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 Financial Ledger Platform
Write-Heavy Transactional Platform is the simpler architecture. Write-Heavy Transactional Platform carries lower operational risk. They share 6 component(s). Financial Ledger Platform has 2 unique risk(s); Write-Heavy Transactional Platform has 2.
12
Nodes
9
Edges
4
Risks
2
Seeds
6
Strengths
4
Adv. Risks
11
Nodes
5
Edges
4
Risks
3
Seeds
2
Strengths
4
Adv. Risks
Comparison Dimensions
Complexity
Financial Ledger Platform
expert complexity, 12 nodes, 9 edges, 4 risks, 2 simulation seeds
Write-Heavy Transactional Platform
high complexity, 11 nodes, 5 edges, 4 risks, 3 simulation seeds
Write-Heavy Transactional Platform is simpler: high operational complexity with 11 topology nodes vs 12 for Financial Ledger Platform.
Operational Risk
Financial Ledger Platform
4 risks (top: high), 4 high/critical, 0 confirmed by simulation
Write-Heavy Transactional Platform
4 risks (top: high), 3 high/critical, 0 confirmed by simulation
Write-Heavy Transactional Platform has lower operational risk: weighted severity score 14 vs 16 (0 vs 0 simulation-confirmed).
Scalability
Financial Ledger Platform
4 scaling thresholds, 3 migration paths, 4 advisor scaling signals
Write-Heavy Transactional Platform
4 scaling thresholds, 3 migration paths, 4 advisor scaling signals
Both scenarios have comparable scaling paths. The best choice depends on your specific growth trajectory. Financial Ledger Platform and Write-Heavy Transactional Platform offer similar numbers of defined evolution steps.
Operational Maturity
Financial Ledger Platform
Advisor assessment: Advanced; recommended team: Platform Engineering Team; 7 operational requirements
Write-Heavy Transactional Platform
Advisor assessment: Advanced; recommended team: Experienced Backend Team; 7 operational requirements
Both scenarios require equivalent team maturity: Advanced.
Observability
Financial Ledger Platform
4 watched metrics, 5 observability recommendations, 2 simulation seeds
Write-Heavy Transactional Platform
6 watched metrics, 4 observability recommendations, 3 simulation seeds
Both scenarios have similar observability requirements: 4 and 6 watched metrics respectively.
Generator Readiness
Financial Ledger Platform
generator relevance documented; topology generation relevance noted; simulation relevance noted; 2 seeds with generator notes
Write-Heavy Transactional Platform
generator relevance documented; topology generation relevance noted; simulation relevance noted; 3 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 (6)
Only in Financial Ledger Platform (6)
Only in Write-Heavy Transactional Platform (5)
Operational Risks
Only in Financial Ledger Platform (2)
Only in Write-Heavy Transactional Platform (2)
Consistency Guarantees
Both provide (1)
Moving from Financial Ledger Platform to Write-Heavy Transactional Platform
Moving from Write-Heavy Transactional Platform to Financial Ledger Platform
Both scenarios claim 1 shared guarantee(s): atomic_multi_object.
Tradeoff Summary
Complexity vs Risk
Financial Ledger Platform has expert complexity. Write-Heavy Transactional Platform has high complexity. Simpler systems often carry different (not necessarily fewer) risks.
Financial Ledger Platform
Financial Ledger Platform: 4 risks (top: high), 4 high/critical, 0 confirmed by simulation
Write-Heavy Transactional Platform
Write-Heavy Transactional Platform: 4 risks (top: high), 3 high/critical, 0 confirmed by simulation
Scaling Path
Financial Ledger Platform offers 4 defined scaling thresholds. Write-Heavy Transactional Platform offers 4. More defined paths means clearer evolution steps but also more anticipated growth.
Financial Ledger Platform
4 scaling thresholds, 3 migration paths, 4 advisor scaling signals
Write-Heavy Transactional Platform
4 scaling thresholds, 3 migration paths, 4 advisor scaling signals
Team Maturity Requirement
Write-Heavy Transactional Platform can be operated by a less experienced team. Financial Ledger Platform requires deeper operational expertise.
Financial Ledger Platform
Advisor assessment: Advanced; recommended team: Platform Engineering Team; 7 operational requirements
Write-Heavy Transactional Platform
Advisor assessment: Advanced; recommended team: Experienced Backend Team; 7 operational requirements
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.
Financial Ledger Platform
6 strengths, 4 risks
Write-Heavy Transactional Platform
2 strengths, 4 risks
Migration Considerations
Migration Step 1
Financial Ledger Platform
Mutable account balance table with no event history → Event sourced ledger with append-only events and projected balance view
Write-Heavy Transactional Platform
Single PostgreSQL with synchronous dual-write (DB + Kafka in application code) → PostgreSQL + outbox pattern + WAL CDC relay to Kafka
Both scenarios define a migration step at this stage. Financial Ledger Platform: triggered by 'Audit requirement to reconstruct account state at any histor'. Write-Heavy Transactional Platform: triggered by 'Dual-write inconsistency events observed in production: DB w'.
