The serverless way to deploy web applications in AWS usually involves hooking up API Gateway with Lambda. If you done this, chances are you have ran into a built-in constraint: the timeout. These products are designed to run microservices, which are expected to be micro, duration-wise as well 😆. However, there are some use cases for which our web app needs to handle long runnings tasks. In this article, I will share with you an HTTP pattern to accomplish that while still being serverless.
So the first realization is that the full Lambda timeout is useless because the request is fronted by API Gateway. So for serverless architectures using API Gateway and whatever integration backend, we have to do our thing under 29s! 😓
There are plenty of use cases I can see that would need more than 29 s. But that’s not important right now. We have first to understand our pattern’s goal:
We want to respond to the request quickly and perform the long running task on the background.
Simple, right ? This is also called Asynchronous processing.
Let’s it break it down into three parts: i) initial request/response; ii) background processing; iii) polling.
The goal of this step is to receive the request, start executing the long running job via non-block (asynchronous) call, and finally create a persistent data object to hold the information about the running job. After all that, we will make use of a nice HTTP status code to produce our response:
The 202 (Accepted) status code indicates that the request has been accepted for processing, but the processing has not been completed.
That’s precisely what we are doing here. The last piece of information we need to include is the URI
job. We do this by adding the
Location header to the HTTP response, which will look like:
All of this can be easily executed in under 29s. 😁
You might be wondering where we will execute the long running job. We want to be serverless, so let’s use another Lambda function. As mentioned in the last step, this function will be called asynchronously, detached from the original Lambda. That means, we can now take advantage of the full 5 min Lambda timeout. That time should be enough for us to execute our long running task.
Once processing is completed, the function must update the status of
Job in the database. Something like
my_job.status = 'DONE'.
So how will the HTTP client know when the job is completed ? Simple, the client will poll for the job status from time to time. It will send a GET request to the location returned in the first step:
Clients should send requests with care so not to overload the server with too many requests. A good strategy is to perform polls with Exponential Backoff.
If we were going to use, say, DynamoDB for our data layer, here’s how our full serverless architecture would look like:
Thanks to Cloudcraft for this nice diagram 🙂
Post on the comments section below if have any questions.
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