Debug This Architecture

We need to analyze the given architecture for a real-time collaborative document editor, similar to Google Docs. The description includes components, data flow, sync strategy, and scaling plan. We are asked to identify potential failure modes, race conditions, and scaling bottlenecks. For each issue, propose a specific solution with trade-offs.

We need to be thorough: consider each component and interaction. The architecture seems to have several potential issues: WebSocket connections per server, broadcast only to clients connected to that server, other servers polling PostgreSQL every 2 seconds, conflict resolution using client timestamps, JWT in localStorage, snapshot storage, etc.

Let's break it down:

Components:

• Frontend: React SPA, WebSocket connections to backend servers.
• Backend: Node.js API servers behind load balancer (round-robin). Each server maintains its own WebSocket connections.
• Database: PostgreSQL for document storage, Redis for session cache.
• Real-time sync: Each server broadcasts changes to its own clients only.
• Sync strategy: Last-write-wins with timestamps from client clocks.
• Storage: Full HTML snapshots every 30 seconds.
• Auth: JWT tokens with 24-hour expiry, stored in localStorage.
• CDN: CloudFront for static assets, also caches API responses for 5 minutes.

Data Flow:

1. User types → change event sent via WebSocket to connected server.
2. Server writes change to PostgreSQL.
3. Server broadcasts change to all WebSocket clients connected to THAT server.
4. Other servers poll PostgreSQL every 2 seconds for changes.
5. Conflict resolution: if two users edit same paragraph, last timestamp wins.

Scaling Plan:

• Horizontal scaling by adding more API servers.
• Database read replicas for read-heavy operations.
• Document partitioning by organization ID.

Now, we need to identify issues:

Potential Failure Modes: These are scenarios where the system may fail to operate correctly, such as data loss, inconsistency, unavailability, security issues, etc.

Race Conditions: Situations where the outcome depends on the sequence or timing of events, leading to unexpected behavior.

Scaling Bottlenecks: Points where the system cannot scale efficiently as load increases.

We'll go through each aspect.

1. WebSocket Connections and Load Balancer

• The load balancer uses round-robin for distributing requests. For WebSocket connections, typical load balancers need to support sticky sessions or WebSocket routing because WebSocket is a persistent connection. If the load balancer is round-robin without stickiness, a client's HTTP requests (e.g., API calls) may go to a different server than its WebSocket connection, causing issues if session state is stored per server. But the architecture says each API server maintains its own WebSocket connections to clients. That implies that after initial handshake, the WebSocket connection is established directly to a specific server. The load balancer likely routes the initial WebSocket upgrade request to a server, and then that connection remains. That's fine if the load balancer supports WebSocket and does not re-route subsequent packets. But round-robin for WebSocket initial connection assignment is okay; once connected, it's sticky by nature (TCP). However, there might be issues with reconnection: if a client disconnects and reconnects, it might connect to a different server. That's okay if there is no server-specific state. But we have session cache in Redis, so maybe it's fine. However, the real-time broadcast is limited to the server's own clients, so if a client reconnects to a different server, it will miss broadcasts from the previous server? Actually, the system relies on polling from other servers to get changes. So if a client switches servers, it will eventually receive updates via polling? Let's examine.

Data flow: When a user types, change is sent via WebSocket to the server they are connected to (say Server A). Server A writes to PostgreSQL and broadcasts to all clients connected to Server A. Other servers (B, C) poll PostgreSQL every 2 seconds for changes, and then presumably they broadcast to their own clients. So eventually all clients receive updates, regardless of which server they are connected to, because other servers will pick up changes from DB and broadcast to their clients. So server affinity is not required for correctness. However, there is a delay: up to 2 seconds for cross-server propagation. That might be acceptable for some collaborative editing but not ideal. Also, if a client disconnects and reconnects, it may get a different server, but that's okay.

Potential failure modes:

• Single point of failure: Load balancer? Usually load balancers can be made highly available. But if it fails, no new connections can be established. But existing WebSocket connections might still be alive if they bypass the LB? Usually LB is in front, so if LB fails, all connections go down. So need HA.

• WebSocket server failure: If a server crashes, all its WebSocket connections are lost. Clients need to reconnect. Their unsent changes? Possibly they were in flight. The server might have written some changes to DB before crashing, but changes not yet written could be lost. Also, the server's broadcast might not have reached all its clients. However, because other servers poll DB, they might eventually get the changes that were persisted. But if the server crashed before writing to DB, the change is lost. Need to ensure durability.

• Load balancer not WebSocket-aware: Some LBs may not handle WebSocket upgrade properly. But we assume it does.

Race Conditions:

• Because polling is every 2 seconds, there is a window where two servers may have conflicting changes. Consider two users on different servers editing the same paragraph. Both send changes to their respective servers. Each server writes to DB and broadcasts to its own clients. The writes to DB will have timestamps from client clocks. If they both write nearly simultaneously, the DB will have two updates. Conflict resolution is last-write-wins based on timestamps. But because servers poll every 2 seconds, the other server's clients won't see the change immediately; they may see the change after polling, and then they might overwrite with older timestamp? Actually, the conflict resolution is applied when? It says "if two users edit the same paragraph, last timestamp wins." But where is this resolved? Possibly at the client? Or at the server when applying changes? The description: "Sync Strategy: Last-write-wins with timestamps from client clocks." And step 5: "Conflict resolution: if two users edit the same paragraph, last timestamp wins." So presumably when a server receives a change, it writes to PostgreSQL. But what if there are concurrent writes to the same paragraph from two servers? The DB might simply store the latest based on timestamp? Or the application logic might compare timestamps before writing. But the data flow says: Server writes change to PostgreSQL. It doesn't mention any read-modify-write or conditional update. So if two servers write to the same row/document concurrently, one may overwrite the other. Since they are writing changes (maybe they store each change as a log entry?), but they say "writes change to PostgreSQL". We need to interpret what "change" means. Possibly they store each operation (like an operation transform) in a table, and then broadcast. But the description is ambiguous. They also mention "Storage: Documents saved as full HTML snapshots every 30 seconds." So the main document state is stored as snapshots, and changes might be logged? However, the real-time sync likely uses operational transforms or differential updates. But they said "Last-write-wins with timestamps from client clocks", which suggests a simpler approach: each change includes the full paragraph content and a timestamp, and the server overwrites the paragraph if the incoming timestamp is newer. But then polling other servers might fetch changes and apply to local state. However, if two servers write conflicting changes to the DB, the later write (based on DB transaction ordering) may win, but that may not correspond to the last timestamp if timestamps are from clients and clocks may be skewed. Also, if they write to the same row without reading first, they might overwrite a newer change with an older one if the older arrives later due to network delays. So there is a race condition.

Let's think deeper.

2. Real-time Sync and Conflict Resolution

The architecture uses last-write-wins with client timestamps. This is problematic because client clocks cannot be trusted; they may be out of sync, or malicious users could set their clock forward to always win. Also, network delays can cause ordering issues. This is a classic issue: using client timestamps for conflict resolution leads to inconsistencies and potential data loss. Need a better approach like Operational Transform (OT) or Conflict-free Replicated Data Types (CRDTs), or using a central server with logical timestamps (e.g., vector clocks, sequence numbers). The trade-off is increased complexity.

Also, the polling interval of 2 seconds introduces a delay in cross-server propagation. For real-time collaboration, 2 seconds might be noticeable. But it could be acceptable for some use cases, but ideally we'd want lower latency. The delay also increases the chance of conflicts because users on different servers may not see each other's changes for up to 2 seconds.

Race condition: Two users on different servers edit same paragraph at nearly same time. Both servers receive the changes, write to DB, and broadcast to their own clients. The writes to DB: if they are updating the same field (e.g., paragraph content) with a timestamp, the second write (based on DB commit time) will overwrite the first, regardless of timestamp. Then when the other server polls, it will see the second write (maybe) and broadcast to its clients. But the first server's clients already saw the first change locally, and now they might receive the second change via polling? Actually, the first server broadcasted the first change to its clients. Then later, when it polls DB, it might see the second change (if it's later) and broadcast to its clients, overwriting the first. But the order of application may cause flickering or lost edits. If conflict resolution is done at client side, similar issues.

