Debug This Architecture

Let’s analyze the architecture of this real-time collaborative document editor for potential failure modes, race conditions, and scaling bottlenecks. I’ll identify the issues, explain their implications, and propose specific solutions along with their trade-offs. The issues are categorized based on the components and strategies in the system.

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1. Failure Modes

1.1 WebSocket Connection Failures

• Issue: Since each API server maintains its own WebSocket connections to clients, if a server crashes or a client loses connection to their specific server, the user is disconnected and may lose real-time updates until they reconnect to another server.
• Impact: Users experience interruptions in real-time collaboration, potentially missing critical updates or being unable to edit during downtime.
• Solution: Implement a WebSocket connection manager using a pub/sub system like Redis Pub/Sub. When a client connects, the connection is registered in Redis, and messages are broadcast through Redis channels. If a server fails, clients can reconnect to any other server without losing state since the connection state and messages are managed centrally.
  • Trade-offs:
    • Pros: Improved fault tolerance and seamless reconnection for clients.
    • Cons: Adds complexity to the system with additional Redis dependency and potential latency in message delivery due to pub/sub overhead.

1.2 API Server Failure During Broadcast

• Issue: If an API server fails after writing a change to PostgreSQL but before broadcasting it to its connected clients, those clients miss the update until another server polls the database (every 2 seconds).
• Impact: Inconsistent document state for some users, leading to temporary desynchronization.
• Solution: Use a reliable message queue (e.g., RabbitMQ or Kafka) for broadcasting updates. After writing to PostgreSQL, the server publishes the update to the queue, and all servers subscribe to it for broadcasting to their clients.
  • Trade-offs:
    • Pros: Ensures updates are not lost even if a server crashes.
    • Cons: Increases system complexity and operational overhead for maintaining a message queue. May introduce slight latency in message delivery.

1.3 Database Failure or Outage

• Issue: If PostgreSQL goes down, the system cannot save or retrieve document changes, halting collaboration. Redis session cache failure would also disrupt user sessions.
• Impact: Complete service outage for writes and potentially reads if no fallback is in place.
• Solution: Implement database replication with failover mechanisms. Use PostgreSQL streaming replication with a hot standby for failover, and set up Redis with a master-slave setup or cluster mode for high availability.
  • Trade-offs:
    • Pros: Minimizes downtime and ensures data availability.
    • Cons: Increases infrastructure costs and complexity for maintaining replicas and failover processes.

1.4 JWT Token Expiry or Compromise

• Issue: JWT tokens stored in localStorage are vulnerable to XSS attacks, and with a 24-hour expiry, a compromised token can be used for an extended period. No refresh token mechanism is mentioned.
• Impact: Security risks and potential unauthorized access to documents.
• Solution: Use HttpOnly cookies for storing JWT tokens to prevent XSS attacks, implement refresh tokens with shorter-lived access tokens (e.g., 1-hour expiry for access tokens, 7 days for refresh tokens), and store refresh tokens securely on the server side.
  • Trade-offs:
    • Pros: Enhances security by reducing exposure of tokens to client-side scripts and limiting damage from compromised tokens.
    • Cons: Increases complexity with token refresh logic and requires additional server-side storage for refresh tokens.

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2. Race Conditions

2.1 Last-Write-Wins with Client Clock Timestamps

• Issue: The sync strategy relies on client-side timestamps for conflict resolution. If client clocks are out of sync (e.g., due to incorrect system time or time zone issues), an older edit might overwrite a newer one based on inaccurate timestamps.
• Impact: Data loss or incorrect conflict resolution, leading to user frustration.
• Solution: Use server-side timestamps or a hybrid approach like Lamport clocks or vector clocks for conflict resolution. Server-side timestamps ensure consistency since the server’s clock is authoritative.
  • Trade-offs:
    • Pros: More accurate conflict resolution and prevention of data loss due to clock skew.
    • Cons: Server-side timestamps may not fully capture causality in distributed systems; vector clocks add complexity and storage overhead.

2.2 Concurrent Updates to PostgreSQL

• Issue: Multiple servers or users may write updates to the same document in PostgreSQL simultaneously, and without proper locking or transaction isolation, this could lead to data corruption or inconsistency.
• Impact: Overwritten updates or inconsistent document state.
• Solution: Use database transactions with row-level locking (e.g., SELECT FOR UPDATE) or optimistic concurrency control with version numbers for documents. Each update checks the version before committing.
  • Trade-offs:
    • Pros: Prevents data corruption and ensures consistency.
    • Cons: Row-level locking can introduce contention and reduce write throughput; version checks add overhead to every update operation.

2.3 Polling-Based Sync Across Servers

• Issue: Servers poll PostgreSQL every 2 seconds for changes. If multiple servers poll and process the same update concurrently, or if a poll misses an update due to timing, it could lead to duplicate broadcasts or missed updates.
• Impact: Inconsistent state across clients connected to different servers or unnecessary network overhead.
• Solution: Replace polling with a pub/sub mechanism using Redis or a message queue. Updates are published to a channel after being written to PostgreSQL, and all servers subscribe to this channel to receive changes in real-time.
  • Trade-offs:
    • Pros: Eliminates polling overhead and ensures immediate propagation of updates.
    • Cons: Adds dependency on an additional system (Redis or message queue) and increases complexity for handling subscription failures.

