Certainly! Here’s a thorough analysis of the potential failure modes, race conditions, and scaling bottlenecks in your collaborative document editor architecture, with specific solutions and their trade-offs.

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1. WebSocket Connection Affinity & Message Propagation

Issue:

• Clients connect to random API servers (due to round-robin LB), and each server only broadcasts edits to its own connected clients.
• Servers poll the DB every 2s for changes from other servers. This introduces latency (up to 2s) and possible message ordering issues.

Problems:

• Delayed sync: Real-time edits may not reach all users instantly.
• Lost updates: If polling interval is too high, rapid edits may conflict or appear out-of-order.
• Duplicate/conflicting edits: With last-write-wins and client-side timestamps, edits may override each other in unexpected ways.

Solution:

• Adopt a shared pub/sub (e.g., Redis Pub/Sub, Kafka): Each server publishes change events to a channel; all servers subscribe and rebroadcast to their WebSocket clients.
• Alternative: Use a managed WebSocket service (e.g., AWS API Gateway WebSocket, Pusher) for global broadcast, bypassing server-to-server sync.

Trade-offs:

• Pub/Sub: Adds operational complexity but ensures low-latency, ordered delivery.
• Managed services: May incur cost; less flexibility but removes operational burden.

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2. Conflict Resolution (Last-write-wins with client timestamps)

Issue:

• Client clocks are unreliable; NTP drift can cause edits to be "lost" or overwritten incorrectly.
• No deterministic merge: Last-write-wins is not user-friendly for text collaboration.

Problems:

• Data loss: User A's edit may be silently overwritten by User B with a slightly ahead clock.
• Poor UX: Edits can disappear or flicker.

Solution:

• Move conflict resolution to server: Use server-generated, monotonic timestamps.
• Implement Operational Transformation (OT) or Conflict-Free Replicated Data Types (CRDTs): For true real-time collaborative editing without data loss.

Trade-offs:

• Server-side timestamps: Adds one round-trip latency, but higher consistency.
• OT/CRDT: More complex to implement and reason about, but best for collaborative text.

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3. Database Write Contention & Scaling

Issue:

• All servers write directly to PostgreSQL for every change.
• High write throughput (many small updates) can overwhelm the DB.

Problems:

• Write amplification: Small edits = many writes.
• Locking/contention: Multiple servers writing to same document/row.
• DB scaling limits: Hard to scale writes horizontally.

Solution:

• Buffer edits in memory (Redis, or in-process) and batch writes: E.g., store "edit log" and flush every few seconds.
• Document partitioning: Already planned, but ensure sharding by orgID is implemented.
• Move to append-only log model: Store changes as events, reconstruct document on-demand.

Trade-offs:

• Batching: Risk of data loss if process crashes before flushing.
• Event sourcing: More complex, but enables easier recovery, audit, and scaling.

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4. WebSocket Session Stickiness

Issue:

• Load balancer is round-robin; WebSocket connections are long-lived.
• On reconnect, clients may hit a different server and lose unsynced state.

Problems:

• Lost state: Unsynced edits may be lost on reconnect.
• Inconsistent experience: No session affinity.

Solution:

• Enable sticky sessions at the load balancer (e.g., based on JWT userID or session cookie).
• Or: Centralize session state in Redis, so any server can recover state on reconnect.

Trade-offs:

• Sticky sessions: Reduces load balancing effectiveness.
• Central state store: More network hops, but better fault tolerance.

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5. Authentication and Token Security

Issue:

• JWT tokens stored in localStorage are vulnerable to XSS attacks.
• 24-hour tokens increase the attack window.

Problems:

• Token theft: Compromised browser can leak JWTs.
• Long expiry: Stolen token is valid for 24h.

Solution:

• Store JWTs in HttpOnly cookies (if feasible).
• Shorten expiry, use refresh tokens: E.g., 15 minutes access + 24h refresh.
• Implement XSS protections: CSP, input sanitization.

Trade-offs:

• HttpOnly cookies: May not work with all SPAs; can complicate CORS.
• Shorter expiry: More auth traffic, but better security.

