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
Analytics Data Platform is both simpler and lower-risk than Write-Heavy Transactional Platform
Analytics Data Platform is the simpler architecture. Analytics Data Platform carries lower operational risk. They share 4 component(s). Analytics Data Platform has 3 unique risk(s); Write-Heavy Transactional Platform has 4.
11
Nodes
5
Edges
3
Risks
1
Seeds
4
Strengths
3
Adv. Risks
11
Nodes
5
Edges
4
Risks
3
Seeds
2
Strengths
4
Adv. Risks
Comparison Dimensions
Complexity
Analytics Data Platform
high complexity, 11 nodes, 5 edges, 3 risks, 1 simulation seeds
Write-Heavy Transactional Platform
high complexity, 11 nodes, 5 edges, 4 risks, 3 simulation seeds
Analytics Data Platform is simpler: high operational complexity with 11 topology nodes vs 11 for Write-Heavy Transactional Platform.
Operational Risk
Analytics Data Platform
3 risks (top: high), 2 high/critical, 0 confirmed by simulation
Write-Heavy Transactional Platform
4 risks (top: high), 3 high/critical, 0 confirmed by simulation
Analytics Data Platform has lower operational risk: weighted severity score 10 vs 14 (0 vs 0 simulation-confirmed).
Scalability
Analytics Data 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. Analytics Data Platform and Write-Heavy Transactional Platform offer similar numbers of defined evolution steps.
Operational Maturity
Analytics Data Platform
Advisor assessment: Advanced; recommended team: Experienced Backend Team; 9 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
Analytics Data Platform
4 watched metrics, 3 observability recommendations, 1 simulation seeds
Write-Heavy Transactional Platform
6 watched metrics, 4 observability recommendations, 3 simulation seeds
Analytics Data Platform has lower observability burden: 4 watched metrics vs 6.
Generator Readiness
Analytics Data Platform
generator relevance documented; topology generation relevance noted; simulation relevance noted; 1 seeds with generator notes
Write-Heavy Transactional Platform
generator relevance documented; topology generation relevance noted; simulation relevance noted; 3 seeds with generator notes
Write-Heavy Transactional Platform has more documented generator readiness signals. Note: this is still preliminary.
Architecture Components
Shared (4)
Only in Analytics Data Platform (7)
Only in Write-Heavy Transactional Platform (7)
Operational Risks
Only in Analytics Data Platform (3)
Only in Write-Heavy Transactional Platform (4)
Consistency Guarantees
Only Write-Heavy Transactional Platform (1)
Moving from Write-Heavy Transactional Platform to Analytics Data Platform
Green = kept, red = lost (the target explicitly excludes it), amber = unproven (the target has made no claim, not the same as losing it).
Only 'Write-Heavy Transactional Platform' claims: atomic_multi_object.
Tradeoff Summary
Complexity vs Risk
Analytics Data Platform has high complexity. Write-Heavy Transactional Platform has high complexity. Simpler systems often carry different (not necessarily fewer) risks.
Analytics Data Platform
Analytics Data Platform: 3 risks (top: high), 2 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
Analytics Data Platform offers 4 defined scaling thresholds. Write-Heavy Transactional Platform offers 4. More defined paths means clearer evolution steps but also more anticipated growth.
Analytics Data 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
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.
Analytics Data Platform
4 strengths, 3 risks
Write-Heavy Transactional Platform
2 strengths, 4 risks
Migration Considerations
Migration Step 1
Analytics Data Platform
Analytics queries running directly against PostgreSQL OLTP primary → Read replica serving analytics queries via polling ETL
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. Analytics Data Platform: triggered by 'OLTP query p99 degrading during analytics reporting windows;'. Write-Heavy Transactional Platform: triggered by 'Dual-write inconsistency events observed in production: DB w'.
Migration Step 2
Analytics Data Platform
Polling ETL from read replica to analytics store → WAL CDC → Kafka → ClickHouse streaming ingestion
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. Analytics Data Platform: triggered by 'Sub-minute analytics freshness SLA required; ETL scheduling '. Write-Heavy Transactional Platform: triggered by 'Primary write CPU > 70% sustained during peak windows; WAL v'.
Migration Step 3
Analytics Data Platform
ClickHouse with raw event tables only → ClickHouse with materialized views and pre-aggregated summary tables
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. Analytics Data Platform: triggered by 'Dashboard query p95 > 5s on frequently accessed aggregation '. Write-Heavy Transactional Platform: triggered by 'Audit completeness requirements grow beyond point-in-time ba'.
Advisor Notes
Strength: Analytics-heavy workloads pre-compute expensive aggregations and joins into materialized views, reducing repeated full-scan query…
Analytics-heavy workloads pre-compute expensive aggregations and joins into materialized views, reducing repeated full-scan query cost from minutes per query to milliseconds per lookup. Key trade-off: Materialized views add write overhead (refresh cost) and storage overhead (duplicate data). Operational note: Refresh interval determines data freshness: every 1 hour is typical for BI dashboards. Evidence: Snowflake automatic clustering and materialized views reduce repeated full-scan aggregation by 10-100x.
