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
Write-Heavy Transactional Platform is both simpler and lower-risk than Social Feed Platform
Write-Heavy Transactional Platform is the simpler architecture. Write-Heavy Transactional Platform carries lower operational risk. They share 4 component(s). Write-Heavy Transactional Platform has 4 unique risk(s); Social Feed Platform has 5.
11
Nodes
5
Edges
4
Risks
3
Seeds
2
Strengths
4
Adv. Risks
18
Nodes
0
Edges
5
Risks
4
Seeds
0
Strengths
5
Adv. Risks
Comparison Dimensions
Complexity
Write-Heavy Transactional Platform
high complexity, 11 nodes, 5 edges, 4 risks, 3 simulation seeds
Social Feed Platform
high complexity, 18 nodes, 0 edges, 5 risks, 4 simulation seeds
Write-Heavy Transactional Platform is simpler: high operational complexity with 11 topology nodes vs 18 for Social Feed Platform.
Operational Risk
Write-Heavy Transactional Platform
4 risks (top: high), 3 high/critical, 0 confirmed by simulation
Social Feed Platform
5 risks (top: high), 4 high/critical, 1 confirmed by simulation
Write-Heavy Transactional Platform has lower operational risk: weighted severity score 14 vs 18 (0 vs 1 simulation-confirmed).
Scalability
Write-Heavy Transactional Platform
4 scaling thresholds, 3 migration paths, 4 advisor scaling signals
Social Feed Platform
4 scaling thresholds, 3 migration paths, 6 advisor scaling signals
Social Feed Platform has more defined scaling paths: 4 thresholds and 3 migration paths.
Operational Maturity
Write-Heavy Transactional Platform
Advisor assessment: Advanced; recommended team: Experienced Backend Team; 7 operational requirements
Social Feed Platform
Advisor assessment: Advanced; recommended team: Experienced Backend Team; 12 operational requirements
Both scenarios require equivalent team maturity: Advanced.
Observability
Write-Heavy Transactional Platform
6 watched metrics, 4 observability recommendations, 3 simulation seeds
Social Feed Platform
12 watched metrics, 6 observability recommendations, 4 simulation seeds
Write-Heavy Transactional Platform has lower observability burden: 6 watched metrics vs 12.
Generator Readiness
Write-Heavy Transactional Platform
generator relevance documented; topology generation relevance noted; simulation relevance noted; 3 seeds with generator notes
Social Feed Platform
generator relevance documented; topology generation relevance noted; simulation relevance noted; 4 seeds with generator notes
Both scenarios have comparable generator readiness at this stage. Generator support is preliminary. Neither scenario should be treated as fully generation-ready.
Architecture Components
Shared (4)
Only in Write-Heavy Transactional Platform (7)
Only in Social Feed Platform (14)
Operational Risks
Only in Write-Heavy Transactional Platform (4)
Consistency Guarantees
Only Write-Heavy Transactional Platform (1)
Moving from Write-Heavy Transactional Platform to Social Feed Platform
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. Social Feed Platform has high 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
Social Feed Platform
Social Feed Platform: 5 risks (top: high), 4 high/critical, 1 confirmed by simulation
Scaling Path
Write-Heavy Transactional Platform offers 4 defined scaling thresholds. Social Feed Platform offers 4. 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
Social Feed Platform
4 scaling thresholds, 3 migration paths, 6 advisor scaling signals
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
Social Feed Platform
0 strengths, 5 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
Social Feed Platform
Monolithic feed built on PostgreSQL timeline queries → Redis pre-materialized feed with Kafka async fan-out workers
Both scenarios define a migration step at this stage. Write-Heavy Transactional Platform: triggered by 'Dual-write inconsistency events observed in production: DB w'. Social Feed Platform: triggered by 'PostgreSQL timeline read query p99 > 500ms; query plan for "'.
Migration Step 2
Write-Heavy Transactional Platform
PostgreSQL + PgBouncer + outbox + Kafka CDC → Domain-partitioned PostgreSQL + separate write services per partition
Social Feed Platform
Uniform fan-out-on-write for all accounts → Hybrid fan-out model (fan-out-on-write for <10k followers, fan-out-on-read for high-follower accounts)
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'. Social Feed Platform: triggered by 'Fan-out worker queue lag during celebrity post events > 5 mi'.
Migration Step 3
Write-Heavy Transactional Platform
PostgreSQL + Kafka CDC → Event sourcing: append-only event log with read model projections
Social Feed Platform
Single Redis primary for all feed data → Redis Cluster with feed keys sharded by user_id range
Both scenarios define a migration step at this stage. Write-Heavy Transactional Platform: triggered by 'Audit completeness requirements grow beyond point-in-time ba'. Social Feed Platform: triggered by 'Redis memory utilization approaching 80% of a single node; R'.
