Lambda is the leading AWS service for serverless code execution. It is a serverless event-based computing service that allows you to run codes for any type of application or back-end service without provisioning or managing servers. You can activate Lambda from over 200 AWS services and software applications as a service (SaaS), and pay only for what you use.
You can use AWS Lambda to extend other AWS services with custom logic, or to build your own backend services that take advantage of AWS scalability, performance, and security. AWS Lambda can automatically execute code in response to multiple events, such as HTTP requests through Amazon API Gateway, changes to objects in Amazon Simple Storage Service (Amazon S3) buckets, updating tables in Amazon DynamoDB, and state transitions in AWS Step Functions.
Lambda runs your code on a highly available compute infrastructure and performs all compute resource administration tasks. These include server and operating system maintenance, capacity provisioning and autoscaling, security code and patch implementation, and code monitoring and logging. All you have to do is provide the code. These and other features make Lambda particularly suitable for the following use cases:
AWS Lambda is a rather powerful and widely used service. And because the service runs on AWS, it’s inherently very secure thanks to Amazon’s high-quality architecture.
It is important to keep in mind that the CPU for Lambda is assigned proportionally to the memory assigned to the lambda function. So sometimes allocating more memory helps a function run faster and lower costs.
This can be tricky as memory increases and costs also increase and it is important to note that after a certain level the reduction in execution becomes much less significant than the increase in costs and is clearly represented in the graph below. Therefore, it is very important to find the right balance between memory and execution time to make sure lambda operates in an optimized environment.
The CriticalCase approach is precisely to study the right balance in the pre-allocation of resources, both in the design phase of the Serverless solution and in the subsequent phases of monitoring and fine-tuning of the solution over time.
Memory allocation versus execution time in a Lambda function
That said, there are some special considerations that need to be taken to secure AWS Lambda. Here are some AWS Lambda security best practices to consider:
To avoid data leaks, AWS recommends that you do not store user data, events, and other data with security implications in the execution environment. Our advice is to create separate functions and separate function versions for each user, for functions that rely on a mutable state that cannot be stored in memory within the manager.
Using an API gateway to invoke Lambda functions is highly recommended, plus it’s a highly integrated AWS service. An API gateway can act as a gateway to the Lambda function, ensuring that only authorized identities access it. Amazon offers its own API gateway to create and document web APIs that route requests to specific Lambda functions. Using API Gateway, you can secure access to each Lambda function and maintain strict authorization authentication. For more information on how to use AWS Lambda with the Amazon API Gateway, read the documents.
AWS Identity and Access Management (IAM) allows administrators to control access to AWS resources. This service allows administrators to control who is authenticated and authorized to use Lambda resources, as well as to configure what lambdas themselves are privileged to do.
Our advice is to programmatically manage all serverless deployments via CI / CD pipeline. Because the speed of development with a serverless architecture will be much more frequent. Creating manual deployments and updates coupled with the need for more frequent deployments can cause bottlenecks and errors.
Our advice is to use the suite of tools for configuring a CI / CD pipeline, made available directly by AWS:
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