Practice/Stripe/Design Google Docs

Design Google Docs

System DesignMust

Problem Statement

Design a cloud-based code editor platform where software engineering teams can collaboratively write, review, and execute code in real-time. Multiple developers should be able to edit the same file simultaneously, see each other's cursors and selections, communicate through inline comments, and run code in isolated execution environments. The system must handle teams ranging from small startups (5-10 users per workspace) to large enterprises (500+ concurrent editors across thousands of files), with a focus on minimizing edit-to-visibility latency and ensuring no user changes are lost during network partitions or conflicts.

Think of this as combining the collaboration features of Google Docs with the specialized needs of a development environment: syntax highlighting, language servers for autocomplete, debugging capabilities, and secure code execution. The platform should support popular programming languages, maintain file version history, and integrate with source control systems while providing sub-200ms propagation of edits between collaborators.

Key Requirements

Functional

Collaborative editing -- multiple developers can edit the same code file simultaneously with real-time synchronization of changes, cursor positions, and selections
Code execution -- users can run code snippets or full programs in sandboxed environments with support for multiple languages and capture output/errors
Inline commenting -- developers can attach threaded comments to specific lines or code blocks for code review and discussion
Version history -- the system maintains a complete audit trail of changes with ability to browse, compare, and restore previous file states
Workspace organization -- users can create projects with folder hierarchies, manage file permissions, and invite team members with role-based access

Non-Functional

Scalability -- support 100,000+ concurrent editing sessions across 1 million active workspaces, with individual files having up to 50 simultaneous editors
Reliability -- 99.9% uptime for editing services, with no data loss even during server failures or network partitions
Latency -- edits propagate to all collaborators within 200ms under normal conditions; code execution starts within 2 seconds
Consistency -- eventual consistency for collaborative edits with guaranteed convergence; strong consistency for file metadata and permissions

What Interviewers Focus On

Based on real interview experiences, these are the areas interviewers probe most deeply:

1. Operational Transformation and Conflict Resolution

This is the heart of any collaborative editor. Interviewers want to see if you understand how to merge concurrent edits to the same text region without losing intent or creating divergent states. Simply saying "we'll use operational transformation" isn't enough -- you need to explain how operations are transformed, ordered, and applied.

Hints to consider:

Consider how you'll represent text operations (insert at position N, delete M characters starting at position P) and what happens when two users insert text at the same position simultaneously
Discuss whether you'll use a centralized server that orders all operations or a peer-to-peer CRDT approach, and justify the tradeoffs
Think about how you'll handle the transform function complexity -- do you need to transform against multiple concurrent operations or just against a single acknowledged state
Address how cursor positions and selections stay correct as the document changes underneath them

2. Real-Time Communication Architecture

Collaborative editing requires bidirectional, low-latency communication channels. Interviewers expect you to design a scalable WebSocket infrastructure that handles connection management, message routing, and failover gracefully.

Hints to consider:

Explain how you'll assign users editing the same file to connection servers and handle horizontal scaling when a single file has dozens of editors
Discuss how you'll detect and handle disconnections, including buffering operations during brief network hiccups versus marking clients as offline
Consider pub-sub patterns for broadcasting changes and how you'll shard or route messages efficiently to avoid broadcasting every operation to every server
Think about how presence information (who's online, cursor positions) is tracked and distributed separately from document operations to reduce message volume

3. Code Execution Sandboxing and Resource Isolation

Running untrusted user code safely is a critical security and reliability challenge. Interviewers want to see you reason about container orchestration, resource limits, and attack vectors.

Hints to consider:

Discuss how you'll isolate execution environments (containers, VMs, serverless functions) and what execution time and memory limits you'll impose
Consider how you'll handle language-specific runtimes and dependencies -- do you maintain pre-built images or allow users to customize their environment
Think about preventing malicious code from attacking infrastructure (infinite loops, fork bombs, network scanning) and how you'll kill runaway processes
Address how you'll queue execution requests during high load and whether you'll support long-running processes versus just short scripts

4. Storage Strategy for Files and Operation Logs

You need to balance durability, query performance, and cost when storing both the current file state and the complete history of operations. Interviewers look for understanding of append-only logs versus snapshot-based approaches.

Hints to consider:

Consider storing files as periodic snapshots plus deltas to enable fast loading without replaying thousands of operations from the beginning
Discuss how you'll partition operation logs (by file, by workspace, by time window) and when you'll compact or archive old operations
Think about how you'll support features like "show me all changes in the last hour" or "compare version from yesterday with current" efficiently
Address the tension between keeping a complete audit trail for compliance versus managing storage costs for large files with extensive edit histories

5. Permission Model and Access Control

Code repositories often have complex sharing needs -- some files are public within a workspace, others restricted to specific teams, and permissions can change dynamically. This intersects with search, file listing, and real-time collaboration.