Better to use a log of operations with server-assigned sequence numbers, and each client applies operations in order. That's the typical approach (OT/CRDT). The trade-off is complexity.

3. Database Polling

Polling PostgreSQL every 2 seconds for changes from all servers. As number of servers increases, each server polls, causing load on DB. If many servers (say 100), each polling every 2 seconds, that's 50 queries per second per server? Actually, 100 servers * 0.5 Hz = 50 queries per second. That's not huge, but each query may scan for recent changes. If the changes table is large, scanning could be expensive. They might use a "last_updated" timestamp or a sequence ID. Still, polling can be inefficient. Alternative: use a message queue or pub/sub (like Redis Pub/Sub) to broadcast changes between servers in real-time, eliminating polling delay and reducing DB load. Trade-off: adds another component, but improves latency and scalability.

Also, the polling interval of 2 seconds means that changes are not immediately propagated across servers, causing a lag. For a collaborative editor, sub-second latency is desirable.

4. WebSocket Broadcast Only to Local Clients

When a server receives a change, it broadcasts to its own clients. That's fine. But for clients on other servers, they rely on polling. So if a server receives a change, it doesn't immediately notify other servers; they have to wait up to 2 seconds. This increases latency for cross-server updates. Also, if a server crashes after broadcasting to its clients but before writing to DB? Actually, step 2: Server writes change to PostgreSQL, then step 3: broadcasts. So the write to DB is before broadcast. So if the write is successful, the change is persisted, and then broadcasted. If the server crashes after broadcast but before DB commit? Actually, order is important: they write then broadcast. But if the write fails, presumably they wouldn't broadcast. So the DB is the source of truth. Then other servers will eventually poll and get the change. So the local broadcast is an optimization for low latency for clients on the same server. However, if the server fails after write but before broadcast, the local clients won't get the change, but they might get it later via polling when they reconnect to another server? Actually, if the server crashes, its clients lose connection. They will reconnect to another server, and that server will poll DB and send the latest state. So the change is not lost. But there is a period where the user who made the change might not see it confirmed if the server crashes before broadcasting back to the originating client? The client might have sent the change and expects an echo or confirmation. If the server crashes before sending the broadcast, the client might not receive acknowledgment. It might resend, causing duplication. So need idempotency.

5. Data Storage and Snapshots

Documents saved as full HTML snapshots every 30 seconds. This means that changes are written to PostgreSQL presumably as incremental updates, but every 30 seconds a snapshot is taken. The snapshots could be used for recovery or for loading documents quickly. However, if the system only stores snapshots and not a log of operations, it's hard to reconstruct the document at arbitrary points for collaboration. The description says "Sync Strategy: Last-write-wins with timestamps from client clocks." This suggests that the document is stored as a set of paragraphs with timestamps, and each change updates the paragraph. The snapshot might be the full HTML of the document. But if we only have snapshots every 30 seconds, we risk losing changes if a crash occurs between snapshots. However, the changes are also written to PostgreSQL (presumably as updates to the document). But if they are writing each change directly to the document record, then the document is always up-to-date in the DB (except for replication lag). The snapshot might be a backup or for versioning. But the description: "Storage: Documents saved as full HTML snapshots every 30 seconds." Could be that the primary storage is the snapshot, and changes are applied to the snapshot in memory? Actually, we need to interpret: It says "Storage: Documents saved as full HTML snapshots every 30 seconds." That might mean that the document is persisted only every 30 seconds, not after each change. But step 2 says "Server writes change to PostgreSQL". So maybe they write each change to a changes table, and periodically create a snapshot from the log. That is common: store operations, and periodically compact into a snapshot. But the description is ambiguous. We'll assume they store each change in PostgreSQL, and also periodically save snapshots for faster loading. That's fine.

Potential failure: If the server crashes before snapshot is taken, the change log might grow large, and recovery might be slow. But that's manageable.

6. Authentication and JWT

JWT tokens with 24-hour expiry stored in localStorage. This is a security concern: localStorage is vulnerable to XSS attacks. If an attacker can inject JavaScript, they can steal the token. Better to use HttpOnly cookies for storing tokens, but then need to handle CSRF protection. Trade-off: localStorage is easier for SPAs but less secure. Using cookies with HttpOnly and Secure flags is more secure but requires CSRF tokens or SameSite attributes. Also, JWT expiry 24 hours is long; could be shortened and use refresh tokens.

Also, JWT tokens are stateless, but they are stored in Redis for session cache? It says "Redis for session cache." Possibly they store something else. But if they use JWT, they might not need session cache unless they invalidate tokens. JWT is self-contained; if they want to invalidate, they need a blacklist, which could be in Redis. That's okay.

Potential failure: If the token is stolen, an attacker can impersonate the user until expiry. Mitigation: use short-lived tokens and refresh tokens with rotation.

7. CDN Caching API Responses

CDN caches API responses for 5 minutes. For a collaborative editor, many API responses are user-specific or document-specific and dynamic. Caching for 5 minutes could lead to stale data. For example, GET /document/{id} might be cached, but the document changes frequently. If the CDN caches it, users might see outdated content. They should avoid caching dynamic data or use cache invalidation. Possibly they only cache static assets, but they said "also caches API responses for 5 minutes." That's a potential issue. They might have misconfigured CDN. We'll flag it.

8. Database Scaling

Scaling plan: Horizontal scaling by adding more API servers, database read replicas for read-heavy operations, document partitioning by organization ID.

Potential bottlenecks:

• Write scalability: PostgreSQL single primary for writes. As number of writes increases (many users editing many documents), the primary may become a bottleneck. Partitioning by org ID helps, but still all writes go to the primary unless sharding is implemented. They mention partitioning, which could be table partitioning within the same PostgreSQL instance, which doesn't help with write scaling across machines. Actually, "document partitioning by organization ID" could mean sharding across different database instances or clusters. But they didn't specify if it's horizontal sharding. Typically, partitioning in PostgreSQL is logical within a single database, but can help with management and indexing. For scaling writes, you need to distribute writes across multiple database nodes (sharding). They might intend to use separate databases per organization, but that's not trivial.

• Polling load: As number of servers grows, polling load increases linearly. Could be mitigated with a message bus.

• WebSocket connections per server: Node.js can handle many WebSocket connections, but there is a limit per server (memory, file descriptors). Horizontal scaling helps.

• Redis for session cache: Redis can be a bottleneck if heavily used. But it's in-memory and can be clustered.

9. Real-time Consistency

Because each server broadcasts changes to its own clients and others poll, there is eventual consistency with up to 2 seconds delay. For collaborative editing, this may be acceptable but not ideal. Also, conflict resolution via client timestamps can lead to inconsistent final states if clocks are skewed. Need to consider stronger consistency models.

10. Failure Modes: Network Partitions

If a server becomes partitioned from the database, it cannot write changes, so it should reject edits or queue them? Currently, it would likely fail to write and maybe not broadcast. But the client might be left hanging. Need to handle gracefully.

