Python QSL Versioning: Runtime, Build & Pipelines

Nov 28, 2025 by Alex Johnson 50 views

Unveiling the QSL Version: A Comprehensive Guide

Determining the current QSL version in a Python environment is crucial for debugging, maintenance, and ensuring compatibility across various stages of your project. This is particularly relevant when working with QGIS Server Light (QSL), where understanding the version of a running worker instance becomes essential. This comprehensive guide delves into methods for obtaining the QSL version at different points in your workflow: during runtime, package builds, and within your pipelines.

Currently, the QSL version is often defined within the setup.py file. This file acts as the central repository for project metadata, including the version number. This version is then used during the package build process, where the Python package is constructed, making it available for distribution and installation. Furthermore, the version information is leveraged in pipelines, such as those implemented on GitHub, to track deployments, manage dependencies, and ensure that the correct version of QSL is being used throughout the development and deployment lifecycle. However, for enhanced maintainability, debugging, and overall clarity, it's beneficial to access the QSL version directly within a running worker instance.

This article will explore strategies to achieve this, promoting best practices and simplifying your QSL-related projects. The core objective is to provide developers with a clear understanding of how to easily access the version number in several contexts. This information is invaluable when troubleshooting issues, monitoring deployments, and ensuring that all components of your system are aligned with the intended QSL version. By implementing these methods, you gain greater control over your deployments and reduce the likelihood of version-related problems. We will cover how to make the version information readily available, not just during the build process, but also during runtime.

The Importance of Versioning Versioning is a fundamental concept in software development, particularly for projects like QSL that evolve over time. Proper versioning allows developers to track changes, manage dependencies, and ensure that different components of a system are compatible. Without a clear understanding of the version number, it's challenging to identify the specific features and bug fixes present in a particular release. This can lead to significant problems during debugging and maintenance.

For QSL, versioning is crucial because it helps: track feature additions, manage bug fixes, coordinate with other related libraries, and ensure that users are running the intended version of the software. This helps to prevent conflicts and ensure that users can reliably deploy and use the software. By being able to quickly determine which version of QSL is running, developers can correlate the code with reported issues, accurately track dependencies, and maintain a history of changes. This level of detail is also particularly valuable in larger deployment scenarios. Furthermore, versioning facilitates automated deployments in pipelines, where the correct version must be specified for installation and subsequent use. With the knowledge of the QSL version, you can configure your continuous integration and continuous delivery (CI/CD) pipelines to install the right version automatically, based on the project requirements. Overall, having readily accessible version information is a critical practice for creating and maintaining reliable QSL-based applications.

Accessing the QSL Version at Runtime

Accessing the QSL version during runtime enables you to retrieve the version information while your QSL worker instance is actively operating. Several methods can achieve this, improving debugging and maintenance capabilities. The most straightforward approach is to import the version directly from your QSL package.

Importing the Version from the Package

If the setup.py file properly defines the version, it's generally accessible during runtime through the package's metadata. This method requires a well-structured project. After installing your QSL package, you can access the version using a few simple lines of code within your Python script. Assuming that your QSL version is specified within a module named __init__.py or another designated module within your QSL package, you can import it like so:

from qsl import __version__

print(f"QSL Version: {__version__}")

Here, qsl represents your QSL package name. The __version__ variable is a standard convention for storing version information. If the version is not found, you can modify it to suit your project structure. This enables you to directly use the version number within your running application. It's readily available without needing to parse files or use external tools. This makes it an ideal solution for many use cases where version information must be dynamically acquired.

Using the pkg_resources Module

The pkg_resources module is part of the setuptools library and offers a more robust way of accessing package metadata, including the version. This method is particularly useful when you need to retrieve the version information without assuming anything about the package structure.

import pkg_resources

try:
    version = pkg_resources.get_distribution('qsl').version
    print(f"QSL Version: {version}")
except pkg_resources.DistributionNotFound:
    print("QSL is not installed or not found.")

This code uses get_distribution() to get the distribution object for your package. The .version attribute then provides the version string. The try...except block handles the DistributionNotFound exception, which can occur if the package isn't installed in the current environment. This method is versatile and works well across different Python environments.

