Scaling to 100M Requests/Day with Laravel and React: Advanced Techniques and Insider Knowledge

Introduction to Scaling

When I first started working on our application, we had about 1,000 users. Fast forward to today, and we're handling over 100 million requests per day. This journey has taught me a lot about what it takes to scale a Laravel and React application to meet the demands of a large user base.

The Problem We Faced

Last quarter, our team discovered that our database connection pool was maxed out, causing significant delays in query execution. We tried increasing the pool size, but that only led to more issues with memory usage. It was clear that we needed a more sustainable solution.

Advanced Techniques for Scaling Laravel

One of the most significant improvements we made was implementing a queues system using Laravel's built-in queue functionality. By offloading tasks like sending emails and processing payments to a queue, we were able to reduce the load on our database and improve response times.

// Example of a queued job in Laravel
namespace AppJobs;

use IlluminateBusQueueable;
use IlluminateQueueSerializesModels;
use IlluminateQueueInteractsWithQueue;
use IlluminateContractsQueueShouldQueue;

class ProcessPayment implements ShouldQueue
{
    use Queueable, SerializesModels, InteractsWithQueue;

    public function handle()
    {
        // Process the payment here
    }
}

Deep Dive into React Optimization

On the frontend, we optimized our React application by using a combination of memoization, shouldComponentUpdate, and React Query to reduce unnecessary re-renders and improve data fetching.

// Example of using React Query to fetch data
import { useQuery } from 'react-query';

function UserProfile() {
  const { data, error, isLoading } = useQuery('userProfile', async () => {
    const response = await fetch('/api/user-profile');
    return response.json();
  });

  if (isLoading) return <div>Loading...</div>;
  if (error) return <div>Error: {error.message}</div>;

  return <div>User Profile: {data.name}</div>;
}

Production-Ready Code and Configurations

In production, we use a combination of NGINX, Redis, and PostgreSQL to handle the high traffic. Our NGINX configuration is set up to handle connection pooling and caching, while Redis is used for session management and caching.

# Example of NGINX configuration
http {
    upstream backend {
        server localhost:8000;
    }

    server {
        listen 80;
        location / {
            proxy_pass http://backend;
            proxy_set_header Host $host;
            proxy_set_header X-Real-IP $remote_addr;
        }
    }
}

Step-by-Step Implementation

To implement these changes, follow these steps:

1. Set up a queues system: Install the necessary packages and configure your queue driver.
2. Optimize your React application: Use memoization, shouldComponentUpdate, and React Query to reduce unnecessary re-renders and improve data fetching.
3. Configure NGINX and Redis: Set up NGINX to handle connection pooling and caching, and Redis for session management and caching.

Conclusion

Scaling to 100 million requests per day is a challenging task that requires careful planning, optimization, and implementation. By using advanced techniques like queuing, memoization, and caching, and configuring our infrastructure for high traffic, we were able to improve the performance and reliability of our application. I hope that by sharing our experience, you can learn from our successes and failures and apply these techniques to your own applications.

What's Next

In our next article, we'll dive deeper into the specifics of our database optimization and share more tips on how to improve the performance of your Laravel and React applications.