Compare Scenarios
Side-by-side comparison with decision path analysis. Every dimension traces back to topology, risk propagation, simulation, and advisor intelligence.
Select Scenarios to Compare
Left Scenario
AI / RAG
Multi-Tenant SaaS
Analytics
Financial Ledger
Read-Heavy
Multi-Tenant SaaS
Write-Heavy
Marketplace
Event-Driven
Financial Ledger
Realtime Collab
Realtime Collab
Financial Ledger
Write-Heavy
AI / RAG
Multi-Tenant SaaS
Event-Driven
Analytics
Read-Heavy
Realtime Collab
Search-Heavy
Event-Driven
Event-Driven
Marketplace
Write-Heavy
Right Scenario
AI / RAG
Multi-Tenant SaaS
Analytics
Financial Ledger
Read-Heavy
Multi-Tenant SaaS
Write-Heavy
Marketplace
Event-Driven
Financial Ledger
Realtime Collab
Realtime Collab
Financial Ledger
Write-Heavy
AI / RAG
Multi-Tenant SaaS
Event-Driven
Analytics
Read-Heavy
Realtime Collab
Search-Heavy
Event-Driven
Event-Driven
Marketplace
Write-Heavy
Topology at a Glance
Architecture Comparison
Realtime Collaborative Editor is both simpler and lower-risk than Write-Heavy Transactional Platform
Realtime Collaborative Editor is the simpler architecture. Realtime Collaborative Editor carries lower operational risk. They share 3 component(s). Write-Heavy Transactional Platform has 4 unique risk(s); Realtime Collaborative Editor has 1. Write-Heavy Transactional Platform requires lower team maturity to operate.
11
Nodes
5
Edges
4
Risks
3
Seeds
2
Strengths
4
Adv. Risks
5
Nodes
2
Edges
1
Risks
1
Seeds
1
Strengths
1
Adv. Risks
Comparison Dimensions
Complexity
Write-Heavy Transactional Platform
high complexity, 11 nodes, 5 edges, 4 risks, 3 simulation seeds
Realtime Collaborative Editor
expert complexity, 5 nodes, 2 edges, 1 risks, 1 simulation seeds
Realtime Collaborative Editor is simpler: expert operational complexity with 5 topology nodes vs 11 for Write-Heavy Transactional Platform.
Operational Risk
Write-Heavy Transactional Platform
4 risks (top: high), 3 high/critical, 0 confirmed by simulation
Realtime Collaborative Editor
1 risks (top: high), 1 high/critical, 1 confirmed by simulation
Realtime Collaborative Editor has lower operational risk: weighted severity score 4 vs 14 (1 vs 0 simulation-confirmed).
Scalability
Write-Heavy Transactional Platform
4 scaling thresholds, 3 migration paths, 4 advisor scaling signals
Realtime Collaborative Editor
3 scaling thresholds, 2 migration paths, 6 advisor scaling signals
Both scenarios have comparable scaling paths. The best choice depends on your specific growth trajectory. Write-Heavy Transactional Platform and Realtime Collaborative Editor offer similar numbers of defined evolution steps.
Operational Maturity
Write-Heavy Transactional Platform
Advisor assessment: Advanced; recommended team: Experienced Backend Team; 7 operational requirements
Realtime Collaborative Editor
Advisor assessment: Expert Only; recommended team: Enterprise Architecture Team; 6 operational requirements
Write-Heavy Transactional Platform requires lower team maturity (Advanced) vs Expert Only for Realtime Collaborative Editor.
Observability
Write-Heavy Transactional Platform
6 watched metrics, 4 observability recommendations, 3 simulation seeds
Realtime Collaborative Editor
4 watched metrics, 2 observability recommendations, 1 simulation seeds
Realtime Collaborative Editor has lower observability burden: 4 watched metrics vs 6.
