Scaling Serverless Architecture using AWS and Lambda for Asynchronous Processing

Scaling Serverless Architecture using AWS and Lambda for Asynchronous Processing

Using AWS Lambda it is possible to develop serverless functions in which the container it is deployed to is maintained by AWS. As well as managing the resource allocation to the environment it will also handle scaling of the lambda function to meet demand. So for example if you have an API Gateway whose endpoints trigger lambda functions AWS can spawn multiple containers horizontally running your Lambda function to meet lets say a sudden burst of hits against your endpoint.

 

Even though AWS is managing the scaling of the lambda function you may still hit other choke points that AWS won’t manage for you. Keeping things simple, lets say the hit against the API Gateway is to trigger some kind of asynchronous task in which you hit a small RDS deployment of Maria DB. The sudden bursts against the DB could cause issues with other functions which may be using the DB. This is where you are at a point where you need to control the flow of queries so that they are triggered at a rate in which you know your current DB can handle.

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