Debug This Architecture

Of course. This is an excellent exercise. The proposed architecture has several critical flaws that would lead to data loss, poor performance, and a broken user experience. Here is a detailed breakdown of the failure modes, race conditions, and bottlenecks, along with specific solutions and their trade-offs.

1. Real-Time Sync & Conflict Resolution

Issue: Last-Write-Wins (LWW) with Client Clocks This is the most critical flaw. Client clocks are not reliable or synchronized. Using them for conflict resolution is a recipe for data loss and bizarre, unpredictable behavior.

• Race Condition: User A (with a fast clock 10 seconds ahead) makes a change that gets timestamp T+10. User B (with a slow, correct clock) makes a change 5 seconds later, but it gets timestamp T+5. User B's change, which happened after User A's, will be overwritten and lost because its timestamp is earlier.
• Failure Mode: Massive user frustration due to lost work and an editor that feels "buggy."

Solution: Use a Centralized, Monotonic Sequencing System Abandon client timestamps entirely. The server must be the single source of truth for ordering events.

• Operational Transform (OT) or Conflict-Free Replicated Data Type (CRDT): These are algorithms designed for this exact problem. They resolve conflicts based on the semantics of the change (e.g., position in text) rather than a unreliable timestamp.
• Simpler Alternative: Centralized Sequence Numbers: For each document, maintain a sequence number (e.g., in Postgres) that increments with every change. Every change from a client must reference the last sequence number it knew about. The server processes changes in strict sequence order. If a change arrives with an old sequence number, it is rejected or transformed.

Trade-offs:

• OT/CRDT: High implementation complexity. Requires deeply understanding the algorithms. OT requires a central server to transform operations, while CRDTs are more decentralized but can have larger data footprints.
• Sequence Numbers: Much simpler to implement but less robust for truly offline operation. It moves the entire conflict resolution burden to the central server.

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2. Data Flow & Broadcast Inconsistency

Issue: Broadcasting Only to Clients on the Same Server This architecture creates "islands" of users. A change made by a user on Server A will be instantly visible to others on Server A, but users on Servers B, C, and D won't see it until their server polls Postgres (up to 2 seconds later). This is an unacceptable delay for real-time collaboration and breaks the "real-time" illusion.

Scaling Bottleneck: The system cannot scale horizontally without introducing this massive latency and inconsistency.

Solution: Introduce a Pub/Sub Messaging Layer Introduce a dedicated, fast Pub/Sub system like Redis Pub/Sub or Apache Kafka.

• New Data Flow:
  1. User types → change event sent via WebSocket to their connected server.
  2. Server writes change to PostgreSQL and increments the central sequence number.
  3. Server publishes the change to a dedicated channel on the Pub/Sub system (e.g., document:123).
  4. Every API server (A, B, C, D...) is subscribed to the channel document:123.
  5. Upon receiving a message from Pub/Sub, each server immediately broadcasts the change to all its connected WebSocket clients watching that document.

Trade-offs:

• Pros: Eliminates the 2-second delay. Provides true real-time sync across all servers. Enables proper horizontal scaling.
• Cons: Introduces a new critical infrastructure component (Redis/Kafka) that must be managed and scaled for high availability. Adds a small amount of network overhead.

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3. State Synchronization & Polling

Issue: Other Servers Polling PostgreSQL Polling the database every 2 seconds is a terrible scaling anti-pattern.

• Scaling Bottleneck: As the number of documents and servers grows, the load on the database from these pointless polls becomes immense, even if no changes are occurring. It's wasteful and does not scale.

Solution: The Pub/Sub solution above completely eliminates the need for this poll. The Pub/Sub system becomes the real-time event bus, and the database is used for persistence and as the source of truth for the current full state.

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4. Storage & Performance

Issue: Saving Full HTML Snapshots Every 30 Seconds This is extremely inefficient.

• Scaling Bottleneck: A single character change forces the entire document (which could be megabytes large) to be rewritten to disk every 30 seconds. This creates huge I/O load on the database and needlessly consumes storage space and bandwidth.