Generator Readiness
Write-Heavy Transactional Platform
generator relevance documented; topology generation relevance noted; simulation relevance noted; 3 seeds with generator notes
Realtime Collaborative Editor
generator relevance documented; topology generation relevance noted; simulation relevance noted; 1 seeds with generator notes
Write-Heavy Transactional Platform has more documented generator readiness signals. Note: this is still preliminary.
Architecture Components
Shared (3)
Only in Write-Heavy Transactional Platform (8)
Only in Realtime Collaborative Editor (2)
Operational Risks
Only in Write-Heavy Transactional Platform (4)
Only in Realtime Collaborative Editor (1)
Consistency Guarantees
Only Write-Heavy Transactional Platform (1)
Moving from Write-Heavy Transactional Platform to Realtime Collaborative Editor
Green = kept, red = lost (the target explicitly excludes it), amber = unproven (the target has made no claim, not the same as losing it).
Only 'Write-Heavy Transactional Platform' claims: atomic_multi_object.
Tradeoff Summary
Complexity vs Risk
Write-Heavy Transactional Platform has high complexity. Realtime Collaborative Editor has expert complexity. Simpler systems often carry different (not necessarily fewer) risks.
Write-Heavy Transactional Platform
Write-Heavy Transactional Platform: 4 risks (top: high), 3 high/critical, 0 confirmed by simulation
Realtime Collaborative Editor
Realtime Collaborative Editor: 1 risks (top: high), 1 high/critical, 1 confirmed by simulation
Scaling Path
Write-Heavy Transactional Platform offers 4 defined scaling thresholds. Realtime Collaborative Editor offers 3. More defined paths means clearer evolution steps but also more anticipated growth.
Write-Heavy Transactional Platform
4 scaling thresholds, 3 migration paths, 4 advisor scaling signals
Realtime Collaborative Editor
3 scaling thresholds, 2 migration paths, 6 advisor scaling signals
Team Maturity Requirement
Write-Heavy Transactional Platform can be operated by a less experienced team. Realtime Collaborative Editor requires deeper operational expertise.
Write-Heavy Transactional Platform
Advisor assessment: Advanced; recommended team: Experienced Backend Team; 7 operational requirements
Realtime Collaborative Editor
Advisor assessment: Expert Only; recommended team: Enterprise Architecture Team; 6 operational requirements
Event-Driven vs Synchronous Processing
Write-Heavy Transactional Platform uses event stream infrastructure (Kafka/Kinesis), enabling decoupled async processing. Realtime Collaborative Editor does not, keeping the stack simpler but less decoupled.
Write-Heavy Transactional Platform
Event stream: async decoupling, consumer lag risk, higher ops burden
Realtime Collaborative Editor
No event stream: simpler stack, synchronous dependencies
Architecture Strengths vs Risks Balance
The advisor identifies strengths and risks grounded in knowledge relationships. A higher strengths-to-risks ratio suggests better mitigation coverage in the current topology.
Write-Heavy Transactional Platform
2 strengths, 4 risks
Realtime Collaborative Editor
1 strengths, 1 risks
Migration Considerations
Migration Step 1
Write-Heavy Transactional Platform
Single PostgreSQL with synchronous dual-write (DB + Kafka in application code) → PostgreSQL + outbox pattern + WAL CDC relay to Kafka
Realtime Collaborative Editor
Short-polling API with version-based conflict detection → WebSocket + Redis pub/sub live propagation
Both scenarios define a migration step at this stage. Write-Heavy Transactional Platform: triggered by 'Dual-write inconsistency events observed in production: DB w'. Realtime Collaborative Editor: triggered by 'User-visible edit conflicts > 5% of sessions; poll interval '.
