Integr8scode
Integr8sCode lets you run Python scripts online by executing them in isolated K8s pods with resource limits. Just input your code!
Integr8sCode
Welcome to Integr8sCode! This is a platform where you can run Python scripts online with ease. Just paste your script, and the platform run it in an isolated environment within its own Kubernetes pod, complete with resource limits to keep things safe and efficient. You'll get the results back in no time.
How to deploy
Backend:
- Change directory to
backend
cd backend
- Call
docker-compose up --build
docker-compose up --build
- The backend will be available at
https://0.0.0.0:443. Docs are available athttps://0.0.0.0:443/docs.
Frontend:
- Change directory to
frontend
cd frontend
- Install dependencies:
npm install
npm install
- Build the app:
npm run dev
npm run dev
- The frontend will be available at
https://localhost:5001/.
Architecture Overview
The platform is built on three main pillars:
- Frontend: A sleek Svelte app that users interact with.
- Backend: Powered by FastAPI, Python, and MongoDB to handle all the heavy lifting.
- Kubernetes Cluster: Each script runs in its own pod, ensuring isolation and resource control.
Components diagram
Backend Details
Script Execution Workflow
Here's how your script gets executed:
- Receive Script: You send your code via the
/executeendpoint. - Spin Up Pod: K8s creates a new pod for your script.
- Run Script: Your code is executed in the pod.
- Capture Output: Any output or errors are recorded.
- Store Results: Everything gets saved in MongoDB.
- Update Status: Execution status is updated for you.
Database Design
MongoDB setup includes an executions collection:
- Fields:
execution_id: Unique ID for each execution.script: The code provided.output: What the script printed out.errors: Any errors that occurred.status: Whether your script is in the process (queued,running,completed,failed).created_atandupdated_at: Timestamps for tracking.
Frontend Details
User Interface Components
Svelte app includes:
- Code Editor: A place to write or paste Python code.
- Run Button: Kick off the execution.
- Output Area: See the results or errors from the script.
- Status Display: Know if your script is queued, running, or done.
State Management
- Stores: Svelte's built-in stores are used to keep track of your script and its execution status.
- API Calls: Functions that talk to backend endpoints and handle responses smoothly.
Kubernetes Integration
Pod Setup
- Docker Image:Lightweight Python image with just what we need is used.
- Isolation: Every script gets its own pod for security and reliability.
- Cleanup: Once your script is done, the pod goes away to keep things tidy.
Resource Management
[!TIP] By limiting resources, we ensure fair usage and prevent any single script from hogging the system.
- CPU & Memory Limits: Each pod has caps to prevent overuse (128 Mi for RAM and 100m for CPU).
- Timeouts: Scripts can't run forever—they'll stop after a set time (default: 5s).
- Disk Space: Limited to prevent excessive storage use.
You can find actual limits in dropdown above execution output.
Security Considerations
[!CAUTION] Running user-provided code is risky. We take security seriously to protect both our system and other users.
- Network Restrictions: Pods can't make external network calls.
- No Privileged Access: Pods run without elevated permissions.
User Authentication
- Accounts: Optional—users can sign up to save scripts.
- Security: We use JWT tokens to secure API endpoints.
Logging and Monitoring
- Logs: Centralized logging helps us track what's happening across pods.
- Monitoring Tools: Using Prometheus and Grafana to keep an eye on system health.
- Alerts: Set up notifications for when things go wrong.
To access:
- Prometheus: http://localhost:9090
- Grafana: http://localhost:3000 (login with admin/admin123)
Testing Strategy
Unit Tests
Repository Tests: Testing individual database operations
- Located in
tests/unit/test_repositories/ - Testing CRUD operations for each model
- Using real MongoDB test instance
- Ensuring data integrity and constraints
- Running with pytest-asyncio for async operations
Service Tests: Testing business logic and service layer
- Located in
tests/unit/test_services/ - Testing service methods independently
- Using actual repositories with test database
- Ensuring proper error handling
- Verifying state changes and data transformations
Integration Tests
API Endpoint Tests: Testing complete HTTP workflows
- Located in
tests/integration/test_api_endpoints.py - Testing all REST endpoints
- Using FastAPI TestClient
- Verifying response codes and payloads
- Testing authentication and authorization
- Ensuring proper error responses
Kubernetes Integration Tests: Testing pod execution
- Located in
tests/integration/test_k8s_integration.py - Testing script execution in pods
- Verifying resource limits and constraints
- Testing cleanup and error scenarios
- Using test Kubernetes cluster
Load Testing
Performance Scenarios: Using Locust for load testing
- Located in
tests/load/ - Different load profiles:
- Smoke Test: 1 user, basic functionality
- Light Load: 10 users, 5 minutes
- Medium Load: 50 users, 10 minutes
- Heavy Load: 100 users, 15 minutes
- Stress Test: 200 users, 30 minutes
- Measuring:
- Response times
- Error rates
- System resource usage
- Database performance
- Kubernetes scaling
Main results:
Test Configuration
Environment Setup:
.env.testfor test environment variablespytest.inifor pytest configurationconftest.pyfor shared fixtures- Docker compose for test dependencies
Test Database:
- Separate MongoDB instance for testing
- Fresh database for each test run
- Automated cleanup after tests
Test Coverage:
pytest-covfor coverage reporting- 92% coverage of core functionality
- Coverage reports in HTML and XML
