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

Introduction to Scaling Laravel

When I first started working with Laravel, I was amazed by its simplicity and ease of use. However, as our application grew to 10M requests/day, we encountered numerous scaling issues. In this article, I'll share our journey, the challenges we faced, and the advanced techniques we used to scale our Laravel application to 100M requests/day.

Understanding the Challenges

As we grew, our database connection pool was maxed out, and our queries took forever to execute. We tried surface codes first, but they failed miserably. It wasn't until we dove deep into the Laravel documentation and consulted with the Laravel community that we discovered the right approach.

Advanced Techniques for Scaling Laravel

One of the most critical techniques we used was implementing a queue system using Laravel's built-in queue feature. This allowed us to offload tasks from our main application and process them in the background. We also implemented a caching layer using Redis, which significantly reduced the load on our database.

Queue System Implementation

To implement the queue system, we first installed the laravel/queue package and configured it to use Redis as our queue driver. We then created a new queue job that would process our tasks in the background.

// app/Jobs/ProcessTask.php

namespace AppJobs;

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

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

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

Caching Layer Implementation

To implement the caching layer, we installed the predis/predis package and configured it to use Redis as our cache driver. We then created a new cache store that would store our cache data in Redis.

// config/cache.php

'stores' => [
    'redis' => [
        'driver' => 'redis',
        'connection' => 'redis',
    ],
],

Performance Optimization

To optimize the performance of our application, we used a combination of techniques, including query optimization, indexing, and caching. We also used Laravel's built-in debugging tools to identify and fix performance bottlenecks.

Query Optimization

To optimize our queries, we used Laravel's query builder to simplify our queries and reduce the number of database queries. We also used indexing to speed up our queries.

// app/Models/User.php

use IlluminateDatabaseEloquentModel;

class User extends Model
{
    protected $guarded = [];

    public function scopeActive($query)
    {
        return $query->where('active', true);
    }
}

Conclusion

Scaling a Laravel application to 100M requests/day requires a combination of advanced techniques, including queue systems, caching layers, and performance optimization. By using these techniques, we were able to significantly improve the performance and scalability of our application. I hope this article has provided you with the knowledge and insights you need to scale your own Laravel application.

What's Next

In our next article, we'll dive deeper into the details of implementing a queue system and caching layer in Laravel. We'll also explore other advanced techniques for scaling Laravel applications.