Introduction
I recently heard about the term ‘serverless containers’. I was already working with cloud and DevOps, Docker, and container orchestration, so I had a question for myself: “What exactly is a serverless container, and how can containers be serverless when they run on servers?”
With that curiosity, I explored serverless containers on AWS, which I found was a powerful approach that blends the flexibility of containers with the simplicity of serverless computing
In this post, I’ll discuss what serverless containers really mean, how AWS IMPLEMENTS THEM, and where they make sense in real-world environments
Serverless Containers:
Serverless containers are containerized workloads that run the app without managing the underlying servers. Serverless does not mean that there are no servers. Servers exist, but we don't need to manage them. AWS takes full responsibility for them.
For the traditional container deployments, you have to:
- Choose the EC2 types
- Provision and manage clusters
- Patch operating systems
- Plan for scaling and capacity
But with serverless, AWS handles all of that. You simply need to:
- Build your container image
- Define CPU and memory needs
- Deploy the workloads
AWS manages the provisioning of compute resources, launching containers, dynamically scaling them as needed, and managing the infrastructure lifecycle.
So, preserving all of the benefits of container-based applications portability, consistency, and flexibility but significantly reducing operational overhead.
Serverless Containers on AWS
AWS provides two options for deploying serverless containers.
Serverless computing for containers (Fargate) is offered by the AWS Cloud for Amazon ECS (Elastic Container Service) or Amazon EKS (Elastic Kubernetes Service) without requiring customers to manage any EC2 instances or worker nodes. Instead, you define your application's task or pod specifications, and AWS takes care of running those specifications for you.
AWS Lambda with Container Images
AWS Lambda also allows you to package your function as a container image with a maximum size of 10 GB instead of a traditional ZIP file. Package your function in a container format when you require a custom runtime environment, and you utilise native dependencies or want to follow a Docker-based build process. When you deploy your container image to AWS Lambda, you receive all of the benefits associated with AWS Lambda's Event-Driven Model, Auto Scaling, and Pay-As-You-Go pricing, along with complete compatibility of your container image(s) with AWS Lambda.
Both services are classified as serverless because they allow customers to describe how to run their applications; AWS takes care of provisioning, scaling, and maintaining hardware resources on behalf of the customer.
Serverless Container Key Features
No Server Management
No EC2 Instances to Size, Patch, or Monitor. AWS fully manages the Compute Layer.
Automatic Scaling
The Compute Layer Automatically Scales Up when Demand increases
and down when Demand decreases, without requiring any human work.
Pay As You Go Pricing
You only pay for CPU, Memory, and Time that your containers
actually consume. And No Idle Capacity Costs.
Native AWARE (AWS) Integration
AWS is Fully Integrated with IAM and VPC, as well as ALB for
networking, Security, and Observability.
Container Flexibility
Use Docker Images and Existing CI/CD Pipelines to Ease in Move of
Containers to the Cloud.
Lambda Containers vs AWS Fargate
Lambda and Fargate provide a serverless container option, but each supports applications with varying workload types.
Lambda has many use cases for short-lived tasks that respond to events (S3, API Gateway, Amazon SQS, EventBridge) that can run for up to 15 minutes and handle inconsistent and unexpected spikes in traffic with lightweight APIs or background jobs.
Examples of event-driven workloads are an image resize service or file processing, webhooks, or cron jobs.
Fargate is intended for long-term applications, like microservices that must keep persistent connections, such as long-running services or REST APIs with multiple connections, or microservices that must maintain their states or persist between requests. In addition, Fargate offers users more options for controlling how their architecture scales, as they can deploy their workloads using ECS or Kubernetes.
Examples of applications that can be deployed using Amazon Fargate include (but are not limited to) internal tools, streaming processors, back-end services, and REST APIs.
Real-World Example of Video Processing Pipeline
Let's say you're creating a video processing application that generates thumbnail images every time someone uploads a video file. Upload traffic is unknown; it may have periods of low volume and sudden influxes (also known as "spikes").
Using Containers As Part Of A Lambda Computing Platform
When a user uploads a video, the system first saves it to Amazon S3 Storage. Next, an event trigger in Amazon S3 initiates a Lambda function. Inside the Lambda function, you may call a container image to create thumbnails utilizing applications such as FFmpeg. Lastly, the Lambda function saves the generated thumbnails back to Amazon S3 Storage for users to view.
Here's how it works:
- There won't be any idle servers.
- You have scalable Lambda computing capabilities when there is high increase in traffic.
- You pay for each time you process; therefore, you do not need to maintain and manage a large EC2 fleet of instances that only process occasionally; you still use complex & powerful container-based software development tools.
Where AWS Fargate Fits Better
Imagine a microservice architecture that supports a mobile or web application.
An example of this type of microservice architecture might have multiple APIs (user, order, payment, inventory) and must be up continuously. Load balancing and service discovery are also necessary for this type of architecture to work.
When using ECS on Fargate:
Each of your microservice tasks runs a container task.
The Application Load Balancer routes traffic.
Your services scale based on either CPU/memory or request count.
There will be no need to manage an EC2 cluster.
Additionally, it provides a nice solution for DevOps teams that require a way to orchestrate containers without having to manage nodes.
Getting Started with Serverless Containers on AWS
Steps: Set Up Serverless Containers on AWS
- Determine which service you want to use, based on what you're trying to achieve
AWS Lambda is a good option when your service will be short-lived (only active while processing an event) and your application is event-driven.
AWS Fargate is a better option when you need to set up a long-running service that requires containerized applications.
- Create Your Application in a Docker Container
Create a Dockerfile with best-practice guidelines:
- Keep base images slim
- Use multiple stages to build
- Minimize the number of layers in your image
- Upload Your Image to Amazon Elastic Container Registry (ECR)
ECR is AWS's completely Managed, scalable image storage/management for storing, managing and delivering Docker container images.
- Deploy Your Container Based Applications
Most people use the AWS Console, the AWS CLI, Terraform or CI/CD pipelines to define AWS Lambda tasks or Amazon Elastic Container Services (ECS) tasks.
- Improve your system's throughput and cost management by keeping track of performance metrics using AWS CloudWatch logs, metrics and alarms.
Key Takeaways
- Container management infrastructure is removed by serverless, but the large degree of container flexibility remains.
- Two serverless options on AWS: Lambda Container and Fargate.
- You should choose Choose based on runtime duration, architecture, or scalability needs
- Serverless containers offer faster deployments, simplify operational processes, and provide greater cost-effectiveness.
- They are best for modern cloud-native DevOps workflows.
Conclusion
Serverless containers fill the gap between traditional containers and serverless computing; it allow you to continue using your existing Docker workflow without the hassles of managing servers, clusters, or even performing capacity planning.
AWS allows you to develop and deploy event-driven functions in AWS Lambda as well as scalable microservices using AWS Fargate. This means that not only can you deploy applications faster but also run them more efficiently than before, thanks to the extensive capabilities of AWS.
Feel free to reach out in the comments if you have questions or want to share your experiences with serverless containers!
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