How to Perform Load Testing at Scale With Real Browsers Using Flood Element

As a web developer, few things are more frustrating than legacy load testing approaches. Configuring complex tests across disparate tools often leaves you with more questions than answers.

"Are these scripted user flows even realistic?"

"Why are the test results not matching reality?"

"How do I make sense of all this noisy data?"

These questions are understandable when code-based tools attempt to approximate real-world browser behavior. The reality is today‘s modern web applications don‘t function like neatly organized API calls. They are complex systems – with dynamic JavaScript, third-party integrations, distributed architectures, and real user interactions defining the experience.

To keep up, a radical shift is needed in how we load test complex browser-based apps. Enter Flood Element…

Flood Element is a blazingly fast load generation tool built for today’s dynamic web apps. It uses real browsers like Chrome and Firefox to simulate realistic user scenarios that capture the true performance profile of your site or application.

With its easy-to-use scripting syntax and distributed testing infrastructure, you can run thousands of concurrent real browser users across 50+ global regions quickly and effortlessly. And Flood Element‘s advanced analytics give you instant insight into real user experiences via visual timelines, waterfall analysis, and interactive dashboards.

Are you ready to fix web performance issues before your customers notice? If so, this comprehensive guide will skill you up on everything you need to know to conduct large scale, real-browser load tests with Flood Element.

Getting Started: Your First Flood Element Test Script

Flood Element scripts are powered by a natural language DSL that allows you to define user flows in easy terms like "click", "type", and "navigate".

user.click(‘.signup-button‘);
user.type(‘inputName‘, ‘John Wick‘); 
user.navigate(‘/home‘);

Combined with JavaScript/TypeScript, you have the flexibility to express any complex browser-based scenario.

Let‘s walk through installing Flood Element and writing your first test script locally:

Step 1: Install the Element CLI

npm install -g @flood/element-cli

Step 2: Init New Script

element new my-first-test

This scaffolds a new Flood Element project locally with everything you need to start writing tests.

Step 3: Write Basic Script

Edit the generated test script to navigate to a homepage, click around the site, fill out forms, assert for elements – whatever represents a real user flow.

Here is what a basic e-commerce order script could look like:

user.navigate(‘https://my-shop.com‘);
user.waitFor(‘.products‘);
user.click(‘#product-1‘); 
user.type(‘#qty‘, ‘2‘) 
user.click(‘#add-to-cart‘);
user.click(‘#checkout‘);  
user.assert.urlContains(‘/checkout‘); 
user.type(‘#fullname‘,‘John Wick‘);
user.type(‘#cardnumber‘,‘1234123412341234‘);
user.click(‘#pay‘);

Notice how easy it is to express real user interactions like clicking, typing, assertions!

Step 4: Debug Script Locally

Flood Element makes debugging a breeze with its local run capability:

element run my-test

Local runs spawn a browser instance right on your own machine to test out your script. And of course, the full Element IDE lets you visually inspect script execution to fix any issues.

Now that you have a script locally validated and running cleanly without errors, it‘s time take it to the next level by load testing at scale!

Running Massive Load Tests on Flood

Once you have a Flood Element script ready, running a full load test at thousands of users is straightforward:

  1. Sign up for free Flood account
  2. Create a New Test
  3. Configure test parameters
    • Browsers (Chrome, Firefox)
    • Regions (US, Europe, Asia…)
    • Duration
    • Users
  4. Upload Script & Start the Flood!

Flood‘s optimized browser engine will distribute your test globally across 50+ regions to accurately simulate worldwide traffic hitting your system.

But how many users should you test with?

Determining Appropriate Load Levels

A common mistake first timers make is drastically undershooting the number of users needed. Instead, take a usage-based approach:

  • Current Traffic – Minimum 2x current daily/monthly users
  • Future Scale – 10x to account for future growth
  • Concurrency – Max number online simultaneously

For example, if your app averages 50K users a month currently, run tests of at least 100K users. And dial up to 500K to benchmark capacity limits.

Get these ballpark estimates by enabling Google Analytics and extracting key metrics around current traffic, growth trends, and peak concurrency.

