AWS Cloud Practitioner Certification

8.Other Compute Section

Index

1. Docker
2. ECS
3. Fargate
4. ECR
5. Why Lambda?
6. AWS API Gateway
7. aws Batch
8. lambda vs Batch
9. Light sail

What is Docker?

1. Docker is a software development platform to deploy apps,
2. Apps are packaged in containers that can be run on any OS
3. Apps run the same, regardless of where they’re run
   • Any machine
   • No compatibility issues
   • Predictable behavior
4. 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) [in layman terms – its kind of source code/image storing aws managed service]

Docker versus Virtual Machines

In Docker 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 – [Mind-map – abstraction over ECS]

• 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?

Shortcoming of EC2

1. Virtual Servers in the Cloud
2. Limited by RAM and CPU
3. Continuously running
4. Scaling means intervention to add / remove servers

Lambda Working –

1. Virtual functions – no servers to manage!
2. Limited by time – short executions
3. Run on-demand
4. Scaling is automated

Benefits of AWS Lambda

1. Easy Pricing:
   • Pay per request and compute time
   • Free tier of 1,000,000 (i.e 1M) AWS Lambda requests and 400,000 GBs of compute time
2. Integrated with the whole AWS suite of services
3. Event–Driven: functions get invoked by AWS when needed
4. Integrated with many programming languages
5. Easy monitoring through AWS CloudWatch
6. Easy to get more resources per functions (up to 10GB of RAM!)
7. Increasing RAM will also improve CPU and network!

Example: Serverless CRON Job

AWS Lambda Pricing: example

1. Pay per calls:
   • First 1,000,000 requests are free
   • $0.20 per 1 million requests thereafter ($0.0000002 per request)
2. 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
3. It is usually very cheap to run AWS Lambda so it’s very popular

Amazon API Gateway

• 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

1. • Fully managed batch processing at any scale
2. • Efficiently run 100,000s of computing batch jobs on AWS
3. • A “batch” job is a job with a start and an end (opposed to continuous)
4. • Batch will dynamically launch EC2 instances or Spot Instances
5. • AWS Batch provisions the right amount of compute / memory
6. • You submit or schedule batch jobs and AWS Batch does the rest!
7. • Batch jobs are defined as Docker images and run on ECS
8. • Helpful for cost optimizations and focusing less on the infrastructure

Batch vs Lambda

Lambda:
• Time limit
• Limited runtimes
• Limited temporary disk space
• Serverless

Batch:
• No time limit
• Any runtime as long as it’s packaged as a Docker image
• Rely on EBS / instance store for disk space
• Relies on EC2 (can be managed by AWS)

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