How Do You Use Playwright with AWS Lambda?

Let me share my journey of integrating Playwright with AWS Lambda. When I first started exploring serverless browser automation, I faced numerous challenges that taught me valuable lessons about both tools. Today, I'll walk you through my experiences and show you how I made them work together seamlessly.

My Journey with Playwright and AWS Lambda

I remember the first time I tried to run Playwright in a serverless environment - it was both exciting and challenging. Like many developers, I started with a simple setup on my local machine, but things got interesting when I needed to scale my automation tasks without managing servers.

Why I Chose This Combination

You might be wondering why I picked Playwright and AWS Lambda. Well, let me tell you - it wasn't an immediate decision. I had tried running Chrome on EC2 instances, but I was tired of managing servers and paying for idle time. That's when I discovered the power of combining Playwright's automation capabilities with Lambda's serverless model.

Here's what convinced me:

I only pay for actual usage - no more idle servers
My automation tasks scale automatically with demand
I don't have to worry about server maintenance anymore

The Challenges I Faced

Let me be honest - it wasn't all smooth sailing. Here are some real challenges I encountered and how I solved them:

import { chromium } from '@sparticuz/chromium';
import { chromium as playwright } from 'playwright-core';
 
// This was my first working implementation
export const handler = async (event) => {
  let browser;
  try {
    // I learned to use @sparticuz/chromium after trying several alternatives
    browser = await playwright.launch({
      args: chromium.args,
      executablePath: await chromium.executablePath(),
      headless: true,
    });
 
    // The rest of my automation logic...
  } catch (error) {
    console.error('I hit a snag:', error);
    throw error;
  } finally {
    if (browser) await browser.close();
  }
};

Lessons I Learned About Memory Management

One of my biggest "aha!" moments came when dealing with memory constraints. Here's a pattern I developed after several iterations:

// This helper has saved me countless hours of debugging
async function createOptimizedBrowser() {
  return playwright.launch({
    args: [
      ...chromium.args,
      '--no-sandbox',
      '--single-process', // I found this crucial for Lambda
      '--disable-dev-shm-usage'
    ],
    executablePath: await chromium.executablePath(),
  });
}

My Tips for Better Performance

After months of running this in production, here are some optimizations I've discovered:

Browser Reuse: I learned to cache the browser instance between invocations:

let browserInstance = null;
 
async function getBrowser() {
  if (!browserInstance) {
    browserInstance = await createOptimizedBrowser();
  }
  return browserInstance;
}

Smart Resource Management: Here's how I handle cleanup:

export const handler = async (event) => {
  const page = await (await getBrowser()).newPage();
  try {
    // Your automation logic here
    return { statusCode: 200, body: 'Success!' };
  } finally {
    await page.close(); // I always clean up my pages
  }
};

Real-World Use Cases from My Experience

Let me share some actual scenarios where this setup has helped me:

Screenshot Service: I built a service that captures screenshots of web pages on demand:

async function captureScreenshot(url: string) {
  const page = await (await getBrowser()).newPage();
  try {
    await page.goto(url, { waitUntil: 'networkidle' });
    return await page.screenshot();
  } finally {
    await page.close();
  }
}

PDF Generation: Here's a pattern I use for generating PDFs:

async function generatePDF(html: string) {
  const page = await (await getBrowser()).newPage();
  try {
    await page.setContent(html);
    return await page.pdf();
  } finally {
    await page.close();
  }
}

My Deployment Process

Here's the deployment workflow I've refined over time:

# My tried-and-tested deployment steps
npm install --production
zip -r function.zip .
aws lambda update-function-code \
  --function-name my-playwright-function \
  --zip-file fileb://function.zip

Conclusion

Looking back at my journey with Playwright and AWS Lambda, I can say it's been incredibly rewarding. Yes, there were challenges, but the benefits of serverless browser automation have far outweighed the initial setup complexity.

If you're considering this path, I encourage you to give it a try. Start small, experiment, and don't be afraid to hit some roadblocks - they're all part of the learning process. And if you need any help, feel free to reach out. I'm always happy to share more details about my experience!

Note: If you're looking for a managed solution without the setup complexity, check out screenshotsapi.dev. It's what I recommend to teams who want the benefits without managing the infrastructure.

Want to explore more of my Playwright guides? Here are some I've written:

Mastering PDF Generation with Playwright - Learn to generate PDFs programmatically
Handling Themes in Playwright Screenshots - Master theme-aware screenshot capture
Using Proxies with Playwright - Configure and use proxies effectively
Rendering HTML with Playwright - Explore HTML rendering capabilities