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6. CloudFront Caching API Responses

Issue:

• CloudFront caches API responses for 5 minutes.
• Stale data for APIs that should be real-time (e.g., document fetch).

Problems:

• Stale reads: Users may see old snapshots, not latest state.

Solution:

• Disable caching for document API endpoints (set Cache-Control: no-store).
• Only cache truly static API responses (e.g., config, public docs).

Trade-offs:

• More backend load: For uncached endpoints.
• No trade-off for real-time APIs; correctness is required.

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7. Failure Recovery & Data Loss

Issue:

• Documents saved as full HTML snapshots every 30 seconds.
• Edits between snapshots may be lost on crash.

Problems:

• Data loss: If server crashes, recent edits may not be persisted.

Solution:

• Persist edit logs incrementally (see above), not just snapshots.
• Leverage Redis persistence (AOF) or use a queue (Kafka) to buffer incoming edits before writing to DB.

Trade-offs:

• More write IOPS: But much lower data loss risk.
• Event log size management: Need compaction/cleanup strategy.

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8. Polling Interval for Cross-server Sync

Issue:

• 2-second polling interval is arbitrary; trades off latency vs. DB load.

Problems:

• Higher latency: Edits can take up to 2s to propagate.
• DB load: Frequent polling = wasted reads.

Solution:

• Switch to push (pub/sub) model as above.

Trade-offs:

• Pub/sub infra: Slightly harder to operate, but far better scalability and latency.

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9. Document Partitioning by Organization ID

Issue:

• If one org is very large, that partition becomes a hotspot.

Problems:

• Hot partition: One org can overwhelm a single DB node.

Solution:

• Sub-partition by document ID within org.
• Consider further sharding or isolating "VIP" orgs.

Trade-offs:

• Complexity: Need to manage cross-partition queries.
• Better scalability: Can add more capacity as needed.

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10. Session Cache Consistency

Issue:

• Redis is used for session cache, but if not set up with HA, can lose session data.

Problems:

• Session loss on Redis failover.

Solution:

• Use Redis Sentinel or Redis Cluster for high availability.
• Periodically checkpoint session data to a persistent store.

Trade-offs:

• More ops overhead: But necessary for reliability.

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11. Frontend/Backend Version Skew

Issue:

• CDN caches static assets and API responses; version mismatches possible.

Problems:

• Incompatible API calls: If frontend is updated but backend isn't (or vice versa).

Solution:

• Cache-busting URLs for assets.
• Version API endpoints and ensure backward compatibility.

Trade-offs:

• Slightly more build/deploy complexity.

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Summary Table

Problem	Solution	Trade-offs
Delayed sync, message loss	Redis/Kafka pub-sub for servers	Infra complexity, more ops
Client timestamp conflicts	Server timestamps, OT/CRDT	OT/CRDT is complex but robust
DB write contention	Batch writes, event log	Batch risk, compaction needed
WebSocket stickiness	LB stickiness, central session	Stickiness limits balancing
JWT security	HttpOnly, short expiry, refresh	More auth round-trips
CDN caches API	No-store for dynamic endpoints	More backend load
Data loss on crash	Incremental event logging	More storage, compaction
Polling interval	Push (pub/sub) sync model	New infra
Org partition hotspots	Sub-partition by docID	More complex sharding
Redis cache HA	Redis Sentinel/Cluster	Ops overhead
Frontend/backend version skew	Cache busting, API versioning	Deploy complexity

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TL;DR:

• The main bottlenecks are the lack of real-time cross-server communication (use pub/sub!), DB write contention, and unreliable conflict resolution with client clocks.
• The main failure modes are data loss (between snapshots, on crash) and stale/incorrect sync due to polling and caching.
• Secure session/auth, ensure partitioning supports large orgs, and use server timestamps or OT/CRDT for proper conflict resolution.
• Most critical: Implement a global pub/sub system for real-time sync, move away from client clocks for conflict resolution, and persistent incremental logging for edits.