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): Queue Backlog Accumulation
Message queue or event stream consumer processing rate falls below producer write rate, causing consumer lag to grow unboundedly: eventually leading to increased end-to-end latency, producer backpressure, data expiry, or queue resource exhaustion.
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 · 13 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.
- ·4 seed type(s) across both scenarios do not have execution previews. Risk confirmation for those types is unavailable.
Analytics Data Platform is the recommended starting point over Write-Heavy Transactional Platform
Analytics Data Platform leads on 3 weighted dimension(s): Complexity, Operational Risk, Observability. Weighted score: 5.5 vs 1.0 for Write-Heavy Transactional Platform.
Decision Intelligence
Architecture Decision Path
Structured reasoning for choosing between Analytics Data Platform and Write-Heavy Transactional Platform. Every condition and trigger traces back to comparison dimensions, advisor insights, and topology evidence.
Analytics Data Platform is the recommended starting point over Write-Heavy Transactional Platform
Analytics Data Platform leads on 3 weighted dimension(s): Complexity, Operational Risk, Observability. Weighted score: 5.5 vs 1.0 for Write-Heavy Transactional Platform. The architectures share 4 component(s), reducing migration cost if you switch later. Analytics Data Platform is the operationally simpler choice.
Where to Start
Start with Analytics Data Platform
LeftAnalytics Data 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, 3 risks, 1 simulation seeds
Migrate when:
- Kafka consumer group lag (bytes or offsets) growing for the analytics topic group; ClickHouse dashboard timestamps falling behind wall clock by > 60s; ClickHouse insert throughput < Kafka produce rate → Tune ClickHouse insert buffer size and async_insert settings; increase consumer parallelism up to the Kafka partition count; batch inserts into ClickHouse using the Buffer engine or materialized views with merge trees
- One Kafka partition offset growing significantly faster than others; one consumer instance CPU/network saturated while others are idle → Add a secondary hash suffix to the partition key to distribute load; increase topic partition count (note: keyed ordering breaks for existing messages); re-evaluate partition key selection based on actual cardinality measurements
- ClickHouse system.parts shows parts_to_merge growing; SELECT queries showing slower p99 despite stable data volume; ClickHouse background merge thread CPU saturation → Reduce insert frequency by increasing batch size; tune parts_to_delay_insert and parts_to_throw_insert; consider a Buffer table as an insert intermediary
Decision Flow
Does your team have the operational maturity to run Analytics Data Platform (advanced rating)?
If Yes
Your team can operate Analytics Data 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 Analytics Data 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
Analytics Data Platform is the simpler choice: Analytics Data Platform is simpler: high operational complexity with 11 topology nodes vs 11 for Write-Heavy Transactional Platform.
If No
If capability and scalability ceiling matter more than simplicity, evaluate the higher-complexity scenario against your specific load model.
When to Choose Each Scenario
Analytics Data Platform
LeftWhen operational simplicity is a top priority
HighAnalytics Data Platform has lower operational complexity: fewer moving parts, easier to reason about and debug.
When stability and predictability matter most
CriticalAnalytics Data Platform carries lower overall risk weight per the advisor's assessment.
When you want to minimise monitoring setup overhead
ModerateAnalytics Data Platform has a lower observability burden: fewer watched metrics and monitoring targets.
When your architecture benefits from: analytics-heavy workloads pre-compute expensive aggregations and joins into materialized views, reducing repeated full-scan query…
ModerateAnalytics-heavy workloads pre-compute expensive aggregations and joins into materialized views, reducing repeated full-scan query cost from minutes per query to milliseconds per lookup. Key trade-off: Materialized views add write overhead (refresh cost) and storage overhead (duplicate data). Operational note: Refresh interval determines data freshness: every 1 hour is typical for BI dashboards. Evidence: Snowflake automatic clustering and materialized views reduce repeated full-scan aggregation by 10-100x.
When your architecture benefits from: clickhouse's columnar storage engine, vectorized query execution, and mergetree family of table engines are specifically designed…
ModerateClickHouse's columnar storage engine, vectorized query execution, and MergeTree family of table engines are specifically designed for analytics-heavy workloads: high-throughput aggregations over billions of rows with sub-second query latency. Key trade-off: ClickHouse has limited transaction support: ACID transactions are not a design goal. Operational note: ClickHouse is optimized for inserts, not updates: use ReplacingMergeTree or CollapsingMergeTree for mutable data. Evidence: ClickHouse processes 100 million rows/second per core for aggregation queries in documented benchmarks.
When your system requires decoupled async event processing
HighAnalytics Data Platform includes event stream infrastructure (e.g., Kafka/Kinesis), enabling async decoupling between producers and consumers.
