Spiketestaus

Spiketestaus, or spike testing, is a type of performance testing that evaluates how a system handles sudden, extreme increases in workload. It focuses on the system’s behavior during bursts and on how quickly it recovers after the peak.

It differs from load testing, which measures performance under a gradual or sustained increase, and from stress testing, which pushes the system beyond its normal capacity. Spike tests simulate rapid spikes in transactions, users, or requests to reveal bottlenecks, elasticity limits, and potential failure modes.

Execution of spike testing involves several steps. Define the spike pattern, including the starting level, peak level, duration, ramp rate, and frequency. Establish baselines and clear success criteria for response times, error rates, throughput, and resource utilization. Instrument the system with monitoring for CPU, memory, disk I/O, network usage, error rates, queue depths, and database contention. Run multiple spikes to observe variability. Use automated load generators such as JMeter, Gatling, Locust, or k6, and consider cloud-based load-testing services. Ensure the testing environment is representative and isolated from production, with appropriate safeguards and rollback plans.

Outcomes include identifying bottlenecks, evaluating auto-scaling policies, assessing capacity margins, and validating recovery procedures. Spike testing helps optimize caching, connection pools, and database configurations, and informs capacity planning and incident response.

Considerations include recognizing that spike tests do not represent all real-world patterns, the need for careful risk management, and the value of combining spike tests with other testing approaches for a comprehensive picture of system resilience. Typical use cases involve e-commerce peak periods, API surge scenarios, and authentication or search spikes.