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.
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): Thundering Herd (Cache Stampede)
When a popular cached key expires or a service recovers from downtime, all requests that were waiting or arrive simultaneously miss the cache and hit the origin database concurrently, producing a request spike that can overwhelm the database within seconds.
Shared Operational Requirements
Both scenarios require: Apache Kafka: scenario has team_maturity below senior, Apache Kafka: scenario uses Kafka for event streaming or CDC, Event stream operations expertise.
Supporting Evidence · 16 items
Coverage Warnings
- ⚠Social Feed Platform: 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.
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.
- ·6 seed type(s) across both scenarios do not have execution previews. Risk confirmation for those types is unavailable.
Write-Heavy Transactional Platform is the recommended starting point over Social Feed Platform
Write-Heavy Transactional Platform leads on 3 weighted dimension(s): Complexity, Operational Risk, Observability. Weighted score: 5.5 vs 2.0 for Social Feed Platform.
Decision Intelligence
Architecture Decision Path
Structured reasoning for choosing between Write-Heavy Transactional Platform and Social Feed Platform. Every condition and trigger traces back to comparison dimensions, advisor insights, and topology evidence.
Write-Heavy Transactional Platform is the recommended starting point over Social Feed Platform
Write-Heavy Transactional Platform leads on 3 weighted dimension(s): Complexity, Operational Risk, Observability. Weighted score: 5.5 vs 2.0 for Social Feed Platform. The architectures share 4 component(s), reducing migration cost if you switch later. Write-Heavy Transactional Platform is the operationally simpler choice.
Where to Start
Start with Write-Heavy Transactional Platform
LeftWrite-Heavy Transactional Platform 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: high complexity, 11 nodes, 5 edges, 4 risks, 3 simulation seeds
Migrate when:
- PgBouncer wait_queue > 0 sustained; application p99 write latency rising faster than PostgreSQL p99; pool_mode=transaction showing >80% utilization → 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 → Tune checkpoint_completion_target to 0.9; increase wal_buffers to 64MB; move PostgreSQL WAL to a dedicated NVMe volume separate from data directory
- pg_locks shows contended rows with wait events > 5ms; write throughput plateauing despite available CPU; deadlock errors appearing in application logs → Partition the hot table by entity range or hash; introduce optimistic locking with retry for high-contention entities; consider queue-per-entity serialization via application-level lock tokens
Decision Flow
Does your team have the operational maturity to run Write-Heavy Transactional Platform (advanced rating)?
If Yes
Your team can operate Write-Heavy Transactional Platform. Continue to Step 2 to refine based on risk tolerance and workload fit.
If No
Prefer the lower-maturity option: right scenario.
Is operational stability and minimising production risk your primary concern over feature richness or scalability ceiling?
If Yes
Prefer Write-Heavy Transactional Platform: 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: Kafka consumer group lag for fan-out worker group growing steadily; feed propagation latency (time from post write to follower feed update) exceeding 30s p95; Redis write rate on feed keys elevated but not saturated; post activity rate normal ?
If Yes
Right scenario has more defined scaling evolution paths for this growth pattern.
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.
Is operational simplicity (fewer moving parts, easier debugging, lower ops burden) more important than maximum architectural capability?
If Yes
Write-Heavy Transactional Platform is the simpler choice: Write-Heavy Transactional Platform is simpler: high operational complexity with 11 topology nodes vs 18 for Social Feed 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 operational simplicity is a top priority
HighWrite-Heavy Transactional Platform has lower operational complexity: fewer moving parts, easier to reason about and debug.
When stability and predictability matter most
CriticalWrite-Heavy Transactional Platform carries lower overall risk weight per the advisor's assessment.
When you want to minimise monitoring setup overhead
ModerateWrite-Heavy Transactional Platform has a lower observability burden: fewer watched metrics and monitoring targets.
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.
Social Feed Platform
RightWhen you need well-defined scaling thresholds and migration paths
HighSocial Feed Platform has more documented scaling evolution steps (4 thresholds, 3 migration paths).
When your system requires decoupled async event processing
HighSocial Feed Platform includes event stream infrastructure (e.g., Kafka/Kinesis), enabling async decoupling between producers and consumers.
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.
Social Feed Platform
RightWhen your team cannot mitigate: thundering herd (cache stampede)
HighThis architecture is significantly exposed to Thundering Herd (Cache Stampede). When a popular cached key expires or a service recovers from downtime, all requests that were waiting or arrive simultaneously miss the cache and hit the origin database concurrently, producing a request spike that can overwhelm the database within seconds.
