Integr8sCode

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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.

Full demo

full_demo.mov

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:

1. Receive Script: You send your code via the /execute endpoint.
2. Spin Up Pod: K8s creates a new pod for your script.
3. Run Script: Your code is executed in the pod.
4. Capture Output: Any output or errors are recorded.
5. Store Results: Everything gets saved in MongoDB.
6. 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_at and updated_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.test for test environment variables
• pytest.ini for pytest configuration
• conftest.py for 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-cov for coverage reporting
• 92% coverage of core functionality
• Coverage reports in HTML and XML