The concept of serverless architectures or serverless engineering revolves entirely around understanding the concept of functions as a service. The most technical and accurate definition of serverless computing on the internet is as follows:
Now, let's go into the details of each part of that definition to understand the paradigm of serverless computing better. We shall start with the term function as a service. It means that every serverless model has a function that is executed on the cloud. These functions are nothing but blocks of code, that are executed depending on the trigger that is associated with the function. This is a complete list of triggers in the AWS Lambda environment:
Now let's understand what manages the starting and stopping of a function. Whenever a function is triggered via one of these available triggers, the cloud provider launches a container in which the function executes. Also, after the function is successfully executed the function has returned something, or if the function has run out of time, the container gets thatched away or destroyed. The thatching happens so that the container can be reused in the event of high demand and whenever there is very little time between two triggers. Now, we come to the next part of the sentence, the function's container. This means that the functions are launched and executed in containers. This is the standard definition of a container from Docker, a company that made the concept of containers very popular:
This helps in packaging the code, the runtime environment, and so on of the function into a single deployment package for seamless execution. The deployment package contains the main code file for the function, all the non-standard libraries which are required for the function to execute. The creation process of a deployment package looks very similar to that of a virtual environment in Python.
So, we can clearly make out that there are no servers running round the clock in the case of serverless infrastructures. There is a clear benefit for this, which includes not having a dedicated Ops team member for monitoring the server boxes. So the extra member, if any, can focus on better things, such as software research, and so on. Not having servers running through the entire day saves a lot of money and resources for the company and/or personally. This benefit can be very clearly seen among machine learning and data engineering teams who make use of GPU instances for their regular workload. So having on-demand serverless GPU instances running, saves a lot of money without the developers or the Ops team needing to maintain them around the clock.
