QA Team: Required Load Testing Tool Requirements

ABF.

The three requirements that the QA team should include when rolling out a new load-testing tool to test the scalability of primary cloud services that run on Google Compute Engine with Cloud Bigtable are:

A. Ensure that the load tests validate the performance of Cloud Bigtable: Since Cloud Bigtable is a crucial component of the system architecture, it is essential to validate its performance during load testing. The load-testing tool should be configured to simulate real-world traffic patterns, including a mix of read and write operations, to measure the performance of Cloud Bigtable accurately.

B. Create a separate Google Cloud project to use for the load-testing environment: Creating a separate Google Cloud project for load testing is important to ensure that the load testing environment does not interfere with the production environment. A separate project also allows for better resource management, security, and isolation.

C. Schedule the load-testing tool to regularly run against the production environment: Load testing should not be a one-time event. To ensure that the system can handle anticipated traffic spikes, the load-testing tool should be scheduled to run regularly against the production environment. This will help identify performance bottlenecks and enable the development team to proactively address them.

D. Ensure all third-party systems your services use are capable of handling high load: The services running on Google Compute Engine with Cloud Bigtable may rely on third-party systems. It is important to ensure that these systems are capable of handling high loads. The QA team should validate the third-party systems' performance under load before conducting load testing.

E. Instrument the production services to record every transaction for replay by the load-testing tool: Instrumenting the production services to record every transaction is useful for load testing. The recorded transactions can be replayed by the load-testing tool to simulate realistic traffic patterns. This can help identify performance bottlenecks and validate the system's ability to handle anticipated traffic.

F. Instrument the load-testing tool and the target services with detailed logging and metrics collection: Detailed logging and metrics collection are essential for identifying performance issues and validating the system's ability to handle anticipated traffic. The load-testing tool and target services should be instrumented to capture metrics such as response time, error rate, and throughput. This data can be analyzed to identify performance bottlenecks and enable the development team to proactively address them.