Design a Coding Platform Like LeetCode: A Step-by-Step Guide

Online coding platforms look simple at first glance–you log in, pick a problem, write some code, and run it. But under the hood, they are powered by complex distributed systems. That’s why interviewers love asking you to design LeetCode in System Design interviews.

At its core, LeetCode is a coding practice platform that allows users to browse problems, submit solutions, run test cases, and track progress. But when you think about scale, like millions of users, billions of submissions, and thousands of problems across multiple languages, the design challenge becomes clear. You have to account for secure code execution, sandboxing, fairness, scalability, and reliability.

In this guide, we’ll walk through how to approach a System Design problem like designing LeetCode step by step. You’ll start by clarifying requirements, then move through feature design, architecture, code execution, scalability, and trade-offs. By the end, you’ll have a complete framework you can use to confidently approach this question in interviews.

12 Steps for Designing a Coding Platform Like LeetCode

When you’re asked to design LeetCode, the best approach is to break the problem into smaller steps. This keeps your answer structured and shows you can prioritize what matters. In the sections below, we’ll walk through 12 practical steps that cover everything from problem storage and user management to secure execution and scaling.

Step 1: Understand the Problem Statement

When you’re faced with a prompt like “design LeetCode,” your first step isn’t to jump into code execution or databases. It’s to clarify the problem statement. This proves that you can define scope and avoid missing important requirements.

At its simplest, LeetCode is a platform for solving coding challenges online. A user picks a problem, writes code in an editor, submits it, and gets instant feedback on correctness and performance.

Functional Requirements

• Browse problems: Users can filter by difficulty, tags, and company.
• Online IDE: Support for multiple programming languages.
• Submit code: Run code against test cases.
• Track history: Store past submissions and problem-solving progress.
• Leaderboards: Rank users by contests and solved problems.
• Discussions: Provide a forum for sharing approaches.

Non-Functional Requirements

• Scalability: Handle millions of users and submissions daily.
• Low latency: Submissions should return results within seconds.
• Reliability: No lost submissions, even if servers fail.
• Security: Isolate user code to prevent malicious behavior.
• Fairness: Ensure contest submissions are judged consistently.

Step 2: Define Core Features

Once you’ve defined the problem, the next step is to list the core features. This gives you a roadmap before you dive into System Design.

When you design LeetCode, the must-have features are:

• Problem browsing
  • Users can search and filter by difficulty, tags, and companies.
  • Problems are stored with metadata for fast retrieval.
• Online coding environment
  • Lightweight IDE in the browser.
  • Syntax highlighting and language selection.
  • Error feedback and test case results.
• Submission and evaluation
  • User submits code to the backend.
  • Code runs in an isolated environment with time/memory limits.
  • Results are returned in real time.
• User profiles and history
  • Store solved problems, streaks, and submission history.
  • Track achievements and badges.
• Leaderboards and contests
  • Track user rankings globally and within contests.
  • Update rankings asynchronously to avoid bottlenecks.
• Discussion forums
  • Allow users to post solutions, ask questions, and collaborate.

Extended Features (Optional in Interviews)

• Mock interviews with timed sessions.
• Company-specific prep kits.
• AI-powered hints and feedback.
• Progress dashboards with personalized recommendations.

You can also use Educative’s Grokking the System Design Interview for further prep. It is one of the best System Design interview resources and covers designing different platforms in detail.

Step 3: High-Level Architecture

After defining requirements and features, you should sketch the high-level System Design. When you design LeetCode, you want to show how users interact with the system and how code flows through different components.

Core Components

• Clients (Web + Mobile apps)
  • Provide the user interface for browsing problems, writing code, and viewing submissions.
• API Gateway
  • Entry point for all requests.
  • Handles authentication, rate limiting, and request routing.
• Backend Services
  • User Service: manages registration, login, profiles, and progress.
  • Problem Service: stores problem metadata, difficulty, tags.
  • Submission Service: accepts code, validates requests.
  • Execution Service: runs user code in isolated sandboxes.
  • Leaderboard Service: tracks rankings and contest results.
  • Discussion Service: supports forums and community interaction.
• Databases
  • User DB: user info, solved problems, subscription tier.
  • Problem DB: metadata and problem details.
  • Submission DB: solutions, results, timestamps.
  • Leaderboard DB: contest and ranking data.
• Message Queues
  • For asynchronous tasks like code execution and notifications.

High-Level Flow Example

1. User selects a problem via the Problem Service.
2. They write code in the IDE and submit.
3. Submission Service validates the request and places it on a queue.
4. Execution Service pulls the request, runs it in a sandbox, and stores results in the Submission DB.
5. The result is returned to the user and updates leaderboards if needed.

Step 4: User Management and Authentication

Every coding platform needs a robust way to handle users, sessions, and profiles. When you design LeetCode, this forms the foundation for progress tracking and premium access.

User Profiles

• Basic details: user_id, username, email, profile picture.
• Progress: number of problems solved, contest history.
• Premium status: subscription tier for locked problems.

