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 Gaming Backend Platform: Gaming Backend Platform is the simpler choice
Gaming Backend Platform is the simpler architecture. Financial Ledger Platform carries lower operational risk. They share 5 component(s). Financial Ledger Platform has 3 unique risk(s); Gaming Backend Platform has 4.
12
Nodes
9
Edges
4
Risks
2
Seeds
6
Strengths
4
Adv. Risks
18
Nodes
0
Edges
5
Risks
1
Seeds
0
Strengths
5
Adv. Risks
Comparison Dimensions
Complexity
Financial Ledger Platform
expert complexity, 12 nodes, 9 edges, 4 risks, 2 simulation seeds
Gaming Backend Platform
high complexity, 18 nodes, 0 edges, 5 risks, 1 simulation seeds
Gaming Backend Platform is simpler: high operational complexity with 18 topology nodes vs 12 for Financial Ledger Platform.
Operational Risk
Financial Ledger Platform
4 risks (top: high), 4 high/critical, 0 confirmed by simulation
Gaming Backend Platform
5 risks (top: high), 4 high/critical, 1 confirmed by simulation
Financial Ledger Platform has lower operational risk: weighted severity score 16 vs 18 (0 vs 1 simulation-confirmed).
Scalability
Financial Ledger Platform
4 scaling thresholds, 3 migration paths, 4 advisor scaling signals
Gaming Backend Platform
4 scaling thresholds, 3 migration paths, 7 advisor scaling signals
Gaming Backend 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
Gaming Backend Platform
Advisor assessment: Advanced; recommended team: Experienced Backend Team; 9 operational requirements
Both scenarios require equivalent team maturity: Advanced.
Observability
Financial Ledger Platform
4 watched metrics, 5 observability recommendations, 2 simulation seeds
Gaming Backend Platform
4 watched metrics, 5 observability recommendations, 1 simulation seeds
Both scenarios have similar observability requirements: 4 and 4 watched metrics respectively.
Generator Readiness
Financial Ledger Platform
generator relevance documented; topology generation relevance noted; simulation relevance noted; 2 seeds with generator notes
Gaming Backend Platform
generator relevance documented; topology generation relevance noted; simulation relevance noted; 1 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 (5)
Only in Financial Ledger Platform (7)
Only in Gaming Backend Platform (13)
Operational Risks
Shared (1)
Only in Financial Ledger Platform (3)
Only in Gaming Backend Platform (4)
Consistency Guarantees
Only Financial Ledger Platform (1)
Moving from Financial Ledger Platform to Gaming Backend 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. Gaming Backend 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
Gaming Backend Platform
Gaming Backend Platform: 5 risks (top: high), 4 high/critical, 1 confirmed by simulation
Scaling Path
Financial Ledger Platform offers 4 defined scaling thresholds. Gaming Backend 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
Gaming Backend Platform
4 scaling thresholds, 3 migration paths, 7 advisor scaling signals
Team Maturity Requirement
Gaming Backend 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
Gaming Backend Platform
Advisor assessment: Advanced; recommended team: Experienced Backend Team; 9 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
Gaming Backend Platform
0 strengths, 5 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
Gaming Backend Platform
Single-server game backend with in-memory game room state → Redis-backed distributed game room state with consistent hashing affinity
Both scenarios define a migration step at this stage. Financial Ledger Platform: triggered by 'Audit requirement to reconstruct account state at any histor'. Gaming Backend Platform: triggered by 'Single game server instance running at connection ceiling; h'.
Migration Step 2
Financial Ledger Platform
Synchronous Kafka publish in transaction (dual-write pattern) → Outbox pattern with CDC relay to Kafka
Gaming Backend Platform
Post-game event publishing via direct PostgreSQL writes in game server → Kafka-based post-game event streaming for analytics and anti-cheat
Both scenarios define a migration step at this stage. Financial Ledger Platform: triggered by 'Kafka publish failures causing financial transaction rollbac'. Gaming Backend Platform: triggered by 'Game server instances blocking on PostgreSQL writes at end-o'.
