- Other Compute
- Docker is a software development platform to deploy apps
- Apps are packaged in containers that can be run on any OS
- Apps run the same, regardless of where they’re run
- Any machine
- No compatibility issues
- Predictable behavior
- Less work
- Easier to maintain and deploy
- Works with any language, any OS, any technology
- Scale containers up and down very quickly (seconds)
- Docker images are stored in Docker Repositories
- Public: Docker Hub https://hub.docker.com/
- Find base images for many technologies or OS:
- Ubuntu
- MySQL
- NodeJS, Java…
- Private: Amazon ECR (Elastic Container Registry)
- Docker is ”sort of” a virtualization technology, but not exactly
- Resources are shared with the host => many containers on one server
- ECS = Elastic Container Service
- Launch Docker containers on AWS
- You must provision & maintain the infrastructure (the EC2 instances)
- AWS takes care of starting / stopping containers
- Has integrations with the Application Load Balancer
- Launch Docker containers on AWS
- You do not provision the infrastructure (no EC2 instances to manage) – simpler!
- Serverless offering
- AWS just runs containers for you based on the CPU / RAM you need
- Elastic Container Registry
- Private Docker Registry on AWS
- This is where you store your Docker images so they can be run by ECS or Fargate
- Serverless is a new paradigm in which the developers don’t have to manage servers anymore…
- They just deploy code
- They just deploy… functions !
- Initially... Serverless == FaaS (Function as a Service)
- Serverless was pioneered by AWS Lambda but now also includes anything that’s managed: “databases, messaging, storage, etc.”
- Serverless does not mean there are no servers…
- it means you just don’t manage / provision / see them
| EC2 | Lambda |
|---|---|
| Virtual Servers in the Cloud | Virtual functions – no servers to manage! |
| Limited by RAM and CPU | Limited by time - short executions |
| Continuously running | Run on-demand |
| Scaling means intervention to add / remove servers | Scaling is automated! |
- Easy Pricing:
- Pay per request and compute time
- Free tier of 1,000,000 AWS Lambda requests and 400,000 GBs of compute time
- Integrated with the whole AWS suite of services
- Event-Driven: functions get invoked by AWS when needed
- Integrated with many programming languages
- Easy monitoring through AWS CloudWatch
- Easy to get more resources per functions (up to 10GB of RAM!)
- Increasing RAM will also improve CPU and network!
- Node.js (JavaScript)
- Python
- Java (Java 8 compatible)
- C# (.NET Core)
- Golang
- C# / Powershell
- Ruby
- Custom Runtime API (community supported, example Rust)
- Lambda Container Image
- The container image must implement the Lambda Runtime API
- ECS / Fargate is preferred for running arbitrary Docker images
- You can find overall pricing information here: https://aws.amazon.com/lambda/pricing/
- Pay per calls:
- First 1,000,000 requests are free
- $0.20 per 1 million requests thereafter ($0.0000002 per request)
- Pay per duration: (in increment of 1 ms)
- 400,000 GB-seconds of compute time per month for FREE
- == 400,000 seconds if function is 1GB RAM
- == 3,200,000 seconds if function is 128 MB RAM
- After that $1.00 for 600,000 GB-seconds
- It is usually very cheap to run AWS Lambda so it’s very popular
- Example: building a serverless API
- Fully managed service for developers to easily create, publish, maintain, monitor, and secure APIs
- Serverless and scalable
- Supports RESTful APIs and WebSocket APIs
- Support for security, user authentication, API throttling, API keys, monitoring.
- Fully managed batch processing at any scale
- Efficiently run 100,000s of computing batch jobs on AWS
- A “batch” job is a job with a start and an end (opposed to continuous)
- Batch will dynamically launch EC2 instances or Spot Instances
- AWS Batch provisions the right amount of compute / memory
- You submit or schedule batch jobs and AWS Batch does the rest!
- Batch jobs are defined as Docker images and run on ECS
- Helpful for cost optimizations and focusing less on the infrastructure
| Batch | Lambda |
|---|---|
| No time limit | Time limit |
| Any runtime as long as it’s packaged as a Docker image | Limited runtime |
| Rely on EBS / instance store for disk space | Limited temporary disk space |
| Relies on EC2 (can be managed by AWS) | Serverless |
- Virtual servers, storage, databases, and networking
- Low & predictable pricing
- Simpler alternative to using EC2, RDS, ELB, EBS, Route 53…
- Great for people with little cloud experience!
- Can setup notifications and monitoring of your Lightsail resources
- Use cases:
- Simple web applications (has templates for LAMP, Nginx, MEAN, Node.js…)
- Websites (templates for WordPress, Magento, Plesk, Joomla)
- Dev / Test environment
- Has high availability but no auto-scaling, limited AWS integrations
- Lambda is Serverless, Function as a Service, seamless scaling, reactive
- Lambda Billing:
- By the time run x by the RAM provisioned
- By the number of invocations
- Language Support: many programming languages except (arbitrary) Docker
- Invocation time: up to 15 minutes
- Use cases:
- Create Thumbnails for images uploaded onto S3
- Run a Serverless cron job
- API Gateway: expose Lambda functions as HTTP API
- Docker: container technology to run applications
- ECS: run Docker containers on EC2 instances
- Fargate:
- Run Docker containers without provisioning the infrastructure
- Serverless offering (no EC2 instances)
- ECR: Private Docker Images Repository
- Batch: run batch jobs on AWS across managed EC2 instances
- Lightsail: predictable & low pricing for simple application & DB stacks
Databases & Analytics 
List Deploying and Managing Infrastructure at Scale 