Porting a Linux Container (LXC) to Docker might seem like a straightforward task given that both are containerization technologies. However, there are several caveats and pitfalls that one should be aware of before attempting this process.

LXC and Docker are both used to create and manage containers, but they operate on different underlying principles. LXC is more of a lightweight virtualization method that provides an environment similar to a full-fledged virtual machine. Docker, on the other hand, is a platform for developing, shipping, and running applications inside containers, optimized for microservices and cloud-native applications.

Here are the key issues to be aware of when porting an LXC container to Docker:

• Different Container Philosophies:
  • LXC containers are closer to traditional virtual machines, providing a more complete operating system environment.
  • Docker containers are designed to run a single application or service, following a microservice architecture.

• File System Differences:
  • LXC uses a full OS filesystem, while Docker uses layered filesystems.
  • You may need to manually adjust file paths and configurations when moving to Docker.

• Process Management:
  • LXC containers manage multiple processes similarly to a traditional Linux environment.
  • Docker is designed to run a single process per container. This might require refactoring of the services running in the LXC container.

• Networking:
  • LXC networking configurations can be complex and highly customized.
  • Docker abstracts much of the networking and provides simpler, yet different, networking options which might require reconfiguration.

• Security Contexts:
  • LXC provides a more traditional Linux security model.
  • Docker has a different security model, with its own set of user namespaces, capabilities, and SELinux/AppArmor profiles, which might affect how applications behave.

• Resource Allocation:
  • LXC provides finer control over resource allocation (CPU, memory, etc.).
  • Docker abstracts and automates much of this, which could lead to different performance characteristics.

The process of containerizing the Flask application involved several key steps, including setting up the necessary dependencies, creating a Dockerfile, and configuring the `docker-compose.yml` file.

When transitioning from LXC containers to Docker, it's often more efficient and cleaner to use base Docker images for your applications rather than attempting to convert existing LXC containers. This approach offers several advantages:

• Universal Compatibility: Docker images are designed to be highly portable and compatible across various cloud platforms, making it easier to deploy and manage your applications.
• Separation of Concerns: Running different services in separate containers (e.g., Python apps in one container and MariaDB in another) aligns with Docker’s microservices architecture, promoting better scalability and maintenance.
• Cleaner Environment: Starting with a clean base image ensures that only the necessary dependencies and configurations are included, reducing potential conflicts and bloat.

Here’s a general outline of the steps to create Docker containers for a Python application and a MariaDB database.

Before containerizing the application, it was crucial to define the required dependencies in a `requirements.txt` file. The Flask application used the following dependencies:

Flask==2.1.2
mariadb==1.1.4

These versions ensured compatibility and stability, avoiding issues related to breaking changes in more recent versions.

The next step was to create a `Dockerfile` to define the environment for the Flask application. The Dockerfile included the following instructions:

FROM python:3.9-slim
 
# Install MariaDB Connector/C library and other dependencies
RUN apt-get update && apt-get install -y \
    libmariadb3 \
    libmariadb-dev \
    gcc \
    && apt-get clean \
    && rm -rf /var/lib/apt/lists/*
 
WORKDIR /app
 
COPY requirements.txt requirements.txt
RUN pip install -r requirements.txt
 
COPY . .
 
CMD ["python", "main.py"]

Explanation:

• `FROM python:3.9-slim`: This uses a lightweight Python 3.9 image as the base.
• `WORKDIR /app`: Sets the working directory inside the container.
• `COPY requirements.txt requirements.txt`: Copies the `requirements.txt` file into the container.
• `RUN pip install -r requirements.txt`: Installs the required Python packages.
• `COPY . .`: Copies all the application files into the container.

To orchestrate the Flask application in a multi-container environment, a `docker-compose.yml` file was created with the following content:

version: '3.1'

services:
  app:
    build: .
    ports:
      - "5000:5000"
    environment:
      - FLASK_ENV=development
      - API_TOKEN=bXktbG9uZy***LongAssBase64String***cmluZw==
      - DB_HOST=api.facundoitest.space
      - DB_PASSWORD=***myPassword***

Explanation:

• `services`: Defines the application service.
• `build: .`: Tells Docker Compose to build the image using the Dockerfile in the current directory.
• `ports: “5000:5000”`: Maps port 5000 on the host to port 5000 in the container.

After setting up the Dockerfile and `docker-compose.yml`, the following commands were used to build and run the Flask application in a Docker container:

docker-compose build
docker-compose up -d

Explanation:

• `docker-compose build`: Builds the Docker image based on the instructions in the Dockerfile.
• `docker-compose up -d`: Starts the container in detached mode.

from flask import Flask, request, jsonify, abort, g
from datetime import datetime
import mariadb
import re
import os
 
# Get passwords and tokens from environment variables
API_TOKEN = os.environ.get('API_TOKEN')
DB_PASSWORD = os.environ.get('DB_PASSWORD')
DB_HOST = os.environ.get('DB_HOST')
 
app = Flask(__name__)
 
def get_db():
    if 'db' not in g:
        g.db = mariadb.connect(
            host=DB_HOST,
            port=3306,
            user="facundo",
            password=DB_PASSWORD,
            database="VeeamReports"
        )
    return g.db
 
@app.before_request
def before_request():
    get_db()
 
@app.teardown_request
def teardown_request(exception=None):
    db = g.pop('db', None)
    if db is not None:
        db.close()
 
@app.route('/upload', methods=['POST'])
def upload_file():
    db = get_db()  # Get the database connection
    cursor = db.cursor()  # Now 'db' should be defined
 
