Lambda Functions

Lambda Functions

In this tutorial, we are going to explore about overview of the Lambda functions and its suitability for applications that don’t require continuous availability.

AWS Lambda is a serverless compute service by Amazon Web Services (AWS) that allows you to run code without provisioning or managing servers. You simply upload your code, and AWS Lambda takes care of executing it whenever it’s triggered by an event, with automatic scaling to handle variable traffic levels.

AWS Lambda allows us to run code without managing servers. It lets us execute functions in response to events, such as changes in data, user actions, or time triggers. We pay only for the compute time our code consumes, making it cost-effective and scalable. It supports various programming languages, making it accessible to various applications and developers. AWS Lambda is amongst the core serverless computing services offered by AWS.

What is serverless computing?

Serverless computing is a computing model where developers can build and run applications without managing or worrying about the underlying servers. In contrast to this, EC2 provides virtual servers that must be provisioned, managed, and scaled manually, and users pay for the server’s uptime, regardless of the workload. Lambda is ideal for event-driven, short-lived tasks, while EC2 offers more control but requires more operational overhead.

Serverless architecture allows us to focus on the functions rather than spending time managing a server. Functions are stateless and event-driven, executed when the function is triggered. This saves us a lot of running costs for the servers since functions are short-lived and are billed on pay-per-execution. The diagram below highlights a serverless architecture.

Serverless architecture
AWS Lambda in action

AWS Lambda allows us to run code without managing servers. Lambda is an ideal computing service for applications that don’t require continuous availability, application scenarios that require rapid scaling, and where the flow of an application is based on triggering events. When working with Lambda, we are only responsible for the code. Lambda provisions the compute resource to offer a balanced memory, CPU, network and other resources. As Lambda itself manages the infrastructure, we can not customize compute instances or operating systems provided on runtimes.

Lambda runtimes offer a language-specific environment to execute the function code. Each programming language has a runtime with a unique identifier, such as nodejs20.x or python3.12. Keeping the Lambda code updated to the latest runtime is important as AWS Lambda does not offer backward compatibility between major versions.

Example: Adding watermarks to images

Let’s take an example of a web application that adds watermarks to images, ensuring they are not misused. The static web pages of the application are hosted on the S3 bucket. Whenever an image is uploaded, the application processes the image and uploads it back to the S3 bucket, making it available for users.

Lambda Functions

This operation can be efficiently handled using a Lambda function. Since the compute resource is only required when a new image is uploaded and does not require a dedicated compute resource running 24/7. The Lambda function executes the code when the image is uploaded to the S3 bucket.

Features of Lambda Functions

Here are some core features of AWS Lambda:

  1. Event-Driven: AWS Lambda can be triggered by events from various AWS services, including API Gateway, S3, DynamoDB, Kinesis, and CloudWatch, as well as external HTTP requests. Lambda responds to events almost instantaneously, making it suitable for event-driven applications.
  2. Serverless and Auto-Scaling: Lambda automatically scales the number of concurrent instances based on incoming requests. There’s no need to provision or manage infrastructure, making it highly efficient and cost-effective for sporadic workloads or burst traffic.
  3. Supported Languages: Lambda Functions supports a range of programming languages including Node.js, Python, Java, Ruby, Go, and .NET Core, as well as custom runtime APIs for other languages.
  4. Function Configuration and Code Packaging:
    • Lambda Function: The main unit of execution in AWS Lambda, consisting of your code and configuration settings.
    • Execution Role: Each Lambda function requires an IAM role to define permissions and access to AWS resources.
    • Environment Variables: Lambda functions can be customized at runtime with environment variables.
  5. Integration with AWS Services: Lambda integrates with services like S3, DynamoDB, API Gateway, CloudWatch, and Step Functions for advanced workflows and automation, making it ideal for microservices, data processing, and automation tasks.
  6. Provisioned Concurrency: Allows you to pre-allocate a specific number of function instances to reduce cold-start latency for applications with predictable traffic.
  7. Deployment Packages and Layers:
    • Deployment Packages: Lambda functions are packaged as ZIP files or container images (up to 10 GB in size), making it flexible for different development and deployment scenarios.
    • Lambda Layers: Allows you to include additional libraries and dependencies separately, reducing package size and simplifying updates.
Use Cases for Lambda Functions
  • Real-Time File and Data Processing: Process data from sources like S3, DynamoDB, or Kinesis in real time.
  • Backends for Web and Mobile: Use Lambda with API Gateway to create RESTful or GraphQL APIs without managing servers.
  • IoT Backend Processing: Handle IoT data streams and process device-generated events in real time.
  • Automation and Scheduled Tasks: Schedule Lambda functions to run at specific times using Amazon CloudWatch Events or perform automation across services.
  • Microservices Architecture: Build microservices where each service can be a Lambda function, enabling modular design and easy scaling.

Pricing

AWS Lambda Functions charges based on the number of requests and the compute time used by each request. You pay for the time your code executes, rounded up to the nearest millisecond, and the memory you allocate to your function.

How Lambda Functions Works

To use AWS Lambda Functions, you:

  1. Create a Lambda Function: Upload your code, define the memory, timeout, and handler (entry point) in a Lambda function.
  2. Set Triggers: Connect the Lambda function to specific event sources, such as an S3 bucket or an API Gateway endpoint.
  3. Monitor and Optimize: Use CloudWatch to observe logs, performance metrics, and set alarms for unexpected behavior.

That’s all about overview of the Lambda functions and its suitability for applications that don’t require continuous availability.. If you have any queries or feedback, please write us at contact@waytoeasylearn.com. Enjoy learning, Enjoy AWS Tutorials.!!

Lambda Functions
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