development-stress-test.js

/* Stress Test GET route */
const { check, sleep } = require('k6');
const http = require('k6/http');

// const baseURL = process.env.BASE_URL || 'http://localhost:3002/';
const baseURL = 'http://localhost:3002/';

export const options = {
  vus: 1000,
  duration: '60s',
  thresholds: {
    // http_req_duration: ['p(95)<2000'],
  },
};

const idList = [];

for (let i = 9899999; i <= 9900099; i += 1) {
  idList.push(i);
}

const randomID = () => (
  idList[Math.floor(Math.random() * idList.length)]
);

export default () => {
  const result = http.get(`${baseURL}api/about/${randomID()}`, {
    tags: {
      name: 'service-endpoint',
    },
  });

  check(result, {
    '200 status code': (res) => res.status === 200,
  });

  sleep(1);
};

/*

          /\      |‾‾| /‾‾/   /‾‾/   
     /\  /  \     |  |/  /   /  /    
    /  \/    \    |     (   /   ‾‾\  
   /          \   |  |\  \ |  (‾)  | 
  / __________ \  |__| \__\ \_____/ .io

  execution: local
     script: stress/get.js
     output: -

  scenarios: (100.00%) 1 scenario, 1000 max VUs, 1m30s max duration (incl. graceful stop):
           * default: 1000 looping VUs for 1m0s (gracefulStop: 30s)


running (1m02.4s), 0000/1000 VUs, 22214 complete and 0 interrupted iterations
default ✓ [======================================] 1000 VUs  1m0s

     ✓ 200 status code

     checks.........................: 100.00% ✓ 22214  ✗ 0     
     data_received..................: 70 MB   1.1 MB/s
     data_sent......................: 2.2 MB  34 kB/s
     http_req_blocked...............: avg=12.6ms  min=900ns  med=2.2µs  max=1.04s  p(90)=3.8µs  p(95)=11.8µs 
     http_req_connecting............: avg=12.59ms min=0s     med=0s     max=1.04s  p(90)=0s     p(95)=0s     
     http_req_duration..............: avg=1.76s   min=4.89ms med=1.9s   max=2.61s  p(90)=1.93s  p(95)=1.95s  
       { expected_response:true }...: avg=1.76s   min=4.89ms med=1.9s   max=2.61s  p(90)=1.93s  p(95)=1.95s  
     http_req_failed................: 0.00%   ✓ 0      ✗ 22214 
     http_req_receiving.............: avg=51.86µs min=16.4µs med=50.9µs max=1.12ms p(90)=65.4µs p(95)=74.1µs 
     http_req_sending...............: avg=75.84µs min=4.4µs  med=7.6µs  max=6.62ms p(90)=20.4µs p(95)=43.93µs
     http_req_tls_handshaking.......: avg=0s      min=0s     med=0s     max=0s     p(90)=0s     p(95)=0s     
     http_req_waiting...............: avg=1.76s   min=4.83ms med=1.9s   max=2.6s   p(90)=1.93s  p(95)=1.95s  
     http_reqs......................: 22214   355.720877/s
     iteration_duration.............: avg=2.77s   min=1s     med=2.9s   max=4.08s  p(90)=2.94s  p(95)=2.95s  
     iterations.....................: 22214   355.720877/s
     vus............................: 664     min=664  max=1000
     vus_max........................: 1000    min=1000 max=1000

*/


/* Stress Test POST route */
const { check, sleep } = require('k6');
const http = require('k6/http');

