AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers. With AWS Lambda, you can run code for virtually any type of application or backend service, all with zero administration.
Here are some key features of AWS Lambda:
- Event-driven: AWS Lambda automatically runs your code in response to specific events, such as changes to data in an Amazon S3 bucket or a new line of a stream in an Amazon Kinesis stream.
- Automatic scaling: AWS Lambda automatically scales your application in response to incoming requests.
- Flexible: AWS Lambda supports a wide variety of languages and runtimes, including Node.js, Java, Python, C#, and Go.
- Cost-effective: You only pay for the compute time that you consume, and there are no upfront costs or long-term commitments.
- Secure: AWS Lambda automatically handles the patching and updating of the underlying infrastructure, and you can use IAM roles and policies to secure access to your Lambda functions.
To create a Lambda function, you need to provide the following details:
- Function Name
- Role (IAM Role)
Once the function is created, you can test it or trigger it using various triggers like API Gateway, Cloudwatch events, S3, SNS etc. You can also configure the function’s settings, such as the timeout, environment variables, and VPC settings.
AWS Lambda can be used for a variety of use cases such as image and video processing, IoT data processing, and building serverless applications and microservices.
How does AWS Lambda work?
AWS Lambda works by allowing you to upload your code and then automatically running it in response to certain events. Here’s an overview of the process:
- You write your code and package it up as a deployment package, which is typically a .zip file containing your code and any dependencies.
- You upload your deployment package to AWS Lambda using the AWS Management Console, the AWS Command Line Interface (CLI), or the AWS SDKs.
- You create a Lambda function, which is a blueprint for your code that includes information such as the runtime and the amount of memory allocated to the function.
- You configure triggers for your Lambda function, which specify the events that will invoke the function. For example, you can configure a Lambda function to be invoked when an object is created in an Amazon S3 bucket, when a new line is added to an Amazon Kinesis stream, or when an API Gateway receives an HTTP request.
- When an event occurs that triggers the Lambda function, AWS automatically provisions the necessary resources and runs your code. The function’s code is executed within a container, which provides the required runtime environment.
- Your code runs and performs its intended task, such as processing data or calling other AWS services.
- When the function completes, the container is terminated and the resources are freed up.
- AWS Lambda automatically scales the number of instances of your function up or down based on the incoming request rate and the function’s memory usage, so you don’t have to worry about capacity planning.
AWS Lambda automatically monitors the performance of your functions and adjusts the capacity accordingly. It also automatically scales the number of instances of your function up or down based on the incoming request rate and the function’s memory usage, so you don’t have to worry about capacity planning.