Debug This Architecture

Below is a comprehensive analysis of the provided architecture for a real-time collaborative document editor. I'll identify potential failure modes (events or conditions that could lead to system unavailability, data loss, or incorrect behavior), race conditions (situations where concurrent operations lead to unpredictable or inconsistent outcomes), and scaling bottlenecks (limitations that hinder performance or growth as load increases). For each issue, I'll describe it briefly, explain its impact, propose a specific solution, and discuss trade-offs.

I've grouped the issues by category for clarity. Note that some issues overlap categories (e.g., a failure mode could exacerbate a race condition). The analysis assumes typical real-world constraints like network unreliability, clock drift, and hardware failures, based on the described architecture.

Race Conditions

These arise from concurrent user actions, server polling, and timestamp-based conflict resolution, potentially leading to data inconsistency or lost edits.

1. Client Clock Skew in Last-Write-Wins Conflict Resolution
   Timestamps rely on client-side clocks, which can drift or be manually adjusted, causing incorrect ordering of edits. For example, User A (clock 10 minutes ahead) and User B (accurate clock) edit the same paragraph; A's later edit might be deemed "earlier" due to skew, overwriting B's valid change.
   Solution: Implement server-side timestamps (e.g., using NTP-synchronized server clocks for each edit). Each change event includes a server-assigned timestamp upon receipt.
   Trade-offs: Increases server load for timestamp assignment (minor, as it's a simple operation). Clients must trust the server clock, which could introduce slight latency (e.g., 10-50ms added to edit propagation). Requires handling out-of-order arrivals if network delays occur, but improves accuracy over client clocks.

2. Delayed Sync Across Servers Due to Polling
   Servers poll PostgreSQL every 2 seconds for changes, creating a window where users on different servers see stale data. If User A on Server 1 edits a doc and User B on Server 2 edits the same part within a 2-second window, Server 2 might overwrite A's edit before seeing it.
   Solution: Replace polling with a publish-subscribe (pub/sub) mechanism, such as using Redis pub/sub channels per document. Each server publishes changes to the channel upon writing to PostgreSQL, and all servers subscribe to receive notifications instantly.
   Trade-offs: Adds complexity (e.g., managing Redis channels and ensuring message ordering). Increases Redis load (though it's lightweight for pub/sub). Eliminates polling's inefficiency but requires handling Redis failures (see failure modes below). Slightly higher latency for initial setup compared to polling.

3. Intra-Server Broadcasting Only
   Changes are broadcast only to WebSocket clients on the same server, but users might be connected to different servers. This can cause temporary inconsistencies: e.g., User A sees an edit, but User B (on another server) doesn't until their server polls and broadcasts, potentially leading to conflicting local edits.
   Solution: Use a centralized message broker (e.g., extend Redis pub/sub to include client notifications). Servers subscribe to document-specific channels and relay messages to their WebSockets.
   Trade-offs: Centralizes real-time logic, reducing per-server isolation but improving consistency. Increases Redis dependency and potential single-point load (mitigate with Redis clustering). Adds a small hop (server-to-broker-to-server), introducing ~10-20ms extra latency per broadcast.

4. Conflict Resolution for Complex Edits
   Last-write-wins on entire paragraphs (or similar units) doesn't handle granular conflicts, like one user inserting text and another deleting it in the same area. Timestamps might resolve the winner, but the losing edit is discarded entirely, causing data loss.
   Solution: Adopt operational transformation (OT) or conflict-free replicated data types (CRDTs) for finer-grained merging. For example, implement a CRDT-based library (e.g., like those in Google Docs) where edits are commutative and can be merged automatically.
   Trade-offs: Significantly increases complexity in the frontend and backend (e.g., needing to track operations like insert/delete at character level). Requires more CPU for merging on servers, potentially slowing edits. Improves user experience by preserving most changes, but OT/CRDTs can have edge cases in highly concurrent scenarios.

Failure Modes

These could cause downtime, data loss, or incomplete functionality, affecting service reliability.

1. API Server Failure
   If a server crashes, all its WebSocket connections drop, users lose unsaved edits (since snapshots are every 30 seconds), and they must reconnect (potentially to a different server, disrupting sync).
   Solution: Implement auto-reconnection with session persistence in Redis (store user state and recent edits). Upon reconnection, replay missed changes from PostgreSQL or a Redis-backed event log.
   Trade-offs: Adds Redis overhead for state storage. Ensures continuity but introduces a brief sync delay (e.g., 1-5 seconds) during failover. Requires client-side logic for reconnection, increasing frontend complexity.

2. Database (PostgreSQL) Failure
   Writes fail, halting document saves and syncing. Full snapshots could be lost if a crash occurs mid-save, and polling servers would error out.
   Solution: Use PostgreSQL high availability (e.g., with Patroni for automatic failover to replicas) and enable synchronous replication. Add a write-ahead log (WAL) for recovery.
   Trade-offs: Increases operational complexity (e.g., managing replicas). Improves uptime but adds latency to writes (due to sync replication, ~20-50ms). Read replicas already help reads, but writes remain centralized.

3. Redis Failure
   Session cache loss could invalidate user sessions prematurely, forcing logins. Pub/sub would break if used, halting cross-server sync.
   Solution: Deploy Redis in a cluster with replication and automatic failover (e.g., using Redis Sentinel or Redis Cluster). Persist critical data (e.g., sessions) to PostgreSQL as a fallback.
   Trade-offs: Higher setup cost and memory usage. Ensures reliability but doesn't eliminate all risks (e.g., brief outages during failover). Clustering adds network overhead.

