HashCodex — Building a Distributed, Secure Code Execution Platform Like LeetCode

Rohit Shah — Mon, 20 Oct 2025 04:16:06 +0000

Have you ever wonder how platforms like LeetCode or HackerRank execute your code instantly and securely - even when thousands of users are submitting simultaneously?

That question led me to build HashCodex — an open-source, distributed, real-time code execution and evaluation platform, inspired by online judges like LeetCode and HackerRank.

🧩 What is HashCodex?

HashCodex is a full-stack online code execution system that allows users to:

🧑‍💻 Solve coding problems directly in the browser
⚙️ Run or submit code in multiple languages (C++, Java, Python)
📦 Execute user code securely inside sandboxed Docker containers
🔄 Receive real-time results via Server-Sent Events (SSE)

It’s a distributed system — meaning the platform is composed of multiple services (frontend, backend, worker, message queue, etc.), all communicating asynchronously.

🧱 System Architecture

Components Overview

Component	Technology	Responsibility
Frontend	Next.js (React)	UI + Code Editor
Backend API	Spring Boot	Auth, SSE, Queue Management
Worker Service	Go	Executes user code inside Docker sandbox
Message Broker	RabbitMQ	Async communication between backend & workers
Database	PostgreSQL	Stores users, problems, testcases, submissions
Cache	Redis	Used for email verification & password reset tokens

⚙️ How It Works

User selects a problem and writes code in the browser.
The frontend sends code, language, and problem ID to the backend.
The backend creates a submission, assigns a unique ID, and publishes the request to a RabbitMQ queue.
A Go Worker Service consumes the queue, runs the code inside a Docker container (isolated with CPU/memory limits).
Once executed, results are sent back to another queue.
The backend consumes the result, updates the database, and streams the final output to the frontend in real-time via SSE.

💾 Database Design

🔐 Security Highlights

Each user’s code runs inside sandboxed containers with:

No network access (NetworkMode: "none")
No privileged operations (CapDrop: ["ALL"], no-new-privileges)
Limited CPU & memory using Docker’s NanoCPUs and memory quotas
Process limit (PidsLimit) to prevent fork bombs or infinite loops

This ensures that no user code can affect the system or other submissions.

🧠 Key Challenges I Solved

Safe sandboxing: Running arbitrary user code without exposing the host system.
Async processing: Decoupling code execution from HTTP requests using queues.
Real-time updates: Streaming results with Server-Sent Events.
Testcase management: Handling multi-param inputs and comparing expected vs actual output.
Error alignment: Adjusting compiler error line numbers to match user code context.

🧭 Learnings

This project taught me:

How distributed systems communicate using message queues
How to securely run untrusted code
How to stream real-time updates without WebSockets
How to design scalable and fault-tolerant architectures

Check out the github repository of hashcodex github.com/shahrohit/hashcodex to learn more about hashcodex.

💬 I’d love to hear your thoughts — how would you improve scalability or isolation in such systems?

Drop a comment below! 👇

DEV Community: Rohit Shah