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
Search-Heavy Content Platform vs Write-Heavy Transactional Platform: Write-Heavy Transactional Platform is the simpler choice
Write-Heavy Transactional Platform is the simpler architecture. Search-Heavy Content Platform carries lower operational risk. They share 2 component(s). Search-Heavy Content Platform has 4 unique risk(s); Write-Heavy Transactional Platform has 4. Search-Heavy Content Platform requires lower team maturity to operate.
13
Nodes
6
Edges
4
Risks
1
Seeds
5
Strengths
4
Adv. Risks
11
Nodes
5
Edges
4
Risks
3
Seeds
2
Strengths
4
Adv. Risks
Comparison Dimensions
Complexity
Search-Heavy Content Platform
high complexity, 13 nodes, 6 edges, 4 risks, 1 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 13 for Search-Heavy Content Platform.
Operational Risk
Search-Heavy Content Platform
4 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
Search-Heavy Content Platform has lower operational risk: weighted severity score 12 vs 14 (0 vs 0 simulation-confirmed).
Scalability
Search-Heavy Content 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. Search-Heavy Content Platform and Write-Heavy Transactional Platform offer similar numbers of defined evolution steps.
Operational Maturity
Search-Heavy Content Platform
Advisor assessment: Intermediate; recommended team: Experienced Backend Team; 9 operational requirements
Write-Heavy Transactional Platform
Advisor assessment: Advanced; recommended team: Experienced Backend Team; 7 operational requirements
Search-Heavy Content Platform requires lower team maturity (Intermediate) vs Advanced for Write-Heavy Transactional Platform.
Observability
Search-Heavy Content Platform
2 watched metrics, 3 observability recommendations, 1 simulation seeds
Write-Heavy Transactional Platform
6 watched metrics, 4 observability recommendations, 3 simulation seeds
Search-Heavy Content Platform has lower observability burden: 2 watched metrics vs 6.
Generator Readiness
Search-Heavy Content 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
Only in Search-Heavy Content Platform (11)
Only in Write-Heavy Transactional Platform (9)
Operational Risks
Only in Search-Heavy Content Platform (4)
Only in Write-Heavy Transactional Platform (4)
Consistency Guarantees
Only Write-Heavy Transactional Platform (1)
Moving from Write-Heavy Transactional Platform to Search-Heavy Content 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
Search-Heavy Content Platform has high complexity. Write-Heavy Transactional Platform has high complexity. Simpler systems often carry different (not necessarily fewer) risks.
Search-Heavy Content Platform
Search-Heavy Content Platform: 4 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
Search-Heavy Content Platform offers 4 defined scaling thresholds. Write-Heavy Transactional Platform offers 4. More defined paths means clearer evolution steps but also more anticipated growth.
Search-Heavy Content 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
Event-Driven vs Synchronous Processing
Write-Heavy Transactional Platform uses event stream infrastructure (Kafka/Kinesis), enabling decoupled async processing. Search-Heavy Content Platform does not, keeping the stack simpler but less decoupled.
Search-Heavy Content Platform
No event stream: simpler stack, synchronous dependencies
Write-Heavy Transactional Platform
Event stream: async decoupling, consumer lag risk, higher ops burden
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.
Search-Heavy Content Platform
5 strengths, 4 risks
Write-Heavy Transactional Platform
2 strengths, 4 risks
Migration Considerations
Migration Step 1
Search-Heavy Content Platform
PostgreSQL full-text search (tsvector) serving all search queries → Elasticsearch for full-text and faceted search, PostgreSQL as source of truth
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. Search-Heavy Content Platform: triggered by 'Search query p99 > 500ms on full-text queries; faceted navig'. Write-Heavy Transactional Platform: triggered by 'Dual-write inconsistency events observed in production: DB w'.
Migration Step 2
Search-Heavy Content Platform
Synchronous dual-write (application writes to PostgreSQL then Elasticsearch) → Asynchronous CDC-based indexing pipeline (PostgreSQL → WAL CDC → Kafka → Elasticsearch)
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. Search-Heavy Content Platform: triggered by 'Application write latency increasing due to Elasticsearch in'. Write-Heavy Transactional Platform: triggered by 'Primary write CPU > 70% sustained during peak windows; WAL v'.
Migration Step 3
Search-Heavy Content Platform
Single Elasticsearch cluster serving all query types → Separate read-optimized and write-optimized Elasticsearch indexes
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. Search-Heavy Content Platform: triggered by 'High write throughput (> 10k documents/min) causing segment '. Write-Heavy Transactional Platform: triggered by 'Audit completeness requirements grow beyond point-in-time ba'.
