This architecture contains several critical design flaws that would lead to data loss, high latency, security vulnerabilities, and poor scalability. Below is a detailed breakdown of the failure modes, race conditions, and bottlenecks, along with specific solutions and trade-offs.

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1. Real-Time Consistency & Synchronization

Issue: Client-Clock-Based Last-Write-Wins (LWW)

• Failure Mode: Client clocks are not synchronized. If User A's clock is 1 minute fast and User B's is 1 minute slow, User A's edits will overwrite User B's edits regardless of actual arrival time.
• Race Condition: Two users edit the same character range simultaneously. LWW resolves this by arbitrarily choosing one, effectively deleting the other user's work. This makes concurrent editing impossible.
• Solution: Implement CRDTs (Conflict-free Replicated Data Types) or Operational Transformation (OT) (e.g., Yjs, Automerge, Google Docs' OT). Assign server-side sequence numbers to operations, not client timestamps.
• Trade-off:
  • Pro: Guarantees eventual consistency without data loss during concurrent edits.
  • Con: Increased complexity in data modeling and frontend state management. Requires a robust state synchronization library.

Issue: Siloed WebSocket Connections (Server Partitioning)

• Failure Mode: The architecture states: "Server broadcasts change to all WebSocket clients connected to THAT server." If User A connects to Server 1 and User B connects to Server 2, User A will never see User B's changes until the polling interval hits.
• Scaling Bottleneck: As you add API servers, the probability of two collaborators connecting to different servers increases, degrading the "real-time" experience to "eventually consistent" (up to 2s delay).
• Solution: Implement a Pub/Sub Layer (e.g., Redis Pub/Sub or NATS) between API servers. When Server 1 receives a change, it publishes to a channel; Server 2 subscribes and pushes to its local clients.
• Trade-off:
  • Pro: Enables true real-time collaboration across horizontally scaled servers.
  • Con: Introduces a single point of failure (Redis cluster) and adds network latency for cross-server message propagation.

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2. Database & Storage Architecture

Issue: Direct PostgreSQL Writes for Every Keystroke

• Scaling Bottleneck: Writing every keystroke directly to PostgreSQL creates massive I/O contention. A single document with 100 users typing fast could generate 500+ writes per second.
• Failure Mode: Database connection pool exhaustion during peak usage, causing write failures and lost edits.
• Solution: Implement Write Buffering. Buffer changes in Redis (sorted set or list) for a short window (e.g., 100ms) or batch them, then flush to PostgreSQL asynchronously. Alternatively, use Event Sourcing: write operations to a log, snapshot state periodically.
• Trade-off:
  • Pro: Reduces DB load by orders of magnitude.
  • Con: Increases complexity. Requires handling buffer persistence to prevent data loss if the Node process crashes.

Issue: Polling PostgreSQL Every 2 Seconds

• Scaling Bottleneck: If you have 50 API servers, that is 50 queries every 2 seconds just to check for updates. This is $O(N)$ load on the database that scales linearly with infrastructure cost.
• Failure Mode: Database CPU saturation under load, increasing latency for all operations.
• Solution: Use Database Change Data Capture (CDC) or PostgreSQL LISTEN/NOTIFY. Instead of polling, the DB pushes notifications to the API servers when a document changes.
• Trade-off:
  • Pro: Eliminates polling overhead; near-zero latency.
  • Con: Tightly couples architecture to PostgreSQL specific features. Requires handling notification backpressure.

Issue: Full HTML Snapshots Every 30 Seconds

• Failure Mode: 30 seconds is too long for a crash window. If the server crashes at 29 seconds, 29 seconds of data is lost.
• Data Integrity: Storing full HTML makes calculating diffs impossible. You cannot merge changes efficiently if the storage is just raw HTML.
• Solution: Store Operation Logs (text insert/delete events) in the DB. Generate snapshots on demand or via a background worker that compiles the log into a state file.
• Trade-off:
  • Pro: Full history audit trail; allows "undo" to any point in time.
  • Con: Storage costs grow over time; requires log compaction/cleanup strategies.

