Great prompt. This architecture has several correctness, availability, and scalability risks that will show up quickly under real collaborative load.

Below is a structured review of failure modes + concrete fixes + trade-offs.

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1) Real-time propagation is partitioned by server

Failure mode

Each API server only broadcasts to WebSocket clients connected to that server.
Users on different servers won’t see updates immediately (or at all until poll catches up).

Impact

• Inconsistent document views across users
• “Phantom overwrite” behavior
• Bad UX (“I typed but others didn’t see it”)

Fix

Add a cross-node pub/sub layer (Redis Pub/Sub, NATS, Kafka, or managed equivalent).
On receiving an edit, publish to document channel; all servers subscribed fan out to local sockets.

Trade-offs

• Extra infra complexity and cost
• Need delivery semantics (at-most-once vs at-least-once)
• Redis Pub/Sub is low-latency but not durable; Kafka durable but higher ops complexity

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2) Polling PostgreSQL every 2s for changes

Failure mode

Polling creates lag + load, and can miss ordering nuances.

Impact

• 0–2s replication delay between nodes (very noticeable for typing)
• DB hammered by frequent “any changes?” queries
• Thundering herd as server count grows

Fix

Replace polling with push:

• DB logical decoding / LISTEN-NOTIFY (small scale)
• Better: application-level event bus (Redis Streams/Kafka/NATS JetStream)

Trade-offs

• Event infrastructure required
• Need consumer offsets/retries for reliability

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3) Last-write-wins based on client timestamps

Failure mode

Clock skew, bad device clocks, malicious clients can “win” incorrectly.

Impact

• Lost edits
• Non-deterministic conflicts
• Easy abuse (send far-future timestamp)

Fix

Use server-assigned monotonic versioning per document (sequence numbers or Lamport/vector clocks).
For true collaborative editing, use OT or CRDT rather than paragraph-level LWW.

Trade-offs

• OT/CRDT implementation complexity
• Higher metadata overhead
• Much better correctness under concurrency

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4) Paragraph-level LWW conflict resolution

Failure mode

Two users edit different words in same paragraph; one overwrites the other.

Impact

• Frequent data loss in real collaboration

Fix

Use operation-based model:

• OT (Google Docs style transform)
• CRDT (Yjs/Automerge style merge)

Trade-offs

• Harder algorithmically
• Requires client and server protocol redesign
• Massive improvement in merge quality

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5) No total ordering of edits across servers

Failure mode

Edits can arrive in different orders on different nodes.

Impact

• Divergent document state
• Hard-to-reproduce consistency bugs

Fix

Per-document sequencer (single writer shard) or ordered log partition by doc_id.

Trade-offs

• Sequencer can become hotspot
• Needs partitioning/sharding strategy
• Gives deterministic replay/state rebuild

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6) Direct write to PostgreSQL for each change event

Failure mode

Typing generates huge write QPS; DB becomes bottleneck.

Impact

• High latency, lock contention
• DB saturation, cascading failures

Fix

Buffer edits in memory/event log, persist in batches (e.g., every N ops / seconds), plus periodic snapshots.

Trade-offs

• Potential small data loss window unless WAL/event log is durable
• More complex recovery path

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7) Full HTML snapshot every 30 seconds

Failure mode

Write amplification + storage bloat + hard diff/replay semantics.

Impact

• Large storage costs
• Slow load/save for long docs
• Limited audit/history fidelity

Fix

Store:

• Base snapshot + incremental ops (event sourcing light)
• Periodic compaction checkpoints

Trade-offs

• More logic for reconstructing current state
• Better history, lower average write size, better scalability

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8) API cached by CloudFront for 5 minutes

Failure mode

Dynamic/authenticated API responses cached and served stale or to wrong users if cache key misconfigured.

Impact

• Stale document metadata/content
• Potential data leakage across users/tenants

Fix

Do not cache mutable/auth APIs at CDN by default.
If caching, use strict cache keys (Authorization, tenant, doc), short TTL, and Cache-Control: private, no-store where needed.

Trade-offs

• Lower cache hit ratio
• Slightly higher origin load
• Major correctness/security gain

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9) JWT in localStorage (24h)

Failure mode

XSS can steal token; long-lived token increases blast radius.

Impact

• Account/session hijacking
• Difficult incident containment

Fix

Use httpOnly, secure, sameSite cookies + short-lived access tokens + refresh rotation + token revocation list.

Trade-offs

• CSRF considerations (mitigate with sameSite + anti-CSRF token)
• Slightly more auth complexity

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10) No mention of WebSocket auth refresh/revalidation

Failure mode

Socket stays alive after token expiry/revocation.