11. Race Conditions in DB Writes

As mentioned, if two servers concurrently update the same paragraph based on client timestamps, they might both read the current state, decide to update, and the later commit overwrites the earlier. Example: Server A reads paragraph with timestamp T1. Client sends change with timestamp T2 (T2 > T1). Server A updates the row, setting content and timestamp to T2. Server B, around the same time, reads the same paragraph before A's update (so sees T1). Client sends change with timestamp T3 (T3 > T1). If T3 < T2, then B's update will overwrite A's update with older timestamp, losing A's change. This is a classic lost update problem. To avoid, they need to use conditional update (compare-and-set) where they update only if the current timestamp is older than the incoming timestamp, or use a version number. But even with conditional update, if both updates have timestamps that are both newer than the read timestamp, whichever commits later will win, but the earlier might be lost. But if they both check the current timestamp before writing, they can avoid overwriting a newer change. However, with concurrent transactions, it's still possible that both see the same old timestamp and both succeed? Actually, suppose both transactions read row with timestamp T1. They both check that their incoming timestamp > T1, which is true. They both attempt to update the row. The first commit will set timestamp to its value (say T2). The second commit will then see that the current timestamp is now T2 (if it re-reads before update, but in a typical UPDATE ... WHERE current_timestamp < incoming_timestamp, the WHERE clause will check the current value at time of update. So the second update's WHERE clause will compare incoming T3 with the current timestamp (which after first commit is T2). If T3 > T2, it will succeed and overwrite; if T3 < T2, it will not update (0 rows affected). So that prevents overwriting with older timestamp. So conditional update can work. But they didn't specify that. They just say "writes change to PostgreSQL". So likely they are doing a simple update, leading to lost updates.

Thus, a race condition exists.

12. Client Clock Skew

Client timestamps can be arbitrarily wrong. A malicious user could set their clock far ahead to always win conflicts. This is a security issue. Need to use server-generated timestamps or logical clocks.

13. Data Integrity with Snapshots

If snapshots are taken every 30 seconds, and the system crashes right before a snapshot, the last snapshot might be old. But if changes are logged, recovery can replay logs. However, if they rely solely on snapshots and not a persistent log, they could lose data. The description says "Server writes change to PostgreSQL", so changes are persisted. Snapshots are just periodic dumps. So that's okay.

14. Load Balancer Round-Robin for WebSocket

Round-robin is fine for initial assignment, but if the load balancer does not support WebSocket persistence, it may route subsequent HTTP requests to different servers, which might be okay if the application uses tokens and stateless servers. However, for WebSocket, the upgrade request is just an HTTP request, so the LB can route it to a server, and then the TCP connection stays with that server. That's typical. So not a problem.

15. Redis Session Cache

Redis is used for session cache. If Redis fails, sessions might be lost, and users may need to re-authenticate. Could be mitigated with replication and failover. But it's a potential single point of failure.

16. CDN Caching API Responses

As mentioned, caching dynamic data is problematic. Also, if the CDN caches API responses that are supposed to be real-time, it breaks the collaborative experience. They should not cache API responses for the document endpoints, or at least use cache-control: no-cache. They might be caching static assets only, but they said "also caches API responses for 5 minutes." That is likely a mistake.

17. Scalability of WebSocket Broadcast

When a server broadcasts to all its clients, if it has many clients (thousands), broadcasting a change to all could be heavy and block the event loop. Node.js can handle it with careful management (e.g., using ws library and iterating over clients). But as number of clients per server grows, broadcast latency increases. Could use a pub/sub system where each server subscribes to document channels and pushes to clients via WebSocket, offloading the broadcast logic? Actually, the current design: each server broadcasts only to its own clients, which is fine because it's only the clients connected to that server. The total broadcast load is distributed across servers. So that scales horizontally. However, if a document has many collaborators all on the same server (due to LB distribution), that server may have to broadcast to many clients. That's okay as long as the server can handle the load. Could be optimized by using a shared pub/sub (like Redis) to fan out messages to all servers, each then sends to its own clients. That would also reduce the need for polling.

18. Polling Overhead and Latency

Polling every 2 seconds is not real-time and adds load. Could use LISTEN/NOTIFY in PostgreSQL to get notifications of changes, eliminating polling. That would be more efficient and reduce latency. But NOTIFY has limitations in scalability (each connection can listen). However, with many servers, each connection can listen to channels. PostgreSQL's NOTIFY can handle many listeners, but there might be performance implications. Alternatively, use a message queue like RabbitMQ or Kafka.

19. Data Partitioning

Partitioning by organization ID helps distribute data. But if some organizations have huge numbers of documents and heavy editing, they may still be a hotspot. Need to consider further sharding.

20. Failure Recovery

If a server crashes, clients reconnect. But there might be in-memory state about pending changes. If the server was holding unsent broadcasts or buffered operations, they could be lost. But since changes are written to DB before broadcast, the persisted state is safe. However, the server might have acknowledged to the client before writing to DB? The flow says write then broadcast, but does the server send an ACK to the client? Not specified. Typically, the server might broadcast the change to all clients including the sender, and the sender sees its own change applied. But if the server crashes after writing to DB but before broadcasting, the originating client may not see its change reflected, and might think it failed. The client could resend, causing duplication. To handle, use idempotent operations with client-generated IDs, so resending doesn't cause duplicate changes.

21. Security: WebSocket Connections

WebSocket connections are persistent and may need to be authenticated. Typically, the connection starts with an HTTP request containing the JWT. The server validates the token and upgrades. If the token expires during the connection, the server should close the connection or request re-authentication. With 24-hour expiry, it's less likely but still possible. Need to handle token refresh via a separate API call, and possibly re-establish WebSocket.

Now, let's list the issues systematically.

We'll categorize:

1. Real-time Sync and Conflict Resolution

   • Client timestamps for last-write-wins
   • Polling delay (2 seconds)
   • Race conditions due to concurrent updates
   • Potential data loss

2. Database Polling

   • Latency
   • Scalability (load on DB)
   • Inefficiency

3. WebSocket and Load Balancing

   • Single point of failure at LB
   • Server failure handling
   • Reconnection and duplicate changes

4. Data Storage and Snapshots

   • Snapshot frequency may cause data loss if only snapshots are used? But they also write changes. However, if they only write changes to a log, snapshots are for compaction. But we need to confirm: they might be writing each change to PostgreSQL, so fine.

5. Authentication and Security

   • JWT in localStorage
   • Long expiry
   • CDN caching API responses
   • Potential XSS

6. Scalability Bottlenecks

   • Write scalability of PostgreSQL primary
   • Polling load
   • Redis as single point
   • WebSocket per server limits

7. Consistency and Fault Tolerance

   • Network partitions
   • Crash recovery
   • Eventual consistency delay

We'll produce a list of specific issues with solutions and trade-offs.

Let's think of more nuanced issues:

Issue: Client clock skew leading to unfair conflict resolution. Solution: Use server-generated timestamps or logical clocks (e.g., vector clocks, sequence numbers). Trade-off: Increases server load and complexity.

Issue: Lost updates due to concurrent writes without conditional checks. Solution: Use optimistic concurrency control with version numbers (e.g., incrementing version per document or per paragraph). Trade-off: Requires reading before writing, and handling failed updates (retry). Could also use Operational Transform or CRDTs for collaborative editing, which are more robust but complex.

Issue: Polling for changes introduces up to 2 seconds latency for cross-server updates. Solution: Replace polling with a pub/sub system (e.g., Redis Pub/Sub, Kafka, or PostgreSQL NOTIFY) to push changes between servers in real-time. Trade-off: Adds complexity and new components, but reduces latency and DB load.

Issue: Database polling every 2 seconds by each server can cause high load on DB as number of servers grows. Solution: Use a message bus as above, or batch polling, or increase polling interval, but best is pub/sub. Trade-off: same.

Issue: Single point of failure at load balancer. Solution: Deploy multiple load balancers with DNS round-robin or anycast, or use cloud provider's managed LB with HA. Trade-off: Cost, complexity.

Issue: WebSocket server failure may cause clients to lose connection and unsent changes if not acknowledged. Solution: Implement client-side buffering and retry with idempotent operation IDs. On server side, ensure changes are persisted before acknowledging to client. Use heartbeats to detect failure quickly. Trade-off: Client code complexity, potential duplicate operations.

Issue: JWT stored in localStorage vulnerable to XSS. Solution: Store JWT in HttpOnly cookie with Secure and SameSite=Strict. Use CSRF tokens. Trade-off: More complex to implement, but more secure. Also, cookies are sent automatically, which could be a risk for CSRF; but SameSite and CSRF tokens mitigate.