// const baseURL = process.env.BASE_URL || 'http://localhost:3002/';
const baseURL = 'http://localhost:3002/';
const record = {
  course_id: 1,
  recent_views: 6676565,
  description: 'Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you\'ll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you\'ll learn about some of Silicon Valley\'s best practices in innovation as it pertains to machine learning and AI. \n\nThis course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you\'ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.',
  learner_career_outcomes: [
    {
      icon: 'fa-map-signs',
      pct: 0.33,
      outcome: 'started a new career after completing these courses',
    },
    {
      icon: 'fa-briefcase',
      pct: 0.32,
      outcome: 'got a tangible career benefit from this course',
    },
    {
      icon: 'fa-money',
      pct: 0.12,
      outcome: 'got a pay increase or promotion',
    },
  ],
  metadata: [
    {
      icon: 'certificate',
      title: 'Shareable Certificate',
      subtitle: 'Earn a Certificate upon completion',
    },
    {
      icon: 'globe',
      title: '100% online',
      subtitle: 'Start instantly and learn at your own schedule',
    },
    {
      icon: 'calendar',
      title: 'Flexible deadlines',
      subtitle: 'Reset deadlines in accordance to your schedule',
    },
    {
      icon: 'clock',
      title: 'Approx. 60 hours to complete',
      subtitle: '',
    },
    {
      icon: 'speechbubble',
      title: 'English',
      subtitle: 'Subtitles: Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Vietnamese, German, Russian, English, Hebrew, Spanish, Hindi, Japanese',
    },
  ],
  what_you_will_learn: [
    'Identify a subset of data needed from a column or set of columns and write a SQL query to limit to those results.',
    'U​se SQL commands to filter, sort, and summarize data.',
    'Create an analysis table from multiple queries using the UNION operator.',
    'Manipulate strings, dates, & numeric data using functions to integrate data from different sources into fields with the correct format for analysis.',
  ],
  skills_you_will_gain: [
    'Logistic Regression',
    'Artificial Neural Network',
    'Machine Learning (ML) Algorithms',
    'Machine Learning',
  ],
};

export const options = {
  vus: 1000,
  duration: '60s',
  thresholds: {
    // http_req_duration: ['p(95)<2000'],
  },
};

export default () => {
  const result = http.post(`${baseURL}api/about/new`, {
    body: JSON.stringify(record),
    headers: {
      'Content-Type': 'application/json',
    },
  }, {
    tags: {
      name: 'service-post-endpoint',
    },
  });

  check(result, {
    '201 status code': (res) => res.status === 201,
  });

  sleep(1);
};


/*

          /\      |‾‾| /‾‾/   /‾‾/   
     /\  /  \     |  |/  /   /  /    
    /  \/    \    |     (   /   ‾‾\  
   /          \   |  |\  \ |  (‾)  | 
  / __________ \  |__| \__\ \_____/ .io

  execution: local
     script: stress/post.js
     output: -

  scenarios: (100.00%) 1 scenario, 1000 max VUs, 1m30s max duration (incl. graceful stop):
           * default: 1000 looping VUs for 1m0s (gracefulStop: 30s)


running (1m01.7s), 0000/1000 VUs, 21950 complete and 0 interrupted iterations
default ✓ [======================================] 1000 VUs  1m0s

     ✓ 201 status code

     checks.........................: 100.00% ✓ 21950  ✗ 0     
     data_received..................: 5.4 MB  88 kB/s
     data_sent......................: 88 MB   1.4 MB/s
     http_req_blocked...............: avg=25.45ms min=1.3µs  med=2.5µs  max=1.07s   p(90)=4.11µs p(95)=13.3µs
     http_req_connecting............: avg=25.44ms min=0s     med=0s     max=1.07s   p(90)=0s     p(95)=0s    
     http_req_duration..............: avg=1.76s   min=8.53ms med=1.85s  max=2.57s   p(90)=1.91s  p(95)=1.96s 
       { expected_response:true }...: avg=1.76s   min=8.53ms med=1.85s  max=2.57s   p(90)=1.91s  p(95)=1.96s 
     http_req_failed................: 0.00%   ✓ 0      ✗ 21950 
     http_req_receiving.............: avg=54.03µs min=19µs   med=51.6µs max=1.17ms  p(90)=73.4µs p(95)=86.9µs
     http_req_sending...............: avg=68.17µs min=6.2µs  med=29µs   max=16.48ms p(90)=40µs   p(95)=60.1µs
     http_req_tls_handshaking.......: avg=0s      min=0s     med=0s     max=0s      p(90)=0s     p(95)=0s    
     http_req_waiting...............: avg=1.76s   min=8.4ms  med=1.85s  max=2.57s   p(90)=1.91s  p(95)=1.96s 
     http_reqs......................: 21950   355.995599/s
     iteration_duration.............: avg=2.79s   min=1s     med=2.85s  max=4.06s   p(90)=2.92s  p(95)=2.96s 
     iterations.....................: 21950   355.995599/s
     vus............................: 982     min=982  max=1000
     vus_max........................: 1000    min=1000 max=1000

*/