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Udacity Capstone Cloud DevOps

  • Author: Devin Yang
  • Date: 22.02.2020

Description

In this project we assume that we gonna use python flask to develop a "Great Application", all the application files are stored under directory app. Here are some technical methods we gonna take to make the development efficient in future:

  • This application will deployed on AWS cloud
  • Use docker to containerize all necessary packages and dependencies
  • Use kubernetes to manage the contanerized application
  • Use jenkins for CI/CD

How to make it work?

  1. Create a GitHub repository, push all the development file into it

    • Makefile
    • Dockerfile
    • Jenkinsfile
    • application source codes
    • ... The github repo: you can find it HERE.

  2. Create a IAM role policy for EC2 whole properties access, With this role we can at least manipulate EKS, EC2, S3 Bucket.

  3. Create a Keypairs on aws console manually, and change the ParameterValue of "KeyName" in the file "eks-params.json"

  4. Create a EC2 instance manually as Jenkins master

    • create a ec2 instance manually, assume the installed ec2 system owns a ubuntu 18.0.4 os
    • through ssh connect to this instance

  5. Setup all tools & environments in instance

    • clone source code from github repo into instance
    • install awscli 
      source install_scripts/install_awscli.sh
    • install jenkins in the instance trhough: 
      source install_scripts/install_jenkins.sh
    • install neccessary jenkins plugins:
      • Blue Ocean
      • Docker
      • AWS Pipeline
    • setup credential between jenkins and docker hub, also between jenkins and aws
    • install docker 
      source install_scripts/install_docker.sh
    • install kubectl 
      source install_scripts/install_kubectl.sh
    • install dockerlint 
      source install_scripts/install_dockerlint.sh

      It seems that hadolint is not friendly to Ubuntu system(not easy to find how to install), here dockerlint is used for Dockerfile linting

  6. Create a stack including a aws cluster and its necessary resources.

    • setup aws cli credential 
      aws configure
    • create aws cluster 
      aws cloudformation create-stack \
--stack-name udacityclouddevopscapstone \
--region us-west-2 \
--template-body file://infrastructure/aws-eks.yml \
--parameters file://infrastructure/eks-params.json \
--capabilities CAPABILITY_IAM

    Of course if something goes wrong, you can update the yaml file then update stack with script stack_update.sh.

  7. Copy Cluster IAM Role ARN and change the file in aws-auth.yml correspondingly

  8. Add jenkins master instance into work node

    kubectl apply -f infrastructure/aws-auth.yml

  9. setup a pipeline with repo in github in blue ocean interface

Experiences & Remarks

  • For a instance to initialize a kubenetes cluster, the minimal CPU counts will be 2, t2.micro can not meet the requirements.

  • To push image to private docker hub repository, it is necessary to create a credential in jenkins credential by using "Username/Password", meanwhile ID must be set for jenkins pipeline invoking it to log into docker hub repository

  • In jenkins pipeline when try to log into docker, it came out an error:

    "Got permission denied while trying to connect to the Docker daemon socket at unix:///var/run/docker.sock: Post http://%2Fvar%2Frun%2Fdocker.sock/v1.40/auth: dial unix /var/run/docker.sock: connect: permission denied" Solution:

    sudo chmod 777 /var/run/docker.sock