This architecture contains several critical flaws that will lead to data loss, poor user experience, and system instability at scale. The "Last-write-wins" strategy combined with client-side timestamps is particularly dangerous for collaborative editing.

Here is a detailed breakdown of failure modes, race conditions, and bottlenecks, organized by domain, with specific solutions and trade-offs.

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1. Data Consistency & Conflict Resolution (Critical)

Issue A: Client-Side Timestamps for Conflict Resolution

• Failure Mode: Client clocks are not synchronized. User A (clock fast by 5s) types at T. User B (clock slow by 5s) types at T. User A's change appears to have a later timestamp and overwrites User B's change, even though B typed later.
• Race Condition: Two users edit the same character simultaneously. The server accepts the one with the higher timestamp, silently deleting the other user's input.
• Solution: Server-Authoritative Timestamps & CRDTs/OT.
  • Stop trusting client clocks. Use server-side logical clocks (e.g., Lamport timestamps) or Vector Clocks.
  • Replace "Last-Write-Wins" with CRDTs (Conflict-free Replicated Data Types, e.g., Yjs, Automerge) or Operational Transformation (OT, e.g., ShareDB).
• Trade-off:
  • Pros: Guarantees eventual consistency; no data loss; true collaboration.
  • Cons: High engineering complexity; requires client-side library integration; higher CPU usage on server to merge operations.

Issue B: Coarse-Grained Conflict Resolution (Paragraph Level)

• Failure Mode: If two users edit the same paragraph, the entire paragraph is overwritten by the "winner."
• Race Condition: User A changes the first sentence; User B changes the last sentence. User A's timestamp wins, and User B's changes to the last sentence are lost.
• Solution: Fine-Grained Data Structure.
  • Store documents as a sequence of characters or rich-text blocks (e.g., ProseMirror JSON), not HTML paragraphs.
  • Apply CRDT/OT logic at the character or node level.
• Trade-off:
  • Pros: Seamless merging of concurrent edits.
  • Cons: Requires significant refactoring of the frontend editor and backend storage schema.

Issue C: Database Replication Lag

• Failure Mode: User writes to Primary DB. Load balancer routes next read to Read Replica. Replica hasn't synced yet. User sees "old" version of document immediately after saving.
• Race Condition: "Read-your-writes" consistency violation.
• Solution: Sticky Reads or Transactional Consistency.
  • Route "read after write" requests to the Primary DB for a short window (e.g., 5 seconds).
  • Or, use a session token to force read from Primary until last_write_time + latency_buffer.
• Trade-off:
  • Pros: Ensures users see their own edits immediately.
  • Cons: Increases load on the Primary DB, reducing the effectiveness of Read Replicas.

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2. Real-Time Architecture & Latency

Issue D: Server Polling (The "Thundering Herd" on DB)

• Failure Mode: Every API server polls PostgreSQL every 2 seconds. With 10 servers, that's 5 queries/sec minimum just for polling. At 100 servers, it's 50 queries/sec. This blocks write throughput.
• Bottleneck: PostgreSQL connection pool exhaustion; high CPU on DB due to constant SELECT queries.
• Latency: Up to 2 seconds delay for updates to propagate between servers.
• Solution: Redis Pub/Sub or Change Data Capture (CDC).
  • When Server A writes to Postgres, it publishes an event to a Redis channel.
  • All other API servers subscribe to this channel and push updates to their connected clients immediately.
• Trade-off:
  • Pros: Near real-time propagation; eliminates DB polling load.
  • Cons: Adds dependency on Redis; requires handling message ordering/delivery guarantees.

Issue E: WebSocket Connection State & Load Balancing

• Failure Mode: Load Balancer uses Round-Robin. User connects to Server A. User refreshes page. LB sends request to Server B. Server B has no WebSocket state for this user.
• Failure Mode: Server A crashes. Users connected to it lose connection. They reconnect, but if LB sends them to Server B, Server B doesn't know their session.
• Solution: Sticky Sessions or Externalized Session State.
  • Option 1: Enable Sticky Sessions on the Load Balancer (Session Affinity).
  • Option 2: Use a WebSocket adapter (e.g., Socket.io Redis Adapter) to share connection state across servers.
• Trade-off:
  • Option 1 Pros: Simple.
  • Option 1 Cons: Uneven load distribution; if Server A dies, all its clients disconnect.
  • Option 2 Pros: High availability; clients can reconnect to any server.
  • Option 2 Cons: Redis becomes a bottleneck for connection metadata.