Issue: CDN caching API responses for 5 minutes leads to stale data. Solution: Configure CDN to not cache dynamic API responses, or use appropriate Cache-Control headers (no-cache, private). Trade-off: Increased load on origin servers but ensures freshness.

Issue: Horizontal scaling of writes to PostgreSQL is limited. Solution: Shard the database by organization ID across multiple PostgreSQL instances or use a distributed database like CockroachDB. Trade-off: Increased operational complexity, potential cross-shard queries harder.

Issue: Redis as session cache single point of failure. Solution: Use Redis Cluster or sentinel for high availability. Trade-off: More complex setup.

Issue: Broadcast to many clients on same server may block event loop. Solution: Use non-blocking I/O, and consider using a dedicated WebSocket server library that handles broadcast efficiently (e.g., using ws and iterating). Could also offload to a pub/sub where each client subscribes to a channel and Redis pushes directly? Not directly; server still needs to send. But can use worker threads? Not needed. Node.js can handle many WebSocket connections; broadcasting to all clients of a document might be O(n) per change, which could be heavy if many clients. Could use a fanout mechanism: each client connection is independent, and Node.js can send messages asynchronously. It's manageable with moderate numbers. For large scale, consider using a dedicated real-time backend like Socket.IO with Redis adapter, which distributes broadcasts across servers via pub/sub. Actually, that's a good point: currently, each server only broadcasts to its own clients. If a document has collaborators spread across multiple servers, each server needs to broadcast to its own clients. That's fine. But if a server has many clients for a popular document, it might be a bottleneck. However, the load balancer should distribute clients evenly. If a particular document is very popular, its collaborators may still be on multiple servers? Actually, if many users are editing the same document, they could be connected to different servers. So the load is distributed. So broadcast per server is not a huge issue. But still, the server needs to send the same message to all its clients interested in that document. That's O(clients_per_server). That's okay.

Issue: Polling interval may cause merge conflicts to be more frequent because users don't see others' changes quickly. Solution: Reduce polling interval, but that increases DB load. Use pub/sub to push changes instantly.

Issue: Using full HTML snapshots every 30 seconds could be inefficient if documents are large and changes are small. Solution: Store incremental changes (operational transforms) and rebuild snapshots asynchronously. Trade-off: More complex but saves storage and I/O.

Issue: Round-robin load balancing may not account for server load, leading to uneven distribution. Solution: Use load-aware LB (e.g., least connections, CPU). Trade-off: More sophisticated LB.

Issue: No mention of monitoring, logging, or alerting. But not asked.

Now, we need to produce a comprehensive answer listing each potential failure mode, race condition, and scaling bottleneck, with specific solutions and trade-offs.

We should structure the answer clearly. Possibly group related issues.

Let's outline:

1. Real-time Sync Issues

   • Conflict resolution using client timestamps
     • Problem: Clock skew, malicious manipulation, lost updates.
     • Solution: Use server-assigned sequence numbers or vector clocks; or implement Operational Transform (OT) / CRDT.
     • Trade-off: Increased server complexity, higher latency for assigning sequence numbers (needs coordination), but ensures consistency.
   • Polling delay (2 seconds)
     • Problem: Cross-server updates delayed up to 2 seconds, leading to poor real-time experience.
     • Solution: Use a pub/sub system (e.g., Redis Pub/Sub, Kafka) to notify other servers immediately of changes.
     • Trade-off: Introduces new component, adds operational overhead, but reduces latency.
   • Race condition in concurrent updates
     • Problem: Without conditional writes, last DB write wins, disregarding timestamps, causing lost updates.
     • Solution: Use optimistic concurrency control with version numbers or conditional updates (compare-and-set).
     • Trade-off: Requires reading before writing, handling retries, and may increase DB load.

2. Database Polling Scalability

   • Problem: Each server polls PostgreSQL every 2 seconds; as number of servers grows, polling frequency increases linearly, causing high read load on DB.
   • Solution: Replace polling with a message queue or pub/sub as above; or use PostgreSQL LISTEN/NOTIFY to push changes.
   • Trade-off: LISTEN/NOTIFY may not scale to thousands of connections; message queue adds complexity.

3. WebSocket and Load Balancer Failure Modes

   • Load balancer single point of failure
     • Problem: If load balancer fails, new connections cannot be established and existing connections may drop.
     • Solution: Use a highly available load balancer setup (active-passive or active-active) with health checks and failover.
     • Trade-off: Additional cost and configuration.
   • WebSocket server failure
     • Problem: Clients lose connection; unsent changes may be lost if not acknowledged; reconnection may cause duplicate changes.
     • Solution: Implement idempotent operation IDs, client-side queue with retry, and ensure server writes to DB before acknowledging. Use heartbeats to detect failure.
     • Trade-off: Client-side complexity, additional logic for idempotency.
   • Load balancer affinity not needed but round-robin may cause uneven load distribution.
     • Problem: Some servers may become overloaded if clients are not evenly distributed.
     • Solution: Use load balancer with least connections or weighted round-robin based on server metrics.
     • Trade-off: More sophisticated LB needed.

4. Authentication and Security

   • JWT in localStorage vulnerable to XSS
     • Problem: Malicious scripts can steal token, leading to account compromise.
     • Solution: Store JWT in HttpOnly, Secure, SameSite=Strict cookie; use CSRF tokens.
     • Trade-off: Requires CSRF protection, slightly more complex frontend handling.
   • Long expiry (24 hours)
     • Problem: If token is stolen, attacker has long window.
     • Solution: Use short-lived access tokens (e.g., 15 minutes) with refresh tokens stored securely (HttpOnly cookie). Rotate refresh tokens.
     • Trade-off: More frequent token refresh, need to handle refresh logic.
   • CDN caching API responses
     • Problem: Dynamic data like document content may be cached, causing stale reads.
     • Solution: Set Cache-Control: no-cache, private for API endpoints; or configure CDN to bypass cache for /api/*.
     • Trade-off: Increased load on origin, but ensures data freshness.

5. Data Storage and Snapshots

   • Snapshot frequency may lead to data loss if system crashes before snapshot and changes are only in memory? But they write changes to DB, so maybe not. However, if they rely solely on snapshots for persistence, that's a problem. We'll assume they persist changes, but still:
   • Problem: Storing full HTML snapshots every 30 seconds could be inefficient for large documents with frequent changes; also recovery point objective is 30 seconds.
   • Solution: Store incremental changes (operation log) and generate snapshots asynchronously; use Write-Ahead Logging or event sourcing.
   • Trade-off: More complex to implement, but reduces storage and provides finer recovery.

6. Scalability Bottlenecks

   • Write scalability of PostgreSQL primary
     • Problem: All writes go to a single primary database; as user base grows, writes may become bottleneck.
     • Solution: Shard the database by organization ID across multiple database instances; use a distributed SQL database.
     • Trade-off: Application must route queries to correct shard; cross-shard operations difficult; increased complexity.
   • Redis as session cache single point of failure
     • Problem: If Redis fails, session data lost, users may be logged out or experience errors.
     • Solution: Use Redis Cluster with replication and automatic failover.
     • Trade-off: More complex setup, but improves availability.
   • WebSocket connections per server limit
     • Problem: Node.js has limits on number of concurrent connections (file descriptors, memory). A single server may not scale beyond a few tens of thousands.
     • Solution: Horizontally scale API servers; use a WebSocket gateway service optimized for many connections (e.g., using Elixir, Go). But Node.js can handle many with tuning.
     • Trade-off: Adding more servers is straightforward; but may need to optimize per-server settings.

7. Consistency and Partition Tolerance

   • Network partition between server and DB
     • Problem: Server cannot write changes, edits fail.
     • Solution: Design client to queue changes offline and sync when connectivity restored; use conflict resolution strategies (CRDTs) to merge later.
     • Trade-off: Complex offline support, but improves user experience.
   • Eventual consistency delay due to polling
     • Already addressed with pub/sub.

