Performance (Load) Testing: Objectives, Metrics, Steps, Scenario Design, and Tool Overview

Load testing (pressure testing) is a method of continuously applying load to a system to determine its maximum service capacity, typically performed after functional testing and system stabilization.

Its main purposes include observing performance metric changes as load increases, detecting errors or crashes under high concurrency, and identifying the maximum load the system can sustain before reaching bottlenecks.

Key focus areas are response slowdown, HTTP 4xx/5xx errors, server unresponsiveness, and crashes. Common metrics are TPS, response time, error rate, and server resource usage (CPU, memory, I/O).

Typical steps: parameterize test data, design realistic scenarios, execute tests, analyze results, and iterate with optimizations.

Scenario design should consider concurrent user count, request mix and ratios, think times, ramp‑up and ramp‑down rates, test duration, monitored metrics, error handling, and required load generators.

An example scenario with 1000 client machines, heartbeat intervals, and a mix of MySQL and Redis back‑ends estimates a TPS of about 2500, with average response time 160 ms and required monitoring of CPU, memory, MySQL lock time, and Redis connections.

Common issues such as high MySQL I/O causing low TPS can be mitigated by relocating databases; after optimization, TPS doubled and response time halved.

Tool overview includes JMeter (script creation, HTTP header and request configuration, CSV data files, BeanShell pre‑processor, response assertions, concurrency and ramp‑up settings) and Locust, with screenshots of script editors and report views.

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