timeout issue when running queued jobs in production

It seems like you're running into a memory limit issue when processing a large amount of data for your Excel export. The --memory=120 option you're using with php artisan queue:work sets the memory limit to 120MB, which might still not be enough for the amount of data you're trying to process.

Here are a few strategies you can try to resolve this issue:

1. Increase the memory limit: You can increase the memory limit for your queue worker by passing a higher value to the --memory option. However, be cautious with this approach as setting the memory limit too high can cause issues with your server if you don't have enough available RAM.

php artisan queue:work --memory=256 --queue=excel-export

1. Optimize your query: If you're loading a lot of data into memory, consider optimizing your query to reduce the memory footprint. This could involve using eager loading for relationships (with()) if you're not already doing so, or using chunk() to process the data in smaller sets.

Result::query()
    ->with('relatedTable') // Eager load relationships
    ->chunk(100, function ($results) {
        foreach ($results as $result) {
            // Process each result
        }
    });

1. Increase PHP memory limit: You can also increase the memory limit in your PHP configuration (php.ini). This is a more global setting that will affect all PHP processes.

memory_limit = 256M

1. Use a generator: If you're using Laravel's Eloquent ORM to fetch the data, consider using lazy() which uses a PHP generator to process large datasets in a memory-efficient manner.

$results = Result::query()->lazy()->each(function ($result) {
    // Process each result
});

1. Optimize your job: If your job is doing a lot of heavy lifting, try breaking it down into smaller jobs if possible. This way, each job will consume less memory.

2. Configure your environment: Ensure that your production environment has enough RAM to handle the memory requirements of your jobs. If you're on a shared host or a server with limited resources, you might need to upgrade your hosting plan.

3. Monitor and log: Implement monitoring and logging to get more insight into memory usage. This can help you identify the exact point at which the memory limit is reached and adjust your strategy accordingly.

Remember to test these changes in a staging environment before deploying to production to ensure that they resolve the issue without introducing new ones.