Migration Step 2
Write-Heavy Transactional Platform
PostgreSQL + PgBouncer + outbox + Kafka CDC → Domain-partitioned PostgreSQL + separate write services per partition
Realtime Collaborative Editor
Last-write-wins conflict resolution → Operational transformation (OT) or CRDT-based conflict resolution
Both scenarios define a migration step at this stage. Write-Heavy Transactional Platform: triggered by 'Primary write CPU > 70% sustained during peak windows; WAL v'. Realtime Collaborative Editor: triggered by 'Data loss complaints from users editing simultaneously; conf'.
Migration Step 3
Write-Heavy Transactional Platform
PostgreSQL + Kafka CDC → Event sourcing: append-only event log with read model projections
Realtime Collaborative Editor
No further migration step defined
Write-Heavy Transactional Platform has a defined migration; Realtime Collaborative Editor does not at this stage.
Advisor Notes
Strength: Write-heavy transactional workloads that emit downstream events (order placed, payment captured) benefit from the outbox pattern…
Write-heavy transactional workloads that emit downstream events (order placed, payment captured) benefit from the outbox pattern to ensure events are published exactly when the database transaction commits: never before, never after. Key trade-off: Adds one INSERT per transaction to the outbox table: minor but nonzero write amplification. Operational note: Outbox table grows with write volume: implement TTL-based cleanup or partition pruning. Evidence: Payment processors like Stripe use outbox-style patterns to ensure webhook delivery matches transaction commits.
Strength: A connection pool bounds the total database connections an application can open, preventing connection storms during traffic…
A connection pool bounds the total database connections an application can open, preventing connection storms during traffic spikes and protecting the database server from exceeding its connection limit. Key trade-off: Pooler becomes a new single point of failure if not replicated. Operational note: PgBouncer transaction-mode pooling is most effective for stateless APIs. Evidence: PgBouncer reduces PostgreSQL connections by 10–100x in typical deployments.
Risk (high): Write Amplification Cascade
Each logical application write triggers multiple physical writes through index maintenance, WAL generation, MVCC versioning, and replication, causing actual disk IOPS to exceed the provisioned I/O ceiling while the logical write rate appears modest.
Risk (high): Connection Pool Exhaustion
All database connections in the pool are in use; new requests queue and then time out, causing cascading latency and errors across all dependent services.
Shared Operational Requirements
Both scenarios require: PostgreSQL: scenario includes high_write_throughput or write_heavy workload, Runbooks and alerting for high-severity risks.
Supporting Evidence · 14 items
Coverage Warnings
- ⚠Realtime Collaborative Editor: fewer than 2 architectural strengths identified. the risk/complexity dimensions are present but the strength analysis is thin. Consider enriching the relationship YAMLs referenced by this scenario to improve coverage.
- ⚠Realtime Collaborative Editor: fewer than 2 operational risks identified. the risk comparison dimension will reflect low advisor coverage, not a low-risk architecture.
Limitations
- ·Comparison grounded in YAML knowledge only. Not measured from any production system.
- ·Winner assessments are deterministic heuristics, not absolute recommendations. Context, team preferences, and workload specifics may change the conclusion.
- ·3 seed type(s) across both scenarios do not have execution previews. Risk confirmation for those types is unavailable.
Realtime Collaborative Editor is the recommended starting point over Write-Heavy Transactional Platform
Realtime Collaborative Editor leads on 3 weighted dimension(s): Complexity, Operational Risk, Observability. Weighted score: 4.5 vs 2.0 for Write-Heavy Transactional Platform.
Decision Intelligence
Architecture Decision Path
Structured reasoning for choosing between Write-Heavy Transactional Platform and Realtime Collaborative Editor. Every condition and trigger traces back to comparison dimensions, advisor insights, and topology evidence.
Realtime Collaborative Editor is the recommended starting point over Write-Heavy Transactional Platform
Realtime Collaborative Editor leads on 3 weighted dimension(s): Complexity, Operational Risk, Observability. Weighted score: 4.5 vs 2.0 for Write-Heavy Transactional Platform. The architectures share 3 component(s), reducing migration cost if you switch later. Realtime Collaborative Editor is the operationally simpler choice.
