Debug This Architecture

Below is a system‑level failure‑mode / race‑condition / scaling‑bottleneck map for the architecture you described, followed by a concrete mitigation (or redesign) for each item together with the main trade‑offs to consider.
I grouped the items by the logical layer they belong to, because many of the problems cascade from one layer to the next.

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1. Real‑time Collaboration Layer (WebSocket → Change Propagation)

#	Issue (Failure Mode / Race Condition)	Why it Happens / Impact	Mitigation / Solution	Trade‑offs
1.1	WebSocket connection loss (client disconnect, server crash, LB timeout)	Client stops receiving updates → stale view, possible data loss if local edits are not flushed.	• Use sticky sessions (source‑IP affinity) or a centralized WebSocket broker (e.g., Redis Pub/Sub, NATS, or a dedicated socket‑server cluster with a shared connection registry). <br>• Implement client‑side reconnection with exponential back‑off and message replay (store last N operations per document in Redis).	Sticky sessions limit load‑balancer flexibility; a broker adds extra hop and operational cost but gives true fan‑out and fail‑over.
1.2	Server‑side broadcast limited to “that server”	Changes made on Server A are not pushed to clients attached to Server B until the 2‑second poll. This creates visible latency spikes and can cause out‑of‑order delivery.	Replace polling with event‑driven publish/subscribe: every server publishes its change to a Redis channel (or Kafka topic) and all servers subscribe. The broadcast becomes instantaneous and ordering can be enforced per‑document.	Requires a reliable message broker and handling of broker failures; adds a small memory footprint for the channel.
1.3	Polling every 2 s on every API server	As the number of servers grows, the aggregate read load on PostgreSQL scales linearly. With 50 servers you have 25 RPS of full‑table scans (or at least index scans). This quickly saturates the primary or read replicas.	• Switch to logical replication or LISTEN/NOTIFY in PostgreSQL so that changes are pushed to listeners. <br>• Or use Change Data Capture (CDC) with Debezium/Kafka Connect to stream row‑level changes.	Requires extra infrastructure (Kafka, Debezium) but eliminates wasteful polling. LISTEN/NOTIFY works only for modest traffic; CDC scales better.
1.4	Last‑write‑wins (LWW) with client‑provided timestamps	Clock skew (malicious or mis‑configured client) can overwrite newer edits, leading to data loss. Also, concurrent edits to the same paragraph can be silently discarded.	• Move to operational transformation (OT) or conflict‑free replicated data type (CRDT) algorithms that resolve conflicts based on intent, not on timestamps. <br>• If LWW must stay, replace client timestamps with server‑generated monotonic sequence numbers (e.g., a per‑document incrementing counter stored in Redis).	OT/CRDT adds algorithmic complexity and higher CPU per edit; server‑generated sequence numbers require a fast, strongly consistent counter (Redis INCR is cheap).
1.5	Duplicate or out‑of‑order messages (network jitter, retries)	Client may apply the same edit twice or apply an older edit after a newer one, corrupting the document state.	• Make every edit idempotent (include a UUID; server deduplicates). <br>• Use per‑document version numbers; server rejects edits with a version ≤ current version.	Version check forces the client to keep the latest version, slightly increasing client state size.
1.6	WebSocket connection‑count limits (ulimit, OS socket limits)	A single API server can only hold a few tens of thousands of concurrent sockets before hitting OS limits, causing new connections to be refused.	• Scale out the socket layer (more servers) and raise OS limits (net.core.somaxconn, file‑descriptor ulimit). <br>• Use a gateway such as AWS API Gateway WebSocket or Cloudflare Workers that terminates the socket and forwards messages via HTTP/2 to backend workers.	Raising OS limits is cheap but requires proper monitoring; a managed gateway removes socket‑scale concerns but adds latency and cost.
1.7	Message size explosion (full HTML snapshot every 30 s)	If many users edit a large document, a 30‑second snapshot can be several MBs, overwhelming both DB write bandwidth and network.	• Store incremental diffs (e.g., Quill Delta, JSON‑Patch) instead of full snapshots. <br>• Keep the full snapshot only in a cold‑storage bucket (S3) and keep a rolling delta log in Redis/Postgres for fast recovery.	Diff generation adds CPU; you need a compaction job to periodically coalesce deltas into a new full snapshot.
1.8	Back‑pressure on the server (burst of edits)	A sudden spike (e.g., copy‑paste of a large block) can flood the Node.js event loop, leading to increased latency or dropped messages.	• Use write‑through queue (e.g., BullMQ backed by Redis) to serialize writes to Postgres. <br>• Apply rate‑limiting per user (tokens per second).	Queue introduces additional latency (few ms) but protects the event loop. Rate‑limiting may affect power users.

