Skip to main content
Asynchronous workflows help reduce request times for expensive operations that would otherwise be performed in-line. They can also help by doing time-consuming work in advance, such as periodic aggregation of data. Asynchronism

​
Message Queues

Message queues receive, hold, and deliver messages. If an operation is too slow to perform inline, you can use a message queue with the following workflow:
  1. An application publishes a job to the queue, then notifies the user of job status
  2. A worker picks up the job from the queue, processes it, then signals the job is complete
The user is not blocked and the job is processed in the background. During this time, the client might optionally do a small amount of processing to make it seem like the task has completed. For example, if posting a tweet, the tweet could be instantly posted to your timeline, but it could take some time before your tweet is actually delivered to all of your followers. Redis is useful as a simple message broker but messages can be lost. RabbitMQ is popular but requires you to adapt to the ‘AMQP’ protocol and manage your own nodes. Amazon SQS is hosted but can have high latency and has the possibility of messages being delivered twice.

​
Task Queues

Tasks queues receive tasks and their related data, runs them, then delivers their results. They can support scheduling and can be used to run computationally-intensive jobs in the background. Celery has support for scheduling and primarily has python support.

​
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 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.

​
How Back Pressure Works

  1. Monitor queue size
  2. When queue reaches capacity threshold, reject new requests
  3. Return HTTP 503 (Service Unavailable) to clients
  4. Clients retry with exponential backoff
  5. System maintains high throughput for existing jobs
Message Queues vs Task Queues: Message queues focus on reliable message delivery between services, while task queues are designed for executing background jobs and computational tasks.
  • Use cases such as inexpensive calculations and realtime workflows might be better suited for synchronous operations, as introducing queues can add delays and complexity.

​
Use Cases for Asynchronous Processing

  • Email notifications
  • Image/video processing
  • Data analytics and aggregation
  • Batch processing
  • Long-running computations
  • Third-party API calls
  • Report generation

​
Source(s) and Further Reading

Build docs developers (and LLMs) love

Get started for freeTalk to us