# Other Compute

What is Docker?

* 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)

Where Docker images are stored?

* 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 versus Virtual Machines

* Docker is ”sort of” a virtualization technology, but not exactly
* Resources are shared with the host => many containers on one server

## ECS

* 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

## Fargate

* 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

## ECR

* 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

## What’s serverless?

* 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

## Why AWS Lambda ?

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!

## Benefits of AWS Lambda

* 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!

## AWS Lambda language support

* 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

## AWS Lambda Pricing: example

* 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**

## Amazon API Gateway

* 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.

## AWS Batch

* 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 vs Lambda

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

## Amazon Lightsail

* 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 Summary

* 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

## Other Compute Summary

* 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