Migration Step 2
Financial Ledger Platform
Synchronous Kafka publish in transaction (dual-write pattern) → Outbox pattern with CDC relay to Kafka
Write-Heavy Transactional Platform
PostgreSQL + PgBouncer + outbox + Kafka CDC → Domain-partitioned PostgreSQL + separate write services per partition
Both scenarios define a migration step at this stage. Financial Ledger Platform: triggered by 'Kafka publish failures causing financial transaction rollbac'. Write-Heavy Transactional Platform: triggered by 'Primary write CPU > 70% sustained during peak windows; WAL v'.
Migration Step 3
Financial Ledger Platform
Single PostgreSQL primary serving all reads and writes → CQRS with separate read model and write model
Write-Heavy Transactional Platform
PostgreSQL + Kafka CDC → Event sourcing: append-only event log with read model projections
Both scenarios define a migration step at this stage. Financial Ledger Platform: triggered by 'Financial dashboard query p99 > 500ms causing dashboard-driv'. Write-Heavy Transactional Platform: triggered by 'Audit completeness requirements grow beyond point-in-time ba'.
Advisor Notes
Strength: The outbox pattern eliminates split-brain between a database write and a message broker publish by writing both the domain record…
The outbox pattern eliminates split-brain between a database write and a message broker publish by writing both the domain record and the outbox event in a single ACID transaction, ensuring events are published if and only if the database write committed. Key trade-off: Adds ~1ms write overhead per transaction for the outbox INSERT. Operational note: Outbox table requires a relay process: this is an additional operational component to monitor. Evidence: Atomic write to both business table and outbox table in one transaction: no window for inconsistency.
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): Lock Contention
Concurrent writers to the same rows serialize behind each other's row locks, so latency is set not by the work a transaction does but by how long it waits for the writers ahead of it. On a hot row the queue depth, and therefore the tail latency, grows with concurrency while throughput flattens. Blocked writers hold connections open, so a single contended row can drain the connection pool as a secondary failure.
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.
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
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.
- ·5 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 Financial Ledger Platform
Write-Heavy Transactional Platform leads on 2 weighted dimension(s): Complexity, Operational Risk. Weighted score: 5.0 vs 1.5 for Financial Ledger Platform.
Decision Intelligence
Architecture Decision Path
Structured reasoning for choosing between Financial Ledger Platform and Write-Heavy Transactional 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 Financial Ledger Platform
Write-Heavy Transactional Platform leads on 2 weighted dimension(s): Complexity, Operational Risk. Weighted score: 5.0 vs 1.5 for Financial Ledger Platform. The architectures share 6 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
RightWrite-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 Financial Ledger Platform (advanced rating)?
If Yes
Your team can operate Financial Ledger 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: 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.
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 12 for Financial Ledger 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
Financial Ledger Platform
LeftWhen your architecture benefits from: the outbox pattern eliminates split-brain between a database write and a message broker publish by writing both the domain record…
ModerateThe outbox pattern eliminates split-brain between a database write and a message broker publish by writing both the domain record and the outbox event in a single ACID transaction, ensuring events are published if and only if the database write committed. Key trade-off: Adds ~1ms write overhead per transaction for the outbox INSERT. Operational note: Outbox table requires a relay process: this is an additional operational component to monitor. Evidence: Atomic write to both business table and outbox table in one transaction: no window for inconsistency.
When your architecture benefits from: financial transaction workloads benefit from event sourcing because the event log provides an immutable audit trail, enables…
ModerateFinancial transaction workloads benefit from event sourcing because the event log provides an immutable audit trail, enables temporal queries (balance at any past date), and makes the derivation of current state fully traceable: meeting regulatory requirements that state-mutation databases cannot satisfy. Key trade-off: Event log growth is unbounded for long-lived accounts: snapshot and archival strategy required. Operational note: Financial event logs must be retained for 7-10 years (regulatory requirement): plan storage accordingly. Evidence: PCI-DSS and SOX require immutable audit trails: event sourcing provides this structurally.
When your system requires decoupled async event processing
HighFinancial Ledger Platform includes event stream infrastructure (e.g., Kafka/Kinesis), enabling async decoupling between producers and consumers.
Write-Heavy Transactional Platform
RightWhen 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 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.
When to Avoid Each Scenario
Financial Ledger Platform
LeftWhen your team cannot mitigate: lock contention
HighThis architecture is significantly exposed to Lock Contention. Concurrent writers to the same rows serialize behind each other's row locks, so latency is set not by the work a transaction does but by how long it waits for the writers ahead of it. On a hot row the queue depth, and therefore the tail latency, grows with concurrency while throughput flattens. Blocked writers hold connections open, so a single contended row can drain the connection pool as a secondary failure.
When your team cannot mitigate: split-brain
HighThis architecture is significantly exposed to Split-Brain. A failover mechanism promotes a new leader without confirming the old one has stopped, so two nodes simultaneously believe they hold the primary role and both accept writes. The two histories diverge, and when the partition that triggered the failover heals, one set of committed transactions must be discarded.