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3. Security & Authentication

Issue F: JWT in LocalStorage

• Failure Mode: XSS vulnerability on the React SPA allows an attacker to read localStorage. They steal the JWT and impersonate the user for 24 hours.
• Bottleneck: None, but high risk.
• Solution: HttpOnly, Secure, SameSite Cookies.
  • Move JWT to an HttpOnly cookie. JavaScript cannot access it, mitigating XSS token theft.
  • Implement CSRF protection (double submit cookie or token in header) if using cookies.
• Trade-off:
  • Pros: Significant security improvement.
  • Cons: Requires backend changes to handle cookies; CSRF protection adds slight implementation overhead.

Issue G: CDN Caching API Responses

• Failure Mode: CloudFront caches API responses for 5 minutes. User A edits doc. User B requests doc. CloudFront serves stale cached version.
• Race Condition: Data staleness. In a collaborative editor, 5 minutes of latency is unacceptable.
• Solution: Cache Busting / No-Cache Headers.
  • Set Cache-Control: no-store for all document content API endpoints.
  • Only cache static assets (JS/CSS/Images).
• Trade-off:
  • Pros: Ensures data freshness.
  • Cons: Increases traffic to Origin (API servers/DB). Mitigate with aggressive DB read caching (Redis).

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4. Storage & Persistence

Issue H: Full HTML Snapshots Every 30 Seconds

• Failure Mode: High storage costs and slow recovery. If a document is 1MB, saving every 30s consumes massive I/O.
• Bottleneck: Disk I/O on Postgres (BLOB storage); slow to load version history.
• Solution: Operational Logs + Checkpoints.
  • Store a stream of Operations (JSON deltas) in a time-series DB or append-only log.
  • Take "snapshots" (state) every 5-10 minutes. To restore, load snapshot + replay ops.
• Trade-off:
  • Pros: Efficient storage; easier to implement version history/diffs.
  • Cons: Complex logic to reconstruct state from logs; requires version compaction logic.

Issue I: Document Partitioning by Organization ID

• Failure Mode: "Noisy Neighbor" / Hot Partition. If Organization X is huge, all their data lands on the same DB shard. That shard becomes a bottleneck while others are idle.
• Bottleneck: Single shard write throughput limit.
• Solution: Hash-Based Sharding.
  • Shard by Document_ID (hashed) rather than Organization_ID.
  • Or use a managed sharding service (e.g., Citus for Postgres).
• Trade-off:
  • Pros: Even data distribution across shards.
  • Cons: Queries across organizations (e.g., "Global Admin View") become expensive (requires sharding key routing or aggregation).

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5. Summary of Critical Fixes

Priority	Component	Problem	Immediate Fix
P0 (Critical)	Sync Strategy	Client Timestamps + LWW	Switch to Server Timestamps + CRDT/OT
P0 (Critical)	Real-Time	Polling Postgres every 2s	Implement Redis Pub/Sub
P0 (Critical)	Security	JWT in LocalStorage	Move to HttpOnly Cookies
P1 (High)	CDN	Caching API responses	Disable caching for document endpoints
P1 (High)	DB	Read Replicas Lag	Route "read-after-write" to Primary
P2 (Medium)	Storage	Full HTML Snapshots	Switch to Ops Log + Periodic Checkpoints

Recommended Architecture Revision

1. Frontend: React + Yjs (for CRDT handling) + WebSocket.
2. Auth: JWT in HttpOnly Cookie (shorter expiry, refresh tokens).
3. Backend: Node.js + Socket.io (with Redis Adapter for cross-server communication).
4. DB: Postgres (Primary) + Redis (Pub/Sub + Cache).
5. Sync: Client sends Operations → Server validates & timestamps → Server broadcasts via Redis → Server persists Ops to DB.
6. Caching: CloudFront for static assets only; Redis for hot document state.

This revision moves the system from a fragile, polling-based architecture to an event-driven, consistency-guaranteed model suitable for real-time collaboration.