Docker Support for Issue Docker Support #85

[SANTHOSH-SACHIN]

Docker Support for Data Formulator

Overview

This PR adds comprehensive Docker support to Data Formulator, enabling easier deployment and development. The implementation includes multi-stage builds, environment variable management, and production-ready configurations.

Key Changes

• Added multi-stage Dockerfile optimized for both development and production
• Implemented docker-compose.yml for simplified deployment
• Added docker-entrypoint.sh with production/development mode support
• Enhanced environment variable handling for API keys and configuration
• Updated documentation with Docker setup instructions

Features

• Multi-stage Build: Optimized image size by separating frontend build from runtime
• Environment Management: Flexible configuration through environment variables
• Development Mode: Volume mounting for live code updates
• Production Mode: Gunicorn support for production deployments
• API Key Management: Secure handling of provider API keys

Documentation Updates

• Added Docker deployment instructions to README.md
• Created comprehensive DOCKER.md guide
• Updated DEVELOPMENT.md with Docker workflow
• Added configuration documentation

Related Files

• Dockerfile
• docker-compose.yml
• docker-entrypoint.sh
• DOCKER.md
• Updated README.md and DEVELOPMENT.md

Dependencies

• Python 3.12
• Node.js 18
• Required Python packages in requirements.txt
• Frontend dependencies in package.json

Security Considerations

• API keys managed through environment variables
• No sensitive data in Docker images
• Production-ready Gunicorn configuration
• Proper permission handling in containers

Breaking Changes

None. This is an additive change that maintains backward compatibility with existing deployment methods.

Future Work

• Container orchestration support (Kubernetes)
• Additional deployment environment configurations
• CI/CD pipeline integration
  @Chenglong-MS @danmarshall

[SANTHOSH-SACHIN]

@microsoft-github-policy-service agree

[danmarshall]

Thanks for the PR, but we already have an existing PR for this: #90 . Would you mind looking at that one and let us know how this differs?

[Copilot]

PR Overview

This PR introduces Docker support for Data Formulator to simplify both development and production deployments. Key changes include:

• A multi-stage Dockerfile and docker-compose.yml for streamlined container management.
• New documentation in DOCKER.md and updates to README.md and DEVELOPMENT.md describing Docker deployment and development workflows.

Reviewed Changes

File	Description
DOCKER.md	Adds a complete Docker deployment guide outlining build, run, and configuration steps.
README.md	Updates deployment instructions with code examples for Docker.
docker-compose.yml	Introduces a Docker Compose configuration to simplify container orchestration.
DEVELOPMENT.md	Updates local development instructions to include Docker-based workflows.

Copilot reviewed 6 out of 6 changed files in this pull request and generated 1 comment.

[Copilot]

The code snippet marker incorrectly includes ':README.md'; this could affect markdown rendering. Please change it to '```bash' for proper syntax highlighting.

Suggested change

	```bash:README.md
	```bash

[SANTHOSH-SACHIN]

Thanks for the PR, but we already have an existing PR for this: #90 . Would you mind looking at that one and let us know how this differs?

I've implemented the streamlined Docker approach in this PR #85 based on several technical advantages that will improve performance, resource utilization, and deployment efficiency:

Optimized Multi-stage Build Pipeline:

• This PR leverages a clean frontend-to-backend build pipeline that minimizes artifact transfer between stages
• Reduced layer complexity results in more efficient Docker image caching

Reduced Container Size and Resource Footprint:

• Final image contains only the compiled frontend assets and necessary backend code
• Elimination of development dependencies in the production container (no Node.js runtime)
• Smaller image size means faster deployments and reduced registry storage costs

Enhanced CI/CD Integration:

• Single Dockerfile approach simplifies CI/CD pipeline configuration
• Consistent build process across environments ensures deployment reliability
• No conditional logic in the build process that could create environment-specific bugs

Runtime Performance Improvements:

• Automatic production detection with Gunicorn implementation for superior request handling
• On-demand installation of production dependencies only when needed
• Direct volume mounting provides better I/O performance for active development

Centralized Environment Configuration:

• All environment variables managed at the docker-compose level for better secrets management integration
• Environment variable inheritance aligns with modern container orchestration practices

@danmarshall @Chenglong-MS

[Chenglong-MS]

I have briefly compared both PRs, mostly differs from how files are organized and options to deal with API keys etc. Though I currently have trouble work out either... so I'll try to look into what's the issues now.

[SANTHOSH-SACHIN]

organized and options to deal with API keys etc

Sure .. If you need any help or clarification , reach out to me in this discussion