Write-Heavy Transactional Platform
RightWhen 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
Analytics Data Platform
LeftWhen your team cannot mitigate: queue backlog accumulation
HighThis architecture is significantly exposed to Queue Backlog Accumulation. Message queue or event stream consumer processing rate falls below producer write rate, causing consumer lag to grow unboundedly: eventually leading to increased end-to-end latency, producer backpressure, data expiry, or queue resource exhaustion.
When your team cannot mitigate: hot partition
HighThis architecture is significantly exposed to Hot Partition. One partition (a database shard, a Kafka topic partition, a Redis hash slot) receives traffic so far above its peers that it saturates while the others sit idle. Aggregate capacity looks healthy, but the hot partition throttles or lags, and everything routed to it degrades. The cause is skew in how keys map to partitions, and the fix depends on whether the skew is spread across many keys or concentrated in one.
When your team is early-stage or solo
HighAnalytics Data 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 6 predicted bottlenecks for Analytics Data 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
LeftAnalytics Data 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)
LeftAnalytics Data 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. Analytics Data Platform: triggered by 'OLTP query p99 degrading during analytics reporting windows;'. 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. Analytics Data Platform: triggered by 'Sub-minute analytics freshness SLA required; ETL scheduling '. 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. Analytics Data Platform: triggered by 'Dashboard query p95 > 5s on frequently accessed aggregation '. Write-Heavy Transactional Platform: triggered by 'Audit completeness requirements grow beyond point-in-time ba'.
Kafka consumer group lag (bytes or offsets) growing for the analytics topic group; ClickHouse dashboard timestamps falling behind wall clock by > 60s; ClickHouse insert throughput < Kafka produce rate
Tier 1: Consumer Lag and Freshness Degradation: ClickHouse insert throughput insufficient for Kafka produce rate. Recommended evolution: Tune ClickHouse insert buffer size and async_insert settings; increase consumer parallelism up to the Kafka partition count; batch inserts into ClickHouse using the Buffer engine or materialized views with merge trees .
One Kafka partition offset growing significantly faster than others; one consumer instance CPU/network saturated while others are idle
Tier 2: Hot Partition and Skewed Consumer Load: Skewed partition key distribution: high-cardinality entity routing the same high-volume key to one partition. Recommended evolution: Add a secondary hash suffix to the partition key to distribute load; increase topic partition count (note: keyed ordering breaks for existing messages); re-evaluate partition key selection based on actual cardinality measurements .
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
Minimum team maturity: Experienced Backend Team
BothThis scenario has high operational complexity. It is recommended for Experienced Backend Team teams or higher.
Required maturity: experienced_backend_team
PostgreSQL: scenario includes high_write_throughput or write_heavy workload
BothDeploy PgBouncer in transaction-mode pooling before relying on vertical scaling
Required maturity: mid_level
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
Both2 high-severity risks identified. Each requires a documented runbook, alerting threshold, and on-call response procedure before running in production.
ClickHouse: scenario has analytics_olap or event_aggregation workload
LeftBatch inserts to ClickHouse in minimum 1k-row batches; single-row inserts cause part fragmentation
Required maturity: mid_level
ClickHouse: scenario uses ClickHouse for OLTP workloads
LeftClickHouse is an OLAP engine: mutations (UPDATE/DELETE) are async and expensive; use PostgreSQL for transactional workloads
Required maturity: mid_level
Generator Constraints
Analytics Data Platform
LeftGenerator relevance documented but not yet production-ready.
For product briefs requiring operational or large-scale analytics with streaming freshness, the generator should propose the WAL CDC → Kafka → ClickHouse composition as the canonical analytics path. Polling ETL should be presented as the lower-complexity starting point for basic_reporting needs. Materialized views in ClickHouse should be generated as optional acceleration for identified high-cost query patterns.
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_analytics_data_platform_vs_write_heavy_transactional_platform | Full comparison of Analytics Data Platform vs Write-Heavy Transactional Platform: 6 dimensions, 4 shared components, 0 shared risks. |
| Advisor | advisor_analytics_data_platform | Advisor for Analytics Data Platform: 4 strengths, 3 risks, maturity: advanced. |
| Advisor | advisor_write_heavy_transactional_platform | Advisor for Write-Heavy Transactional Platform: 2 strengths, 4 risks, maturity: advanced. |
| Scenario | analytics_data_platform | Scenario 'Analytics Data Platform': 4 scaling thresholds, 3 migration paths, complexity: high. |
| Scenario | write_heavy_transactional_platform | Scenario 'Write-Heavy Transactional Platform': 4 scaling thresholds, 3 migration paths, complexity: high. |
| Risk Path | prop_failure_mode_slow_consumer_risk_queue_backlog_accumulation | Slow Consumer → Queue Backlog Accumulation |
| 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_failure_mode_slow_consumer_risk_queue_backlog_accumulation | Referenced by the operational risk comparison dimension. |
| Risk Path | prop_workload_profile_write_heavy_transactional_risk_wal_saturation | 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.