capturing frontend and backend performance. It identifies bottlenecks across layers, ensuring holistic optimization for user experience.

61. When to use k6 for WebSocket testing?

Use k6 for WebSocket testing when:

• Validating real-time connections.
• Simulating chat applications.
• Testing message throughput.
• Integrating with scenarios.
• Versioning in Git.
• Monitoring latency.
• Handling disconnections.

This tests interactive features.

62. Where are WebSocket scripts defined?

WebSocket scripts are defined in:

• JavaScript default functions.
• ws module imports.
• Scenario configurations.
• Git repositories.
• CI/CD pipelines.
• Cloud test setups.
• Monitored environments.

This enables real-time validation.

63. Who implements WebSocket tests?

Full-stack developers implement WebSocket tests. They:

• Write connection logic.
• Simulate message exchanges.
• Test error handling.
• Integrate with thresholds.
• Version in Git.
• Monitor connection metrics.
• Troubleshoot drops.

This ensures functionality.

64. Which module supports WebSocket in k6?

k6/ws supports WebSocket by:

• Establishing connections.
• Sending binary/text messages.
• Handling events.
• Integrating with checks.
• Versioning in Git.
• Monitoring latency.
• Scaling connections.

This tests real-time apps.

65. How do you test browser interactions?

Test browser interactions by:

• Using experimental.browser module.
• Launching Chromium pages.
• Simulating clicks and navigation.
• Capturing screenshots.
• Versioning scripts in Git.
• Monitoring Web Vitals.
• Integrating with load tests.

This validates frontend performance.

66. What are Web Vitals in k6?

Web Vitals measure user-centric performance, including LCP, FID, CLS. k6 captures them during browser tests, with:

• Real-time metric collection.
• Threshold integration.
• Versioning in Git.
• Monitoring in Grafana.
• Scaling for scenarios.
• Alerting on degradation.
• Analysis for optimization.

This focuses on experience.

67. Why use k6 for spike testing?

k6 for spike testing simulates sudden traffic surges, identifying breaking points. It uses ramping executors for quick VU increases, ensuring systems handle peaks without crashing.

68. When to perform soak testing with k6?

Perform soak testing when:

• Validating long-duration stability.
• Detecting memory leaks.
• Testing endurance under load.
• Integrating with CI/CD.
• Versioning in Git.
• Monitoring trends.
• Setting durations.

This uncovers gradual issues.

69. Where are soak test results analyzed?

Soak test results are analyzed in:

• Grafana dashboards.
• InfluxDB time-series.
• JSON exports.
• CI/CD reports.
• Git-versioned summaries.
• Cloud platforms.
• Observability tools.

This reveals patterns.

70. Who runs soak tests?

QA engineers run soak tests. They:

• Configure long durations.
• Monitor resource usage.
• Analyze trends.
• Test in staging.
• Version configs in Git.
• Integrate with alerts.
• Report findings.

This ensures longevity.

71. Which executor fits spike testing?

Ramping-vus fits spike testing by:

• Quick VU increases.
• Simulating sudden loads.
• Supporting short durations.
• Integrating with thresholds.
• Versioning in Git.
• Monitoring peaks.
• Handling bursts.

This tests resilience.

72. How do you simulate real-time data in scripts?

Simulate real-time data by:

• Using random functions.
• Reading from CSV.
• Generating UUIDs.
• Integrating with APIs.
• Testing in staging.
• Versioning data in Git.
• Ensuring variability.

This, with network simulation, mimics users.

Troubleshooting and Best Practices

73. Why follow k6 scripting best practices?

Following best practices ensures maintainable, efficient scripts. They promote modularity, error handling, and realism, reducing debugging time by 30%. Integration with GitOps supports collaboration and version control.

74. When to use environment variables in scripts?

Use environment variables when:

• Parameterizing endpoints.
• Handling secrets securely.
• Configuring thresholds.
• Integrating with CI/CD.
• Versioning in Git.
• Testing environments.
• Avoiding hardcoding.

