Introduction ¶

The author reflects on evolving development practices, noting that “back then the standard approach of attaching your debug interpreter was by creating a virtual environment in your python project.” Previously, debugging containerized Django projects required SSH daemons and remote interpreter configuration. JetBrains recently enabled native Docker Compose interpreter support, resolving earlier limitations around environment variable handling.

Key Improvements in PyCharm ¶

Recent versions now support:

• Multiple Docker Compose recipes with local overrides
• Environment file inclusion
• Local-to-container directory mapping
• Native environment variable inheritance in run configurations

Requirements ¶

The tutorial assumes familiarity with:

• Git and GitHub workflows
• Docker and Docker Compose fundamentals
• Linux, macOS, or WSL environments
• Python and Django basics
• Debugging methodologies
• PyCharm Pro Edition

Standard Setup Without IDE ¶

The author provides a sample Django microservice repository demonstrating initial setup. Running docker-compose up --build -d deploys services without IDE dependencies, maintaining workflow independence. Configuration uses environment variables through .env files and docker-compose.override.yml templates, enabling declarative deployments.

PyCharm Configuration Steps ¶

Docker Compose Integration ¶

PyCharm’s built-in Docker plugin enables visual service management through the Services tab, supporting deployment creation, status monitoring, and configuration updates.

Python Interpreter Setup ¶

Configure interpreters by specifying Docker Compose configuration files and service selection, allowing PyCharm to build custom container images with debugging helpers.

Path Mapping ¶

Establish mappings between host and container directories to enable proper code navigation and debugging synchronization.

Django Integration ¶

Enable Django support through project settings, allowing access to Django Shell and template debugging capabilities.

Run Configuration ¶

Create Django server configurations that utilize environment variables from .env, supporting both execution and debugging modes with real-time breakpoint attachment.

Testing Capabilities ¶

Developers can execute Django tests directly through context menus or run individual test methods using IDE shortcuts, eliminating lengthy command-line invocations.

Optional Beta Features ¶

PyCharm supports storing run configurations as shareable files within repositories, facilitating team collaboration. However, the feature currently lacks modular interpreter storage, limiting discoverability.

Conclusion ¶

This workflow demonstrates Docker-compose debugging without sacrificing IDE-independent development. The integration proves especially valuable for teams with developer turnover, as “everything has already been prepared from the start,” enabling rapid onboarding through declarative configurations.