Armed with real numbers, you can configure robust Flood Element tests spanning across a globe.

Monitoring Tests in Real-Time

One amazing aspect of Flood Element is getting immediate visibility into test execution through its command center dashboards.

As thousands of browsers kick off across regions, all key test diagnostics are monitored in real-time:

  • Total Users – Test progression toward target
  • Response Time – Latency and slowdowns
  • Throughput – Requests per second
  • Errors – 4xx/5xx status codes
  • Log Events – Debug script failures

Flood Element empowers you to slice and dice across any dimension in real-time – by region, by browser, by individual page or asset. It makes pinpointing problems under load a breeze.

But to analyze results post-test, you need to level up your analytics game…

Leveling Up Your Web Performance Analytics

When tests complete, Flood Element delivers comprehensive CSV reports covering all user actions and network requests. But to analyze results like a performance pro, you need to understand metrics beyond basic response times and error rates.

Here are the key real user metrics to master:

Metric Description
Time to Interactive When primary content is visible and ready for input. Key for UX.
First CPU Idle Time to main thread available. Affects responsiveness.
First Input Delay Lag when clicking or typing. Key for interactions.
Visually Complete Page elements done rendering. Key for perception.
Largest Contentful Paint Render time of main content. Critical for SEO.

Leverage Element‘s rich CSV reports to distill key user-centric metrics like Time-to-Interactive and First Input Delay. Analyze against performance budgets and optimize regressions.

Now let‘s walk through a proven methodology to fix web performance issues using Flood Element data.

The Methodology: How to Use Flood Tests to Optimize Web App Performance

Flood Element opens the door to continuously improving real end user experiences by revealing performance gaps quickly.

Here is a step-by-step guide to leveraging Element tests for better web app optimization:

Step 1: Establish Performance Baselines

Run initial benchmark tests across critical user journeys – signup, purchase, search etc. Capture baseline metrics like Time-to-Interactive and Largest Contentful Paint per page.

Step 2: Make Targeted Code Improvements

Use Flood Element metrics to identify optimization opportunities – areas for Lighthouse performance wins, critical third party scripts to eliminate, bottlenecked pages dragging down Time-to-Interactive.

Step 3: Validate Gains with A/B Testing

Configure two Flood Element tests – one representing the original version of site, another testing changes. Compare user-centric metrics across runs to validate performance lift of optimizations.

Step 4: Confidently Release Changes to Production

Only release changes to live after verified improvements in lab environment testing. This ensures optimizations actually translate to better real user performance!

Rinse and repeat running continuous cycles of benchmarking, optimizing, confirming, releasing for end-to-end web performance gains.

Let‘s walk through a real world example applying this methodology…

Case Study: Improving Performance of a Retail Product Page

Step 1: Baseline Testing

Starting Largest Contentful Paint (LCP): 5.5s
Goal: Improve LCP < 3s

Step 2: Optimization Analysis

Use Element crawl waterfall charts to identify/remove heavy third-party tag blocking rendering.

Step 3: A/B Testing Validation

Test Original Version vs. Optimized in Flood Element.

Results:
LCP improved from 5.5s to 2.8s!

Step 4: Confidently Release

Push changes live only after validating gains in testing environment resulting in much faster LCP.

Just like that, we‘ve validated a methodology to make quantified web performance wins leveraging Flood Element data!

Conclusion & Next Steps

This guide explored a powerful approach to level up web application testing using Flood Element – a blazingly fast way to generate load across thousands of real browser users and global regions.

With its easy scripting API and robust analytics, Flood Element empowers you solve two key challenges:

1. Testing Complexity
Break free from legacy code-based tools with Flood Element‘s easy real browser scripts.

2. Performance Insights
How fast is fast enough? Find out with Flood Element‘s real user metrics identifying optimization opportunities.

Think your web app is ready handle crushing loads in the real world? Schedule a demo today to discuss use cases and get set up running large scale, real browser tests with Flood:

https://flood.io/demo

Unleash Flood Element to push your web application‘s speed, scale and efficiency to new heights!