This architecture broadly covers a real-time collaborative editor but has several potential failure modes, race conditions, and scaling bottlenecks due to design choices like polling, last-write-wins, and distributed WebSocket connections. Below is a detailed analysis and recommendations:

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1. Failure Modes and Race Conditions

1.1. Clock Skew & Last-Write-Wins (LWW) Conflict Resolution

• Issue:
  Using client-side timestamps for LWW is unreliable. Client clocks can be skewed or manipulated, causing inconsistent conflict resolution and data loss.

• Impact:
  Edits may be overwritten incorrectly, leading to lost user data or inconsistent document state.

• Solution:
  Use server-generated timestamps or a logical clock (Lamport clock or vector clocks) to order changes deterministically.

  • Trade-off:
    Slightly increased backend complexity and latency, but vastly improved consistency and trustworthiness.

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1.2. Polling for Changes Across Servers (2-second interval)

• Issue:
  Polling PostgreSQL every 2 seconds for changes introduces latency in syncing edits across servers. This affects real-time collaboration experience.

• Race Condition:
  If two servers receive concurrent edits for the same paragraph, polling delay may cause conflicting states before reconciliation.

• Scalability Bottleneck:
  Frequent polling can overload the database, especially with increasing server count and users.

• Solution:
  Implement a centralized message broker or pub/sub system (e.g., Redis Pub/Sub, Kafka) to propagate changes instantly to all servers.

  • Servers publish changes to a channel; others subscribe and push updates to their clients immediately.
  • Trade-off:
    Additional infrastructure and complexity, but dramatically improves real-time sync and reduces DB load.

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1.3. WebSocket Connections Distributed Across Servers

• Issue:
  WebSocket clients connected to different servers do not share state natively. Server-to-server communication is needed for real-time sync.

• Failure Mode:
  If a server crashes, all its WebSocket connections drop, disconnecting users.

• Solution:

  • Use a stateful connection manager or shared session store to allow seamless failover.
  • Or implement sticky sessions at the load balancer to keep clients on the same server.
  • Consider WebSocket proxies or shared pub/sub for message distribution.
  • Trade-off:
    Sticky sessions reduce load balancer flexibility; shared state adds complexity.

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1.4. Document Writes to PostgreSQL on Every Change

• Issue:
  Writing every keystroke or small change immediately to PostgreSQL is a performance bottleneck.

• Scaling Bottleneck:
  High write throughput can overwhelm the DB, causing latency spikes and possible downtime.

• Solution:

  • Buffer changes in-memory and batch write periodically (e.g., every few seconds or after N changes).
  • Use an append-only operation log or event sourcing pattern to store changes efficiently.
  • Trade-off:
    Slightly reduced durability on crashes between batch writes; more complex recovery logic.

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1.5. Full HTML Snapshots Saved Every 30 Seconds

• Issue:
  Saving entire document snapshots causes large write operations and storage use.

• Scaling Bottleneck:
  Large documents and frequent snapshots increase DB size, IO, and backup times.

• Solution:

  • Store incremental diffs/patches instead of full snapshots.
  • Use a combination of snapshots and diffs for efficient reconstruction (similar to Git).
  • Trade-off:
    Increased complexity in document reconstruction logic.

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1.6. JWT Tokens Stored in LocalStorage

• Security Risk:
  LocalStorage is vulnerable to XSS attacks, risking token theft and session hijacking.

• Solution:
  Store tokens in HttpOnly Secure cookies with proper SameSite flags to mitigate XSS risks.

  • Trade-off:
    Slightly more complex CSRF protection needed.

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1.7. CloudFront Caching API Responses for 5 Minutes

• Issue:
  Caching API responses for 5 minutes can cause clients to receive stale data, breaking real-time collaboration consistency.

• Solution:

  • Disable caching for real-time API endpoints (WebSocket endpoints are not cached anyway).
  • Use cache-control headers to prevent caching on dynamic content.
  • Trade-off:
    Slightly increased load on API servers, but necessary for consistency.

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2. Scaling Bottlenecks and Solutions

2.1. Load Balancer Using Simple Round-Robin

• Issue:
  Round-robin does not respect WebSocket session affinity, causing frequent reconnects or broken sessions.

• Solution:
  Implement sticky sessions or session-aware load balancing to keep WebSocket connections stable.

  • Alternatively, use a WebSocket gateway or message broker to decouple connection handling from API servers.
  • Trade-off:
    Sticky sessions reduce flexibility; gateway adds infrastructure.

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2.2. Database Write Scalability

• Issue:
  Single PostgreSQL write master can become bottleneck for high concurrent writes.

• Solution:

  • Use sharding or partitioning more aggressively (by document or organization).
  • Consider multi-master or distributed databases optimized for high write throughput (e.g., CockroachDB).
  • Implement write queues or event sourcing to smooth spikes.
  • Trade-off:
    Added operational complexity.

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2.3. Redis Session Cache as Single Point of Failure

• Issue:
  Redis used for session cache can become a bottleneck or single point of failure.

• Solution:

  • Use Redis in cluster mode with replication and failover.
  • Implement local caching with fallback to Redis.
  • Trade-off:
    Increased infrastructure complexity.

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2.4. Document Partitioning by Organization ID

• Potential Bottleneck:
  Uneven distribution of users per organization can cause hotspots.

• Solution:

  • Monitor partition sizes and dynamically rebalance partitions.
  • Possibly shard within organizations if needed.
  • Trade-off:
    Complexity in partition management.

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3. Summary Table of Issues and Solutions

Issue	Impact	Solution	Trade-off
Client-side timestamps for LWW	Data inconsistency and loss	Use server or logical clocks	Increased complexity
Polling DB every 2 seconds	Latency, DB overload	Use pub/sub message broker	Added infra, complexity
Distributed WebSocket connections	User disconnect on server crash	Sticky sessions or shared state	Reduced LB flexibility, complexity
Immediate DB writes per change	DB write bottleneck	Batch writes or event sourcing	Durability risk on crash
Full HTML snapshot every 30s	Large storage and IO	Store diffs + snapshots	Complex reconstruction logic
JWT in localStorage	XSS vulnerability	Use HttpOnly Secure cookies	Need CSRF protection
CloudFront caches API for 5 minutes	Stale data, broken real-time	Disable caching for API endpoints	More API load
Round-robin LB for WebSockets	Connection instability	Sticky sessions or WebSocket gateway	LB flexibility, added infra
Single DB write master	Write bottleneck	Sharding, multi-master DBs	Operational complexity
Redis as single session cache	SPOF	Redis cluster with replication	Infra complexity
Uneven document partitioning	Hotspots	Dynamic rebalancing	Management complexity

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Final Recommendations

• Replace polling with pub/sub for real-time sync.
• Use server-side or logical clocks for ordering edits.
• Batch DB writes and store incremental diffs to reduce load.
• Improve WebSocket connection management with sticky sessions or gateway.
• Secure JWT storage with cookies, not localStorage.
• Disable CDN caching on dynamic API responses.
• Scale DB writes via sharding and consider distributed DBs.
• Make Redis highly available via clustering.

Implementing these will improve consistency, scalability, security, and user experience in the collaborative document editor.