Asynchronous Task Processing in Cloud
When you have an application that processes data with sizes ranging from small to large and if your system processes them one by one, there is a high chance that one of the tasks fails. This could lead to application breakage or high latency due to retry.
Imagine, you are now pushing the computing part of your application to a queue. This queue would handle every task independently, minimizing user latency and ensuring that even if one of them fails, the rest of the task/data gets processed. This is Asynchronous task processing.
IronMQ is a flexible, lightning-fast message queue solution to manage your queues. Check out the posts on https://blog.iron.io/ for detailed documentation on Message Queue Architecture.
Types of Queues
Queue is a data structure that is open at both ends and follows a particular order in which the operations are performed. There are two styles of queue. They are usually First in First Out or First In Last Out.
Synchronous Queue vs. Asynchronous Queue
| Synchronous (Normal) Queue | Asynchronous Queue |
| Tasks* are handled in a specific order, and execution takes place in FIFO (First in- First out) | Not executed in FIFO or any specific order. It is executed randomly |
| Queue size is limited | Infinite capacity but the rate of processing is limited |
| Each task, once executed, is taken out of the queue | On a large scale, some tasks might be executed multiple times. However, 99.99% it is always executed only once |
| Tasks are taken in-memory buffer or in a database | Queue persists items. So, even if an application fails, tasks are still available in the queue. |
*Task is an independent piece of work that contains information
When to use Synchronous and Asynchronous Task Processing?
As mentioned above, in asynchronous task processing, it might not be applicable for all use cases. You can choose Asynchronous Task processing only when you are dealing with Independent tasks. In cases where you have a dependency on the completion of previous task(s), you need to go with Synchronous processing.
Also, if the same resource is going to be used for multiple tiny tasks, it is recommended to choose synchronous processing to avoid deadlocks.
Let us take an example of reimburse claim processing workflow. Starting with the submission of claims, related documents, system verification, secondary approvals, and then initiation of payment to the bank account, mentioned steps need to be processed only in the following manner. If you are designing an automated system for the above-mentioned workflow, your system must follow synchronous processing.
To put it simply, an asynchronous process executes a task in the background without asking the customer/user/other services to wait until the task is completed.
Benefits of Asynchronous Task Processing
With the task being processed in parallel, you will have a shorter delivery time and will be able to fully utilize your workers.
It will be easier for you to isolate your failures and help avoid service going down because of one single failure. Failed tasks can be pushed to Dead Letter Queue(DLQ) to be reviewed later.
You can smoothen your traffic spikes by removing non-user-facing tasks from the main user queue and split among your workers.
Asynchronous processing helps the customers to keep using the application/service while the processing happens in the background.
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Task Schedulers
Task Schedulers allow to schedule specific delivery times and manage the number of concurrent tasks that need to be processed. This is to ensure that you don’t overrun your worker. This also helps to configure retry behavior. (Number of retries, how long should it wait before retrying). Iron MQ supports all these features.
Use cases for Asynchronous Processing
- When you are using a third-party API that you depend on for production use cases. You would not have control over that API which could possibly go down or be overused. In such cases, you would need a queue to preserve your requests.
- When you have a front-end UI supporting customer requests and a backend computing calculations/data extraction/image processing based on requests, you would need to run them in parallel so that new customers don’t have to wait. In this case, you can effectively process jobs in the background with Iron Worker.
- On Image Rendering services, where you get a request to web scrape and render an image from the given URL. Each request can be split, and each action can be queued so that extractions of larger images don't stop the processing of other smaller images.
. When you own a cab aggregator service, the customer gets a front-end UI on the mobile app to book a cab. However, in the background, a map service would be running, dynamic pricing service would be involved, nearest cab finder, and a lot more. All these aren't necessarily running in one order but randomly.
. On applications where you are trying to order your food, it has a mixed set of synchronous as well as asynchronous processing. In the background, the application checks for the status of the restaurant and the availability of food. This is asynchronous, whereas once the food is ordered, the app checks if the food is ready and picked up. Once the food is picked up, it opens up the map to track the delivery. The second part of the application needs to happen in a synchronous fashion.
. When your services write too much information into the database, it might take a long to be written. If performed asynchronously, the user/customer can move on to the next page and while the function continues.
. On a fulfillment center, services process new orders from customers, add new products to the warehouse, check for demand, delivery agent availability for the day, location of delivery and nearest delivery agent, and a lot more which are processed every second asynchronously.
Best Practices in using Queues
- For high duration operations, set the timeout to 5x the function duration. This will avoid messages going to DLQ and retries.
- Set the batching to maximum size possible
- Ensure that timeout is always set greater than function duration to avoid duplicate invocation.
- Configure DLQ for the failed or timed-out messages to be reviewed later.
. Though the queue management system handles data persistently, it is recommended to use acknowledgment wisely so that duplication doesn’t happen.
. Built effective systems to stop processing, when all the queue process ends up in failure. This happens when the whole service goes down, so retry might not help.
. Have processes to retry failed messages from DLQ once the service is recovered from downtime.
Conclusion
To summarize, Asynchronous task processing enables developers and users to handle multiple independent tasks without affecting the execution of other tasks. One such example of a Queue is Iron MQ, which is the fastest cloud-agnostic message Queue service.
It is persistent by design and helps in operational improvement. With a fast response average time of 45 seconds, support is available 24 x 7 through slack. As the service is available as On-premise solution, it can run on any hardware in the cloud and so you can be relaxed so that your information stays secure and confidential.
You can analyse usage using inbuilt reporting and analytics provided along with the service. With its unlimited queue size and high availability, Iron MQ is the best in the market for queuing your tasks. Are you interested to learn how IronMQ could support your business? Check out the demo and use your 14-day free trial
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