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
Financial Ledger Platform vs E-Commerce Order Platform: E-Commerce Order Platform is the simpler choice
E-Commerce Order Platform is the simpler architecture. Financial Ledger Platform carries lower operational risk. They share 7 component(s). Financial Ledger Platform has 3 unique risk(s); E-Commerce Order Platform has 5.
12
Nodes
9
Edges
4
Risks
2
Seeds
6
Strengths
4
Adv. Risks
21
Nodes
0
Edges
6
Risks
2
Seeds
0
Strengths
6
Adv. Risks
Comparison Dimensions
Complexity
Financial Ledger Platform
expert complexity, 12 nodes, 9 edges, 4 risks, 2 simulation seeds
E-Commerce Order Platform
high complexity, 21 nodes, 0 edges, 6 risks, 2 simulation seeds
E-Commerce Order Platform is simpler: high operational complexity with 21 topology nodes vs 12 for Financial Ledger Platform.
Operational Risk
Financial Ledger Platform
4 risks (top: high), 4 high/critical, 0 confirmed by simulation
E-Commerce Order Platform
6 risks (top: high), 5 high/critical, 0 confirmed by simulation
Financial Ledger Platform has lower operational risk: weighted severity score 16 vs 22 (0 vs 0 simulation-confirmed).
Scalability
Financial Ledger Platform
4 scaling thresholds, 3 migration paths, 4 advisor scaling signals
E-Commerce Order Platform
4 scaling thresholds, 3 migration paths, 4 advisor scaling signals
E-Commerce Order Platform has more defined scaling paths: 4 thresholds and 3 migration paths.
Operational Maturity
Financial Ledger Platform
Advisor assessment: Advanced; recommended team: Platform Engineering Team; 7 operational requirements
E-Commerce Order Platform
Advisor assessment: Advanced; recommended team: Experienced Backend Team; 15 operational requirements
Both scenarios require equivalent team maturity: Advanced.
Observability
Financial Ledger Platform
4 watched metrics, 5 observability recommendations, 2 simulation seeds
E-Commerce Order Platform
4 watched metrics, 6 observability recommendations, 2 simulation seeds
Financial Ledger Platform has lower observability burden: 4 watched metrics vs 4.
Generator Readiness
Financial Ledger Platform
generator relevance documented; topology generation relevance noted; simulation relevance noted; 2 seeds with generator notes
E-Commerce Order Platform
generator relevance documented; topology generation relevance noted; simulation relevance noted; 2 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 (7)
Only in Financial Ledger Platform (5)
Only in E-Commerce Order Platform (14)
Operational Risks
Shared (1)
Only in Financial Ledger Platform (3)
Only in E-Commerce Order Platform (5)
Consistency Guarantees
Only Financial Ledger Platform (1)
Moving from Financial Ledger Platform to E-Commerce Order 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 'Financial Ledger Platform' claims: atomic_multi_object.
Tradeoff Summary
Complexity vs Risk
Financial Ledger Platform has expert complexity. E-Commerce Order 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
E-Commerce Order Platform
E-Commerce Order Platform: 6 risks (top: high), 5 high/critical, 0 confirmed by simulation
Scaling Path
Financial Ledger Platform offers 4 defined scaling thresholds. E-Commerce Order 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
E-Commerce Order Platform
4 scaling thresholds, 3 migration paths, 4 advisor scaling signals
Team Maturity Requirement
E-Commerce Order 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
E-Commerce Order Platform
Advisor assessment: Advanced; recommended team: Experienced Backend Team; 15 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
E-Commerce Order Platform
0 strengths, 6 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
E-Commerce Order Platform
Synchronous checkout with direct database payment insert and synchronous payment API call → Saga-orchestrated checkout with outbox-based fulfillment events
Both scenarios define a migration step at this stage. Financial Ledger Platform: triggered by 'Audit requirement to reconstruct account state at any histor'. E-Commerce Order Platform: triggered by 'Payment provider timeout causing full checkout rollback and '.
Migration Step 2
Financial Ledger Platform
Synchronous Kafka publish in transaction (dual-write pattern) → Outbox pattern with CDC relay to Kafka
E-Commerce Order Platform
PostgreSQL full-text search for product discovery → Elasticsearch for product search with CDC-based catalog indexing
Both scenarios define a migration step at this stage. Financial Ledger Platform: triggered by 'Kafka publish failures causing financial transaction rollbac'. E-Commerce Order Platform: triggered by 'Product search p99 > 1s; faceted navigation (category + pric'.
Migration Step 3
Financial Ledger Platform
Single PostgreSQL primary serving all reads and writes → CQRS with separate read model and write model
E-Commerce Order Platform
Monolithic order processing with inline notification delivery → RabbitMQ-based notification fanout with dead-letter handling
Both scenarios define a migration step at this stage. Financial Ledger Platform: triggered by 'Financial dashboard query p99 > 500ms causing dashboard-driv'. E-Commerce Order Platform: triggered by 'Email/push notification provider timeouts causing checkout l'.