Authentication

• Registration/Login with email or third-party (Google, GitHub).
• Session tokens (JWT or OAuth) for API requests.
• Multi-device login support.

Security Considerations

• Passwords must be hashed and salted.
• Rate limit login attempts to prevent brute force attacks.
• Support two-factor authentication for extra safety.

Database Example

• Users Table: user_id (PK), username, email, password_hash, subscription.
• Sessions Table: session_id, user_id, token, expiration.

Why It Matters in Interviews

User management is more than just login. It’s the backbone of personalization, subscriptions, and progress. A good answer shows you can scale user profiles to millions while keeping them secure.

Step 5: Problem Storage and Management

At the heart of LeetCode are problems and test cases. When you design LeetCode, you need to show how problems are stored, updated, and retrieved efficiently.

Problem Metadata

Each problem includes:

• Title and description.
• Difficulty (easy, medium, hard).
• Tags (arrays, dynamic programming, graphs).
• Company association (Amazon, Google, etc.).
• Constraints and sample test cases.

Problem Storage Design

• Store problem statements and metadata in a Problem DB (SQL for structured metadata).
• Store long-form descriptions in a document store (NoSQL).
• Store test cases separately in a Test DB optimized for fast read access.

Features to Support

• Search and filtering by tags, difficulty, company.
• Versioning of problems (to update test cases without breaking submissions).
• Moderation tools for admins to add/edit problems.

Example Schema

• Problems Table: problem_id, title, difficulty, tags, company.
• ProblemDetails Table: problem_id, description, constraints.
• TestCases Table: test_id, problem_id, input, expected_output, is_hidden.

Step 6: Code Execution Environment

The most critical and technically challenging part of LeetCode’s design is the code execution environment. This is where user-submitted code is compiled, executed, and validated against constraints while maintaining security and fairness.

Requirements

• Support multiple programming languages (Python, Java, C++, JavaScript, etc.).
• Isolate user code so malicious code cannot harm the system.
• Enforce time limits and memory limits.
• Scale horizontally to handle thousands of submissions in parallel.

Implementation Approach

• Use sandbox environments:
  • Containers (e.g., Docker) for fast spin-up and teardown.
  • VMs for stronger isolation, but higher overhead.
• Maintain a pool of pre-warmed sandboxes to reduce cold-start latency.
• Language runtimes and compilers are pre-installed inside sandboxes.
• Monitor execution metrics: CPU, memory, runtime.

Security Considerations

• Restrict network access (no outbound connections).
• Limit file system access to sandbox-only space.
• Terminate code that exceeds runtime/memory limits.

Step 7: Test Case Management

Once the code runs, it must be validated against test cases. When you design LeetCode, test case storage and execution efficiency become key bottlenecks.

Types of Test Cases

• Sample test cases: Small inputs shown to the user.
• Hidden test cases: Larger or edge cases hidden to prevent hardcoding.
• Stress test cases: Designed to test performance under constraints.

Storage Strategy

• Store test cases in a Test DB separate from problems for scalability.
• Index by problem_id for fast lookups.
• Mark visibility (is_hidden) to separate public vs private cases.

Execution Flow

1. Submission Service fetches all test cases for a problem.
2. Execution Service runs user code against each test case.
3. Results (pass/fail, runtime, memory) are aggregated.
4. Partial scoring (for contests) may be applied if some test cases fail.

Optimizations

• Batch test case execution inside a single container run.
• Cache frequently used test cases in memory for popular problems.
• Rotate hidden test cases periodically to reduce cheating.

Step 8: Submission Flow

Finally, let’s connect the dots with how a submission flows end-to-end. This is the pipeline that makes or breaks the user experience when you design LeetCode.

Step-by-Step Flow

1. User submits code via the web/mobile client.
2. Submission Service validates request (user logged in, problem exists, language supported).
3. Request is placed on a message queue for asynchronous execution.
4. Execution Service picks up the submission, runs it in a sandbox, and fetches test cases.
5. Code runs against all test cases. Results are recorded:
   • Pass/fail per test case.
   • Runtime and memory usage.
   • Error logs if compilation/runtime errors occur.
6. Results are written to the Submission DB.
7. API Gateway notifies the user (polling or WebSockets).
8. Leaderboard Service and User Profile are updated asynchronously if problem is solved.

Reliability Features

• Use idempotent submission IDs so retries don’t double-count.
• Ensure durable queues so no submissions are lost.
• Support graceful degradation—if Execution Service is down, users can still browse problems and see old submissions.

Database Example

• Submissions Table: submission_id, user_id, problem_id, language, code_ref, status, result, runtime, memory, timestamp.

Step 9: Leaderboards & Ranking

Leaderboards drive engagement and are critical in contests. When you design LeetCode, separate scoring from display so writes don’t block reads.

