For the first container, we will be creating a Dockerfile from scratch. For the other containers, the Dockerfiles are provided.
-
Create a Dockerfile
-
Access the jumpbox
-
In the
~/blackbelt-aks-hackfest/app/web
directory, add a file called "Dockerfile"- If you in in a SSH session, use vi as the editor
- In RDP, you can use Visual Studio Code
-
Add the following lines and save:
FROM node:9.4.0-alpine ARG VCS_REF ARG BUILD_DATE ARG IMAGE_TAG_REF ENV GIT_SHA $VCS_REF ENV IMAGE_BUILD_DATE $BUILD_DATE ENV IMAGE_TAG $IMAGE_TAG_REF WORKDIR /usr/src/app COPY package*.json ./ RUN npm install COPY . . RUN apk --no-cache add curl EXPOSE 8080 CMD [ "npm", "run", "container" ]
-
-
Create a container image for the node.js Web app
From the terminal session:
cd ~/blackbelt-aks-hackfest/app/web docker build --build-arg BUILD_DATE=`date -u +"%Y-%m-%dT%H:%M:%SZ"` --build-arg VCS_REF=`git rev-parse --short HEAD` --build-arg IMAGE_TAG_REF=v1 -t rating-web .
-
Validate image was created with
docker images
In this step, the Dockerfile has been created for you.
-
Create a container image for the node.js API app
cd ~/blackbelt-aks-hackfest/app/api docker build -t rating-api .
-
Validate image was created with
docker images
-
Create a MongoDB image with data files
cd ~/blackbelt-aks-hackfest/app/db docker build -t rating-db .
-
Validate image was created with
docker images
Create a docker bridge network to allow the containers to communicate internally.
docker network create --subnet=172.18.0.0/16 my-network
-
Run mongo container
docker run -d --name db --net my-network --ip 172.18.0.10 -p 27017:27017 rating-db
-
Validate by running
docker ps -a
-
Import data into database
docker exec -it db bash
You will have a prompt inside the mongo container. From that prompt, run the import script (
./import.sh
)root@61f9894538d0:/# ./import.sh 2018-01-10T19:26:07.746+0000 connected to: localhost 2018-01-10T19:26:07.761+0000 imported 4 documents 2018-01-10T19:26:07.776+0000 connected to: localhost 2018-01-10T19:26:07.787+0000 imported 72 documents 2018-01-10T19:26:07.746+0000 connected to: localhost 2018-01-10T19:26:07.761+0000 imported 2 documents
-
Type
exit
to exit out of container
-
Run api app container
docker run -d --name api -e "MONGODB_URI=mongodb://172.18.0.10:27017/webratings" --net my-network --ip 172.18.0.11 -p 3000:3000 rating-api
Note that environment variables are used here to direct the api app to mongo.
-
Validate by running
docker ps -a
-
Test api app with curl
curl http://localhost:3000/api/heroes
-
Run web app container
docker run -d --name web -e "API=http://172.18.0.11:3000/" --net my-network --ip 172.18.0.12 -p 8080:8080 rating-web
-
Validate by running
docker ps -a
-
Test web app
The jumpbox has a Public IP address and port 8080 is open. You can browse your running app with a link such as: http://13.90.246.114:8080
You can also test via curl
curl http://localhost:8080
Now that we have container images for our application components, we need to store them in a secure, central location. In this lab we will use Azure Container Registry for this.
-
In the browser, sign in to the Azure portal at https://portal.azure.com. Your Azure login ID will look something like
[email protected]
-
Click "Create a resource" and select "Azure Container Registry"
-
Provide a name for your registry (this must be unique)
-
Use the existing Resource Group
-
Enable the Admin user
-
Use the 'Standard' SKU (default)
The Standard registry offers the same capabilities as Basic, but with increased storage limits and image throughput. Standard registries should satisfy the needs of most production scenarios.
-
Browse to your Container Registry in the Azure Portal
-
Click on "Access keys"
-
Make note of the "Login server", "Username", and "password"
-
In the terminal session on the jumpbox, set each value to a variable as shown below
# set these values to yours ACR_SERVER= ACR_USER= ACR_PWD= docker login --username $ACR_USER --password $ACR_PWD $ACR_SERVER
# Be sure to replace the login server value
docker tag rating-db $ACR_SERVER/azureworkshop/rating-db:v1
docker tag rating-api $ACR_SERVER/azureworkshop/rating-api:v1
docker tag rating-web $ACR_SERVER/azureworkshop/rating-web:v1
docker push $ACR_SERVER/azureworkshop/rating-db:v1
docker push $ACR_SERVER/azureworkshop/rating-api:v1
docker push $ACR_SERVER/azureworkshop/rating-web:v1
Output from a successful docker push
command is similar to:
The push refers to a repository [mycontainerregistry.azurecr.io/azureworkshop/rating-db]
035c23fa7393: Pushed
9c2d2977a0f4: Pushed
d7b18f71e002: Pushed
ec41608c0258: Pushed
ea882d709aca: Pushed
74bae5e77d80: Pushed
9cc75948c0bd: Pushed
fc8be3acfc2d: Pushed
f2749fe0b821: Pushed
ddad740d98a1: Pushed
eb228bcf2537: Pushed
dbc5f877c367: Pushed
cfce7a8ae632: Pushed
v1: digest: sha256:f84eba148dfe244f8f8ad0d4ea57ebf82b6ff41f27a903cbb7e3fbe377bb290a size: 3028
- Return to the Azure Portal in your browser and validate that the images appear in your Container Registry under the "Repositories" area.
- Under tags, you will see "v1" listed.