Environment Variables

Another approach involves setting the version as an environment variable during the build process or deployment. This provides a global configuration value accessible in Python during runtime.

During build (e.g., in setup.py):

You can pass the version to environment variables during the packaging process. For example, using the setuptools module, you can inject it during the build process.
Deployment (e.g., CI/CD pipeline):

In your deployment scripts, you set this environment variable before starting your QSL worker. For instance, in a bash script:
```
export QSL_VERSION="$(python -c 'from qsl import __version__; print(__version__)')"
python your_qsl_worker.py
```

Access in your Python code:

Access the environment variable in your Python code as follows:

import os

qsl_version = os.environ.get('QSL_VERSION', 'Unknown')
print(f"QSL Version: {qsl_version}")

This method keeps version information separate from your code. It's often helpful in environments where you want to dynamically configure the software. It also allows you to update the version without modifying the code. This makes the system more flexible during deployments.

Integrating Version Information into Package Builds

During the package build process, which typically involves tools like setuptools or poetry, it is very important to include version information in the built package. This guarantees that the version number is available to users who install the package. Integrating version information into your package builds ensures that the version number is correctly included within the built package, accessible via the methods mentioned above, and used throughout the entire deployment process. This section provides an overview of best practices and strategies for seamlessly embedding version information in your package builds.

Using setup.py (or pyproject.toml with build tools like Poetry or Flit)

Define __version__: As previously described, the standard practice is to define the version inside your package. Typically, this is within the __init__.py file or a dedicated _version.py file within your package. The setup.py file should then read this value when it constructs the package.

# In your package/__init__.py or _version.py
__version__ = "0.1.0"

# In your setup.py
from setuptools import setup, find_packages

# Import the version from your package
from your_package import __version__

setup(
    name='your_package',
    version=__version__,
    packages=find_packages(),
)

Dynamic Versioning: For more dynamic approaches, particularly if you are using version control (like Git), you may use tools such as setuptools_scm. This tool automatically determines the version from your Git tags. This is useful for automatically updating the version on every release.
```
# Install setuptools_scm
# pip install setuptools_scm
from setuptools import setup, find_packages
from setuptools_scm import get_version

setup(
    name='your_package',
    version=get_version(),
    packages=find_packages(),
)
```
This approach guarantees that the version in the built package is up-to-date with your source control tags.

Integrating with Build Pipelines

CI/CD Integration: When using CI/CD pipelines (e.g., GitHub Actions, GitLab CI), the build process can automatically insert the version number at build time. This allows you to have a single source of truth for your version.
Environment Variables during Build: Set environment variables in the build configuration that are available during the build process, and inject that version number into your package. This gives flexibility. A common approach is to set an environment variable during the build. This can be directly read during the build process to update the version.

# Example GitHub Actions Workflow
jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Set up Python
        uses: actions/setup-python@v4
        with:
          python-version: '3.x'
      - name: Install dependencies
        run: |
          python -m pip install --upgrade pip
          pip install -r requirements.txt
      - name: Build and Package
        run: python setup.py sdist bdist_wheel
      - name: Get Version
        run: echo "VERSION=$(python -c 'from your_package import __version__; print(__version__)')" >> $GITHUB_ENV
      - name: Upload Artifacts
        uses: actions/upload-artifact@v3
        with:
          name: python-package-artifact
          path: dist/

Using setuptools_scm: Integrate setuptools_scm to derive the version from Git tags. This enables automatic versioning based on your Git repository's tags. By using tools like these, you can seamlessly integrate version information into your build process and ensure that the correct version is always available in the built package.

Incorporating Versioning in Pipelines (e.g., GitHub Actions)

Utilizing versioning in pipelines is essential for automating builds, deployments, and ensuring traceability throughout your software development lifecycle. By integrating version information into your CI/CD pipelines, you can improve deployment reliability, reduce manual errors, and simplify debugging.