Advisor Notes
Strength: Redis caching absorbs repeated read requests at the edge, reducing database load and latency for high read-to-write ratio workloads by orders of magnitude
Redis caching absorbs repeated read requests at the edge, reducing database load and latency for high read-to-write ratio workloads by orders of magnitude. Key trade-off: Introduces eventual consistency: stale reads possible within TTL window. Operational note: Cache-aside (lazy population) is the dominant integration pattern. Evidence: Cache hit rates of 80–95% observed in production read-heavy APIs.
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): 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.
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: Minimum team maturity: Experienced Backend Team, PostgreSQL: scenario includes high_write_throughput or write_heavy workload, Runbooks and alerting for high-severity risks.
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.
Search-Heavy Content Platform is the recommended starting point over Write-Heavy Transactional Platform
Search-Heavy Content Platform leads on 3 weighted dimension(s): Operational Risk, Operational Maturity, Observability. Weighted score: 5.0 vs 1.5 for Write-Heavy Transactional Platform.
Decision Intelligence
Architecture Decision Path
Structured reasoning for choosing between Search-Heavy Content Platform and Write-Heavy Transactional Platform. Every condition and trigger traces back to comparison dimensions, advisor insights, and topology evidence.
Search-Heavy Content Platform is the recommended starting point over Write-Heavy Transactional Platform
Search-Heavy Content Platform leads on 3 weighted dimension(s): Operational Risk, Operational Maturity, Observability. Weighted score: 5.0 vs 1.5 for Write-Heavy Transactional Platform. The architectures share 2 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 Write-Heavy Transactional Platform (advanced rating)?
If Yes
Your team can operate Write-Heavy Transactional Platform. Continue to Step 2 to refine based on risk tolerance and workload fit.
If No
Prefer the lower-maturity option: left scenario.
Is operational stability and minimising production risk your primary concern over feature richness or scalability ceiling?
If Yes
Prefer Search-Heavy Content 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.
Does your team already operate an event streaming platform (e.g., Kafka, Kinesis, Pub/Sub) in production?
If Yes
Write-Heavy Transactional Platform builds on event stream infrastructure. Your existing platform reduces the adoption risk.
If No
If you don't have an event streaming platform, the other scenario avoids adding that infrastructure dependency. Evaluate whether the async decoupling benefit justifies the new ops burden.
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 13 for Search-Heavy Content 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
Search-Heavy Content Platform
LeftWhen stability and predictability matter most
CriticalSearch-Heavy Content Platform carries lower overall risk weight per the advisor's assessment.
When your team has limited operational maturity
CriticalSearch-Heavy Content Platform is rated intermediate , accessible for teams without deep platform expertise.
When you want to minimise monitoring setup overhead
ModerateSearch-Heavy Content Platform has a lower observability burden: fewer watched metrics and monitoring targets.
When your architecture benefits from: redis caching absorbs repeated read requests at the edge, reducing database load and latency for high read-to-write ratio workloads by orders of magnitude
ModerateRedis caching absorbs repeated read requests at the edge, reducing database load and latency for high read-to-write ratio workloads by orders of magnitude. Key trade-off: Introduces eventual consistency: stale reads possible within TTL window. Operational note: Cache-aside (lazy population) is the dominant integration pattern. Evidence: Cache hit rates of 80–95% observed in production read-heavy APIs.
When your architecture benefits from: redis distributed locks (via set nx ex or redlock) prevent thundering herd by ensuring only one caller repopulates a cache entry…
ModerateRedis distributed locks (via SET NX EX or Redlock) prevent thundering herd by ensuring only one caller repopulates a cache entry at a time, with other callers either waiting or returning a stale value until the cache is warm. Key trade-off: Distributed locking adds one Redis round-trip to every cache miss that triggers population. Operational note: Lock TTL must be set longer than the cache population time: if it expires before population completes, lock is acquired again. Evidence: Redis SET key value NX EX ttl atomically sets a lock only if absent: enables single-caller cache population.
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 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
Search-Heavy Content Platform
LeftWhen 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 cannot mitigate: thundering herd (cache stampede)
HighThis architecture is significantly exposed to Thundering Herd (Cache Stampede). When a popular cached key expires or a service recovers from downtime, all requests that were waiting or arrive simultaneously miss the cache and hit the origin database concurrently, producing a request spike that can overwhelm the database within seconds.