This enhances flexibility.

75. Where do you debug k6 scripts?

Debug k6 scripts in:

• Local runs with --verbose.
• VS Code extensions.
• Console.log outputs.
• CI/CD logs.
• Git-versioned branches.
• Cloud debug modes.
• Monitored environments.

This identifies issues.

76. Who troubleshoots k6 test failures?

Performance engineers troubleshoot failures. They:

• Analyze logs and metrics.
• Check thresholds.
• Test isolated scenarios.
• Integrate with dashboards.
• Version fixes in Git.
• Collaborate on optimizations.
• Prevent recurrences.

This resolves problems.

77. Which practice avoids script flakiness?

Using fixed seeds for random avoids flakiness by:

• Reproducible randomness.
• Consistent test runs.
• Integration with checks.
• Versioning seeds in Git.
• Monitoring variances.
• Scaling reliably.
• Troubleshooting consistently.

This ensures repeatability.

78. How do you handle errors in k6 scripts?

Handle errors by:

• Using try-catch blocks.
• Adding custom metrics.
• Integrating with thresholds.
• Testing error paths.
• Versioning in Git.
• Monitoring error rates.
• Logging details.

This improves robustness.

79. What are the steps to troubleshoot high latency?

Troubleshooting high latency involves analyzing metrics, isolating causes, and optimizing. Steps include reviewing percentiles, checking network, and refining scripts for accurate diagnosis.

Examine http_req_duration metrics. Isolate scenarios. Check server resources. Test with fewer VUs. Version changes in Git. Monitor with Grafana.

80. Why modularize k6 scripts?

Modularizing k6 scripts promotes reusability, maintainability, and collaboration. Separate concerns like requests and checks reduce complexity, supporting Git versioning for team workflows.

81. When to use groups in scripts?

Use groups when:

• Organizing related requests.
• Filtering metrics.
• Integrating with thresholds.
• Versioning in Git.
• Monitoring user flows.
• Supporting nested logic.
• Analyzing sections.

This structures tests.

82. Where are custom modules imported?

Custom modules are imported in:

• Script headers with import.
• Local file paths.
• Git repositories.
• CI/CD builds.
• Cloud test setups.
• Monitored environments.
• Versioned bundles.

This extends functionality.

83. Who reviews k6 scripts?

Peer developers review scripts. They:

• Check for best practices.
• Test scenarios locally.
• Validate thresholds.
• Integrate feedback.
• Version in Git.
• Ensure realism.
• Optimize performance.

This improves quality.

84. Which practice optimizes script execution?

Using shared arrays optimizes execution by:

• Reducing memory usage.
• Sharing data across VUs.
• Integrating with loops.
• Versioning in Git.
• Monitoring memory metrics.
• Scaling efficiently.
• Avoiding duplication.

This enhances speed.

85. How do you avoid common pitfalls in scripting?

Avoid pitfalls by:

• Using async properly.
• Handling promises correctly.
• Testing edge cases.
• Versioning in Git.
• Monitoring for leaks.
• Integrating linting.
• Documenting logic.

This ensures reliability.

86. What are the steps to debug a failing threshold?

Debugging a failing threshold involves isolating metrics, reviewing scripts, and retesting. Steps include checking expressions, analyzing data, and adjusting for accuracy.

Review threshold syntax. Isolate scenarios. Run with verbose output. Analyze percentiles. Version fixes in Git. Retest in CI/CD.

87. Why use tags in k6 metrics?

Tags in k6 metrics enable filtering and grouping, improving analysis. They label requests for scenario-specific insights, supporting GitOps for targeted optimizations.

Advanced Topics

88. What is k6 cloud testing?

k6 cloud testing distributes load globally, scaling to millions of VUs. It provides dashboards and collaboration, with:

• Real-time result viewing.
• Integration with Grafana.
• Automated reporting.
• Versioning scripts in Git.
• Supporting hybrid runs.
• Monitoring distributed metrics.
• Handling large scales.

This overcomes local limits.