8. Other Race Conditions

   • When multiple servers poll and broadcast the same change, duplicate broadcasts could occur? Possibly not, but if they poll at different times, they might broadcast the same change multiple times. That's okay if changes are idempotent.
   • Need to ensure that when a server polls, it only gets changes that it hasn't seen. Use a monotonically increasing sequence number per change, and each server remembers the last processed ID. That's a typical approach. The current design might just get all changes in the last 2 seconds, which could cause duplicates if the same change is fetched again (if polling interval overlaps). They should use a cursor.

   Problem: Polling with a time window may miss changes if they are made exactly at the boundary, or may fetch duplicates. Solution: Store changes with an auto-increment ID, and each server tracks last processed ID. Trade-off: Requires additional bookkeeping.

Now, we need to produce a final answer that lists these issues and solutions.

The instruction: "Identify all potential failure modes, race conditions, and scaling bottlenecks in this architecture. For each issue found, propose a specific solution with trade-offs."

We should produce a list, perhaps bullet points or numbered, each with a clear description, problem, solution, trade-off.

We can group but ensure each issue is distinct.

Let's compile a comprehensive list:

Potential Failure Modes (system may fail or behave incorrectly):

1. Client clock skew causing unfair conflict resolution: Clocks may be out of sync; malicious users can set clock ahead to always win. Solution: Use server-assigned timestamps or sequence numbers. Trade-off: Adds latency (need to get timestamp from server) and requires coordination; but ensures fairness and consistency.

2. Lost updates due to concurrent writes without concurrency control: Two servers may overwrite each other's changes. Solution: Use optimistic concurrency control with version numbers (e.g., increment version on each update, check before write). Trade-off: Requires reading before writing, handling retries, may increase DB load.

3. WebSocket server failure leading to lost in-flight changes: If server crashes after receiving change but before persisting or acknowledging, client may think change failed or resend. Solution: Implement idempotent operation IDs, persist change before acknowledgment, and client retries with same ID. Trade-off: Client-side complexity, need to generate unique IDs.

4. Load balancer single point of failure: If load balancer fails, service becomes unavailable. Solution: Use highly available load balancer setup (active-passive with failover) or cloud-managed LB with redundancy. Trade-off: Additional cost and complexity.

5. Redis session cache failure: If Redis goes down, session data lost, users may be logged out. Solution: Use Redis Cluster with replication and automatic failover. Trade-off: Increased operational overhead.

6. Database primary failure: PostgreSQL primary failure can cause downtime. Solution: Set up streaming replication with failover (e.g., using Patroni). Trade-off: Complexity and potential data loss during failover.

7. Network partition between server and DB: Server cannot write, edits fail. Solution: Allow offline editing with local queue and sync later using CRDTs. Trade-off: Significant complexity, but improves availability.

8. CDN caching dynamic API responses: Users may see stale document content. Solution: Configure CDN to not cache API responses, or set proper Cache-Control headers. Trade-off: Increased load on origin servers.

9. JWT stored in localStorage vulnerable to XSS: Attackers can steal tokens. Solution: Store tokens in HttpOnly cookies with Secure and SameSite flags, and implement CSRF protection. Trade-off: More complex to implement, but more secure.

10. Long JWT expiry increases risk if token stolen: 24 hours is long. Solution: Use short-lived access tokens (e.g., 15 min) with refresh tokens stored securely. Trade-off: More frequent token refresh, need refresh endpoint.

Race Conditions (timing issues leading to inconsistency):

1. Concurrent updates to same paragraph without proper locking: Two servers read old state, both update, leading to lost update. (Already covered in lost updates, but it's a race condition). Solution: Conditional updates (compare-and-set) as above.

2. Polling window overlap causing duplicate processing of changes: If servers poll for changes based on timestamp, they may fetch the same change twice, leading to duplicate broadcasts. Solution: Use a monotonically increasing sequence ID for changes, and each server tracks last processed ID. Trade-off: Requires additional bookkeeping per server.

3. Client reconnection after server crash may cause duplicate operations: If client resends change after timeout, but original change was persisted, duplicate may be applied. Solution: Idempotent operation IDs as above.

4. Timestamp-based conflict resolution with network delays: Even with conditional updates, if two clients have timestamps that are both newer than current, the later commit may overwrite the earlier if timestamps are close and one server's write is delayed. Actually, conditional update with timestamp check would prevent overwriting if the incoming timestamp is not greater than current. But if both have timestamps greater than current, the first will succeed, the second will check if its timestamp > current (now updated to first's timestamp). If second's timestamp > first's, it will overwrite; if not, it will fail. So order depends on timestamp order, not commit order. That's fine. But if clocks are skewed, a later edit may have an earlier timestamp and be rejected incorrectly. That's a failure mode, not race. So the race is mitigated by conditional update, but clock skew remains.

Scaling Bottlenecks (limits to growth):

1. Database write scalability: Single PostgreSQL primary handles all writes. As number of concurrent edits grows, writes may become bottleneck. Solution: Shard database by organization ID across multiple PostgreSQL instances or use distributed SQL. Trade-off: Application must route queries to correct shard; cross-organization queries become complex.

2. Polling load on database: Each server polling every 2 seconds causes read load that scales with number of servers. Solution: Replace polling with pub/sub (e.g., Redis Pub/Sub, Kafka) to push changes. Trade-off: Introduces new component, but reduces DB load and latency.

3. WebSocket connections per server: Node.js may hit limits (e.g., ~65k connections per server due to port range, but actually limited by file descriptors). Solution: Scale horizontally, tune OS limits, use a more scalable WebSocket server (e.g., using Go or Erlang). Trade-off: Adding servers is straightforward; may need to adjust architecture.

4. Broadcast to many clients on a single server: If a popular document has many collaborators on the same server, broadcasting a change to all could be CPU-intensive and cause backpressure. Solution: Use a dedicated pub/sub where each client connection is handled by the server, but broadcast can be optimized by using a set of WebSocket connections and iterating; or use Redis Pub/Sub to fan out messages to multiple servers even for same document, but that's already for cross-server. For intra-server, it's still O(N). Could use multicast? Not over internet. Trade-off: Acceptable for moderate N; if N is huge, may need to partition further.

5. Redis as central cache: As number of users grows, Redis may become a bottleneck. Solution: Use Redis Cluster to distribute load. Trade-off: More complex configuration.

6. Snapshot generation overhead: Generating full HTML snapshots every 30 seconds for many documents could consume CPU and I/O. Solution: Generate snapshots asynchronously in background workers, and only for active documents. Trade-off: Delayed snapshots, but reduces peak load.

7. Load balancer round-robin not load-aware: May cause uneven load across servers. Solution: Use load balancer with least connections or based on server health. Trade-off: May require more sophisticated LB features.

Now, we need to present these in a clear, concise manner. The instruction says "Identify all potential failure modes, race conditions, and scaling bottlenecks." So we should list them, perhaps categorized, but ensure each is identified and then a solution with trade-offs.

We should be careful not to over-list; some are duplicates. For example, "lost updates" is both a failure mode and race condition. We can present as one item with both aspects.

Let's produce a structured answer:

• For each issue:
  • Issue: description (type: failure mode/race condition/bottleneck)
  • Solution: specific technical solution
  • Trade-offs: what we give up or added complexity

We can number them.

Given the breadth, we can combine related issues where appropriate, but the instruction says "for each issue found", so each distinct issue should have its own solution.

I'll produce a list of about 10-15 items, covering the main points.

But let's also consider if there are any issues with the "partitioning by organization ID" scaling plan. It might be insufficient if an organization is very large. That's a scaling bottleneck: hotspot organizations. Solution: further shard within organization by document ID or user. Trade-off: complexity.

