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Serverless computing with AWS Lambda
Light Work
For a number of reasons, it makes sense to use today's cloud-native infrastructure to run software without employing servers; instead, you can use an arms-length, abstracted serverless platform such as AWS Lambda.
For example, when you create a Lambda function (source code and a run-time configuration) and execute it, the AWS platform only bills you for the execution time, also called the "compute time." Simple tasks usually book only hundreds of milliseconds, as opposed to running an Elastic Compute Cloud (EC2) server instance all month long along with its associated costs.
In addition to reducing the cost and removing the often overlooked administrative burden of maintaining a fleet of servers to run your tasks, AWS Lambda also takes care of the sometimes difficult-to-get-right automatic scaling of your infrastructure. With Lambda, AWS promises that you can sit back with your feet up and rest assured that "your code runs in parallel" and that the platform will be "scaling precisely with the size of the workload" in an efficient and cost-effective manner [1].
In this article, I show you how to get started with AWS Lambda. Once you've seen that external connectivity is working, I'll use a Python script to demonstrate how you might use a Lambda function to monitor a website all year round, without the need of ever running a server.
For more advanced requirements, I'll also touch on how to get the internal networking set up correctly for a Lambda function to communicate with nonpublic resources (e.g., EC2 instances) hosted internally in AWS. Those Lambda functions will also be able to connect to the Internet, which can be challenging to get right.
On an established AWS infrastructures, most resources are usually segregated into their own virtual private clouds (VPCs) for security and organizational requirements, so I'll look at the workflow required to
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