Debug This Architecture

Here’s a comprehensive analysis of failure modes, race conditions, and scaling bottlenecks in the proposed architecture, along with specific solutions and trade-offs.

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1. Real-Time Sync & WebSocket Issues

Failure Mode: WebSocket Connections Not Shared Across Servers

• Problem: Each Node.js server maintains its own WebSocket connections. If User A is on Server 1 and User B is on Server 2, changes from A won’t reach B in real-time unless Server 2 polls PostgreSQL.
• Race Condition: Polling every 2 seconds means up to 2 seconds of sync delay between users on different servers.
• Scaling Bottleneck: As servers increase, cross-server latency grows, hurting real-time collaboration feel.

Solution: Use a Pub/Sub system (Redis Pub/Sub or dedicated message broker like Kafka) for cross-server real-time notifications.

• Trade-offs:
  • Adds complexity and another infrastructure component.
  • Redis Pub/Sub doesn’t guarantee persistence; if a server is down during broadcast, messages are lost.
  • Alternative: Use a managed service (e.g., Amazon MQ, Socket.IO with Redis adapter) for simpler scaling.

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2. Conflict Resolution & Last-Write-Wins (LWW)

Failure Mode: Client Clock Skew

• Problem: Relying on client timestamps for LWW is dangerous—clients can have incorrect times (intentionally or not), causing valid edits to be overwritten.
• Race Condition: Two users edit the same paragraph simultaneously; the one with a clock set ahead always wins, regardless of actual edit order.

Solution: Use server-generated monotonic timestamps (logical clocks or hybrid logical clocks) or adopt Operational Transformation (OT) / Conflict-Free Replicated Data Types (CRDTs).

• Trade-offs:
  • OT/CRDTs increase implementation complexity and may require a central coordination service.
  • Server timestamps require all events to pass through the server first, adding latency before local UI update.
  • Compromise: Use vector clocks if each user has a unique client ID, but still need server mediation.

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3. Database & Storage Issues

Failure Mode: PostgreSQL Write Contention

• Problem: Every keystroke (or change event) writes to PostgreSQL. Under heavy load, this can cause table locks, slow writes, and become a single point of failure.
• Scaling Bottleneck: Partitioning by organization ID helps, but hot partitions (large active orgs) can still overwhelm a single DB node.

Solution:

1. Buffer writes in Redis and periodically flush to PostgreSQL.
2. Use change log streaming (PostgreSQL logical decoding or Debezium) to stream changes to read replicas and other services.

• Trade-offs:
  • Buffering adds risk of data loss if Redis crashes.
  • Change log streaming increases infrastructure complexity.

Failure Mode: Full HTML Snapshots Every 30 Seconds

• Problem: Large documents cause heavy I/O. If two snapshots are triggered near-simultaneously, they may conflict.
• Race Condition: Snapshot might save an inconsistent state if concurrent edits are mid-flight.

Solution: Store delta-based changes with periodic snapshots (e.g., every 100 changes or 5 minutes). Use event sourcing: store all operations, reconstruct document from log.

• Trade-offs:
  • Increases read complexity (must replay deltas to get current state).
  • Reduces storage I/O but increases storage volume for change logs.

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4. API & Caching Issues

Failure Mode: CDN Caching API Responses for 5 Minutes

• Problem: Dynamic document data cached for 5 minutes will serve stale content. Users may see outdated documents.
• Scaling Bottleneck: If CDN is used for API responses, cache invalidation on document update is difficult.

Solution: Only cache static assets in CDN. For API, use Redis cache with fine-grained invalidation (per document ID). Alternatively, use short-lived CDN TTL (e.g., 5 seconds) and soft purge on update.

• Trade-offs:
  • More cache misses increase load on backend.
  • CDN soft purge may have propagation delays.

Failure Mode: JWT in localStorage

• Problem: Vulnerable to XSS attacks. Token auto-refresh mechanism not described; users may be logged out unexpectedly after 24 hours.
• Race Condition: Multiple tabs might attempt token refresh simultaneously, causing duplicate requests.

Solution: Store JWT in httpOnly cookies (secure, sameSite strict) and implement sliding session renewal via refresh tokens (stored server-side in Redis). Use CSRF tokens for state-changing operations.

• Trade-offs:
  • Slightly more complex auth flow.
  • Cookies have size limits and are sent with every request, increasing bandwidth.