Hints to consider:

Explain how you'll model permissions (role-based, attribute-based, or ACLs) and where you'll enforce them (at the API gateway, in the collaboration server, in storage)
Consider how permission changes propagate to active editing sessions -- do you kick users out immediately or let them finish their current edits
Think about how search and file browsing respect permissions without requiring expensive per-document checks
Discuss how you'll handle inherited permissions in folder hierarchies and what happens when a file is moved between folders with different access rules

Practice/Stripe/Design Google Docs

Design Google Docs

System DesignMust

Problem Statement

Key Requirements

Functional

Collaborative editing -- multiple developers can edit the same code file simultaneously with real-time synchronization of changes, cursor positions, and selections
Code execution -- users can run code snippets or full programs in sandboxed environments with support for multiple languages and capture output/errors
Inline commenting -- developers can attach threaded comments to specific lines or code blocks for code review and discussion
Version history -- the system maintains a complete audit trail of changes with ability to browse, compare, and restore previous file states
Workspace organization -- users can create projects with folder hierarchies, manage file permissions, and invite team members with role-based access

Non-Functional

Scalability -- support 100,000+ concurrent editing sessions across 1 million active workspaces, with individual files having up to 50 simultaneous editors
Reliability -- 99.9% uptime for editing services, with no data loss even during server failures or network partitions
Latency -- edits propagate to all collaborators within 200ms under normal conditions; code execution starts within 2 seconds
Consistency -- eventual consistency for collaborative edits with guaranteed convergence; strong consistency for file metadata and permissions

What Interviewers Focus On

Based on real interview experiences, these are the areas interviewers probe most deeply:

1. Operational Transformation and Conflict Resolution

Hints to consider:

Consider how you'll represent text operations (insert at position N, delete M characters starting at position P) and what happens when two users insert text at the same position simultaneously
Discuss whether you'll use a centralized server that orders all operations or a peer-to-peer CRDT approach, and justify the tradeoffs
Think about how you'll handle the transform function complexity -- do you need to transform against multiple concurrent operations or just against a single acknowledged state
Address how cursor positions and selections stay correct as the document changes underneath them

2. Real-Time Communication Architecture

Hints to consider:

Explain how you'll assign users editing the same file to connection servers and handle horizontal scaling when a single file has dozens of editors
Discuss how you'll detect and handle disconnections, including buffering operations during brief network hiccups versus marking clients as offline
Consider pub-sub patterns for broadcasting changes and how you'll shard or route messages efficiently to avoid broadcasting every operation to every server
Think about how presence information (who's online, cursor positions) is tracked and distributed separately from document operations to reduce message volume

3. Code Execution Sandboxing and Resource Isolation

Running untrusted user code safely is a critical security and reliability challenge. Interviewers want to see you reason about container orchestration, resource limits, and attack vectors.

Hints to consider:

Discuss how you'll isolate execution environments (containers, VMs, serverless functions) and what execution time and memory limits you'll impose
Consider how you'll handle language-specific runtimes and dependencies -- do you maintain pre-built images or allow users to customize their environment
Think about preventing malicious code from attacking infrastructure (infinite loops, fork bombs, network scanning) and how you'll kill runaway processes
Address how you'll queue execution requests during high load and whether you'll support long-running processes versus just short scripts

4. Storage Strategy for Files and Operation Logs

Hints to consider:

Consider storing files as periodic snapshots plus deltas to enable fast loading without replaying thousands of operations from the beginning
Discuss how you'll partition operation logs (by file, by workspace, by time window) and when you'll compact or archive old operations
Think about how you'll support features like "show me all changes in the last hour" or "compare version from yesterday with current" efficiently
Address the tension between keeping a complete audit trail for compliance versus managing storage costs for large files with extensive edit histories

5. Permission Model and Access Control

Hints to consider:

Explain how you'll model permissions (role-based, attribute-based, or ACLs) and where you'll enforce them (at the API gateway, in the collaboration server, in storage)
Consider how permission changes propagate to active editing sessions -- do you kick users out immediately or let them finish their current edits
Think about how search and file browsing respect permissions without requiring expensive per-document checks
Discuss how you'll handle inherited permissions in folder hierarchies and what happens when a file is moved between folders with different access rules