Migration Step 3
Financial Ledger Platform
Single PostgreSQL primary serving all reads and writes → CQRS with separate read model and write model
Gaming Backend Platform
Full event sourcing in PostgreSQL for all game session state → Snapshot-only persistence in PostgreSQL with Kafka for event streaming
Both scenarios define a migration step at this stage. Financial Ledger Platform: triggered by 'Financial dashboard query p99 > 500ms causing dashboard-driv'. Gaming Backend Platform: triggered by 'PostgreSQL event log table approaching 100B rows; reconnect '.
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): 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.
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 · 14 items
Coverage Warnings
- ⚠Gaming Backend 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.
- ·2 seed type(s) across both scenarios do not have execution previews. Risk confirmation for those types is unavailable.
Decision between Financial Ledger Platform and Gaming Backend Platform depends on your specific context
Neither scenario is clearly better: weighted scores are Financial Ledger Platform 3.5 vs Gaming Backend Platform 4.0. 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 Gaming Backend Platform. Every condition and trigger traces back to comparison dimensions, advisor insights, and topology evidence.
Decision between Financial Ledger Platform and Gaming Backend Platform depends on your specific context
Neither scenario is clearly better: weighted scores are Financial Ledger Platform 3.5 vs Gaming Backend Platform 4.0. The best choice depends on your specific workload, team profile, and growth trajectory. The architectures share 5 component(s), reducing migration cost if you switch later. Gaming Backend Platform is the operationally simpler choice.
Where to Start
Start with Gaming Backend Platform
RightGaming Backend 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, 18 nodes, 0 edges, 5 risks, 1 simulation seeds
Migrate when:
- Game server instance file descriptor count approaching OS limit (typically 65k open connections); WebSocket accept latency increasing; new connection establishment p99 > 200ms; CPU on game server instances > 70% during peak concurrent player count → Increase OS fd_max to 512k and application connection accept queue depth; tune SO_REUSEPORT to allow multiple accept threads per socket; add game server instances and update consistent hashing ring; the affinity layer automatically routes new game rooms to the new instances as the ring expands: existing rooms are unaffected
- Redis command throughput > 500k/second; Redis CPU > 60%; per-tick Redis write latency p99 > 5ms (above the acceptable state sync threshold); game tick rate visibly dropping below target (30 ticks/second falling to 20) under load → Implement delta state serialization: only changed fields are written to Redis per tick using HSET with only the modified keys, not full state replacement; profile Redis command distribution per game tick to identify specific state fields with high churn; consider moving ephemeral per-tick state (player positions, projectile states) to local server memory with only durable state (scores, inventory changes) written to Redis
- Match formation latency (time from queue join to match start) p95 > 10s at peak player count; matchmaking Redis key contention visible in MONITOR output; match quality degrading (skill bracket widening under pressure) to maintain formation rate; matchmaking queue depth growing despite available game server capacity → Move matchmaking logic to a dedicated matchmaking service with its own Redis shard (separate from game room state Redis); implement bracket-level partitioning for matchmaking queues using Redis Cluster to distribute hot bracket keys; use a batch formation algorithm that processes multiple pending players per tick rather than first-in-first-out individual matching; tune skill bracket tolerance as a time-in-queue function (expand bracket after 5s, 10s, 15s waiting)
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: Game server instance file descriptor count approaching OS limit (typically 65k open connections); WebSocket accept latency increasing; new connection establishment p99 > 200ms; CPU on game server instances > 70% during peak concurrent player count ?
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
Gaming Backend Platform is the simpler choice: Gaming Backend Platform is simpler: high operational complexity with 18 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 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.
Gaming Backend Platform
RightWhen operational simplicity is a top priority
HighGaming Backend Platform has lower operational complexity: fewer moving parts, easier to reason about and debug.
When you need well-defined scaling thresholds and migration paths
HighGaming Backend Platform has more documented scaling evolution steps (4 thresholds, 3 migration paths).