Setting up Version Information in GitHub Actions

Reading Version from Source: The most straightforward approach is to read the version directly from your Python package during the build process. This involves importing your package's __version__ attribute within your pipeline configuration. Here's a basic example using GitHub Actions:

jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Set up Python
        uses: actions/setup-python@v4
        with:
          python-version: '3.x'
      - name: Install dependencies
        run: |
          python -m pip install --upgrade pip
          pip install -r requirements.txt
      - name: Get Version
        id: package-version
        run: |
          echo "version=$(python -c 'from your_package import __version__; print(__version__)')" >> $GITHUB_OUTPUT
      - name: Build
        run: python setup.py sdist bdist_wheel
      - name: Display Version
        run: echo "Package Version: ${{ steps.package-version.outputs.version }}"
      - name: Upload Artifacts
        uses: actions/upload-artifact@v3
        with:
          name: python-package-artifact
          path: dist/

Using setuptools_scm: For automatic versioning based on Git tags, integrate setuptools_scm into your workflow. This allows your pipeline to derive the version number from your Git repository, making releases more streamlined.
1. Install setuptools_scm: Add setuptools_scm to your requirements.txt or install it within your pipeline configuration. If you do not have it, use the following command:
```
pip install setuptools_scm
```
2. Configure setup.py: Make sure your setup.py or pyproject.toml file uses setuptools_scm to derive the version. This ensures that the package build process can automatically determine the correct version from your Git tags.
3. Use the version in your pipeline: When building or deploying your package, the version derived from your Git tags will automatically be incorporated into your build artifacts.
Environment Variables: Set the version as an environment variable within the pipeline. This provides a way to pass the version to subsequent steps. The variable is defined using the version variable from your Python package. The environment variable can be used for logging, tagging releases, or passing to deployment scripts.
```
- name: Set Version Environment Variable
  run: echo "VERSION=$(python -c 'from your_package import __version__; print(__version__)')" >> $GITHUB_ENV
- name: Deploy
  run: | 
    echo "Deploying version $VERSION"
    # Your deployment scripts here, using $VERSION
```

Benefits of Pipeline Versioning

Automated Tagging: Automatically tag releases in your repository based on the version derived from your package or from Git tags. This provides a clear link between your source code and deployed artifacts.
Artifact Management: Tag and version-specific artifacts during deployment, providing better traceability. Versioned artifacts help track the exact code that was deployed. This greatly simplifies debugging and rollback procedures if issues arise. Versioned artifacts are often stored in an artifact repository or container registry, and version tags can be used to identify specific builds.
Consistent Deployments: Using a defined version in your pipelines guarantees consistent deployments across different environments (development, staging, production). This helps in reducing configuration drift and deployment failures.
Rollbacks: Simplify rollbacks by easily reverting to a previous version if a deployment introduces issues. Rollbacks can be performed by simply deploying an older, known-good version. This is extremely valuable for reducing downtime and resolving production problems. By using automated tools and a version-controlled deployment process, you can ensure a quick and smooth rollback in case of issues.

Conclusion

Effectively managing QSL versioning is critical for maintaining robust and reliable applications. By implementing the techniques described in this article, you can accurately determine the version of QSL in your Python environment during runtime, package builds, and within your pipelines. Properly integrating version information into these processes ensures better debugging, enhanced maintainability, and streamlined deployments. By adopting these best practices, you empower yourself to improve the quality of your QSL-related projects. Make version information readily accessible in your Python projects, and enjoy the benefits of improved maintainability and reliability.

Key Takeaways

Runtime Versioning: Import the version directly from the package, or use the pkg_resources module for comprehensive access to package metadata, including the version.
Package Builds: Include the version in your setup.py (or pyproject.toml) file. Use tools like setuptools_scm to automate versioning based on your Git tags.
Pipelines: Incorporate the version in your CI/CD pipelines using environment variables, and automate tagging and artifact management based on the package version.

By following the recommended practices, you'll ensure that you have the most up-to-date and reliable version of QSL.

For more detailed information and best practices on Python packaging and versioning, you can consult the official Python Packaging User Guide. This will provide you with further insights and resources to enhance your projects.