89. Why extend k6 with modules?

Extending k6 with modules adds protocols like WebSocket or custom metrics. It supports unique needs, promotes reusability, and integrates with CI/CD for comprehensive testing.

90. When to use k6 for API testing?

Use k6 for API testing when:

• Validating endpoints under load.
• Simulating CRUD operations.
• Testing authentication flows.
• Integrating with thresholds.
• Versioning in Git.
• Monitoring response times.
• Handling JSON payloads.

This ensures API reliability.

91. Where are extension modules sourced?

Extension modules are sourced from:

• k6 extensions repository.
• GitHub for community code.
• Local builds.
• CI/CD pipelines.
• Cloud test setups.
• Versioned packages.
• Monitored integrations.

This adds capabilities.

92. Who develops k6 extensions?

Developers develop extensions. They:

• Build in Go for core.
• Test with JavaScript wrappers.
• Integrate with modules.
• Version in Git repositories.
• Document usage.
• Monitor performance.
• Contribute to community.

This customizes k6.

93. Which extension supports WebSocket?

k6/ws extension supports WebSocket by:

• Establishing connections.
• Sending messages.
• Handling events.
• Integrating with scenarios.
• Versioning in Git.
• Monitoring latency.
• Scaling connections.

This tests real-time.

94. How do you handle large-scale k6 tests?

Handle large-scale tests by:

• Using cloud execution.
• Distributing VUs globally.
• Optimizing scripts.
• Monitoring with Grafana.
• Versioning in Git.
• Setting resource limits.
• Analyzing trends.

This scales efficiently.

95. What are the steps to set up k6 cloud?

Setting up k6 cloud enables distributed testing. Steps include account creation, script upload, and configuration for scalable runs.

Sign up for Grafana Cloud. Upload JavaScript scripts. Configure scenarios and thresholds. Schedule tests. Monitor dashboards. Version in Git.

96. Why test with k6 browser module?

k6 browser module tests frontend interactions, capturing Web Vitals. It simulates clicks and navigation, providing end-to-end insights for optimized user experiences.

97. When to use k6 for stress testing?

Use k6 for stress testing when:

• Finding breaking points.
• Simulating overloads.
• Validating recovery.
• Integrating with thresholds.
• Versioning in Git.
• Monitoring crashes.
• Analyzing limits.

This reveals weaknesses.

98. Where are browser test screenshots saved?

Browser test screenshots are saved in:

• Local directories during runs.
• Cloud storage for cloud tests.
• Git repositories for versioning.
• CI/CD artifacts.
• Monitored dashboards.
• Debug folders.
• Export files.

This aids debugging.

99. Who runs browser tests with k6?

Frontend developers run browser tests. They:

• Script interactions.
• Capture Web Vitals.
• Test navigation flows.
• Integrate with load tests.
• Version in Git.
• Monitor performance.
• Optimize UI.

This validates experience.

100. Which module launches browsers in k6?

experimental.browser launches browsers by:

• Using Chromium engine.
• Supporting page objects.
• Integrating with scenarios.
• Versioning in Git.
• Monitoring timings.
• Scaling VUs.
• Capturing screenshots.

This enables frontend testing.

101. How do you capture Web Vitals?

Capture Web Vitals by:

• Using browser module APIs.
• Tracking LCP, FID, CLS.
• Integrating with metrics.
• Testing in scenarios.
• Versioning in Git.
• Monitoring in Grafana.
• Setting thresholds.

This measures experience.

102. What are the steps to troubleshoot k6 failures?

Troubleshooting k6 failures involves log analysis, metric review, and isolation. Steps include checking outputs, re-running with verbose, and optimizing for resolution.

Review console logs. Analyze metrics. Isolate scenarios. Test locally. Version fixes in Git. Consult docs.

103. Why is k6 ideal for real-time testing?

k6 is ideal for real-time testing with its CLI output and cloud dashboards. It provides instant metrics, integrates with Grafana for visualization, and supports distributed execution for accurate, timely insights.