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2. API/Load‑Balancer Layer

#	Issue	Why it Happens / Impact	Mitigation	Trade‑offs
2.1	Round‑robin LB without session affinity → a user’s WebSocket may be re‑routed mid‑session (if LB re‑balances).	The client loses its open socket and must reconnect; any in‑flight edits are lost.	Enable sticky sessions (source‑IP or cookie‑based) for WS endpoints, or use a layer‑7 router that forwards based on a “document‑id” hash.	Sticky sessions reduce true load‑balancing; hash‑based routing may unevenly distribute load if many users work on the same doc.
2.2	LB health‑check timeout (too aggressive) → servers are marked unhealthy while still processing edits.	Traffic shifts to fewer servers, causing overload and increased latency.	Tune health‑check interval and graceful shutdown (drain connections before marking down).	Longer health‑check periods mean slower detection of real failures.
2.3	Single point of failure for LB (no active‑active)	Entire service unavailable if LB crashes.	Deploy multiple LB instances behind a DNS‑based fail‑over (Route 53) or use a managed service (AWS ELB, Cloudflare Load Balancer).	Adds cost and DNS TTL considerations, but eliminates single point of failure.

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3. Persistence Layer (PostgreSQL)

#	Issue	Why it Happens / Impact	Mitigation	Trade‑offs
3.1	Write hotspot on a single primary (every edit hits the same row → high row‑level lock contention)	As concurrency grows, the primary becomes the bottleneck; latency spikes and occasional deadlocks.	• Use partitioned tables per‑organization (already planned) and shard by document‑id across multiple PostgreSQL clusters. <br>• Apply optimistic concurrency (version column) and batch multiple edits into a single UPDATE.	Partitioning adds complexity to queries and migrations; sharding across clusters requires a routing layer.
3.2	Replica lag (read replicas used for “read‑heavy” ops)	The 2‑second poll may read stale data, causing out‑of‑date broadcasts.	• Keep read‑writes on the primary for low‑latency change detection. <br>• If replicas are needed, reduce replication delay by using synchronous replication for the latest commit or using logical replication that streams WAL in near‑real‑time.	Synchronous replication reduces write throughput; logical replication adds operational overhead.
3.3	Transaction loss on crash (no durable write‑ahead log flush)	A server crash before the DB commit can cause lost edits.	Ensure PostgreSQL fsync is enabled and use synchronous_commit = on for critical tables.	Slight performance hit (extra fsync) but guarantees durability.
3.4	Schema migration while servers are running	In‑flight edits may violate new constraints, leading to errors and possible data loss.	Adopt zero‑downtime migration patterns (add new column, back‑fill, switch, then drop old). Use feature flags on the API to toggle between schema versions.	Requires careful coordination and testing.
3.5	Full‑snapshot storage bloat	Every 30 s snapshot creates a new row; after weeks the table can be terabytes.	• TTL / archival: move snapshots older than X days to S3 and delete from DB. <br>• Compaction job: merge deltas into a new snapshot and prune old deltas.	Archival adds retrieval latency for historic versions; compaction needs additional compute.
3.6	SQL injection via malformed client data	If client‑provided HTML is stored unchecked, could lead to XSS when rendered.	Sanitize/escape HTML on the server, store as text but render through a safe sanitizer (DOMPurify) on the client.	Slight CPU overhead; must keep sanitizer version in sync.