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3. Infrastructure & Load Balancing

Issue: Round-Robin Load Balancing for WebSockets

• Failure Mode: WebSockets are stateful. If a Load Balancer (LB) sends a handshake to Server A, but the next request (or message) hits Server B, Server B won't have the connection context.
• Solution: Enable Sticky Sessions (Session Affinity) on the Load Balancer, or use a WebSocket Gateway (like Socket.IO or a dedicated proxy) that handles connection state external to the Node app.
• Trade-off:
  • Pro: Ensures connection stability.
  • Con: Sticky sessions can lead to uneven load distribution (hotspots). A Gateway adds an infrastructure layer.

Issue: CDN Caching API Responses

• Failure Mode: "CloudFront... caches API responses for 5 minutes." This is catastrophic for a collaborative editor. User A edits, User B sees old data for 5 minutes.
• Solution: Disable CDN caching for all API endpoints (/api/*). Only cache static assets (JS, CSS, Images). Use Cache-Control: no-store for dynamic document data.
• Trade-off:
  • Pro: Ensures users always see the latest data.
  • Con: Increases origin server traffic; higher latency for static assets if not properly optimized elsewhere.

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4. Security & Authentication

Issue: LocalStorage JWTs with 24-Hour Expiry

• Failure Mode: XSS Vulnerability. If a script is injected into the page (via a malicious comment or dependency), it can steal the JWT from LocalStorage and impersonate the user for 24 hours.
• Failure Mode: Revocation. If a user is fired, you cannot revoke their access until the token expires (24 hours later).
• Solution: Store Access Tokens in HttpOnly, Secure Cookies. Use a short-lived Access Token (15 mins) + a Refresh Token (stored in HttpOnly Cookie).
• Trade-off:
  • Pro: Mitigates XSS token theft; allows immediate revocation.
  • Con: Requires CSRF protection (e.g., Double Submit Cookie pattern); slightly more complex auth flow.

Issue: Document Partitioning by Organization ID

• Scaling Bottleneck: If one organization has massive traffic (e.g., a large enterprise), it will monopolize the resources of the shard it is assigned to, causing "noisy neighbor" issues.
• Solution: Implement Multi-tenancy with Quotas or Sharding by Hash rather than simple Org ID. Use a Hash Map to distribute Orgs across shards evenly.
• Trade-off:
  • Pro: Better resource isolation and load balancing.
  • Con: More complex data migration logic if a shard becomes too hot.

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Summary of Recommended Architecture Changes

Component	Current State	Recommended State	Reason
Sync Logic	LWW + Client Clocks	CRDT / OT + Server Seq IDs	Prevents data loss on concurrent edits.
Inter-Server	Polling DB (2s)	Redis Pub/Sub	Reduces DB load; improves latency to <100ms.
DB Writes	Immediate PG Write	Buffer / Event Log	Prevents DB I/O saturation.
Storage	HTML Snapshots	Operation Logs + Snapshots	Enables history/undo and efficient merging.
Auth	LocalStorage JWT	HttpOnly Cookies + Refresh	Prevents XSS token theft; allows revocation.
CDN	Caches API	Cache Static Only	Prevents stale document data.
LB	Round-Robin	Sticky Sessions / Gateway	Maintains WebSocket connection state.

Critical "Showstopper" Risks

If you deploy the architecture exactly as described:

1. Users will lose text when editing the same paragraph simultaneously (LWW + Client Clocks).
2. Collaboration will feel broken because users on different servers will see edits with 2+ second delays (Polling).
3. Security will be compromised if a single XSS vulnerability exists (LocalStorage JWT).
4. Users will see stale data due to CDN caching API responses.

Recommendation: Prioritize fixing the Sync Strategy (CRDT/OT) and the Inter-Server Communication (Redis Pub/Sub) immediately, as these directly impact the core value proposition of the product.