Also, the system uses "last-write-wins" which is a simplistic conflict resolution; for collaborative editing, it can lead to poor user experience because typing over each other's changes may be lost. That's a functional issue, not just technical. But we can mention that a more sophisticated approach like OT/CRDT is preferable, but with trade-offs of complexity.

Now, let's draft.

Answer Outline:

1. Conflict Resolution Using Client Timestamps

   • Problem: Clock skew, manipulation, lost updates.
   • Solution: Use server-generated sequence numbers or vector clocks; or adopt Operational Transform / CRDT.
   • Trade-off: Increased complexity and server coordination; may increase latency slightly.

2. Lost Updates Due to Lack of Concurrency Control

   • Problem: Concurrent writes from different servers can overwrite each other.
   • Solution: Implement optimistic concurrency control with version numbers and conditional updates (compare-and-set).
   • Trade-off: Additional read before write, retry logic, and potential increased DB load.

3. Polling Delay and DB Load

   • Problem: 2-second polling introduces latency for cross-server updates and scales poorly with many servers.
   • Solution: Replace polling with a real-time pub/sub system (e.g., Redis Pub/Sub, Kafka) to push changes between servers.
   • Trade-off: Adds new component, increases architectural complexity, but reduces latency and DB load.

4. WebSocket Server Failure and In-Flight Changes

   • Problem: Server crash may lose unacknowledged changes or cause duplicates on retry.
   • Solution: Use idempotent operation IDs, persist changes before acknowledgment, and implement client-side retry queue.
   • Trade-off: Client-side complexity, need for unique ID generation and idempotent processing on server.

5. Load Balancer Single Point of Failure

   • Problem: If load balancer fails, service becomes unavailable.
   • Solution: Deploy load balancer in high-availability mode (active-passive) or use cloud-managed LB with auto-scaling and health checks.
   • Trade-off: Additional cost and configuration.

6. Redis Session Cache Availability

   • Problem: Redis outage leads to session data loss and forced re-authentication.
   • Solution: Use Redis Cluster with replication and automatic failover.
   • Trade-off: Operational overhead, but ensures high availability.

7. Database Write Scalability

   • Problem: Single PostgreSQL primary limits write throughput.
   • Solution: Shard database by organization ID across multiple instances, or use a distributed SQL database.
   • Trade-off: Application must be shard-aware; cross-shard queries become difficult.

8. JWT Stored in localStorage (XSS Risk)

   • Problem: Vulnerable to XSS attacks stealing tokens.
   • Solution: Store tokens in HttpOnly, Secure, SameSite cookies and implement CSRF protection.
   • Trade-off: More complex frontend/backend coordination, need CSRF tokens.

9. Long JWT Expiry

   • Problem: Stolen tokens remain valid for up to 24 hours.
   • Solution: Use short-lived access tokens (e.g., 15 minutes) with refresh tokens stored securely.
   • Trade-off: More frequent token refresh requests, need refresh endpoint and secure storage for refresh token.

10. CDN Caching API Responses

    • Problem: Dynamic document data may be cached, causing stale reads.
    • Solution: Configure CDN to bypass cache for API routes, or set Cache-Control: no-cache, private.
    • Trade-off: Increased load on origin servers, but ensures data freshness.

11. Snapshot Generation Overhead

    • Problem: Full HTML snapshots every 30 seconds for all documents can consume significant resources.
    • Solution: Generate snapshots asynchronously using background workers, only for active documents, and store incremental changes.
    • Trade-off: Delayed snapshots, added complexity of job queue.

12. Polling Duplicate Processing

    • Problem: Servers polling based on time window may fetch the same change multiple times, causing duplicate broadcasts.
    • Solution: Use a monotonically increasing sequence ID for changes, and each server tracks last processed ID.
    • Trade-off: Requires additional bookkeeping and a way to generate global sequence IDs (e.g., database sequence).

13. Load Balancer Not Load-Aware

    • Problem: Round-robin may unevenly distribute connections, overloading some servers.
    • Solution: Use load balancer algorithm like least connections or based on server health metrics.
    • Trade-off: May require more advanced load balancer features.

14. WebSocket Connection Limits

    • Problem: Node.js server may hit OS limits on concurrent connections.
    • Solution: Scale horizontally, tune OS limits (e.g., increase file descriptors), or use a language/runtime optimized for massive concurrency (e.g., Elixir, Go) for WebSocket handling.
    • Trade-off: Additional servers increase cost; switching language may be costly.

15. Offline Support and Network Partitions

    • Problem: If server loses DB connectivity, edits fail; users experience interruption.
    • Solution: Allow offline editing with client-side queue and merge later using CRDTs; or degrade gracefully.
    • Trade-off: Significant complexity to implement conflict resolution and sync.

We might also mention the issue of "Broadcast to many clients on same server" but it's somewhat covered by scaling horizontally and load balancing. Could be added.

But the list is already long. We need to ensure we cover the main points from the description.

Now, we need to write the final answer in a clear, organized way.

I'll structure as:

Potential Failure Modes, Race Conditions, and Scaling Bottlenecks

1. Issue: ...
   • Type: Failure mode / Race condition / Scaling bottleneck
   • Description: ...
   • Solution: ...
   • Trade-offs: ...

But to keep it concise, we can just present each as a paragraph with bullet.

Given the instruction, we can just list each issue and its solution with trade-offs.

I'll produce a numbered list.

Let's start writing.

But before final, double-check the architecture: "Real-time: Each API server maintains its own WebSocket connections to clients" and "Sync Strategy: Last-write-wins with timestamps from client clocks". That is a major red flag. Also "Other servers poll PostgreSQL every 2 seconds for changes". So indeed polling is used.

Now, also note: "Database: PostgreSQL for document storage, Redis for session cache". So Redis is only for session cache, not for real-time. So they might be using Redis for caching sessions, not for pub/sub. So we can propose using Redis Pub/Sub to replace polling, which would also leverage existing Redis, but then we need to ensure Redis is highly available. That's a trade-off: using Redis for both cache and pub/sub might overload it; but can scale.

Also, "Storage: Documents saved as full HTML snapshots every 30 seconds." Could be that they are only saving snapshots, not each change. But step 2 says "Server writes change to PostgreSQL". So maybe they write changes to a table, and then a periodic job creates snapshots. That's plausible. So we should not assume they only have snapshots. But we can still mention that snapshot-only would be problematic; but given they write changes, it's okay. However, they might be writing the change as an update to the document record directly, which would mean the document is always up-to-date, and snapshots are backups. That's fine.

Potential issue: If they update the document record directly on every keystroke, that could be a huge number of writes. Usually collaborative editors buffer changes and send batches. But they didn't specify. That could be a scaling issue: high write rate. But we can mention that sending every keystroke as a DB write might overload DB; batching could help.

But the description says "User types → change event sent via WebSocket". That could be per keystroke, which is a lot. They might want to throttle or batch. But not explicitly a problem? We can add as a scaling bottleneck: high frequency of DB writes. Solution: buffer changes and write in batches, or use a log-based approach with asynchronous compaction. Trade-off: potential data loss if server crashes before flush.

But we already have many items; maybe include.

Let's see: "User types → change event sent via WebSocket to their connected server". That implies each keystroke is sent immediately. That's typical for real-time collaboration. But then "Server writes change to PostgreSQL". Writing each keystroke to PostgreSQL could be heavy. Many systems use an operation log (like a changes table) which is append-only, which is efficient. But still, high volume of writes. They could use a message queue to decouple, or batch. But we can mention.

Also, conflict resolution: if they write each keystroke as an update to the same row (paragraph), that's even worse. Usually collaborative editors use operational transforms where each operation is stored and applied in order. That's more scalable. So the architecture seems naive.

Given the scope, we should highlight that the entire sync strategy is flawed and not suitable for real-time collaborative editing. But the question asks to identify potential failure modes, race conditions, and scaling bottlenecks, and propose solutions with trade-offs. So we can point out that last-write-wins is inappropriate and suggest OT/CRDT.