When your team is early-stage or solo
HighFinancial Ledger 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 Financial Ledger Platform. Rapid growth will surface these limitations quickly.
Write-Heavy Transactional Platform
RightWhen 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.
Team Fit
Solo developer or small startup
LeftFinancial Ledger 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)
LeftFinancial Ledger Platform suits small teams that need to move fast without deep platform tooling investment.
- ↳Consider Write-Heavy Transactional 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.
- ↳Write-Heavy Transactional Platform may require additional runbook coverage and alerting investment.
Platform engineering team or SRE-equipped organisation
RightA platform team can safely operate Write-Heavy Transactional 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. Financial Ledger Platform: triggered by 'Audit requirement to reconstruct account state at any histor'. Write-Heavy Transactional Platform: triggered by 'Dual-write inconsistency events observed in production: DB w'.
Migration Step 2
Both scenarios define a migration step at this stage. Financial Ledger Platform: triggered by 'Kafka publish failures causing financial transaction rollbac'. Write-Heavy Transactional Platform: triggered by 'Primary write CPU > 70% sustained during peak windows; WAL v'.
Migration Step 3
Both scenarios define a migration step at this stage. Financial Ledger Platform: triggered by 'Financial dashboard query p99 > 500ms causing dashboard-driv'. Write-Heavy Transactional Platform: triggered by 'Audit completeness requirements grow beyond point-in-time ba'.
pg_locks shows contended rows on accounts table; write p99 > 50ms; deadlock errors in application logs; pg_stat_activity showing many transactions waiting for RowExclusiveLock on the same account rows
Tier 1: Hot Account Lock Contention: Concurrent debit/credit transactions competing for the same account row versions. Recommended evolution: Implement optimistic locking with version column and retry; or queue concurrent updates for the same account entity through an account-scoped serialization queue at the application layer; or partition the accounts table by account range .
Write p99 > 100ms with synchronous_commit = remote_apply; replica WAL apply lag visible in pg_stat_replication; network jitter between primary and replica causing write latency spikes correlating with replication ACK delays
Tier 2: Synchronous Replication Write Latency: Synchronous replication write-ahead wait amplifying network latency for every committed transaction. Recommended evolution: Co-locate primary and replica in the same availability zone for lowest replication RTT; tune wal_sender_timeout and recovery_min_apply_delay; evaluate whether synchronous_commit = on (durable to primary WAL only) is acceptable for your regulatory risk model .
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 .
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
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
Replica lag monitoring and lag-aware routing
BothRead 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.
Runbooks and alerting for high-severity risks
Both4 high-severity risks identified. Each requires a documented runbook, alerting threshold, and on-call response procedure before running in production.
Minimum team maturity: Platform Engineering Team
LeftThis scenario has expert operational complexity. It is recommended for Platform Engineering Team teams or higher.
Required maturity: platform_engineering_team
Minimum team maturity: Experienced Backend Team
RightThis scenario has high operational complexity. It is recommended for Experienced Backend Team teams or higher.
Required maturity: experienced_backend_team
Generator Constraints
Financial Ledger Platform
LeftGenerator relevance documented but not yet production-ready.
For financial product briefs, the generator must output event sourcing + outbox + CQRS as mandatory components, not optional enhancements. synchronous_commit settings, replication standby configuration, and Kafka min.insync.replicas must be generated as explicit configuration, not left as defaults. Two-phase commit should be presented as a cross-service coordination option with explicit complexity warnings.
Write-Heavy Transactional Platform
RightGenerator 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.
Supporting Evidence
| Type | Reference | Explanation |
|---|---|---|
| Comparison | compare_financial_ledger_platform_vs_write_heavy_transactional_platform | Full comparison of Financial Ledger Platform vs Write-Heavy Transactional Platform: 6 dimensions, 6 shared components, 2 shared risks. |
| Advisor | advisor_financial_ledger_platform | Advisor for Financial Ledger Platform: 6 strengths, 4 risks, maturity: advanced. |
| Advisor | advisor_write_heavy_transactional_platform | Advisor for Write-Heavy Transactional Platform: 2 strengths, 4 risks, maturity: advanced. |
| Scenario | financial_ledger_platform | Scenario 'Financial Ledger Platform': 4 scaling thresholds, 3 migration paths, complexity: expert. |
| Scenario | write_heavy_transactional_platform | Scenario 'Write-Heavy Transactional Platform': 4 scaling thresholds, 3 migration paths, complexity: high. |
| Risk Path | prop_workload_profile_write_heavy_transactional_risk_lock_contention | Write-Heavy Transactional → Lock Contention |
| Risk Path | prop_architecture_pattern_two_phase_commit_risk_split_brain | Two-Phase Commit (2PC) → Split-Brain |
| 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_workload_profile_write_heavy_transactional_risk_lock_contention | Referenced by the operational risk comparison dimension. |
| Risk Path | prop_architecture_pattern_two_phase_commit_risk_split_brain | 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.