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5. Load Balancing & Session Persistence

Failure Mode: Round-Robin Load Balancing with WebSockets

• Problem: WebSocket connections are long-lived. Round-robin may distribute connections unevenly over time, causing some servers to be overloaded.
• Scaling Bottleneck: Without sticky sessions, reconnection after server failure may route a user to a different server, losing in-memory state (if any).

Solution: Use load balancer with sticky sessions (e.g., hash based on user ID or session ID) for WebSocket connections. For health checks, ensure WebSocket endpoints are monitored.

• Trade-offs:
  • Sticky sessions reduce flexibility in load distribution.
  • Server failures still require reconnection, but user can reconnect to any server if state is externalized (Redis).

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6. Polling Mechanism Bottleneck

Failure Mode: Every Server Polling PostgreSQL Every 2 Seconds

• Problem: As server count grows, database load from polling increases linearly (O(n)). This can overwhelm the database with redundant queries.
• Race Condition: Polls may miss changes that occur between intervals, requiring longer poll windows or more frequent polling (which exacerbates load).

Solution: Replace polling with database triggers + notification system (e.g., PostgreSQL LISTEN/NOTIFY) or use change data capture to push changes to a message queue that servers subscribe to.

• Trade-offs:
  • LISTEN/NOTIFY has limited message payload size and no persistence.
  • CDC adds operational overhead but is scalable and reliable.

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7. Data Consistency Across Read Replicas

Failure Mode: Replication Lag

• Problem: Read replicas may be behind the primary. If a user reads from a replica immediately after a write, they might see stale data.
• Race Condition: User edits, UI updates optimistically, but a subsequent fetch (from replica) shows old content, causing UI flicker or overwrite.

Solution: Implement read-after-write consistency by:

1. Directing reads for recently modified documents to the primary.
2. Using monotonic reads (same user always hits same replica).
3. Tracking replication lag and routing queries accordingly.

• Trade-offs:
  • Increased primary load.
  • More complex routing logic.

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8. Horizontal Scaling of WebSocket Servers

Failure Mode: Server Failure Loses In-Memory State

• Problem: If a server dies, all its WebSocket connections are dropped, and any unsaved changes in memory are lost.
• Scaling Bottleneck: Reconnecting all clients simultaneously to other servers may cause thundering herd on those servers.

Solution:

1. Externalize WebSocket session state in Redis (e.g., connection metadata, pending messages).
2. Implement graceful degradation on server shutdown: notify clients to reconnect elsewhere.
3. Use exponential backoff in client reconnection logic.

• Trade-offs:
  • Redis becomes a critical dependency; adds latency to message routing.
  • More network hops for session data.

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9. No Offline Support / Queued Edits

Failure Mode: Network Disconnection Loses Edits

• Problem: If a user goes offline, changes are only in their browser; reconnection might lose unsent changes if not queued.
• Race Condition: Offline edits with old timestamps may overwrite newer changes when reconnected.

Solution: Implement client-side queue with versioning and server-assigned document version numbers. On reconnect, replay queued operations if the base version still matches; otherwise, require merge/resolve.

• Trade-offs:
  • Complex client-side logic.
  • Merge conflicts may require user intervention.

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10. Monitoring & Observability Gaps

Failure Mode: No Visibility into Real-Time Layer

• Problem: No mention of logging, metrics, or alerts for WebSocket message rates, connection churn, or sync delays.
• Scaling Bottleneck: Hard to detect when to add more servers or where bottlenecks are.

Solution: Integrate APM tools (e.g., Datadog, New Relic) for Node.js servers, track WebSocket connections per server, message latency, and end-to-end sync delay. Use structured logging for operations.

• Trade-offs:
  • Added overhead from metric collection.
  • Operational cost of monitoring stack.

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Summary of High-Priority Fixes

1. Replace client timestamps with server-side sequencing (logical clocks or CRDTs).
2. Introduce Pub/Sub (Redis) for cross-server real-time sync instead of polling.
3. Change snapshot strategy to delta-based + periodic snapshots.
4. Secure auth by moving JWT to httpOnly cookies + refresh tokens.
5. Add sticky sessions for WebSocket load balancing.
6. Implement offline queue with versioning for better resilience.

Each solution involves a trade-off between complexity, performance, and user experience, but addressing these issues will significantly improve system reliability and scalability.