Now, let's produce the final answer.

I'll write in a structured manner, with each issue clearly labeled, maybe with a heading.

Answer:

The architecture described has several potential issues that could lead to failures, race conditions, and scaling bottlenecks. Below is a list of these issues, along with specific solutions and their trade-offs.

1. Conflict Resolution Using Client Timestamps

   • Type: Failure mode / Race condition
   • Problem: Client clocks may be skewed or maliciously set, leading to unfair conflict resolution and data loss when two users edit the same paragraph. Last-write-wins based on client timestamps cannot guarantee consistency.
   • Solution: Replace client timestamps with server-assigned sequence numbers or adopt a conflict-free replicated data type (CRDT) or Operational Transform (OT) algorithm.
   • Trade-offs: Increased server complexity; sequence numbers require coordination (e.g., a database sequence) which can become a bottleneck; CRDT/OT implementations are complex but provide robust real-time collaboration.

2. Lost Updates Due to Lack of Concurrency Control

   • Type: Race condition
   • Problem: When two servers concurrently update the same paragraph, both may read the old state and write, causing the later write to overwrite the earlier one regardless of timestamps.
   • Solution: Use optimistic concurrency control: store a version number (or timestamp) for each paragraph, and perform conditional updates (e.g., UPDATE ... WHERE version = read_version). If the update fails, retry with the new state.
   • Trade-offs: Additional read before write increases database load; retry logic adds complexity; version tracking must be maintained.

3. Polling Delay and Database Load

   • Type: Scaling bottleneck / Failure mode (latency)
   • Problem: Servers poll PostgreSQL every 2 seconds for changes, causing up to 2 seconds latency for cross-server updates and increasing read load linearly with the number of servers.
   • Solution: Replace polling with a publish-subscribe system (e.g., Redis Pub/Sub, Apache Kafka) to notify all servers immediately when a change is written. Alternatively, use PostgreSQL's LISTEN/NOTIFY.
   • Trade-offs: Introduces a new component (or uses existing Redis for pub/sub), which adds operational complexity and potential new failure points. LISTEN/NOTIFY may not scale to thousands of connections.

4. WebSocket Server Failure and In-Flight Changes

   • Type: Failure mode
   • Problem: If a server crashes after receiving a change but before persisting it or acknowledging to the client, the change may be lost, or the client may resend causing duplicates.
   • Solution: Make operations idempotent by having the client generate a unique ID for each change. The server persists the change (with the ID) before sending acknowledgment. On reconnection, the client can resend unacknowledged changes with the same IDs.
   • Trade-offs: Client-side complexity (queue, ID generation), server must check for duplicate IDs to ensure idempotency.

5. Load Balancer Single Point of Failure

   • Type: Failure mode
   • Problem: The load balancer is a single point of failure; if it goes down, new connections cannot be established and existing connections may be disrupted.
   • Solution: Deploy the load balancer in a high-availability configuration (active-passive with failover) or use a cloud-managed load balancer that provides automatic redundancy.
   • Trade-offs: Additional cost and configuration effort; some solutions may introduce new failure modes during failover.

6. Redis Session Cache Availability

   • Type: Failure mode / Scaling bottleneck
   • Problem: Redis is used for session cache; if it fails, session data is lost, forcing users to re-authenticate and possibly losing state.
   • Solution: Use Redis Cluster with replication and automatic failover to ensure high availability.
   • Trade-offs: Increased operational overhead; cluster management requires careful planning.

7. Database Write Scalability

   • Type: Scaling bottleneck
   • Problem: All writes go to a single PostgreSQL primary, which will eventually become a bottleneck as the number of concurrent edits grows.
   • Solution: Shard the database by organization ID (or a finer key) across multiple PostgreSQL instances, or migrate to a distributed SQL database like CockroachDB.
   • Trade-offs: Application must be shard-aware; cross-shard queries become difficult; distributed databases add complexity.

8. JWT Stored in localStorage (XSS Risk)

   • Type: Security failure mode
   • Problem: Storing JWTs in localStorage makes them accessible to JavaScript, so a cross-site scripting (XSS) attack can steal the token and impersonate the user.
   • Solution: Store the access token in an HttpOnly, Secure, SameSite=Strict cookie, and use CSRF tokens or SameSite attributes to prevent CSRF attacks.
   • Trade-offs: Frontend must handle CSRF protection; cookies are sent automatically, requiring careful configuration.

9. Long JWT Expiry

   • Type: Security failure mode
   • Problem: A 24-hour expiry gives an attacker a long window to use a stolen token.
   • Solution: Use short-lived access tokens (e.g., 15 minutes) and long-lived refresh tokens stored securely (e.g., in HttpOnly cookies). Implement a token refresh endpoint.
   • Trade-offs: More frequent network calls to refresh tokens; need to secure refresh tokens against theft.

10. CDN Caching API Responses

    • Type: Failure mode (stale data)
    • Problem: Caching API responses for 5 minutes can serve stale document content, breaking real-time collaboration.
    • Solution: Configure CDN to bypass caching for all API routes, or set appropriate Cache-Control: no-cache, private headers on dynamic endpoints.
    • Trade-offs: Increased load on origin servers, but ensures data freshness.

11. Snapshot Generation Overhead

    • Type: Scaling bottleneck
    • Problem: Saving full HTML snapshots of every document every 30 seconds consumes CPU, I/O, and storage, especially for large or many documents.
    • Solution: Generate snapshots asynchronously using a background job queue, only for active documents, and store incremental changes (e.g., an operation log) to reduce snapshot frequency or size.
    • Trade-offs: Adds complexity of a job system; recovery point objective may increase if snapshots are delayed.

12. Polling Duplicate Processing

    • Type: Race condition / inefficiency
    • Problem: Servers polling based on a time window (last 2 seconds) may fetch the same change multiple times, leading to duplicate broadcasts and unnecessary load.
    • Solution: Store each change with a monotonically increasing sequence ID (e.g., a database sequence). Each server keeps track of the last processed ID and fetches only newer changes.
    • Trade-offs: Requires a global sequence generator (could be a bottleneck) and per-server state; but eliminates duplicates.

13. Load Balancer Not Load-Aware

    • Type: Scaling bottleneck
    • Problem: Round-robin load balancing does not account for the current load on servers, potentially overloading some while others are underutilized.
    • Solution: Use a load-aware algorithm such as least connections or based on server health metrics (CPU, memory).
    • Trade-offs: Requires more advanced load balancer features, which may incur additional cost or configuration.

14. WebSocket Connection Limits per Server

    • Type: Scaling bottleneck
    • Problem: A single Node.js server has limits on the number of concurrent WebSocket connections (due to file descriptors, memory). As user count grows, a server may hit these limits.
    • Solution: Scale horizontally by adding more API servers; tune OS limits (increase file descriptors). Alternatively, use a language/runtime better suited for massive concurrency (e.g., Elixir, Go) for the WebSocket layer.
    • Trade-offs: Adding servers increases cost; changing technology stack is a major undertaking.

15. Offline Support and Network Partitions

    • Type: Failure mode
    • Problem: If a server loses connectivity to the database, it cannot persist changes, causing user edits to fail. Network partitions can lead to service interruption.
    • Solution: Allow clients to continue editing offline by queuing changes locally and syncing when connectivity is restored, using CRDTs to merge conflicts.
    • Trade-offs: Significant implementation complexity; client-side storage and conflict resolution logic required.

16. High Frequency of Database Writes

    • Type: Scaling bottleneck
    • Problem: Sending every keystroke as a separate write to PostgreSQL can overwhelm the database, especially during peak usage.
    • Solution: Buffer changes on the server and write them in batches (e.g., every 200ms) or use an append-only log with asynchronous compaction.
    • Trade-offs: Increases latency slightly for persistence; if the server crashes before flushing, recent changes may be lost unless also replicated.

