Debug This Architecture

Let's analyze the architecture step-by-step to identify potential failure modes, race conditions, and scaling bottlenecks, along with proposed solutions and their trade-offs.

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1. Potential Failure Modes

a. WebSocket Connection Failures

• Issue: Clients may disconnect unexpectedly due to network issues, server crashes, or load balancer timeouts.

• Impact: Loss of real-time updates, potential data inconsistency if not handled properly.

• Solution: Implement heartbeat/ping messages to detect dead connections; use WebSocket reconnection strategies on the client.

• Trade-off: Increased complexity and network overhead; potential for reconnection storms under high churn.

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b. Load Balancer and Sticky Sessions

• Issue: Since each API server maintains its own WebSocket connections, load balancer round-robin may distribute WebSocket connections unevenly, causing some servers to be overloaded or underutilized.

• Impact: Inefficient resource utilization; potential for dropped connections or latency.

• Solution: Use sticky sessions (session affinity) or an application-level routing layer for WebSockets, ensuring clients connect to the same server throughout their session.

• Trade-off: Sticky sessions can reduce load balancing flexibility and may require session management.

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c. PostgreSQL Write Failures

• Issue: Network partitions, disk failures, or database overload could cause write failures.

• Impact: Lost changes, inconsistent document state.

• Solution: Implement retries with exponential backoff, write-ahead logging, and ensure transactions are atomic.

• Trade-off: Increased latency during retries; potential for write conflicts if not handled properly.

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d. Redis Cache Failures

• Issue: Redis could crash or become unreachable.

• Impact: Loss of session data or cache invalidation issues.

• Solution: Use Redis persistence modes (RDB or AOF), set up Redis Sentinel for failover, or have a fallback to database for critical data.

• Trade-off: Additional overhead and complexity; slightly increased latency.

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2. Race Conditions & Data Consistency Issues

a. Asynchronous WebSocket Broadcasts

• Issue: Multiple servers broadcast changes to clients connected to different servers, but clients connected to server A might miss updates if server B crashes or is slow.

• Impact: Inconsistent document views among clients.

• Solution: Implement a centralized message bus (e.g., Redis Pub/Sub or Kafka) for broadcasting changes across servers.

• Trade-off: Additional infrastructure complexity and latency.

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b. Conflict Resolution Strategy (Last-Write-Wins)

• Issue: Relying solely on timestamps from client clocks can lead to race conditions, especially if clocks are unsynchronized.

• Impact: Overwritten changes that are actually later, leading to data loss or confusion.

• Solution: Use Lamport timestamps or vector clocks to establish causality, or implement Operational Transformation (OT) or Conflict-free Replicated Data Types (CRDTs) for real-time conflict resolution.

• Trade-off: Increased system complexity; OT/CRDTs require significant engineering effort.

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c. Multiple Servers Polling PostgreSQL

• Issue: Race conditions may occur if servers read stale data or miss updates between polls.

• Impact: Users see outdated content, or conflicting updates.

• Solution: Use PostgreSQL's NOTIFY/LISTEN feature to push change notifications to servers, reducing polling frequency and latency.

• Trade-off: Additional complexity, potential scalability issues with notification channels.

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3. Scaling Bottlenecks

a. Database Write Scalability

• Issue: PostgreSQL writes are centralized; high write volume can cause bottlenecks.

• Impact: Increased latency, potential downtime.

• Solution: Use sharding for documents, or employ CQRS pattern—separate command (write) and query (read) models. Consider distributed databases designed for high write throughput (e.g., CockroachDB, YugabyteDB).

• Trade-off: Increased complexity in data management and querying.

b. Document Storage as Full HTML Snapshots

• Issue: Storing full snapshots every 30 seconds increases storage and I/O load, especially for large documents.

• Impact: Slow backups, increased storage costs, potential I/O bottleneck.

• Solution: Store diffs/patches or OT logs instead of full snapshots, reconstructing full document state as needed.

• Trade-off: More complex storage and retrieval logic; may impact performance.

c. WebSocket Scalability

• Issue: Each server maintains many WebSocket connections; as user base grows, server memory and CPU demand increase.

• Impact: Resource exhaustion, degraded performance.

• Solution: Use WebSocket multiplexing or message brokers (like Redis Pub/Sub) to offload some messaging; horizontally scale WebSocket servers with load balancing.

• Trade-off: Increased system complexity and potential message routing delays.

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4. Additional Concerns and Solutions

Concern	Potential Issue	Proposed Solution	Trade-offs
JWT token storage in localStorage	Vulnerable to XSS attacks	Use HttpOnly cookies	Slightly less convenient in client code
Static asset caching	Cache invalidation issues	Implement cache busting strategies	Increased deployment complexity
Document partitioning	Cross-organization queries or shared documents	Enforce strict partitioning; avoid cross-partition joins	Limits querying flexibility

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Summary of Key Recommendations:

• Implement centralized message bus for WebSocket broadcast consistency.
• Use session affinity or sticky sessions for WebSocket connections.
• Replace last-write-wins with conflict-aware algorithms like CRDTs or OT.
• Use PostgreSQL notifications to reduce polling.
• Store diffs/patches instead of full snapshots for efficiency.
• Employ distributed database solutions for high scalability.
• Add failover and redundancy for Redis, PostgreSQL, and WebSocket servers.
• Implement monitoring and alerting to detect failures early.

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Overall, the architecture is a solid starting point but requires enhancements in conflict resolution, data consistency, connection management, and scalability to handle real-world load and failure scenarios robustly.