When your system requires decoupled async event processing
HighGaming Backend 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.
Gaming Backend Platform
RightWhen 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 cannot mitigate: network partition
HighThis architecture is significantly exposed to Network Partition. A subset of distributed system nodes can reach each other but not another subset, splitting the cluster into groups that disagree about the current state. Partition tolerance is not optional for a system spanning more than one node; the real choice a partition forces is between consistency and availability for the duration it lasts.
When your team is early-stage or solo
HighGaming Backend 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 8 predicted bottlenecks for Gaming Backend 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 Gaming Backend 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.
- ↳Gaming Backend Platform may require additional runbook coverage and alerting investment.
Platform engineering team or SRE-equipped organisation
RightA platform team can safely operate Gaming Backend 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'. Gaming Backend Platform: triggered by 'Single game server instance running at connection ceiling; h'.
Migration Step 2
Both scenarios define a migration step at this stage. Financial Ledger Platform: triggered by 'Kafka publish failures causing financial transaction rollbac'. Gaming Backend Platform: triggered by 'Game server instances blocking on PostgreSQL writes at end-o'.
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'. Gaming Backend Platform: triggered by 'PostgreSQL event log table approaching 100B rows; reconnect '.
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 .
Game server instance file descriptor count approaching OS limit (typically 65k open connections); WebSocket accept latency increasing; new connection establishment p99 > 200ms; CPU on game server instances > 70% during peak concurrent player count
Tier 1: WebSocket Connection Ceiling per Instance: Single game server instance WebSocket connection count limit; OS-level fd_max or application-level connection accept queue saturation. Recommended evolution: Increase OS fd_max to 512k and application connection accept queue depth; tune SO_REUSEPORT to allow multiple accept threads per socket; add game server instances and update consistent hashing ring; the affinity layer automatically routes new game rooms to the new instances as the ring expands: existing rooms are unaffected .
Redis command throughput > 500k/second; Redis CPU > 60%; per-tick Redis write latency p99 > 5ms (above the acceptable state sync threshold); game tick rate visibly dropping below target (30 ticks/second falling to 20) under load
Tier 2: Redis Game State Write Amplification: Game state serialization to Redis per tick producing more writes than expected; unoptimized state struct serialization writing entire state blob on any field change. Recommended evolution: Implement delta state serialization: only changed fields are written to Redis per tick using HSET with only the modified keys, not full state replacement; profile Redis command distribution per game tick to identify specific state fields with high churn; consider moving ephemeral per-tick state (player positions, projectile states) to local server memory with only durable state (scores, inventory changes) written to Redis .
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.
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
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.
Gaming Backend Platform
RightGenerator relevance documented but not yet production-ready.
For multiplayer game product briefs, the generator must produce the three-layer state architecture: (1) in-game tick state in Redis with delta serialization, (2) durable player persistence in PostgreSQL with Redis-cached hot reads, (3) post-game event stream via Kafka. The consistent hashing affinity router configuration, the WebSocket session management pattern, and the snapshot + event log catch-up protocol must be generated as integrated components, not independent modules. Connection affinity must be highlighted as a non-optional architectural constraint.
Supporting Evidence
| Type | Reference | Explanation |
|---|---|---|
| Comparison | compare_financial_ledger_platform_vs_gaming_backend_platform | Full comparison of Financial Ledger Platform vs Gaming Backend Platform: 6 dimensions, 5 shared components, 1 shared risks. |
| Advisor | advisor_financial_ledger_platform | Advisor for Financial Ledger Platform: 6 strengths, 4 risks, maturity: advanced. |
| Advisor | advisor_gaming_backend_platform | Advisor for Gaming Backend Platform: 0 strengths, 5 risks, maturity: advanced. |
| Scenario | financial_ledger_platform | Scenario 'Financial Ledger Platform': 4 scaling thresholds, 3 migration paths, complexity: expert. |
| Scenario | gaming_backend_platform | Scenario 'Gaming Backend 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_connection_exhaustion | Redis → Connection Pool Exhaustion |
| 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.