Call function in Python script in a Gitlab repositry from another script in another Gitlab repository

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03-10-2024

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Calling Python Functions Across GitLab Repositories

Imagine you have two separate projects in GitLab, each with its own Python script. You want to execute a function from one script within the other, but they reside in different repositories. This scenario presents a unique challenge, as direct function calls across repositories are not possible.

Here's a simplified example of the problem:

Repository 1 (utils.py):

def greet(name):
  return f"Hello, {name}!"

Repository 2 (main.py):

# How to call the 'greet' function from 'utils.py'?

This article explores different solutions to bridge this gap and enable cross-repository function calls.

Solutions for Cross-Repository Function Calls

Several strategies can be employed to overcome the repository barrier and call functions from one GitLab project within another:

1. Using GitLab CI/CD Pipelines:

• The most straightforward approach involves utilizing GitLab CI/CD pipelines. You can trigger a pipeline in Repository 2 that calls a script in Repository 1. This script would then execute the desired function and return its output.
• Advantages:
  • Clear separation of concerns and workflow.
  • Suitable for asynchronous tasks and scenarios where immediate function results are not required.
• Example: - In your main.py script, you could use the requests library to make an HTTP request to a specific endpoint in Repository 1 that triggers the CI/CD pipeline. This pipeline would then execute the utils.py script, call the greet() function, and return the result to the main script.

2. Shared Library or Package:

• Create a shared library or package containing the desired function. This library can be hosted in a separate GitLab repository and referenced by both repositories.
• Advantages:
  • Provides a centralized and reusable library.
  • Allows for version control and easy updates.
• Disadvantages:
  • Requires additional setup and maintenance for the library.
• Example: - Create a new GitLab repository for the utils library. - Define the greet function in utils.py within this repository. - Both main.py and utils.py would then import the necessary functions from the shared utils package.

3. Web API Approach:

• Build a simple web API (RESTful API) in Repository 1 that exposes the greet function.
• Repository 2 can then interact with this API using libraries like requests to make HTTP requests and receive the function output.
• Advantages:
  • Flexible and scalable approach.
  • Allows for easy communication across networks and different programming languages.
• Disadvantages:
  • Requires additional development and infrastructure for the API.
• Example: - Create a Flask app in utils.py that defines a route to call the greet function. - In main.py, use the requests library to send an HTTP request to this route and retrieve the result.

4. Utilizing GitLab's Shared Runners:

• Leverage shared runners in GitLab CI/CD to execute scripts in both repositories within a single pipeline.
• Advantages:
  • Enables direct execution of scripts from different repositories in a controlled environment.
  • Simplifies communication between scripts by using environment variables or shared files.
• Disadvantages:
  • Requires careful planning and coordination to ensure scripts execute in the desired order and have access to necessary resources.
• Example: - Define a GitLab CI/CD pipeline that uses shared runners. - The pipeline executes both utils.py and main.py scripts sequentially, passing data between them using environment variables.

Choosing the Right Approach:

The optimal solution depends on your specific requirements, project structure, and technical expertise. For simple scenarios with minimal data exchange, using a shared library or GitLab CI/CD pipelines is a good starting point. If you need more flexibility or a scalable solution, consider building a web API.

Further Considerations:

• Security: Implement appropriate authentication and authorization mechanisms when using web APIs or shared libraries to protect sensitive data.
• Error Handling: Incorporate robust error handling in your code to gracefully manage exceptions and ensure stability.
• Dependencies: Manage external dependencies in both repositories effectively, ensuring compatibility and version control.

By understanding these approaches and applying the best suited solution, you can successfully call Python functions from one GitLab repository within another. This allows you to modularize your codebase, improve collaboration, and leverage the full power of GitLab's ecosystem.