Introduction to Amazon Lambda

• Amazon Lambda is a serverless computing service, which enables code execution in the cloud without server management concerns.
• Lambda automatically adjusts resources in response to demand.
• Functions run in response to triggers like uploading a file to a website.
• Users are billed only for the compute time consumed.
• Lambda allows developers to focus solely on code.

Use Cases

• Lambda facilitates event-driven automation.
• Lambda automatically processes files uploaded to an S3 bucket, performing actions such as resizing images or converting file formats.
• Lambda is used for building serverless APIs using AWS API Gateway.
• Lambda handles backend API logic, processing data, and responding to requests without managing servers. Real-time data transformation and aggregation is possible with Lambda.
• Lambda processes data from streams like Kinesis or DynamoDB Streams, filtering, transforming, or aggregating that data before storing it in a database.
• Lambda handles real-time file processing.
• Lambda can be triggered when a new file is added to an S3 bucket, such as running sentiment analysis or scanning for malware.

Advantages

• Lambda is scalable, adjusting resources dynamically based on the workload.
• Lambda is cost efficient, billing users only for the compute resources.
• Lambda simplifies development, eliminating the need for infrastructure management.
• Lambda functions are typically stateless, meaning each invocation is independent.

Lambda Layers

• Lambda layers facilitate the management of shared code and dependencies across multiple Lambda functions.
• Layers are zip archives containing libraries or runtimes for Lambda functions to use.
• Lambda layers promote code reuse across multiple functions, eliminating code duplication.
• Lambda layers encourage separation of concerns, isolating business logic from dependencies.
• Layers can manage dependencies and external libraries, making codebase management and updating easier.
• The total size of a Lambda function (code + layers) must be within the maximum limit (50 MB for direct uploads, or 250 MB if using Amazon S3).
• Directly uploaded layers must not exceed 50 MB.
• Lambda functions can have multiple layers.
• One layer may have common utilities, another a machine learning model, and another database connectors.
• Layers are versioned, so updates do not affect existing functions until explicitly applied.
• Layers can contain shared libraries
• The Numpy library can be packaged into a layer and reused across functions for multiple Lambda functions.
• Layers can use a custom runtime that is not natively supported by Lambda
• Layers can include utility functions such as for logging and error handling.
• To use layers a user must create and upload a layer.
• When configuring a Lambda function, the user can select the layer to attach to the function.
• When the Lambda function is triggered, it has access to the code inside the layer, just as if it were part of the function itself.
• The Pandas library can be put into a Lambda layer for data processing.
• Updating the layer will give every function using access to the latest version.
• Lambda layers simplify dependencies, reduce function sizes, and ensure consistency across functions.

Amazon Lambda is a serverless computing service. It executes code in the cloud without needing server management. Lambda automatically adjusts resources based on demand and bills users only for the compute time consumed, allowing developers to focus on code.