قوائم انتظار العمل وPrefetch والمستهلكون المتنافسون

A work queue (sometimes called a task queue) lets your Node.js backend push time-consuming jobs to a queue instead of running them inside the HTTP request. Think image resizing, sending emails, or generating PDFs.

• The producer publishes a small message describing the job.
• One or more consumers (worker processes) pull jobs and execute them in the background.
• The HTTP response returns immediately, keeping your API fast.

RabbitMQ stores the messages durably until a worker is free to process them.

The producer connects to RabbitMQ, declares a durable queue, and sends a task message. Each message is just a buffer of bytes — here we send a plain string describing the job.

Notice { durable: true } on the queue and { persistent: true } on the message. Together they let tasks survive a broker restart.

The consumer asserts the same durable queue and registers a callback with ch.consume. Whenever a message arrives, RabbitMQ delivers it to the handler.

Here we simulate work by counting the dots in the message body, then sleeping that many seconds. The queue name is the contract that links producer and consumer.

By default, manual acknowledgements protect you from losing jobs if a worker crashes. With { noAck: false }, RabbitMQ keeps a message in an unacknowledged state until the worker calls ch.ack(msg).

• If the worker dies before acking, RabbitMQ requeues the message and another worker picks it up.
• Always ack after the work succeeds, never before.
• Use ch.nack(msg, false, requeue) to reject a poisoned message.

The competing consumers pattern simply means: run multiple worker processes that all consume from the same queue. RabbitMQ delivers each message to exactly one of them.

• Want more throughput? Start more workers — no code change needed.
• The queue acts as a load buffer during traffic spikes.
• This is how you scale background processing horizontally in Node.js.

But how RabbitMQ chooses which worker gets the next message matters a lot.

Out of the box, RabbitMQ dispatches messages to consumers in a strict round-robin order — it counts messages, not workload.

Imagine two workers. The queue has tasks where odd-numbered ones are heavy and even-numbered ones are light. Round-robin sends every other task to each worker, so one worker may end up with all the heavy jobs while the other sits idle.

RabbitMQ does not look at how busy a consumer already is — unless you tell it to with prefetch.

Prefetch (QoS) caps how many unacknowledged messages a single consumer may hold at once. You set it per channel with ch.prefetch(count).

• ch.prefetch(1) means: don't give a worker a new task until it has acked the previous one.
• This turns dispatch from "round-robin by count" into "give the next task to whoever is free" — fair dispatch.
• Higher values (e.g. 10) increase throughput by pipelining, at the cost of fairness.

The right prefetch depends on your workload:

• prefetch(1) — best for long, uneven, CPU-heavy tasks. Maximum fairness, slight latency between ack and next delivery.
• prefetch(10–50) — best for short, uniform tasks (e.g. tiny webhook fan-out) where round-trip latency would otherwise dominate.

Rule of thumb: short tasks + low latency network → larger prefetch. Long tasks or unpredictable durations → small prefetch. Measure, then tune.

You don't need RabbitMQ to understand fair dispatch. This standalone program models a single shared queue and two workers under a prefetch=1 policy: each worker only takes a new job when it is free.

Heavy jobs take longer, so the free worker keeps grabbing the next task — the load self-balances instead of being split blindly by count.

A common gotcha: ch.prefetch(count) applies to each consumer on that channel by default. If you run one worker process per queue, this is exactly what you want.

• The limit counts only unacked messages, so forgetting to ack will stall the worker once it hits the cap.
• A second optional argument, ch.prefetch(count, true), makes the limit apply across the whole channel (global), which is rarely needed.
• One channel per worker process is the simplest, safest setup.

A production-ready consumer combines all the pieces: a durable queue, fair-dispatch prefetch, manual acks on success, and nack on failure.

Run several copies of this exact file as separate processes and you have competing consumers that share load fairly. Scale up by launching more workers; scale down by stopping some — the queue absorbs the difference.

You run 3 worker processes consuming the same queue. Task durations vary wildly — some take 200ms, some take 30s. You notice one worker is always busy while others go idle, so jobs pile up. What is the most direct fix?

You learned how to distribute background work fairly across Node.js workers with RabbitMQ:

• Work queues offload slow jobs from the request path into a durable queue.
• Competing consumers means multiple workers consume the same queue; each message goes to exactly one worker, and you scale by adding processes.
• Default dispatch is round-robin by count, which can overload one worker with heavy tasks.
• Prefetch (ch.prefetch(n)) caps unacked messages per consumer; prefetch(1) gives fair dispatch, higher values pipeline short tasks.
• Always use manual acks (ch.ack on success, ch.nack on failure) so crashed workers don't lose jobs.