Debug This Architecture

Below, I'll systematically identify potential failure modes, race conditions, and scaling bottlenecks in the described architecture for a real-time collaborative document editor. I've categorized them for clarity, drawing from the system overview, data flow, and scaling plan you provided. For each issue, I've proposed a specific, actionable solution along with its trade-offs (e.g., benefits, drawbacks, complexity, and performance impacts). These are based on common pitfalls in distributed real-time systems like this one, which relies on WebSockets, polling, and a central database.

I've aimed to be comprehensive but focused on the most critical issues directly tied to the architecture. Note that some issues overlap categories (e.g., a race condition could lead to a failure mode), but I've grouped them logically.

1. Failure Modes

These are scenarios where parts of the system could fail, leading to data loss, downtime, or inconsistent behavior.

• Issue: PostgreSQL as a single point of failure. All changes are written to and polled from PostgreSQL. If the primary PostgreSQL instance fails (e.g., due to hardware issues, overload, or network partition), document updates stop propagating, servers can't sync, and the system becomes read-only or unresponsive. Read replicas help with reads but not writes.

  • Solution: Implement PostgreSQL high availability (HA) with automatic failover using tools like Patroni or AWS RDS Multi-AZ. Configure synchronous replication to a standby node, with automatic promotion on failure detection.
  • Trade-offs: Improves reliability and minimizes downtime (failover in seconds), but increases costs (additional nodes/replicas) and complexity (managing replication lag and failover logic). Write latency may increase slightly due to synchronous commits, and there's a risk of split-brain if failover isn't perfectly orchestrated.

• Issue: API server failure disrupts WebSocket connections. Each server manages its own WebSocket clients. If a server crashes or is taken offline (e.g., for maintenance), connected clients lose their real-time updates, and changes they send aren't broadcast or persisted until reconnection (potentially to a different server via the load balancer).

  • Solution: Use a WebSocket-aware load balancer (e.g., AWS ALB with sticky sessions) combined with client-side reconnection logic in the React app (e.g., using libraries like Socket.io with automatic retry). On reconnection, have the client fetch the latest document state from PostgreSQL via an API call.
  • Trade-offs: Enhances user experience by reducing perceived downtime, but adds frontend complexity and potential for brief data inconsistencies during reconnection. Sticky sessions reduce load balancer flexibility, and frequent reconnections could increase API load.

• Issue: Redis session cache failure leads to authentication issues. If Redis goes down, session data (e.g., active user sessions) is lost, forcing users to re-authenticate and potentially disrupting real-time collaboration mid-session.

  • Solution: Set up Redis in a clustered mode with replication and persistence (e.g., Redis Sentinel for HA), and fallback to PostgreSQL for session storage if Redis is unavailable (with a circuit breaker pattern in the Node.js code).
  • Trade-offs: Boosts resilience at low cost, but replication adds latency and data transfer overhead. Falling back to PostgreSQL could degrade performance during outages, as it's slower for cache-like operations.

• Issue: Full HTML snapshots every 30 seconds risk data loss on crashes. If a server crashes between snapshots, any unsaved changes (up to 30 seconds' worth) are lost, as changes are only written to PostgreSQL per-event but snapshots are the "full" persisted state.

  • Solution: Switch to delta-based storage: Store incremental changes (e.g., as JSON diffs) in PostgreSQL transactionally with each WebSocket event, and use a background job to periodically merge them into a full snapshot.
  • Trade-offs: Reduces data loss risk and improves recovery, but increases database write volume and complexity (need diff-merging logic). Merging could become a bottleneck for very active documents.

• Issue: JWT tokens in localStorage are vulnerable to XSS attacks or token expiry mid-session. A 24-hour expiry means tokens can expire during long editing sessions, interrupting workflow. LocalStorage also exposes tokens to client-side attacks.

  • Solution: Implement silent token refresh (e.g., via a /refresh endpoint called periodically from the frontend) and store tokens in HTTP-only cookies instead of localStorage for better security.
  • Trade-offs: Improves security and user experience (seamless sessions), but introduces server-side state management for refreshes, increasing backend load. Cookies add complexity for cross-origin requests and may not work in all browser environments.

• Issue: CDN caching of API responses causes stale data in real-time scenarios. CloudFront caches API responses for 5 minutes, but real-time document changes could make cached responses outdated, leading to users seeing inconsistent document states.

  • Solution: Exclude real-time API endpoints (e.g., those handling document fetches) from CDN caching by setting Cache-Control headers to no-cache, while keeping static assets cached.
  • Trade-offs: Ensures data freshness, but increases origin server load and latency for uncached requests. It simplifies the architecture but may require more backend capacity.

2. Race Conditions

These involve timing issues where concurrent operations lead to inconsistent or lost data.

