AWS Lambda container images allow developers to package and deploy Lambda functions as container images up to 10 GB in size, providing greater flexibility compared to traditional ZIP deployment packages limited to 250 MB uncompressed.
Container images for Lambda must be based on AWS-provided base …AWS Lambda container images allow developers to package and deploy Lambda functions as container images up to 10 GB in size, providing greater flexibility compared to traditional ZIP deployment packages limited to 250 MB uncompressed.
Container images for Lambda must be based on AWS-provided base images or custom images that implement the Lambda Runtime API. AWS offers base images for popular runtimes including Python, Node.js, Java, .NET, Go, and Ruby. These base images are available through Amazon Elastic Container Registry (ECR) Public Gallery.
To create a Lambda container image, developers typically start with a Dockerfile that specifies the base image, copies function code, installs dependencies, and sets the CMD instruction to point to the function handler. The image must implement the Lambda Runtime Interface Client (RIC) to communicate with the Lambda service.
Key benefits of using container images include:
1. Larger package sizes supporting complex applications with many dependencies
2. Consistent development and testing environments using familiar container tooling
3. Ability to use preferred base operating systems and custom runtimes
4. Better dependency management for machine learning models or data processing workloads
Deployment involves building the container image locally or through CI/CD pipelines, pushing it to Amazon ECR, and then creating or updating the Lambda function to reference the ECR image URI. Lambda caches container images to optimize cold start performance.
For testing locally, AWS provides the Lambda Runtime Interface Emulator (RIE), which simulates the Lambda execution environment on your local machine.
Important considerations include ensuring images are stored in ECR within the same AWS region as the Lambda function, implementing proper IAM permissions for ECR access, and optimizing image layers to reduce cold start times. Container image functions support the same Lambda features as ZIP-based functions, including VPC connectivity, provisioned concurrency, and event source mappings.
AWS Lambda Container Images - Complete Guide
Why Lambda Container Images Are Important
Lambda container images represent a significant evolution in serverless computing, allowing developers to package Lambda functions as container images up to 10 GB in size. This capability bridges the gap between traditional containerized applications and serverless architecture, enabling teams to leverage existing container tooling, workflows, and dependencies while benefiting from Lambda's automatic scaling and pay-per-use pricing model.
What Are Lambda Container Images?
Lambda container images are Docker-compatible container images that contain your function code and all its dependencies. Instead of uploading a .zip deployment package, you create a container image and store it in Amazon Elastic Container Registry (ECR). Lambda then pulls and runs this image when your function is invoked.
Key characteristics include: - Maximum image size of 10 GB (compared to 250 MB unzipped for .zip packages) - Must implement the Lambda Runtime API - Stored in Amazon ECR (private repositories only) - Support for custom runtimes and any programming language - Images must be based on Linux
How Lambda Container Images Work
1. Image Creation You create a Dockerfile that includes: - A base image (AWS provides base images for popular runtimes) - Your function code and dependencies - The Lambda runtime interface client
2. Building and Pushing - Build the container image locally using Docker - Tag the image appropriately - Push the image to an Amazon ECR private repository in the same region as your Lambda function
3. Function Configuration - Create a Lambda function specifying the ECR image URI - Lambda pulls the image and caches it for subsequent invocations - The function executes within the container environment
4. Invocation Flow - When invoked, Lambda provisions a microVM with your container image - The Lambda service manages cold starts and warm instances - Your code runs with the same execution model as .zip-based functions
AWS-Provided Base Images
AWS provides base images for: - Python, Node.js, Java, .NET, Go, Ruby - Custom runtime (provided.al2) - These images include the Lambda Runtime Interface Client
Key Differences from Standard Lambda Deployments
| Feature | .zip Package | Container Image | |---------|--------------|-----------------| | Max Size | 250 MB (unzipped) | 10 GB | | Storage | S3 | ECR | | Custom Runtimes | Via layers | Native support | | Local Testing | AWS SAM | Standard Docker tools |
Best Practices
- Use multi-stage builds to minimize image size - Leverage AWS base images when possible for optimization - Store images in ECR repositories in the same AWS region - Use image tags for version control - Test locally using the Lambda Runtime Interface Emulator (RIE)
Exam Tips: Answering Questions on Lambda Container Images
Remember These Key Facts:
1. Size Limit: Container images support up to 10 GB - this is the primary reason to choose container images over .zip packages when dealing with large dependencies
2. ECR Requirement: Images MUST be stored in Amazon ECR private repositories - not Docker Hub, not ECR Public, not S3
3. Region Matching: The ECR repository must be in the same region as the Lambda function
4. Runtime API: Container images must implement the Lambda Runtime API - this is how Lambda communicates with your code
5. Base Images: Know that AWS provides optimized base images for common runtimes
6. Use Cases: Choose container images when: - Dependencies exceed .zip size limits - You need custom runtimes or ML frameworks - Your team has existing container-based CI/CD pipelines - You require specific OS-level packages
7. Linux Only: Lambda container images must be based on Linux - Windows containers are not supported
8. Same Execution Model: Container-based functions have the same timeout limits (15 minutes), memory options (128 MB to 10 GB), and billing model as .zip-based functions
Common Exam Scenarios:
- A company has a large ML model exceeding 250 MB → Use container images - Need to use a language not natively supported → Container image with custom runtime - Existing Docker-based development workflow → Container images maintain consistency - Where to store Lambda container images → Amazon ECR (private repository)