Performance Testing

aj-geddes/useful-ai-prompts/skills/performance-testing

by aj-geddes3f5182cfd739No license355 starsListed Oct 8, 2026Updated Oct 8, 2026Repository updated 7 months ago

Design and execute performance tests to measure response times, throughput, and resource utilization. Use for performance test, load test, JMeter, k6, benchmark, latency testing, and scalability analysis.

Includes scriptsSoftware Development Data & Analytics

AI-generated overview

Design and run performance and load tests to measure latency, throughput, and resource use.

What it does: Guides the design and execution of performance tests, covering response times, throughput, resource utilization, and scalability. It provides reference material for k6 API load testing, Apache JMeter, pytest-benchmark, JMH, database query performance, and real-time monitoring, plus a quick-start k6 script with staged load and thresholds. It also ships a validation script and an API scaffold template, and lists best practices such as using percentiles and production-like environments.
When to use it: Use it when validating response-time requirements, measuring API throughput and latency, testing database query performance, or identifying bottlenecks. It also fits benchmarking before and after optimizations, checking caching effectiveness, and testing concurrent user capacity.
Requirements: Runs scripts: it ships an executable validation script and an API scaffold template. The reference guides assume tools such as k6, Apache JMeter, pytest-benchmark, and JMH, and testing targets typically require network access to the system under test.

Overview

Performance testing measures how systems behave under various load conditions, including response times, throughput, resource utilization, and scalability. It helps identify bottlenecks, validate performance requirements, and ensure systems can handle expected loads.

When to Use

Validating response time requirements
Measuring API throughput and latency
Testing database query performance
Identifying performance bottlenecks
Comparing algorithm efficiency
Benchmarking before/after optimizations
Validating caching effectiveness
Testing concurrent user capacity

Quick Start

Minimal working example:

javascript

// load-test.jsimport http from "k6/http";import { check, sleep } from "k6";import { Rate, Trend } from "k6/metrics";
// Custom metricsconst errorRate = new Rate("errors");const orderDuration = new Trend("order_duration");
// Test configurationexport const options = {  stages: [    { duration: "2m", target: 10 }, // Ramp up to 10 users    { duration: "5m", target: 10 }, // Stay at 10 users    { duration: "2m", target: 50 }, // Ramp up to 50 users    { duration: "5m", target: 50 }, // Stay at 50 users    { duration: "2m", target: 0 }, // Ramp down to 0  ],  thresholds: {    http_req_duration: ["p(95)<500"], // 95% of requests under 500ms    http_req_failed: ["rate<0.01"], // Error rate under 1%    errors: ["rate<0.1"], // Custom error rate under 10%  },};
// ... (see reference guides for full implementation)

// load-test.jsimport http from "k6/http";import { check, sleep } from "k6";import { Rate, Trend } from "k6/metrics";
// Custom metricsconst errorRate = new Rate("errors");const orderDuration = new Trend("order_duration");
// Test configurationexport const options = {  stages: [    { duration: "2m", target: 10 }, // Ramp up to 10 users    { duration: "5m", target: 10 }, // Stay at 10 users    { duration: "2m", target: 50 }, // Ramp up to 50 users    { duration: "5m", target: 50 }, // Stay at 50 users    { duration: "2m", target: 0 }, // Ramp down to 0  ],  thresholds: {    http_req_duration: ["p(95)<500"], // 95% of requests under 500ms    http_req_failed: ["rate<0.01"], // Error rate under 1%    errors: ["rate<0.1"], // Custom error rate under 10%  },};
// ... (see reference guides for full implementation)

Reference Guides

Detailed implementations in the references/ directory:

Guide	Contents
k6 for API Load Testing [blocked]	k6 for API Load Testing
Apache JMeter [blocked]	Apache JMeter
pytest-benchmark for Python [blocked]	pytest-benchmark for Python
JMH for Java Benchmarking [blocked]	JMH for Java Benchmarking
Database Query Performance [blocked]	Database Query Performance
Real-Time Monitoring [blocked]	Real-Time Monitoring

Best Practices

✅ DO

Define clear performance requirements (SLAs)
Test with realistic data volumes
Monitor resource utilization
Test caching effectiveness
Use percentiles (P95, P99) over averages
Warm up before measuring
Run tests in production-like environment
Identify and fix N+1 query problems

❌ DON'T

Test only with small datasets
Ignore memory leaks
Test in unrealistic environments
Focus only on average response times
Skip database indexing analysis
Test only happy paths
Ignore network latency
Compare without statistical significance

Source and attribution

Source:aj-geddes/useful-ai-promptsinskills/performance-testingat commit3f5182c

License: No license

Content belongs to its original authors. SourceWeft indexes it from a public repository.

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Performance Testing

Performance Testing

Table of Contents

Overview

When to Use

Quick Start

Reference Guides

Best Practices

✅ DO

❌ DON'T

Source and attribution

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