• Issue: Client-side timestamps for last-write-wins conflict resolution are unreliable. Client clocks can be desynchronized (e.g., due to time zones, drift, or manipulation), causing incorrect "wins" in conflicts. For example, if two users edit the same paragraph simultaneously, a user with a slightly advanced clock could overwrite valid changes indefinitely.

  • Solution: Use server-generated timestamps (e.g., from a monotonic clock like PostgreSQL's NOW() function) upon receiving changes, and implement operational transformation (OT) or conflict-free replicated data types (CRDTs) for merging edits instead of last-write-wins.
  • Solution Trade-offs: Provides accurate, tamper-proof resolution and preserves more edits, but OT/CRDTs add significant complexity (e.g., implementing algorithms like Yjs) and computational overhead on the server. It may increase latency for conflict-heavy scenarios.

• Issue: Polling delay (every 2 seconds) causes inconsistent views across servers. Users on different servers might see divergent document states for up to 2 seconds (or more under load), leading to races where one user overwrites another's changes before they're visible.

  • Solution: Replace polling with PostgreSQL's LISTEN/NOTIFY for pub-sub notifications. Servers subscribe to change events, triggering immediate broadcasts to their WebSocket clients.
  • Trade-offs: Achieves near-real-time sync with low latency, reducing races, but requires database modifications (e.g., triggers) and could overload PostgreSQL with notifications in high-traffic scenarios. It's more efficient than polling but adds setup complexity.

• Issue: Concurrent writes to PostgreSQL without proper locking. If multiple servers attempt to write conflicting changes to the same document row simultaneously (e.g., during a poll-sync), it could result in lost updates or corruption, especially without transactions or locks.

  • Solution: Use row-level locking in PostgreSQL (e.g., SELECT FOR UPDATE in transactions) when applying changes, ensuring serialized access.
  • Trade-offs: Prevents data corruption, but introduces potential deadlocks and increased latency for contended documents. It scales poorly for very high concurrency without sharding.

3. Scaling Bottlenecks

These are limitations that hinder performance as users/documents grow.

• Issue: Polling PostgreSQL every 2 seconds from each API server creates a read bottleneck. With horizontal scaling (more servers), the database faces exponentially more poll queries, leading to high CPU/load and potential throttling.

  • Solution: As mentioned in race conditions, switch to pub-sub with PostgreSQL LISTEN/NOTIFY or an external message broker like Kafka/Redis PubSub to distribute changes without per-server polling.
  • Trade-offs: Scales better (O(1) per change vs. O(n) polls), reducing DB load, but introduces a new component (broker) with its own management overhead, latency, and failure modes (e.g., message backlog).

• Issue: Write-heavy PostgreSQL operations for every change. Every WebSocket event writes to PostgreSQL, which could bottleneck on IOPS/write throughput as user count grows, especially without sharding beyond org ID partitioning.

  • Solution: Buffer changes in Redis (as a write-ahead log) and batch-write to PostgreSQL every 1-5 seconds, using document partitioning more aggressively (e.g., by document ID hash for even distribution).
  • Trade-offs: Reduces DB write pressure and improves throughput, but risks data loss if Redis fails before batching (mitigable with persistence). Adds latency and complexity for consistency guarantees.

• Issue: Per-server WebSocket connections limit horizontal scaling. Each Node.js server has finite resources (e.g., memory/file descriptors) for WebSockets. As users grow, servers could hit connection limits, and load balancing doesn't distribute WebSockets evenly.

  • Solution: Use a centralized WebSocket service like Pusher or AWS AppSync, or deploy WebSockets on stateless containers (e.g., via Kubernetes) with a shared Redis backend for connection state.
  • Trade-offs: Enables infinite scaling by decoupling WebSockets from API servers, but increases costs (external service fees) and latency (extra hop). It simplifies server management but requires handling state synchronization.

• Issue: Full HTML snapshots bloat storage and slow operations for large documents. Saving entire documents every 30 seconds consumes storage and I/O, especially for large or frequently edited docs, limiting scalability.

  • Solution: Store documents as versioned deltas (e.g., using Git-like diffs in PostgreSQL or S3), reconstructing the full state on-demand.
  • Trade-offs: Saves storage and speeds up saves, but reconstruction adds CPU overhead on reads. It's great for history/audit but complicates conflict resolution.

• Issue: Uneven partitioning by organization ID leads to hotspots. If some organizations have disproportionately active documents, their partitions overload while others idle, bottlenecking the database.

  • Solution: Implement finer-grained sharding (e.g., by document ID range or hash) using PostgreSQL's declarative partitioning or a tool like Citus for distributed scaling.
  • Trade-offs: Improves load distribution and scalability, but adds query complexity (e.g., cross-shard joins) and migration effort. Data movement during resharding could cause temporary downtime.

This architecture has solid foundations but leans heavily on a central database and polling, which are common anti-patterns for real-time scaling. Addressing these would make it more robust, but prioritize based on your expected load (e.g., focus on polling if scaling to many servers). If you provide more details (e.g., expected user count), I can refine these further.