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 ### Back pressure
 
 If queues start to grow significantly, the queue size can become larger than memory, resulting in cache misses, disk reads, and even slower performance.  [Back pressure](http://mechanical-sympathy.blogspot.com/2012/05/apply-back-pressure-when-overloaded.html) can help by limiting the queue size, thereby maintaining a high throughput rate and good response times for jobs already in the queue.  Once the queue fills up, clients get a server busy or HTTP 503 status code to try again later.  Clients can retry the request at a later time, perhaps with [exponential backoff](https://en.wikipedia.org/wiki/Exponential_backoff).
+
+### Disadvantage(s): asynchronism
+
+* Use cases such as inexpensive calculations and realtime workflows might be better suited for synchronous operations, as introducing queues can add delays and complexity.
+
+### Source(s) and further reading
+
+* [It's all a numbers game](https://www.youtube.com/watch?v=1KRYH75wgy4)
+* [Applying back pressure when overloaded](http://mechanical-sympathy.blogspot.com/2012/05/apply-back-pressure-when-overloaded.html)
+* [Little's law](https://en.wikipedia.org/wiki/Little%27s_law)
+* [What is the difference between a message queue and a task queue?](https://www.quora.com/What-is-the-difference-between-a-message-queue-and-a-task-queue-Why-would-a-task-queue-require-a-message-broker-like-RabbitMQ-Redis-Celery-or-IronMQ-to-function)