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4. Caching & Session Layer (Redis, CloudFront)

#	Issue	Why it Happens / Impact	Mitigation	Trade‑offs
4.1	Redis as a single point of failure (session cache, pub/sub)	If Redis crashes, session lookup fails → forced logout; pub/sub channel lost → real‑time updates stop.	Deploy Redis Cluster (sharding + replication) or use a managed service (AWS Elasticache with Multi‑AZ). Enable persistence (AOF/RDB) for session data.	Cluster adds complexity, cross‑slot pub/sub limitations (need to use a single hash slot or a separate channel per node).
4.2	Redis pub/sub message loss (no durability)	If a server restarts while a message is in transit, that edit is never broadcast.	Switch to Redis Streams (or Kafka) which persist messages and support consumer groups with ack/replay.	Streams require consumer offset management; higher memory usage.
4.3	Cache stampede on document load (many users request same doc, cache miss)	All servers hit PostgreSQL simultaneously, causing a spike.	Use request coalescing (single flight) or early‑expire with stale‑while‑revalidate pattern.	Slightly stale data may be served for a few seconds, but read load is drastically reduced.
4.4	JWT stored in localStorage	XSS can steal the token → session hijack.	Store JWT in httpOnly Secure SameSite=Lax cookies; optionally use short‑lived access token + refresh token flow.	Cookies are sent on every request (small overhead) and need CSRF protection (SameSite mitigates most).
4.5	CloudFront caching of API responses (5 min)	Real‑time API endpoints (e.g., “GET /documents/:id”) may return stale content, causing users to see outdated snapshots.	Disable caching for any endpoint that returns mutable data, or use Cache‑Control: no‑store. If static assets only, keep CDN.	Removes CDN benefit for those endpoints (but they are low‑traffic compared to WS).
4.6	Cache invalidation race (snapshot saved, but CDN still serves older version)	Users see an older snapshot for up to 5 min.	Invalidate the CDN object programmatically after each snapshot write (CloudFront invalidation API) or use versioned URLs (e.g., /doc/123?v=timestamp).	Invalidation cost (max 1000 per day free on AWS) and extra query‑string handling; versioned URLs are cheap and more deterministic.

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5. Authentication & Authorization

#	Issue	Why it Happens / Impact	Mitigation	Trade‑offs
5.1	Long‑lived JWT (24 h) with no revocation	If a token is stolen, the attacker can act for a full day.	Switch to short‑lived access tokens (5‑15 min) plus a refresh token stored in httpOnly cookie. Implement token revocation list in Redis for immediate logout.	Requires refresh flow and extra Redis reads on each token refresh, but limits exposure.
5.2	No per‑document ACL enforcement (only org‑level)	Users from the same org could edit any document, violating fine‑grained permissions.	Embed document‑level ACL in the DB and enforce in the API before broadcasting changes. Cache ACL in Redis for fast lookup.	Slight extra DB/Redis read per edit; adds complexity to permission management UI.
5.3	JWT signed with symmetric key stored in code repo	If repo is leaked, anyone can forge tokens.	Use asymmetric RSA/ECDSA keys with the private key only on the auth service; rotate keys regularly.	Slightly larger token size, verification cost is higher but still negligible.

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6. Network & Deployment Concerns

#	Issue	Why it Happens / Impact	Mitigation	Trade‑offs
6.1	Network partition between API servers and DB	Some servers cannot write/read → local edits are lost or become inconsistent.	Deploy DB in a multi‑AZ cluster with automatic failover (Patroni, CloudSQL). Use circuit‑breaker pattern in the API to fallback to a “read‑only” mode and alert users.	Failover may cause brief write pause; circuit‑breaker adds latency when open.
6.2	NTP clock skew between clients (used for LWW)	A fast client can “win” over correct edits.	Do not trust client timestamps; generate server‑side timestamps or monotonic counters.	Removes ability for client‑side offline editing (if you need offline, you must sync and resolve later).
6.3	WebSocket payload size limits (e.g., CloudFront or ALB limits)	Large diff messages may be truncated, causing loss of edits.	Enforce max payload size on the client (e.g., 64 KB) and chunk larger changes into multiple messages.	Slightly more complex client logic.
6.4	DNS TTL mismatch for load‑balancer changes	When you add/remove API servers, clients may keep connecting to old IPs.	Keep low TTL (30 s) for the service DNS and use Service Discovery (Consul, AWS Cloud Map) for the WebSocket endpoint.	More frequent DNS queries; must ensure DNS provider supports low TTL.