What to track

• Per-problem status: accepted, wrong answer, runtime, memory.
• Contest score: points, penalties, submission time, tie-breakers.
• Global stats: total solved, streaks, rating (Elo/Glicko-like).

Storage & models

• Event log (append-only) for submissions and verdicts.
• Materialized views for fast reads:
  • Redis Sorted Sets (score → user_id) for top-N queries.
  • Time-series store for historical rating and streak charts.
• CQRS split: writes go to the log; reads hit precomputed ranks.

Update pipeline

1. Verdict event → scoring service applies contest rules.
2. Update user’s contest state (points, penalties).
3. Push new rank to cache (sorted set).
4. Publish rank changes to notification/inbox.

Performance & fairness

• Asynchronous leaderboard refresh; UI polls every few seconds.
• Windowed recalculation (e.g., every 1–5s) to batch updates.
• Idempotency keys on verdict events prevent double scoring.
• Freeze window near contest end to stop last-second sniping.

Anti-cheat basics

• Plagiarism detection (AST similarity / token n-grams).
• IP/device anomalies.
• Hidden test rotation and differential test buckets for finalists.

Step 10: Scalability Considerations

At peak, platforms like LeetCode process millions of submissions/day. To design LeetCode at scale, isolate hot paths and scale each tier independently.

Execution tier

• Horizontal workers pulling from a partitioned queue (e.g., by language or problem_id).
• Pre-warmed container pools per language/runtime.
• Autoscale on queue lag, CPU, and container pool depth.
• Backpressure: throttle submission API if lag exceeds thresholds.

Data tier

• Problem DB: cache metadata (Redis) + search index for tags/difficulty.
• Submission DB: shard by (user_id % N); keep recent verdicts in hot storage, archive old to cold storage.
• Test cases: CDN or memory cache for popular problems; checksum versioning to invalidate safely.

API & edges

• API Gateway with rate limits per user/IP to prevent floods.
• CDN for static assets (editor bundles, problem statements).
• WebSockets for live verdicts; fallback to polling when needed.

Multi-region

• Active-active for reads (problems, discussions).
• Home region for a user’s writes (submissions, profile).
• Async replication; conflict-free by design (append-only submission log).

Observability & cost

• SLIs: submission queue wait P95, verdict latency P95, sandbox failure rate, cache hit ratio.
• Right-size runtimes (container flavors by language).
• Tiered storage and TTL for large artifacts (logs, stderr/stdout).

Step 11: Reliability & Fault Tolerance

Users won’t accept lost code or missing verdicts. When you design LeetCode, build for failure up front.

Durable delivery

• Idempotent submission IDs across API, queue, and workers.
• Transactional outbox from Submission Service → queue to avoid lost events.
• Dead-letter queues with replay tooling.

Exactly-once effects (practical)

• Accept at-least-once execution; ensure idempotent writes to verdict store.
• Use dedupe on (submission_id, test_batch_id).

Graceful degradation

• If Execution Service is impaired:
  • Keep browse/solve history, practice mode online.
  • Show queue position + ETA.
  • Allow local “Run sample tests” (client-side) with watermark “not official”.

Backups & DR

• Point-in-time recovery for Submission/Problem DBs.
• Periodic test case checksum audits.
• Cross-region failover runbooks, chaos drills on queues and workers.

Safe changes

• Canary workers for new runtimes; shadow-run submissions.
• Feature flags for language enable/disable and limit tweaks.

Step 12: Trade-Offs & Extensions

Strong answers acknowledge constraints and show a roadmap.

Key trade-offs

• VMs vs Containers
  • VMs: stronger isolation, higher cost/latency.
  • Containers: fast/cheap, tighter hardening needed.
• Synchronous vs Asynchronous results
  • Sync: better UX, risks API timeouts.
  • Async: resilient; needs WebSockets/polling UI.
• Hidden test count
  • More tests = fairness, slower verdicts/cost.
  • Fewer tests = speed, higher false-accept risk.
• SQL vs NoSQL
  • SQL: consistency for users/contests.
  • NoSQL/KV: high-throughput verdict/history reads.

Possible extensions

• Mock interviews with interviewer console, shared coding, timed sessions.
• Company-tagged tracks and role-specific ladders.
• AI hints and code feedback (rate-limited, contest-safe mode).
• Editorial workflow: versioned editorials, gated unlock after AC.
• Plagiarism appeals portal with evidence.
• Offline practice kits (downloadable tests; official verdict only via server).
• Education dashboards for classrooms and cohorts.

Wrapping Up

You’ve now walked through how to design LeetCode step by step, from clarifying requirements and defining features to building the execution pipeline, handling test cases, scaling leaderboards, and managing trade-offs.

In a System Design interview, remember to:

• Start with problem framing and requirements.
• Focus on core flows like problem storage, code execution, and submission handling.
• Show you can scale and secure the platform at millions of users and submissions.
• End with trade-offs and extensions to demonstrate forward thinking.

By structuring your answer this way, you’ll give interviewers confidence that you can design complex, real-world systems.