Where to Start
Start with Realtime Collaborative Editor
RightRealtime Collaborative Editor has lower operational complexity. Starting here reduces risk and cognitive load. Migrate to the more capable architecture only when you hit concrete scaling or feature limits.
Complexity: expert complexity, 5 nodes, 2 edges, 1 risks, 1 simulation seeds
Migrate when:
- Server memory growing with active connections; file descriptor limits approached; new WebSocket connections refused → Increase file descriptor limits (ulimit); move to dedicated WebSocket server tier; implement connection multiplexing (multiple documents per connection where safe)
- Consecutive writes to the same document causing lock contention; write latency rising; auto-save batching queue depth increasing → Move to operational transformation or CRDT-based conflict resolution; batch writes and resolve conflicts in-process before database commit; consider append-only event log for document operations
- Redis memory growing; high number of active pub/sub channels per Redis instance; SUBSCRIBE/UNSUBSCRIBE operations becoming significant overhead → Shard Redis pub/sub by document range; implement channel expiry; consider dedicated messaging tier (e.g. Ably, Pusher) for very high session counts
Decision Flow
Does your team have the operational maturity to run Realtime Collaborative Editor (expert only rating)?
If Yes
Your team can operate Realtime Collaborative Editor. Continue to Step 2 to refine based on risk tolerance and workload fit.
If No
Prefer the lower-maturity option: left scenario.
Is operational stability and minimising production risk your primary concern over feature richness or scalability ceiling?
If Yes
Prefer Realtime Collaborative Editor: it carries lower operational risk weight per the advisor's assessment.
If No
Proceed to Step 3 to evaluate based on scaling requirements.
Do you expect your load to reach: high sustained load with clear migration paths?
If Yes
Both scenarios have comparable scaling paths. Choose based on complexity preference.
If No
If you don't expect to hit these scaling signals soon, prefer the simpler architecture and re-evaluate when load patterns become clearer.
Does your team already operate an event streaming platform (e.g., Kafka, Kinesis, Pub/Sub) in production?
If Yes
Write-Heavy Transactional Platform builds on event stream infrastructure. Your existing platform reduces the adoption risk.
If No
If you don't have an event streaming platform, the other scenario avoids adding that infrastructure dependency. Evaluate whether the async decoupling benefit justifies the new ops burden.
Is operational simplicity (fewer moving parts, easier debugging, lower ops burden) more important than maximum architectural capability?
If Yes
Realtime Collaborative Editor is the simpler choice: Realtime Collaborative Editor is simpler: expert operational complexity with 5 topology nodes vs 11 for Write-Heavy Transactional Platform.
If No
If capability and scalability ceiling matter more than simplicity, evaluate the higher-complexity scenario against your specific load model.
When to Choose Each Scenario
Write-Heavy Transactional Platform
LeftWhen your team has limited operational maturity
CriticalWrite-Heavy Transactional Platform is rated advanced , accessible for teams without deep platform expertise.
When your architecture benefits from: write-heavy transactional workloads that emit downstream events (order placed, payment captured) benefit from the outbox pattern…
ModerateWrite-heavy transactional workloads that emit downstream events (order placed, payment captured) benefit from the outbox pattern to ensure events are published exactly when the database transaction commits: never before, never after. Key trade-off: Adds one INSERT per transaction to the outbox table: minor but nonzero write amplification. Operational note: Outbox table grows with write volume: implement TTL-based cleanup or partition pruning. Evidence: Payment processors like Stripe use outbox-style patterns to ensure webhook delivery matches transaction commits.