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.
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): 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.
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 · 15 items
Coverage Warnings
- ⚠E-Commerce Order 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.
- ·4 seed type(s) across both scenarios do not have execution previews. Risk confirmation for those types is unavailable.
Decision between Financial Ledger Platform and E-Commerce Order Platform depends on your specific context
Neither scenario is clearly better: weighted scores are Financial Ledger Platform 4.0 vs E-Commerce Order Platform 3.5. The best choice depends on your specific workload, team profile, and growth trajectory.
Decision Intelligence
Architecture Decision Path
Structured reasoning for choosing between Financial Ledger Platform and E-Commerce Order Platform. Every condition and trigger traces back to comparison dimensions, advisor insights, and topology evidence.
Decision between Financial Ledger Platform and E-Commerce Order Platform depends on your specific context
Neither scenario is clearly better: weighted scores are Financial Ledger Platform 4.0 vs E-Commerce Order Platform 3.5. The best choice depends on your specific workload, team profile, and growth trajectory. The architectures share 7 component(s), reducing migration cost if you switch later. E-Commerce Order Platform is the operationally simpler choice.
Where to Start
Start with E-Commerce Order Platform
RightE-Commerce Order 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, 21 nodes, 0 edges, 6 risks, 2 simulation seeds
Migrate when:
- PostgreSQL pg_locks showing high RowExclusiveLock contention on inventory_items for specific sku_ids; checkout p99 > 2s for contended SKUs; deadlock errors appearing in application logs during sale events; effective checkout throughput for hot SKUs well below per-request checkout latency would predict → Introduce a per-SKU checkout serialization queue at the application layer : all concurrent checkout requests for the same SKU are queued and processed serially, converting lock contention into queue latency. Alternatively, use PostgreSQL advisory locks with non-blocking trylock: requests that cannot acquire the lock immediately return a "sold out" response rather than queuing. For very high flash sale volumes, pre-allocate inventory slots (reserve N slots per sale event, each slot is a row with one reservation) to spread lock contention across N rows instead of one.
- Checkout p99 tracking payment provider p99 almost linearly; connection pool utilization on the payment service rising during payment provider slowdowns; circuit breaker trip events appearing in payment service metrics; saga timeout events correlated with payment provider latency spikes → Decouple the payment step from the synchronous checkout saga: reserve inventory and create the order record synchronously, then process payment asynchronously. The customer receives an "order confirmed, payment processing" state immediately; the payment step runs as a separate saga step triggered by an event. This reduces the synchronous checkout latency to the inventory reservation time, not the payment provider round-trip time.
- CDC consumer lag on the Elasticsearch indexing consumer > 30s during catalog bulk updates; customer complaints about price changes not visible in search; search result prices diverging from checkout prices by more than the acceptable window; Kibana showing indexing throughput below the catalog update rate → Tune Elasticsearch bulk indexing batch size and flush interval to increase indexing throughput; add indexing consumer replicas with partition-based assignment to parallelize indexing across catalog segment partitions. Introduce a "price_as_of" timestamp in search results displayed to customers : this converts an invisible consistency gap into an explicit, auditable staleness signal that satisfies most checkout price dispute scenarios.
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 Financial Ledger 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: PostgreSQL pg_locks showing high RowExclusiveLock contention on inventory_items for specific sku_ids; checkout p99 > 2s for contended SKUs; deadlock errors appearing in application logs during sale events; effective checkout throughput for hot SKUs well below per-request checkout latency would predict ?
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
E-Commerce Order Platform is the simpler choice: E-Commerce Order Platform is simpler: high operational complexity with 21 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 stability and predictability matter most
CriticalFinancial Ledger Platform carries lower overall risk weight per the advisor's assessment.
When you want to minimise monitoring setup overhead
ModerateFinancial Ledger Platform has a lower observability burden: fewer watched metrics and monitoring targets.
When 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.
E-Commerce Order Platform
RightWhen operational simplicity is a top priority
HighE-Commerce Order Platform has lower operational complexity: fewer moving parts, easier to reason about and debug.
When you need well-defined scaling thresholds and migration paths
HighE-Commerce Order Platform has more documented scaling evolution steps (4 thresholds, 3 migration paths).
When your system requires decoupled async event processing
HighE-Commerce Order 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.
E-Commerce Order Platform
RightWhen 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: cascading failure
HighThis architecture is significantly exposed to Cascading Failure. A failure or degradation in one service causes increased load, held resources, or error propagation in its callers, which in turn degrade their callers, until the failure front propagates through the entire dependency graph and brings down services with no direct dependency on the original failure point.
