How would you implement processing large volumes of tasks (millions in queue) without hitting memory and execution time limits?

To process millions of tasks in a queue without exceeding memory and execution time limits, approaches based on distributed processing and staged task handling can be used:

1. Using external message queues — for example, RabbitMQ, Kafka, Redis Streams. They allow storing tasks outside the application's memory and ensure reliable delivery.

2. Batch processing — dividing tasks into small batches, processing them in parts to avoid exceeding time and memory limits.

3. Horizontal scaling — running multiple workers that process tasks from the queue in parallel.

4. Idempotency of tasks — so that reprocessing does not cause side effects.

5. Using cron or task schedulers to run handlers with execution time limits.

6. Storing processing state — so that in case of failure, processing can resume from the last processed item.

Example in PHP using Redis and batch processing:

$redis = new Redis();
$redis->connect('127.0.0.1', 6379);

$batchSize = 100;

while (true) {
    $tasks = [];
    for ($i = 0; $i < $batchSize; $i++) {
        $task = $redis->lPop('task_queue');
        if ($task === false) {
            break;
        }
        $tasks[] = $task;
    }

    if (empty($tasks)) {
        break; // Queue is empty
    }

    foreach ($tasks as $task) {
        // Process task
        processTask($task);
    }

    // Optional sleep to control load
    usleep(100000); // 0.1 seconds
}

function processTask($task) {
    // Processing logic
}

This approach allows controlling memory usage, execution time, and scaling as needed.