When your architecture benefits from: kafka is the standard downstream target for wal-based cdc pipelines: debezium captures database wal records and publishes them to…
ModerateKafka is the standard downstream target for WAL-based CDC pipelines: Debezium captures database WAL records and publishes them to Kafka topics, which downstream consumers process to maintain derived data stores, caches, and event-driven services. Key trade-off: Debezium replication slot holds WAL until consumed: disconnected Debezium can fill primary disk. Operational note: Debezium replication slot on PostgreSQL must be monitored: a lagging or disconnected Debezium causes replication slot WAL accumulation on the primary. Evidence: Debezium (Red Hat) captures PostgreSQL, MySQL, and MongoDB WAL and publishes to Kafka topics.
When your system requires decoupled async event processing
HighWrite-Heavy Transactional Platform includes event stream infrastructure (e.g., Kafka/Kinesis), enabling async decoupling between producers and consumers.
Realtime Collaborative Editor
RightWhen operational simplicity is a top priority
HighRealtime Collaborative Editor has lower operational complexity: fewer moving parts, easier to reason about and debug.
When stability and predictability matter most
CriticalRealtime Collaborative Editor carries lower overall risk weight per the advisor's assessment.
When you want to minimise monitoring setup overhead
ModerateRealtime Collaborative Editor has a lower observability burden: fewer watched metrics and monitoring targets.
When your architecture benefits from: a connection pool bounds the total database connections an application can open, preventing connection storms during traffic…
ModerateA connection pool bounds the total database connections an application can open, preventing connection storms during traffic spikes and protecting the database server from exceeding its connection limit. Key trade-off: Pooler becomes a new single point of failure if not replicated. Operational note: PgBouncer transaction-mode pooling is most effective for stateless APIs. Evidence: PgBouncer reduces PostgreSQL connections by 10–100x in typical deployments.
When to Avoid Each Scenario
Write-Heavy Transactional Platform
LeftWhen your team cannot mitigate: write amplification cascade
HighThis architecture is significantly exposed to Write Amplification Cascade. Each logical application write triggers multiple physical writes through index maintenance, WAL generation, MVCC versioning, and replication, causing actual disk IOPS to exceed the provisioned I/O ceiling while the logical write rate appears modest.
When your team cannot mitigate: wal saturation
HighThis architecture is significantly exposed to WAL Saturation. PostgreSQL WAL (Write-Ahead Log) generation rate exceeds wal_buffers flush capacity or downstream replica/WAL archive bandwidth, causing write transactions to stall waiting for WAL flush and replication lag to grow unboundedly.
When your team is early-stage or solo
HighWrite-Heavy Transactional Platform is rated 'advanced'. It requires experienced backend engineers or platform tooling to operate reliably at scale.
When you expect rapid growth within the next 12–18 months
ModerateThe advisor identifies 7 predicted bottlenecks for Write-Heavy Transactional Platform. Rapid growth will surface these limitations quickly.
Realtime Collaborative Editor
RightWhen your team cannot mitigate: connection pool exhaustion
HighThis architecture is significantly exposed to Connection Pool Exhaustion. All database connections in the pool are in use; new requests queue and then time out, causing cascading latency and errors across all dependent services.
When your team does not have platform engineering expertise
CriticalRealtime Collaborative Editor is rated 'expert only'. It requires deep operational expertise to run safely. Operating it without the right team leads to incidents.
When you expect rapid growth within the next 12–18 months
ModerateThe advisor identifies 3 predicted bottlenecks for Realtime Collaborative Editor. Rapid growth will surface these limitations quickly.
Team Fit
Solo developer or small startup
LeftWrite-Heavy Transactional Platform is more accessible for small teams. Fewer operational moving parts reduces on-call burden.
- ↳Validate that the simpler architecture can handle your projected load before committing.
Small product team (2–6 engineers)
LeftWrite-Heavy Transactional Platform suits small teams that need to move fast without deep platform tooling investment.
- ↳Consider Realtime Collaborative Editor only if your workload pattern specifically requires it.
Experienced backend team
DependsAn experienced team can operate either architecture. Choose based on workload fit, not team capability.
- ↳Prioritise alignment with existing infrastructure and tooling.
