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