These improvements would address the major risks and scalability limits, albeit with added complexity and potential trade-offs in performance, cost, or development effort. The exact choices depend on the required consistency, latency, and scale.The architecture described has several potential failure modes, race conditions, and scaling bottlenecks. Below is a list of these issues, along with specific solutions and their trade-offs.

---

1. Conflict Resolution Using Client Timestamps

• Type: Failure mode / Race condition
• Problem: Client clocks may be skewed or maliciously set, leading to unfair conflict resolution and data loss when two users edit the same paragraph. Last-write-wins based on client timestamps cannot guarantee consistency.
• Solution: Replace client timestamps with server-assigned sequence numbers or adopt a conflict-free replicated data type (CRDT) or Operational Transform (OT) algorithm.
• Trade-offs: Increased server complexity; sequence numbers require coordination (e.g., a database sequence) which can become a bottleneck; CRDT/OT implementations are complex but provide robust real-time collaboration.

2. Lost Updates Due to Lack of Concurrency Control

• Type: Race condition
• Problem: When two servers concurrently update the same paragraph, both may read the old state and write, causing the later write to overwrite the earlier one regardless of timestamps.
• Solution: Use optimistic concurrency control: store a version number (or timestamp) for each paragraph, and perform conditional updates (e.g., UPDATE ... WHERE version = read_version). If the update fails, retry with the new state.
• Trade-offs: Additional read before write increases database load; retry logic adds complexity; version tracking must be maintained.

3. Polling Delay and Database Load

• Type: Scaling bottleneck / Failure mode (latency)
• Problem: Servers poll PostgreSQL every 2 seconds for changes, causing up to 2 seconds latency for cross-server updates and increasing read load linearly with the number of servers.
• Solution: Replace polling with a publish-subscribe system (e.g., Redis Pub/Sub, Apache Kafka) to notify all servers immediately when a change is written. Alternatively, use PostgreSQL's LISTEN/NOTIFY.
• Trade-offs: Introduces a new component (or uses existing Redis for pub/sub), adding operational complexity and potential new failure points. LISTEN/NOTIFY may not scale to thousands of connections.

4. WebSocket Server Failure and In-Flight Changes

• Type: Failure mode
• Problem: If a server crashes after receiving a change but before persisting it or acknowledging to the client, the change may be lost, or the client may resend causing duplicates.
• Solution: Make operations idempotent by having the client generate a unique ID for each change. The server persists the change (with the ID) before sending acknowledgment. On reconnection, the client can resend unacknowledged changes with the same IDs.
• Trade-offs: Client-side complexity (queue, ID generation), server must check for duplicate IDs to ensure idempotency.

5. Load Balancer Single Point of Failure

• Type: Failure mode
• Problem: The load balancer is a single point of failure; if it goes down, new connections cannot be established and existing connections may be disrupted.
• Solution: Deploy the load balancer in a high-availability configuration (active-passive with failover) or use a cloud-managed load balancer that provides automatic redundancy.
• Trade-offs: Additional cost and configuration effort; some solutions may introduce new failure modes during failover.

6. Redis Session Cache Availability

• Type: Failure mode / Scaling bottleneck
• Problem: Redis is used for session cache; if it fails, session data is lost, forcing users to re-authenticate and possibly losing state.
• Solution: Use Redis Cluster with replication and automatic failover to ensure high availability.
• Trade-offs: Increased operational overhead; cluster management requires careful planning.

7. Database Write Scalability

• Type: Scaling bottleneck
• Problem: All writes go to a single PostgreSQL primary, which will eventually become a bottleneck as the number of concurrent edits grows.
• Solution: Shard the database by organization ID (or a finer key) across multiple PostgreSQL instances, or migrate to a distributed SQL database like CockroachDB.
• Trade-offs: Application must be shard-aware; cross-shard queries become difficult; distributed databases add complexity.

8. JWT Stored in localStorage (XSS Risk)

• Type: Security failure mode
• Problem: Storing JWTs in localStorage makes them accessible to JavaScript, so a cross-site scripting (XSS) attack can steal the token and impersonate the user.
• Solution: Store the access token in an HttpOnly, Secure, SameSite=Strict cookie, and use CSRF tokens or SameSite attributes to prevent CSRF attacks.
• Trade-offs: Frontend must handle CSRF protection; cookies are sent automatically, requiring careful configuration.

9. Long JWT Expiry

• Type: Security failure mode
• Problem: A 24-hour expiry gives an attacker a long window to use a stolen token.
• Solution: Use short-lived access tokens (e.g., 15 minutes) and long-lived refresh tokens stored securely (e.g., in HttpOnly cookies). Implement a token refresh endpoint.
• Trade-offs: More frequent network calls to refresh tokens; need to secure refresh tokens against theft.

10. CDN Caching API Responses

• Type: Failure mode (stale data)
• Problem: Caching API responses for 5 minutes can serve stale document content, breaking real-time collaboration.
• Solution: Configure CDN to bypass caching for all API routes, or set appropriate Cache-Control: no-cache, private headers on dynamic endpoints.
• Trade-offs: Increased load on origin servers, but ensures data freshness.

11. Snapshot Generation Overhead

• Type: Scaling bottleneck
• Problem: Saving full HTML snapshots of every document every 30 seconds consumes CPU, I/O, and storage, especially for large or many documents.
• Solution: Generate snapshots asynchronously using a background job queue, only for active documents, and store incremental changes (e.g., an operation log) to reduce snapshot frequency or size.
• Trade-offs: Adds complexity of a job system; recovery point objective may increase if snapshots are delayed.

12. Polling Duplicate Processing

• Type: Race condition / inefficiency
• Problem: Servers polling based on a time window (last 2 seconds) may fetch the same change multiple times, leading to duplicate broadcasts and unnecessary load.
• Solution: Store each change with a monotonically increasing sequence ID (e.g., a database sequence). Each server keeps track of the last processed ID and fetches only newer changes.
• Trade-offs: Requires a global sequence generator (could be a bottleneck) and per-server state; but eliminates duplicates.

13. Load Balancer Not Load-Aware

• Type: Scaling bottleneck
• Problem: Round-robin load balancing does not account for the current load on servers, potentially overloading some while others are underutilized.
• Solution: Use a load-aware algorithm such as least connections or based on server health metrics (CPU, memory).
• Trade-offs: Requires more advanced load balancer features, which may incur additional cost or configuration.

14. WebSocket Connection Limits per Server

• Type: Scaling bottleneck
• Problem: A single Node.js server has limits on the number of concurrent WebSocket connections (due to file descriptors, memory). As user count grows, a server may hit these limits.
• Solution: Scale horizontally by adding more API servers; tune OS limits (increase file descriptors). Alternatively, use a language/runtime better suited for massive concurrency (e.g., Elixir, Go) for the WebSocket layer.
• Trade-offs: Adding servers increases cost; changing technology stack is a major undertaking.

15. Offline Support and Network Partitions

• Type: Failure mode
• Problem: If a server loses connectivity to the database, it cannot persist changes, causing user edits to fail. Network partitions can lead to service interruption.
• Solution: Allow clients to continue editing offline by queuing changes locally and syncing when connectivity is restored, using CRDTs to merge conflicts.
• Trade-offs: Significant implementation complexity; client-side storage and conflict resolution logic required.

16. High Frequency of Database Writes

• Type: Scaling bottleneck
• Problem: Sending every keystroke as a separate write to PostgreSQL can overwhelm the database, especially during peak usage.
• Solution: Buffer changes on the server and write them in batches (e.g., every 200ms) or use an append-only log with asynchronous compaction.
• Trade-offs: Increases latency slightly for persistence; if the server crashes before flushing, recent changes may be lost unless also replicated.

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These improvements would address the major risks and scalability limits, albeit with added complexity and potential trade-offs in performance, cost, or development effort. The exact choices depend on the required consistency, latency, and scale.