Solution: Store a History of Operations (Event Sourcing) Instead of storing snapshots, store every atomic operation (e.g., {type: 'insert', index: 42, text: 'a'}).

• To get the current document, you replay all operations from the beginning.
• To get a recent document, you can take a periodic snapshot (e.g., every 1000 operations) and then only replay the operations since that snapshot.

Trade-offs:

• Pros: Dramatically reduces storage I/O and storage space. Enables powerful features like full history playback ("Time Travel") and undo.
• Cons: Increased application complexity. Rebuilding a document requires CPU cycles. Requires a process for creating and managing snapshots.

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5. Authentication & Security

Issue: JWT Tokens Stored in localStorage localStorage is vulnerable to XSS (Cross-Site Scripting) attacks. If a malicious script is injected into your React app (e.g., via a third-party library), it can steal the JWT token, giving an attacker full access to the user's account for 24 hours.

Solution: Store JWTs in httpOnly Cookies

• httpOnly cookies cannot be accessed by JavaScript, making them immune to XSS theft.
• They must be paired with the SameSite=Lax (or Strict) attribute to help mitigate CSRF attacks. Since this is an API that should only be called by your own frontend, you should also implement CSRF tokens if you are using cookies.

Trade-offs:

• Pros: Vastly improved security against XSS.
• Cons: Slightly more complex to implement. Requires CSRF protection. The frontend can no longer easily read the token expiration, though this can be solved by duplicating that data in a safe payload.

Issue: Caching API Responses for 5 Minutes Caching any authenticated API responses at a CDN level is extremely dangerous. A user's GET /documents/123 response must never be cached and served to another user.

Solution: Do Not Cache Authenticated API Responses in CloudFront

• Use CloudFront only for static assets (JS, CSS, images).
• For API responses, ensure the Cache-Control: private, no-cache, no-store headers are set. Alternatively, do not route API traffic through CloudFront at all; use the load balancer directly.

Trade-offs:

• Pros: Complete data security and privacy.
• Cons: You lose the performance benefit of caching API calls, which is correct because they shouldn't be cached at this level anyway.

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6. WebSocket Connection Stability

Issue: Server-Centric WebSocket Connections If a user's API server crashes or is taken down for deployment, all its WebSocket connections are abruptly terminated. The client's React SPA will have to reconnect, but with round-robin LB, it will likely connect to a different server. The new server has no inherent knowledge of the user's previous connection state.

Solution: Use Redis to Store WebSocket Session State

• Store session data (e.g., which documents a user is watching) in the shared Redis cache, not in the local server memory.
• This allows any server to seamlessly take over a connection after a reconnection.

Implement a Sticky Session Load Balancer Policy:

• Configure the load balancer to use "sticky sessions" (session affinity) based on the JWT or a cookie. This ensures a user consistently connects to the same server while it is healthy, which improves connection stability and can simplify broadcast logic.
• Trade-off: This reduces the statistical fairness of round-robin and can lead to imbalance if some users are much more active than others. It's a trade-off between simplicity and perfect distribution.

Summary of Proposed Architecture Changes

Component	Original Flaw	Proposed Solution	Key Benefit
Conflict Resolution	LWW + Client Clocks	OT, CRDT, or Server Sequence Numbers	Prevents data loss
Real-Time Sync	Server Islands + DB Polling	Redis/Kafka Pub/Sub	True real-time sync, enables scaling
Storage	Full HTML Snapshots	Event Sourcing + Occasional Snapshots	Reduces I/O, enables history
Auth	JWT in localStorage	JWT in httpOnly + SameSite Cookie	Mitigates XSS attacks
CDN	Cached API responses	No API caching on CDN	Prevents data leakage
WebSockets	Server-local state	Sticky Sessions + Redis Session Store	Handles server failures gracefully

Implementing these changes would transform the architecture from a fundamentally broken design into a robust, scalable, and secure system suitable for a real-time collaborative editor.