- ↳Realtime Collaborative Editor may require additional runbook coverage and alerting investment.
Platform engineering team or SRE-equipped organisation
RightA platform team can safely operate Realtime Collaborative Editor and will benefit from its more advanced scaling characteristics.
- ↳Ensure observability and alerting are configured before launch.
Migration Triggers
Migration Step 1
Both scenarios define a migration step at this stage. Write-Heavy Transactional Platform: triggered by 'Dual-write inconsistency events observed in production: DB w'. Realtime Collaborative Editor: triggered by 'User-visible edit conflicts > 5% of sessions; poll interval '.
Migration Step 2
Both scenarios define a migration step at this stage. Write-Heavy Transactional Platform: triggered by 'Primary write CPU > 70% sustained during peak windows; WAL v'. Realtime Collaborative Editor: triggered by 'Data loss complaints from users editing simultaneously; conf'.
Migration Step 3
Write-Heavy Transactional Platform has a defined migration; Realtime Collaborative Editor does not at this stage.
PgBouncer wait_queue > 0 sustained; application p99 write latency rising faster than PostgreSQL p99; pool_mode=transaction showing >80% utilization
Tier 1: Connection Pool Saturation: PgBouncer pool_size too small for write concurrency profile. Recommended evolution: Increase PgBouncer pool_size incrementally; profile transaction duration to right-size pool; consider separate pools for write-heavy and read-only workloads .
PostgreSQL checkpoint_completion_target warnings in logs; wal_buffers flushing more than once per second; pg_stat_bgwriter shows checkpoints_req rising; write p99 > 20ms without query explanation
Tier 2: WAL and Checkpoint Pressure: Write rate exceeding PostgreSQL's WAL flush and checkpoint throughput. Recommended evolution: Tune checkpoint_completion_target to 0.9; increase wal_buffers to 64MB; move PostgreSQL WAL to a dedicated NVMe volume separate from data directory .
Server memory growing with active connections; file descriptor limits approached; new WebSocket connections refused
Tier 1: WebSocket Connection Ceiling: WebSocket server process connection limit or OS file descriptor ceiling. Recommended evolution: Increase file descriptor limits (ulimit); move to dedicated WebSocket server tier; implement connection multiplexing (multiple documents per connection where safe) .
Consecutive writes to the same document causing lock contention; write latency rising; auto-save batching queue depth increasing
Tier 2: Database Write Contention: High-frequency auto-save operations conflicting at the document row level; row-level locking under concurrent user edits . Recommended evolution: Move to operational transformation or CRDT-based conflict resolution; batch writes and resolve conflicts in-process before database commit; consider append-only event log for document operations .
Readiness Requirements
PostgreSQL: scenario includes high_write_throughput or write_heavy workload
BothDeploy PgBouncer in transaction-mode pooling before relying on vertical scaling
Required maturity: mid_level
Runbooks and alerting for high-severity risks
Both3 high-severity risks identified. Each requires a documented runbook, alerting threshold, and on-call response procedure before running in production.
Apache Kafka: scenario has team_maturity below senior
LeftKafka operational complexity requires dedicated expertise: consider MSK or Confluent Cloud to reduce ops burden
Required maturity: senior
Apache Kafka: scenario uses Kafka for event streaming or CDC
LeftSet min.insync.replicas=2 with acks=all; monitor consumer lag as primary health signal
Required maturity: senior
Event stream operations expertise
LeftThis architecture includes event stream infrastructure (Kafka, Kinesis, or similar). Operations requires consumer group management, partition assignment, dead-letter handling, and lag monitoring.
Required maturity: platform_engineering_team
Minimum team maturity: Experienced Backend Team
LeftThis scenario has high operational complexity. It is recommended for Experienced Backend Team teams or higher.