    # Check for Authorization header
    if 'Authorization' not in request.headers:
        abort(401)  # Unauthorized
 
    # Check if the token is correct
    token = request.headers['Authorization']
    if token != API_TOKEN:
        abort(403)  # Forbidden
 
    data = request.get_json()  # Get JSON data from request
    if data is None:
        return jsonify({'error': 'No JSON received.'}), 400
 
    # ---------- The application itself ---------
    # Extract hostname from data
    hostname = data['hostname']
 
    # Create a new table for this hostname if it doesn't exist
    cursor.execute(f"CREATE TABLE IF NOT EXISTS `{hostname}` (id INT AUTO_INCREMENT PRIMARY KEY, creationtime VARCHAR(255), vmname VARCHAR(255), type VARCHAR(255), result VARCHAR(255))")
 
    # Delete all rows from the table
    cursor.execute(f"DELETE FROM `{hostname}`")
 
    for restorePoint in data['restorePoints']:
        creationtime = restorePoint['creationtime']
 
        # Check if creationtime contains Date
        if "Date" in creationtime:
            # Extract the integer from the creationtime string
            match = re.search(r'\((\d+)\)', creationtime)
            if match:
                creationtime = int(match.group(1))  # Convert to integer type
 
        # Check the type and replace it if necessary
        type = str(restorePoint['type'])  # wtf
        if str(restorePoint['type']) == '0':
            type = 'Full'
        elif str(restorePoint['type']) == '2':
            type = 'Incremental'
        elif str(restorePoint['type']) == '4':
            type = 'Snapshot'
 
        # Check the type and replace the result if necessary
        if str(restorePoint['type']) in ["TieringJob", "BackupJob"]:
            if str(restorePoint['result']) == '0':
                result = 'Success'
            elif str(restorePoint['result']) == '1':
                result = 'Warn'
            elif str(restorePoint['result']) == '2':
                result = 'Fail'
        else:
            result = restorePoint['result']
 
        query = f"INSERT INTO `{hostname}` (creationtime, vmname, type, result) VALUES (%s, %s, %s, %s)"
        values = (
            creationtime,
            restorePoint['vmname'],
            type,
            result
        )
        cursor.execute(query, values)
 
    db.commit()  # Commit the transaction
 
    return jsonify({'message': 'Data saved successfully.'}), 200
 
if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5000, debug=True)

#5 11.31       OSError: mariadb_config not found.
#5 11.31
#5 11.31       This error typically indicates that MariaDB Connector/C, a dependency which
#5 11.31       must be preinstalled, is not found.
#5 11.31       If MariaDB Connector/C is not installed, see installation instructions
#5 11.31       If MariaDB Connector/C is installed, either set the environment variable
#5 11.31       MARIADB_CONFIG or edit the configuration file 'site.cfg' to set the
#5 11.31        'mariadb_config' option to the file location of the mariadb_config utility.
#5 11.31
#5 11.31       [end of output]

The error you're seeing indicates that the `mariadb` Python package requires the MariaDB Connector/C library to be preinstalled, and it's not available in the Docker image you're using. You need to install this library before installing your Python requirements.

Here's how you can modify your Dockerfile to install the MariaDB Connector/C library before installing the Python dependencies:

FROM python:3.9-slim
 
# Install MariaDB Connector/C library and other dependencies
RUN apt-get update && apt-get install -y \
    libmariadb3 \
    libmariadb-dev \
    gcc \
    && apt-get clean \
    && rm -rf /var/lib/apt/lists/*
 
WORKDIR /app
 
COPY requirements.txt requirements.txt
RUN pip install -r requirements.txt
 
COPY . .
 
CMD ["python", "main.py"]

• Update and install dependencies: The `RUN apt-get update && apt-get install -y` command installs the required MariaDB Connector/C library (`libmariadb3` and `libmariadb-dev`) and a C compiler (`gcc`).
• Clean up: The `apt-get clean && rm -rf /var/lib/apt/lists/*` commands clean up the apt cache to reduce the image size.

Now, try building your Docker image again with the updated Dockerfile:

docker-compose up --build

This should resolve the issue with the missing `mariadb_config` and allow the `mariadb` Python package to install correctly.

app-flask-app-1  | ImportError: cannot import name 'url_quote' from 'werkzeug.urls' (/usr/local/lib/python3.9/site-packages/werkzeug/urls.py)
app-flask-app-1 exited with code 1

The error you're encountering is due to an incompatibility between Flask and Werkzeug versions. Flask 2.1.2 may not be compatible with the latest version of Werkzeug.

To fix this, you can pin the Werkzeug version to one that is compatible with Flask 2.1.2. Based on the error, pinning Werkzeug to version 2.0.3 should resolve the issue.

Flask==2.1.2
mariadb==1.1.4
Werkzeug==2.0.3

FROM python:3.9-slim
 
# Install MariaDB Connector/C library and other dependencies
RUN apt-get update && apt-get install -y \
    libmariadb3 \
    libmariadb-dev \
    gcc \
    && apt-get clean \
    && rm -rf /var/lib/apt/lists/*
 
WORKDIR /app
 
COPY requirements.txt requirements.txt
RUN pip install -r requirements.txt
 
COPY . .
 
CMD ["python", "main.py"]

1. Update your `requirements.txt` as shown above.
2. Rebuild the Docker image with the updated `requirements.txt`:

docker-compose up --build

This should resolve the incompatibility issue between Flask and Werkzeug and allow your Flask app to start without errors.

Porting from LXC to Docker is not a trivial task due to the differences in how these containers are designed and managed. It's essential to review the services, configurations, and workflows involved before starting the migration to avoid unexpected issues and ensure that your applications run smoothly in Docker.

• Docker documentation: https://docs.docker.com/
• LXC documentation: https://linuxcontainers.org/lxc/introduction/