Docker

1. Introduction

This chapter is a condensed walk-through of the official Docker Getting Started Guide, making use of an example repository Python-Vscode to demonstrate some basic docker commands. Now what's the main difference between a VM and Docker?

Docker virtualizes just the app and its dependencies.

2. Installation

The installation steps may vary between distributions and OS's. Follow the steps to install docker-ce from the official documentation.

After that, pull the repository that is used in this tutorial from https://github.com/Menziess/Python-Vscode:

git clone git@github.com:Menziess/Python-Vscode.git
cd Python-Vscode

3. Run and Deploy

Develop, deploy, and run applications with containers.

  1. Image: a blueprint for a container

  2. Container: a unit of software (instance of an image) that packages all its dependencies so that it run smoothly and reliably

  3. Swarm: a cluster of nodes (running Docker)

  4. Service: a collection of containers running the same image

  5. Stack: multiple services, possibly sharing dependencies, to be scaled and orchestrated together

For example: an application, or a stack, may contain a database service, and a web-app service, described by their respective images, each having three containers (6 in total), running distributed on a swarm.

2.1 Build Image

Verify that the Dockerfile exists:

$ ls
Dockerfile        app.py            requirements.txt

Build the image from the dockerfile:

docker build -t menziess/python-vscode:latest .

Show the image that has been built:

$ docker image ls

REPOSITORY             TAG                 IMAGE ID
menziess/python-vscode latest              326387cea398

Run the app in detached mode:

docker run -p 80:80 menziess/python-vscode

Show the running containers:

$ docker container ls
CONTAINER ID        IMAGE                  COMMAND             CREATED
1fa4ab2cf395        menziess/python-vscode "python app.py"     28 seconds ago

Stop the running container:

docker container stop 1fa4ab2cf395

Push container to your own dockerhub repository:

docker login
docker push <dockerhub-username>/python-vscode

2.2 Scale Service (Multiple Containers - Single Node)

Verify that the docker-compose.yml file exists:

$ ls
Dockerfile        app.py            requirements.txt      docker-compose.yml

We initialize a swarm (of one node, our local computer):

docker swarm init

Then we deploy our service:

docker stack deploy -c docker-compose.yml getstartedlab

And we scale it by increasing the number of replicas in the .yml file, and simply run the previous command again:

docker stack deploy -c docker-compose.yml getstartedlab

We take down the app, and leave the swarm:

docker stack rm getstartedlab
docker swarm leave --force

2.3 Distributed Swarm (Containers Across Cluster - Multiple Nodes)

Start two VM's using virtualbox and docker-machine:

docker-machine create --driver virtualbox myvm1
docker-machine create --driver virtualbox myvm2

Initialize the first VM as a swarm manager, as we did in 3.2:

docker-machine ls
docker-machine ssh myvm1 "docker swarm init --advertise-addr <myvm1 ip>"

Copy the command that is show in terminal, and ssh into the second VM, then paste the command to add it as a worker to the swarm:

docker-machine ssh myvm2 "<the command that is shown>"

Show all the nodes in the swarm:

docker-machine ssh myvm1 "docker node ls"

Now you can walk through 3.2 again, and deploy the stack, but on the distributed swarm this time. If you do this, run docker-machine ls to reveal the VM ip addresses to access the application in the browser.

Finally, we can leave the swarm from within each VM, remove the stack:

docker-machine ssh myvm2 "docker swarm leave"
docker-machine ssh myvm1 "docker swarm leave --force"

docker stack rm getstartedlab
docker-machine stop myvm1
docker-machine stop myvm2

2.4 Stack Services (Adding Database)

We will expand our docker-compose.yml file by adding more services. We will add a docker visualizer and a redis database. The database will require a volume that is stored on the swarm manager called /data, let's make that folder and redeploy:

docker-machine ssh myvm1 "mkdir ./data"
docker stack deploy -c docker-compose.yml getstartedlab

Our application should now display the number of visits.

4. Containers For Development

It is sometimes useful to develop apps in a docker container to ensure consistency between different developers' environments.

4.1 Docker Approach

A container can be run with a shared volume, so that code changes are immediately visible, and the container is deleted after use. Create a docker image with debug enabled by setting the ENV FLASK_DEBUG flag to 1, then run:

docker run -p 80:80 --rm menziess/python-vscode

4.2 Traditional Approach

Create a virtual environment with a tool such as 'virtualenv' in the root folder, and run:

source ./development.sh

This will activate the virtual environment and install dev dependencies and command-line tools. Run the flask app:

flask run

5. Useful Dockerfiles

Installing java
ENV JAVA_HOME=/usr/lib/jvm/jdk1.8.0_111/
RUN mkdir -p /usr/share/man/man1 && \
  apt-get update && \
  apt-get -y --no-install-recommends install openjdk-8-jdk-headless
Installing pipenv dependencies
WORKDIR /app
COPY Pipfile /app
COPY Pipfile.lock /app
RUN pip install pipenv
RUN pipenv install -d --system --deploy --ignore-pipfile
Installing pybuilder whl program, running with entry shell script
WORKDIR /app

COPY Pipfile /app
COPY Pipfile.lock /app

RUN pip3 install pipenv

COPY /target/dist/Pybuilder-1.0.dev0/dist/Pybuilder-1.0.dev0-py3-none-any.whl /app

RUN pipenv install --system
RUN pip3 install /app/Pybuilder-1.0.dev0-py3-none-any.whl

ENTRYPOINT [ "entry" ] # Calls entry script built by pybuilder as entrypoint
Installing pip requirements.txt
# Only copy requirements
COPY requirements.txt /app

# Install any needed packages specified in requirements.txt
RUN pip install --trusted-host pypi.python.org -r requirements.txt
Installing hadoop
FROM kovarn/python-java

# Set environmental variables
ENV HADOOP_HOME /localhadoop/hadoop-2.9.2
ENV JAVA_HOME=/usr/lib/jvm/jdk1.8.0_111/
ENV PATH ${HADOOP_HOME}/bin:${PATH}

# Downloading hadoop and installing config file
RUN wget http://apache.40b.nl/hadoop/common/hadoop-2.9.2/hadoop-2.9.2.tar.gz &&   tar xzf hadoop-2.9.2.tar.gz && \
  rm -rf hadoop-2.9.2.tar.gz

# Create localhadoop folder
RUN mkdir localhadoop && \
  mv -v /hadoop-2.9.2 /localhadoop/hadoop-2.9.2

# Create config folder
RUN mkdir localhadoop/conf && \
  cp -R ${HADOOP_HOME}/etc/hadoop/* localhadoop/conf && \
  wget https://gist.githubusercontent.com/Menziess/52f1064f3b77b4b0b3655ca270a38b6b/raw/cba6dcd237f89538d5a03c57b27278eb15b6d314/hdfs-site.xml -O localhadoop/conf/hdfs-site.xml

RUN hdfs dfs -mkdir localhdfs
Installing pyspark
FROM python:3.6.7-slim-stretch

WORKDIR /app

COPY Pipfile /app
COPY Pipfile.lock /app
RUN pip install pipenv

RUN pipenv install --system

RUN apt update && \
  mkdir -p /usr/share/man/man1 && \
  apt install -y openjdk-8-jre-headless

ENTRYPOINT ["pyspark"] # Expects pipfile contains pyspark
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