4. WebSocket Disconnection and Network Failures
   Clients can disconnect due to network issues, losing edits between snapshots. Round-robin load balancing might route reconnections to different servers, causing sync issues.
   Solution: Enable sticky sessions at the load balancer (e.g., via IP hashing or session cookies) to prefer reconnecting to the same server. Add client-side buffering to queue unsaved edits for replay.
   Trade-offs: Sticky sessions reduce load balancing efficiency (uneven server load). Improves edit persistence but requires more client state management, increasing memory usage on devices. Buffering adds complexity if conflicts arise during replay.

5. JWT Expiry and Auth Failures
   JWTs expire after 24 hours, logging out users mid-session. LocalStorage storage is vulnerable to clearing (e.g., browser crashes or user actions), causing auth loss.
   Solution: Implement refresh tokens: Store a long-lived refresh token in a secure HTTP-only cookie, and use it to auto-renew JWTs without user intervention.
   Trade-offs: Enhances security (less exposure in localStorage) and user experience (seamless renewals). Adds server complexity for handling refreshes. Increases attack surface slightly if cookies are compromised.

6. CDN Caching of API Responses
   API responses cached for 5 minutes in CloudFront could serve stale data for non-static requests (e.g., document lists), conflicting with real-time edits.
   Solution: Configure cache headers to disable caching for dynamic API endpoints (e.g., use Cache-Control: no-cache for edit-related responses) or use signed URLs for sensitive data.
   Trade-offs: Reduces CDN benefits for those endpoints, increasing origin server load. Improves accuracy but might raise costs if more requests hit the backend.

7. Snapshot-Based Storage Loss
   Documents saved as full HTML snapshots every 30 seconds; crashes between saves lose recent edits. No incremental diffing means inefficient storage and recovery.
   Solution: Implement incremental versioning with diffs (e.g., store change deltas in PostgreSQL or a separate log table). Use background jobs to compute and store snapshots less frequently.
   Trade-offs: Reduces storage costs and improves recovery granularity. Adds computation overhead for diffs (e.g., via libraries like diff-match-patch), potentially delaying saves by 1-2 seconds.

Scaling Bottlenecks

These limit horizontal growth or performance under load, especially with increased users or documents.

1. Polling-Induced Database Load
   Each server polling PostgreSQL every 2 seconds scales poorly; with N servers, it's N queries per interval, overwhelming the DB even with read replicas.
   Solution: As noted in race conditions, switch to Redis pub/sub for instant notifications, reducing DB queries to near zero.
   Trade-offs: Shifts load to Redis (which is better suited for pub/sub). Eliminates polling waste but requires Redis to handle high message volumes; if not clustered, it becomes a bottleneck.

2. WebSocket Connection Limits per Server
   Each Node.js server handles its own WebSockets, limited by OS/file descriptors (e.g., ~10k-100k per server). Adding servers helps, but uneven user distribution (e.g., many users editing one doc) overloads a single server.
   Solution: Use a WebSocket proxy or gateway (e.g., based on Nginx or HAProxy) to distribute connections across servers dynamically, or adopt a shared WebSocket server (e.g., via Socket.IO clustering with Redis adapter).
   Trade-offs: Proxy adds a single point of failure and slight latency (~5-10ms). Improves distribution but increases complexity (e.g., ensuring message routing). Clustering shares load but requires coordination.

3. Centralized Database Writes
   All edits write to PostgreSQL master, creating a bottleneck for write-heavy loads (e.g., many concurrent edits). Read replicas help reads but not writes.
   Solution: Shard documents across multiple PostgreSQL instances (e.g., by hash of document ID, beyond just org ID). Use a distributed DB like CockroachDB for built-in sharding and replication.
   Trade-offs: Greatly improves write scalability. Adds complexity in query routing and cross-shard operations (e.g., harder to aggregate org-wide data). Migration from single PostgreSQL requires downtime or tooling.

4. Load Balancer and Uneven Distribution
   Round-robin balancing doesn't account for connection stickiness or load per server, leading to hotspots (e.g., one server with many active WebSockets).
   Solution: Upgrade to a smarter load balancer (e.g., AWS ALB with least-connections algorithm) or use session affinity with health checks.
   Trade-offs: Better distribution improves performance. Adds configuration overhead and potential for affinity-related issues (e.g., users stuck on slow servers).

5. Document Partitioning Limitations
   Partitioning by org ID is good for isolation but doesn't prevent bottlenecks within a large org (e.g., one org with 10k users editing the same doc overloads its partition).
   Solution: Sub-partition by document ID (e.g., use a hash function to distribute docs across multiple DB shards within an org).
   Trade-offs: Enhances parallelism within orgs. Increases routing complexity (e.g., needing a lookup service for shard locations). Requires careful key design to avoid hotspots.

6. Resource Intensity of Broadcasting
   Broadcasting changes to all clients on a server scales poorly for large documents (e.g., 100 users on one server means 100 messages per edit).
   Solution: Implement selective broadcasting (e.g., only send to clients viewing the edited section) using client-reported viewports or subscription models.
   Trade-offs: Reduces network and CPU load per edit. Requires more client-server communication (e.g., viewport updates), adding slight overhead. May miss edge cases if viewports aren't accurately tracked.

This architecture has solid foundations (e.g., horizontal scaling and partitioning), but real-time collaboration demands tight consistency and low latency—many issues stem from the polling-based sync and client-reliance. Prioritizing fixes like server-side timestamps and pub/sub would address core races and bottlenecks. If implemented, thorough testing (e.g., with simulated failures and load) is essential. Let me know if you'd like deeper dives into any solution!