Playwright Loading Time Measurement of Google.com Page with concurrent 1-5 Users (Simulation)

Overview

This script measures the loading time of a webpage using Playwright. It simulates multiple users accessing a specified URL concurrently and calculates the average loading time over multiple iterations.

Prerequisites

• Node.js (latest LTS recommended)
• Playwright package installed
• Python 3.8+ (for data analysis and dashboard)

Installation

Node.js Dependencies

1. Install dependencies:

   npm install playwright

Python Dependencies

1. Install Python dependencies:

   pip install -r requirements.txt

Usage

Running the Measurement Script

1. Run the script:

   ts-node measureLoadingTime.spec.ts

2. The script will:
   • Launch multiple browser instances concurrently.
   • Measure the time taken to load https://google.com.
   • Repeat the process for different user counts.
   • Output the average loading time for each test case.

Data Analysis with Jupyter Notebook

1. Start Jupyter Notebook:

   jupyter notebook

2. Open loading_times_analysis.ipynb to explore the data and get insights:
   • Loading times by hour of day
   • Loading times by day of week
   • Loading times by month
   • Average loading times by number of users
   • Time series analysis
   • Statistical summaries

Interactive Dashboard with Voila

1. Run the dashboard using Voila:

   voila dashboard.ipynb

2. The dashboard will open in your browser and provides:
   • Interactive filtering by number of users
   • Multiple visualization views (Time Series, By Hour, By Day of Week, By Month, Distribution)
   • Real-time statistics
   • Professional, user-friendly interface

Alternatively, you can also view the dashboard in Jupyter:

jupyter notebook dashboard.ipynb

Configuration

• Modify the users parameter to change the number of concurrent browser instances.
• Adjust the iterations parameter to control the number of test repetitions.
• Change the page.goto URL to test a different webpage.

Example Output

Average loading time for 1 user(s) over 5 iterations: 120 ms
Average loading time for 2 user(s) over 5 iterations: 140 ms
...

Data Visualization Options

This project provides multiple ways to visualize and analyze the collected data:

• R Shiny Dashboard (app.R): Interactive web application for real-time data visualization
• Jupyter Notebook Analysis (loading_times_analysis.ipynb): Comprehensive exploratory data analysis
• Voila Dashboard (dashboard.ipynb): Interactive Python-based dashboard for data exploration

Notes

• Ensure network conditions are stable for accurate results.
• Running many concurrent users may consume significant system resources.
• The script defaults to testing https://google.com, but this can be modified as needed.
• Results can be analyzed to track performance trends over time.

Disclaimer

• This project uses https://google.com solely as a stable test case for personal learning and educational purposes.
• It makes a low and limited number of automated requests, and does not involve scraping, user interaction automation, or high-frequency traffic.
• This project is provided as-is for educational use. If you choose to modify or adapt it, you are solely responsible for ensuring that your usage complies with all applicable laws and remains respectful of any target site's terms of service, rate limits, and infrastructure.