When your team cannot mitigate: queue backlog accumulation
HighThis architecture is significantly exposed to Queue Backlog Accumulation. Message queue or event stream consumer processing rate falls below producer write rate, causing consumer lag to grow unboundedly: eventually leading to increased end-to-end latency, producer backpressure, data expiry, or queue resource exhaustion.
When your team is early-stage or solo
HighSocial Feed 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 Social Feed Platform. 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 Social Feed Platform 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.
- ↳Social Feed Platform may require additional runbook coverage and alerting investment.
Platform engineering team or SRE-equipped organisation
RightA platform team can safely operate Social Feed Platform 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'. Social Feed Platform: triggered by 'PostgreSQL timeline read query p99 > 500ms; query plan for "'.
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'. Social Feed Platform: triggered by 'Fan-out worker queue lag during celebrity post events > 5 mi'.
Migration Step 3
Both scenarios define a migration step at this stage. Write-Heavy Transactional Platform: triggered by 'Audit completeness requirements grow beyond point-in-time ba'. Social Feed Platform: triggered by 'Redis memory utilization approaching 80% of a single node; R'.
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 .
Kafka consumer group lag for fan-out worker group growing steadily; feed propagation latency (time from post write to follower feed update) exceeding 30s p95; Redis write rate on feed keys elevated but not saturated; post activity rate normal
Tier 1: Fan-Out Worker Queue Backlog: Fan-out worker pool undersized for burst post activity or a celebrity post creating a sustained high-fan-out event. Recommended evolution: Add fan-out worker replicas; implement fan-out cost routing: route high-follower-count fan-out events to a dedicated high-cost worker pool with separate Kafka consumer group and Redis write quota; use follower count threshold (e.g., >50k followers) as the routing decision. Monitor fan-out cost per post as a first-class metric. .
Redis memory utilization > 75%; eviction rate rising; cache miss rate on feed reads increasing; cold feed read fallback queries appearing in PostgreSQL slow query log; feed read p99 > 100ms despite Redis being online
Tier 2: Redis Feed Memory Ceiling: Redis feed list storage approaching memory limit; feed items being evicted before TTL; or feed list cap set too high for available memory. Recommended evolution: Reduce feed list cap from current value toward 100–150 items; increase Redis cluster capacity or shard feed keys by user_id range across multiple Redis primaries; implement tiered feed storage: hot recent items in Redis, older items fetched from PostgreSQL on demand with explicit product UX affordance .
Readiness Requirements
Apache Kafka: scenario has team_maturity below senior
BothKafka 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
BothSet min.insync.replicas=2 with acks=all; monitor consumer lag as primary health signal
Required maturity: senior
Event stream operations expertise
BothThis 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
BothThis scenario has high operational complexity. It is recommended for Experienced Backend Team teams or higher.
Required maturity: experienced_backend_team
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.
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.
RabbitMQ: scenario has high_throughput_writes exceeding 50k messages/second
RightRabbitMQ throughput ceiling may be insufficient: evaluate Kafka for sustained high-throughput event streams
Required maturity: mid_level
RabbitMQ: scenario requires event replay or consumer catch-up from historical messages
RightRabbitMQ deletes acknowledged messages: use Kafka for replay-capable event streaming
Required maturity: mid_level
RabbitMQ: scenario uses classic mirrored queues for HA
RightMigrate to quorum queues: classic mirrored queues have known split-brain behavior under network partition
Required maturity: mid_level
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.
Social Feed Platform
RightGenerator relevance documented but not yet production-ready.
For social product briefs with user-follows-user semantics, the generator must produce the hybrid fan-out composition: outbox → Kafka → fan-out worker → Redis feed list. The generator must include the follower count routing threshold as a first-class configuration parameter, and must generate the feed merge logic for high-follower-count accounts at read time. Redis feed list schema (LPUSH + LTRIM pattern with feed cap) must be generated with explicit cap configuration and PostgreSQL fallback handling.
Supporting Evidence
| Type | Reference | Explanation |
|---|---|---|
| Comparison | compare_write_heavy_transactional_platform_vs_social_feed_platform | Full comparison of Write-Heavy Transactional Platform vs Social Feed Platform: 6 dimensions, 4 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_social_feed_platform | Advisor for Social Feed Platform: 0 strengths, 5 risks, maturity: advanced. |
| Scenario | write_heavy_transactional_platform | Scenario 'Write-Heavy Transactional Platform': 4 scaling thresholds, 3 migration paths, complexity: high. |
| Scenario | social_feed_platform | Scenario 'Social Feed Platform': 4 scaling thresholds, 3 migration paths, complexity: high. |
| 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_architecture_pattern_fan_out_on_write_risk_fanout_amplification | Fan-Out on Write → Fanout Amplification |
| Risk Path | prop_technology_profile_redis_risk_thundering_herd | Redis → Thundering Herd (Cache Stampede) |
| 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.