When your team is early-stage or solo
HighE-Commerce Order 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 9 predicted bottlenecks for E-Commerce Order 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 E-Commerce Order 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.
- ↳E-Commerce Order Platform may require additional runbook coverage and alerting investment.
Platform engineering team or SRE-equipped organisation
RightA platform team can safely operate E-Commerce Order 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'. E-Commerce Order Platform: triggered by 'Payment provider timeout causing full checkout rollback and '.
Migration Step 2
Both scenarios define a migration step at this stage. Financial Ledger Platform: triggered by 'Kafka publish failures causing financial transaction rollbac'. E-Commerce Order Platform: triggered by 'Product search p99 > 1s; faceted navigation (category + pric'.
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'. E-Commerce Order Platform: triggered by 'Email/push notification provider timeouts causing checkout l'.
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 .
PostgreSQL pg_locks showing high RowExclusiveLock contention on inventory_items for specific sku_ids; checkout p99 > 2s for contended SKUs; deadlock errors appearing in application logs during sale events; effective checkout throughput for hot SKUs well below per-request checkout latency would predict
Tier 1: Flash Sale Inventory Contention: Concurrent saga checkout attempts competing for the same inventory row via row-level locking. Recommended evolution: Introduce a per-SKU checkout serialization queue at the application layer : all concurrent checkout requests for the same SKU are queued and processed serially, converting lock contention into queue latency. Alternatively, use PostgreSQL advisory locks with non-blocking trylock: requests that cannot acquire the lock immediately return a "sold out" response rather than queuing. For very high flash sale volumes, pre-allocate inventory slots (reserve N slots per sale event, each slot is a row with one reservation) to spread lock contention across N rows instead of one. .
Checkout p99 tracking payment provider p99 almost linearly; connection pool utilization on the payment service rising during payment provider slowdowns; circuit breaker trip events appearing in payment service metrics; saga timeout events correlated with payment provider latency spikes
Tier 2: Payment Provider Latency Amplifying Checkout Latency: Checkout saga holding a database connection and an inventory reservation open for the duration of the payment provider call: payment latency directly amplifies connection pool pressure. Recommended evolution: Decouple the payment step from the synchronous checkout saga: reserve inventory and create the order record synchronously, then process payment asynchronously. The customer receives an "order confirmed, payment processing" state immediately; the payment step runs as a separate saga step triggered by an event. This reduces the synchronous checkout latency to the inventory reservation time, not the payment provider round-trip time. .
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
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
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.
Elasticsearch: scenario has full_text_search or log_analytics workload
RightConfigure ILM policies from day one to prevent shard explosion as data grows
Required maturity: senior
Elasticsearch: scenario uses Elasticsearch as a primary datastore
RightElasticsearch is a search index, not a source of truth: add a durable primary store and sync to ES
Required maturity: senior
Elasticsearch: scenario uses dynamic mappings on high-cardinality fields
RightDefine explicit index mappings: dynamic mapping on high-cardinality fields causes mapping explosions and cluster instability
Required maturity: senior
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
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
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.
E-Commerce Order Platform
RightGenerator relevance documented but not yet production-ready.
For e-commerce product briefs, the generator must output the full saga orchestration template: forward path (reserve_inventory → charge_payment → confirm_order → notify_fulfillment) and compensation path (release_inventory, refund_payment, cancel_order) as first-class generated artifacts. Inventory reservation schema (with SELECT FOR UPDATE NOWAIT), outbox table schema, and idempotency key persistence pattern must be generated as required components. RabbitMQ dead-letter exchange configuration must be generated alongside the primary queue configuration.
Supporting Evidence
| Type | Reference | Explanation |
|---|---|---|
| Comparison | compare_financial_ledger_platform_vs_ecommerce_order_platform | Full comparison of Financial Ledger Platform vs E-Commerce Order Platform: 6 dimensions, 7 shared components, 1 shared risks. |
| Advisor | advisor_financial_ledger_platform | Advisor for Financial Ledger Platform: 6 strengths, 4 risks, maturity: advanced. |
| Advisor | advisor_ecommerce_order_platform | Advisor for E-Commerce Order Platform: 0 strengths, 6 risks, maturity: advanced. |
| Scenario | financial_ledger_platform | Scenario 'Financial Ledger Platform': 4 scaling thresholds, 3 migration paths, complexity: expert. |
| Scenario | ecommerce_order_platform | Scenario 'E-Commerce Order 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_technology_profile_redis_risk_thundering_herd | Redis → Thundering Herd (Cache Stampede) |
| Risk Path | prop_workload_profile_financial_transaction_workload_risk_deadlock | Financial Transaction → Deadlock. also affects: PostgreSQL |
| 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.