Impact

• Unauthorized long-lived access

Fix

Revalidate auth on socket connect + periodic checks + forced disconnect on revocation event.

Trade-offs

• Requires session tracking/pubsub for revocations

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11) Round-robin LB for WebSockets without stickiness strategy

Failure mode

Reconnects land on different servers; local in-memory session/state lost.

Impact

• Missed unacked ops
• Presence/cursor flicker
• Duplicates on retry if not idempotent

Fix

Use either:

• Stateless servers + shared state bus (preferred)
• Or sticky sessions (temporary simplification)

Trade-offs

• Sticky sessions hurt balancing/failover
• Stateless design requires robust shared infra

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12) No idempotency/deduplication for client retries

Failure mode

Network retry sends same op multiple times.

Impact

• Duplicate text insertion/deletion
• State corruption

Fix

Attach operation IDs (UUID + client seq), dedupe per document/client.

Trade-offs

• Metadata/state to track recent op IDs

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13) No ACK protocol/backpressure handling on WebSockets

Failure mode

Slow clients/sockets buffer indefinitely; memory blowups.

Impact

• Server OOM
• Latency spikes for all users on node

Fix

ACKed delivery windows, bounded queues, drop/close slow consumers, resumable sync via version catch-up.

Trade-offs

• More protocol complexity
• Better stability under load

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14) PostgreSQL read replicas for “read-heavy” while writes are hot path

Failure mode

Replica lag gives stale reads for active documents.

Impact

• User sees old state after edit (“read your own write” broken)

Fix

Route collaborative doc reads to primary or session-consistent store; use replicas only for analytics/search/non-critical reads.

Trade-offs

• More load on primary
• Better consistency guarantees

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15) Partitioning by organization ID

Failure mode

Hot-tenant problem: one large org overloads a shard.

Impact

• Uneven utilization
• Noisy-neighbor effects

Fix

Partition primarily by document_id (or hash), optionally with org-aware placement constraints.

Trade-offs

• Harder tenant-level data locality/compliance control
• Better load distribution

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16) Server/node failure during active editing

Failure mode

In-flight edits in memory are lost if node crashes before DB persist/broadcast.

Impact

• Silent data loss
• Client divergence

Fix

Write-ahead to durable log before ACK (Kafka/Redis Stream with AOF), then process/broadcast.

Trade-offs

• Slightly higher write latency
• Much stronger durability

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17) No disaster recovery / multi-region consistency plan

Failure mode

Region outage = total downtime/data unavailability.

Impact

• Poor availability SLOs

Fix

Define RPO/RTO; implement cross-region replication + failover strategy. For real-time collab, usually active-passive per document region.

Trade-offs

• Cost and complexity
• Cross-region active-active for collaborative editing is very hard

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18) Security: trusting client-generated HTML snapshots

Failure mode

Stored XSS/script injection in document HTML.

Impact

• Token theft, account compromise, worm-like propagation

Fix

Use structured document model (ProseMirror/Slate schema) and sanitize/render safely server-side + CSP.

Trade-offs

• Migration effort from raw HTML
• Strong security and editing robustness

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19) Missing observability for race/debug

Failure mode

Hard to diagnose ordering and merge bugs without traceability.

Impact

• Long MTTR
• Undetected data corruption

Fix

Instrument per-op tracing: doc_id, op_id, version, server, latency; define SLOs for fanout lag/conflict rate.

Trade-offs

• Logging/telemetry cost

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20) Lack of explicit consistency model

Failure mode

Different components assume different guarantees (eventual vs strong).

Impact

• Hidden correctness bugs in edge cases

Fix

Define contract clearly:

• Per-document linearizable op order (or eventual with CRDT convergence)
• Read-your-own-writes guarantee
• Conflict semantics documented

Trade-offs

• May constrain architecture choices
• Dramatically reduces ambiguity/bugs

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Recommended target architecture (pragmatic)

1. OT/CRDT ops instead of paragraph LWW
2. Per-doc ordered event stream (partitioned bus)
3. Stateless WS gateways + shared pub/sub fanout
4. Durable op log + periodic snapshots
5. Server-side versioning, idempotent op IDs, ACK protocol
6. No CDN caching for mutable auth APIs
7. httpOnly auth tokens + rotation

If you want, I can sketch a concrete “v2” design with specific tech choices (e.g., Redis Streams vs Kafka, OT vs Yjs CRDT) and expected throughput limits.