Required maturity: experienced_backend_team
Replica lag monitoring and lag-aware routing
LeftRead replicas must be monitored for replication lag. The application router must include a max_lag_ms threshold; queries above that threshold must be redirected to the primary.
Cache sizing and eviction policy configuration
RightRedis or equivalent cache requires correct maxmemory configuration, eviction policy selection (allkeys-lru is common), and cold-start warming strategy after restarts.
Minimum team maturity: Enterprise Architecture Team
RightThis scenario has expert operational complexity. It is recommended for Enterprise Architecture Team teams or higher.
Required maturity: enterprise_architecture_team
Redis: scenario has read_heavy workload with high cache miss risk
RightImplement cache stampede protection (probabilistic early expiry or locking) to prevent thundering herd on cold start
Required maturity: junior
Redis: scenario relies on Redis for data that cannot be re-derived
RightRedis is not a durable store: add persistence layer or treat Redis as expendable cache only
Required maturity: junior
Generator Constraints
Write-Heavy Transactional Platform
LeftGenerator relevance documented but not yet production-ready.
For write-heavy product briefs requiring ACID guarantees and event durability, the generator should propose the PostgreSQL + outbox + WAL CDC + Kafka composition as the canonical starting point. Dual-write (synchronous DB + Kafka publish) must be listed as an anti-pattern with explicit consistency hazard documentation. PgBouncer must be included by default: not as an optional enhancement.
Realtime Collaborative Editor
RightGenerator relevance documented but not yet production-ready.
When generating architectures for collaborative editing or presence-aware applications, the WebSocket + Redis pub/sub + PostgreSQL composition should be presented as the baseline. CRDT/OT conflict resolution should be surfaced as a required upgrade path before launch in production collaborative contexts. The knowledge base currently lacks detailed CRDT/OT pattern entries: these should be added as the knowledge base expands.
Supporting Evidence
| Type | Reference | Explanation |
|---|---|---|
| Comparison | compare_write_heavy_transactional_platform_vs_realtime_collaborative_editor | Full comparison of Write-Heavy Transactional Platform vs Realtime Collaborative Editor: 6 dimensions, 3 shared components, 0 shared risks. |
| Advisor | advisor_write_heavy_transactional_platform | Advisor for Write-Heavy Transactional Platform: 2 strengths, 4 risks, maturity: advanced. |
| Advisor | advisor_realtime_collaborative_editor | Advisor for Realtime Collaborative Editor: 1 strengths, 1 risks, maturity: expert_only. |
| Scenario | write_heavy_transactional_platform | Scenario 'Write-Heavy Transactional Platform': 4 scaling thresholds, 3 migration paths, complexity: high. |
| Scenario | realtime_collaborative_editor | Scenario 'Realtime Collaborative Editor': 3 scaling thresholds, 2 migration paths, complexity: expert. |
| Risk Path | prop_workload_profile_write_heavy_transactional_risk_wal_saturation | Write-Heavy Transactional → WAL Saturation |
| Risk Path | prop_workload_profile_write_heavy_transactional_risk_lock_contention | Write-Heavy Transactional → Lock Contention |
| Risk Path | prop_technology_profile_redis_risk_connection_exhaustion | Redis → Connection Pool Exhaustion |
| Risk Path | prop_workload_profile_write_heavy_transactional_risk_wal_saturation | Referenced by the operational risk comparison dimension. |
| Risk Path | prop_workload_profile_write_heavy_transactional_risk_lock_contention | Referenced by the operational risk comparison dimension. |
Limitations
- ·Decision guidance is grounded in YAML knowledge only. Not measured from any production system.
- ·Recommendations are deterministic heuristics based on structured knowledge. Your specific workload, team profile, and business context may lead to different conclusions.
- ·Generator constraints are preliminary. No scenario should be treated as production generation-ready at this stage.
Comparison complete
Profile, topology, simulation, advisor, comparison, and decision path are ready. Your architecture decision is grounded in structured knowledge and deterministic reasoning.