When you expect rapid growth within the next 12–18 months
ModerateThe advisor identifies 5 predicted bottlenecks for Search-Heavy Content 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
LeftSearch-Heavy Content 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)
LeftSearch-Heavy Content 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. Search-Heavy Content Platform: triggered by 'Search query p99 > 500ms on full-text queries; faceted navig'. 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. Search-Heavy Content Platform: triggered by 'Application write latency increasing due to Elasticsearch in'. 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. Search-Heavy Content Platform: triggered by 'High write throughput (> 10k documents/min) causing segment '. Write-Heavy Transactional Platform: triggered by 'Audit completeness requirements grow beyond point-in-time ba'.
Elasticsearch index CDC consumer lag > 10s; search results showing items that no longer exist or missing recently published items; CDC connector health dashboard showing processing rate below write rate
Tier 1: Index Freshness Degradation: CDC consumer or Elasticsearch bulk indexer not keeping pace with PostgreSQL write rate. Recommended evolution: Increase Elasticsearch bulk indexer thread count; tune bulk index batch size and flush interval; profile CDC connector bottleneck (network vs Elasticsearch write throughput vs mapping complexity) .
Elasticsearch JVM heap usage > 75% sustained; GC pause events visible in cluster logs; query p99 latency spikes during GC; cluster health showing yellow (unassigned shards during GC recovery)
Tier 2: Search Cluster Heap Pressure: Large aggregation queries or high document count per shard exceeding JVM heap budget. Recommended evolution: Increase Elasticsearch heap to 50% of node RAM (max 30GB for ZGC); reduce shard count to keep per-shard document count < 50M; disable dynamic mapping and explicitly define all field types; move to doc values for all non-analyzed fields .
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
Minimum team maturity: Experienced Backend Team
BothThis scenario has high operational complexity. It is recommended for Experienced Backend Team teams or higher.
Required maturity: experienced_backend_team
PostgreSQL: scenario includes high_write_throughput or write_heavy workload
BothDeploy PgBouncer in transaction-mode pooling before relying on vertical scaling
Required maturity: mid_level
Runbooks and alerting for high-severity risks
Both2 high-severity risks identified. Each requires a documented runbook, alerting threshold, and on-call response procedure before running in production.
Cache sizing and eviction policy configuration
LeftRedis 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
LeftConfigure ILM policies from day one to prevent shard explosion as data grows
Required maturity: senior
Elasticsearch: scenario uses Elasticsearch as a primary datastore
LeftElasticsearch 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
LeftDefine explicit index mappings: dynamic mapping on high-cardinality fields causes mapping explosions and cluster instability
Required maturity: senior
Redis: scenario has read_heavy workload with high cache miss risk
LeftImplement 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
LeftRedis is not a durable store: add persistence layer or treat Redis as expendable cache only
Required maturity: junior
Apache Kafka: scenario has team_maturity below senior
RightKafka 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
RightSet min.insync.replicas=2 with acks=all; monitor consumer lag as primary health signal
Required maturity: senior
Event stream operations expertise
RightThis 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
Replica lag monitoring and lag-aware routing
RightRead 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.
Generator Constraints
Search-Heavy Content Platform
LeftGenerator relevance documented but not yet production-ready.
For content platform or e-commerce product briefs with full-text or faceted search requirements, the generator should propose the PostgreSQL + Elasticsearch + Redis composition. The CDC pipeline should be generated as the canonical indexing path, not synchronous dual-write. Explicit Elasticsearch mapping templates and blue/green alias configuration should be included as mandatory generated artifacts.
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_search_heavy_content_platform_vs_write_heavy_transactional_platform | Full comparison of Search-Heavy Content Platform vs Write-Heavy Transactional Platform: 6 dimensions, 2 shared components, 0 shared risks. |
| Advisor | advisor_search_heavy_content_platform | Advisor for Search-Heavy Content Platform: 5 strengths, 4 risks, maturity: intermediate. |
| Advisor | advisor_write_heavy_transactional_platform | Advisor for Write-Heavy Transactional Platform: 2 strengths, 4 risks, maturity: advanced. |
| Scenario | search_heavy_content_platform | Scenario 'Search-Heavy Content 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_technology_profile_redis_risk_thundering_herd | Redis → Thundering Herd (Cache Stampede) |
| 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_technology_profile_redis_risk_thundering_herd | 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.