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7. Observability & Operational Safety

#	Issue	Why it Happens / Impact	Mitigation	Trade‑offs
7.1	No visibility into edit latency	You cannot detect when the 2 s polling becomes a bottleneck.	Instrument end‑to‑end latency (client → WS → DB → broadcast) with OpenTelemetry; expose alerts on > 200 ms latency.	Minor instrumentation overhead, but huge operational benefit.
7.2	No back‑pressure metrics (queue depth)	Queue overflows silently, dropping edits.	Export BullMQ queue metrics (waiting, active, completed) to Prometheus; set alerts when waiting > N.	Requires monitoring stack.
7.3	No audit log of document changes	Hard to recover from accidental overwrite.	Write append‑only audit table (or event store) with user‑id, operation, timestamp. Use partitioning to keep it performant.	Extra storage cost; can be pruned after compliance period.

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8. Summary of High‑Impact Refactors

Refactor	What it solves	Rough effort & cost
Replace polling + LWW with event‑driven publish/subscribe + OT/CRDT	Eliminates latency spikes, removes race conditions, provides true collaborative semantics.	Medium‑high: need to integrate OT/CRDT library, add message broker, adjust client model.
Move WebSocket fan‑out to a central broker (Redis Streams / NATS / Kafka)	Guarantees delivery to all servers, removes need for polling, scales linearly with users.	Medium: set up broker cluster, update servers to subscribe/publish.
Use server‑generated monotonic version numbers (or sequence) instead of client timestamps	Removes clock‑skew attacks and data loss from LWW.	Low: add a column, generate via Redis INCR or DB sequence.
Introduce short‑lived JWT + refresh token + revocation list	Improves security, limits impact of stolen tokens.	Low‑medium: change auth flow, add token refresh endpoint.
Shard documents across multiple PostgreSQL clusters (by org‑id)	Breaks write hotspot, improves write scalability.	High: DB provisioning, routing layer, migration scripts.
Persist change logs in an event store (Kafka) and generate snapshots lazily	Reduces DB write volume, enables easy replay, supports offline edits.	Medium: set up Kafka, write consumer for snapshot generation.
Add graceful shutdown & connection draining on API servers	Prevents abrupt loss of in‑flight edits during scaling or deployment.	Low: handle SIGTERM, stop accepting new WS, wait for active edits to finish.

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Final Checklist for a Production‑ready Real‑time Editor

1. Real‑time transport – WS broker (Redis Streams / NATS) with idempotent messages.
2. Conflict resolution – OT/CRDT or server‑generated versioning.
3. Persistence – Write deltas to an event store; snapshot every N minutes to S3; keep recent snapshots in Postgres for fast load.
4. Scalability – Horizontal API servers + sticky sessions or a stateless “gateway” that forwards WS to a pool of dedicated socket workers.
5. Reliability – Multi‑AZ DB + synchronous replication for primary; Redis Cluster with persistence; health‑check + graceful drain.
6. Security – Short‑lived JWT + refresh token + httpOnly cookie; server‑side sanitization; per‑document ACL.
7. Observability – End‑to‑end latency, queue depth, edit‑rate, replica lag, WS disconnect/reconnect metrics.
8. Testing – Chaos testing (kill a DB replica, restart a WS server, inject clock skew) to verify that the above mitigations hold.

Implementing these mitigations in the order of impact vs. effort (start with the low‑effort fixes: server‑generated version numbers, sticky sessions, JWT revocation, health‑check tuning) and then move to the higher‑effort architectural changes (event‑driven propagation, OT/CRDT, sharding) will give you a robust, horizontally‑scalable collaborative editor that can handle real‑world traffic spikes without data loss or user‑visible conflicts.