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3. Scaling Bottlenecks

3.1 WebSocket Connections Per Server

• Issue: Each API server maintains its own WebSocket connections, and as the number of users grows, a single server may become overwhelmed by the number of open connections, leading to resource exhaustion (e.g., file descriptors, memory).
• Impact: Degraded performance or server crashes under high load.
• Solution: Offload WebSocket handling to a dedicated service or use a scalable WebSocket gateway like AWS API Gateway WebSocket or a third-party service like Pusher. Alternatively, use a connection balancer to distribute WebSocket connections across servers more evenly.
  • Trade-offs:
    • Pros: Reduces load on individual API servers and improves scalability for real-time connections.
    • Cons: Increases costs (if using managed services) and introduces additional points of failure or latency.

3.2 PostgreSQL Write Contention

• Issue: As the number of users and documents grows, frequent writes to PostgreSQL for every change (especially without batching) can lead to contention, particularly for hot documents with many concurrent editors.
• Impact: Slow write performance and potential database bottlenecks.
• Solution: Batch updates before writing to PostgreSQL (e.g., aggregate changes over a short window like 500ms) and use sharding to distribute documents across multiple database instances based on organization ID or document ID.
  • Trade-offs:
    • Pros: Reduces write load on the database and improves scalability through sharding.
    • Cons: Batching introduces slight delays in persistence; sharding adds complexity to queries and data migrations.

3.3 Redis Session Cache Overload

• Issue: Redis is used for session caching, but under heavy load (e.g., millions of active users), it may run out of memory or become a performance bottleneck if not scaled properly.
• Impact: Slow session lookups or session data loss, leading to user authentication failures.
• Solution: Use Redis Cluster to distribute session data across multiple nodes and implement a least-recently-used (LRU) eviction policy to manage memory. Alternatively, offload session storage to a distributed cache like Amazon ElastiCache.
  • Trade-offs:
    • Pros: Enhances scalability and reliability of session storage.
    • Cons: Increases operational complexity and costs for managing a distributed cache.

3.4 CDN Caching of API Responses

• Issue: Caching API responses for 5 minutes in CloudFront can lead to stale data being served to users, especially for real-time collaborative features where updates happen frequently.
• Impact: Users may see outdated document states, undermining the real-time nature of the application.
• Solution: Disable caching for real-time API endpoints or set a very short cache TTL (e.g., 10 seconds) with cache invalidation on updates. Use WebSocket for real-time updates instead of relying on cached HTTP responses.
  • Trade-offs:
    • Pros: Ensures users receive the latest data for real-time collaboration.
    • Cons: Increases load on backend servers due to reduced caching benefits; cache invalidation can be complex to implement reliably.

3.5 Polling Overhead for Cross-Server Sync

• Issue: Polling PostgreSQL every 2 seconds by all API servers creates unnecessary database load, especially as the number of servers increases.
• Impact: Wastes database resources and risks performance degradation under scale.
• Solution: Replace polling with a pub/sub system (as mentioned earlier) to notify servers of changes in real-time without querying the database repeatedly.
  • Trade-offs:
    • Pros: Reduces database load and improves responsiveness.
    • Cons: Requires additional infrastructure for pub/sub and careful handling of message delivery guarantees.

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4. Additional Considerations

4.1 Document Snapshot Storage

• Issue: Storing full HTML snapshots every 30 seconds can lead to significant storage overhead, especially for large documents or frequent edits. There’s no mention of versioning or delta storage.
• Impact: Rapidly growing storage costs and potential performance issues when retrieving large snapshots.
• Solution: Store deltas (incremental changes) instead of full snapshots, and periodically consolidate deltas into a baseline snapshot. Use a versioning system to retain historical states.
  • Trade-offs:
    • Pros: Reduces storage usage and enables efficient history tracking.
    • Cons: Increases complexity for reconstructing document state from deltas and may slow down reads for historical data.

4.2 Lack of Operational Transformation or CRDTs

• Issue: The current last-write-wins strategy for conflict resolution can lead to data loss when concurrent edits occur. It doesn’t preserve user intent in collaborative editing.
• Impact: Poor user experience due to overwritten changes.
• Solution: Implement Operational Transformation (OT) or Conflict-Free Replicated Data Types (CRDTs) to merge concurrent edits intelligently without data loss.
  • Trade-offs:
    • Pros: Preserves user intent and improves collaboration experience.
    • Cons: Significantly increases implementation complexity and may require additional state management on the client and server.

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Summary of Key Issues and Recommendations

Issue	Impact	Solution	Trade-offs
WebSocket Connection Failures	User disconnection	Redis Pub/Sub for connection management	Complexity, latency
Client Clock Timestamp Issues	Data loss in conflicts	Server-side or vector clocks	Complexity, storage overhead
PostgreSQL Write Contention	Slow writes, bottlenecks	Batching, sharding	Latency, query complexity
Polling Overhead	Database load	Pub/Sub (Redis, Kafka)	Additional infrastructure
CDN Stale Data	Outdated responses	Short TTL or disable caching	Increased server load
Lack of OT/CRDT	Poor conflict resolution	Implement OT or CRDT	High implementation complexity

This analysis highlights critical areas for improvement in fault tolerance, consistency, and scalability. Addressing these issues will make the system more robust for real-time collaboration at scale, though each solution introduces trade-offs that must be evaluated based on the project’s priorities (e.g., cost, performance, or simplicity).