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 is both simpler and lower-risk than E-Commerce Order Platform
Search-Heavy Content Platform is the simpler architecture. Search-Heavy Content Platform carries lower operational risk. They share 7 component(s). Search-Heavy Content Platform has 3 unique risk(s); E-Commerce Order Platform has 5. Search-Heavy Content Platform requires lower team maturity to operate.
13
Nodes
6
Edges
4
Risks
1
Seeds
5
Strengths
4
Adv. Risks
21
Nodes
0
Edges
6
Risks
2
Seeds
0
Strengths
6
Adv. Risks
Comparison Dimensions
Complexity
Search-Heavy Content Platform
high complexity, 13 nodes, 6 edges, 4 risks, 1 simulation seeds
E-Commerce Order Platform
high complexity, 21 nodes, 0 edges, 6 risks, 2 simulation seeds
Search-Heavy Content Platform is simpler: high operational complexity with 13 topology nodes vs 21 for E-Commerce Order Platform.
Operational Risk
Search-Heavy Content Platform
4 risks (top: high), 2 high/critical, 0 confirmed by simulation
E-Commerce Order Platform
6 risks (top: high), 5 high/critical, 0 confirmed by simulation
Search-Heavy Content Platform has lower operational risk: weighted severity score 12 vs 22 (0 vs 0 simulation-confirmed).
Scalability
Search-Heavy Content 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
Search-Heavy Content Platform
Advisor assessment: Intermediate; recommended team: Experienced Backend Team; 9 operational requirements
E-Commerce Order Platform
Advisor assessment: Advanced; recommended team: Experienced Backend Team; 15 operational requirements
Search-Heavy Content Platform requires lower team maturity (Intermediate) vs Advanced for E-Commerce Order Platform.
Observability
Search-Heavy Content Platform
2 watched metrics, 3 observability recommendations, 1 simulation seeds
E-Commerce Order Platform
4 watched metrics, 6 observability recommendations, 2 simulation seeds
Search-Heavy Content Platform has lower observability burden: 2 watched metrics vs 4.
Generator Readiness
Search-Heavy Content Platform
generator relevance documented; topology generation relevance noted; simulation relevance noted; 1 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 Search-Heavy Content Platform (6)
Only in E-Commerce Order Platform (14)
Operational Risks
Shared (1)
Only in Search-Heavy Content Platform (3)
Only in E-Commerce Order Platform (5)
Consistency Guarantees
Neither scenario has a recorded consistency-guarantee claim.
Neither scenario has recorded a consistency-guarantee claim; no guarantee comparison is possible from the data on record.
Tradeoff Summary
Complexity vs Risk
Search-Heavy Content Platform has high complexity. E-Commerce Order 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
E-Commerce Order Platform
E-Commerce Order Platform: 6 risks (top: high), 5 high/critical, 0 confirmed by simulation
Scaling Path
Search-Heavy Content Platform offers 4 defined scaling thresholds. E-Commerce Order 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
E-Commerce Order Platform
4 scaling thresholds, 3 migration paths, 4 advisor scaling signals
Event-Driven vs Synchronous Processing
E-Commerce Order 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
E-Commerce Order 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
E-Commerce Order Platform
0 strengths, 6 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
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. Search-Heavy Content Platform: triggered by 'Search query p99 > 500ms on full-text queries; faceted navig'. E-Commerce Order Platform: triggered by 'Payment provider timeout causing full checkout rollback and '.
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)
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. Search-Heavy Content Platform: triggered by 'Application write latency increasing due to Elasticsearch in'. E-Commerce Order Platform: triggered by 'Product search p99 > 1s; faceted navigation (category + pric'.
Migration Step 3
Search-Heavy Content Platform
Single Elasticsearch cluster serving all query types → Separate read-optimized and write-optimized Elasticsearch indexes
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. Search-Heavy Content Platform: triggered by 'High write throughput (> 10k documents/min) causing segment '. E-Commerce Order Platform: triggered by 'Email/push notification provider timeouts causing checkout l'.
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.
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): 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: Cache sizing and eviction policy configuration, Elasticsearch: scenario has full_text_search or log_analytics workload, Elasticsearch: scenario uses Elasticsearch as a primary datastore.
Supporting Evidence · 12 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.
- ·3 seed type(s) across both scenarios do not have execution previews. Risk confirmation for those types is unavailable.
- ·Neither scenario has a recorded consistency-guarantee claim. Guarantee comparison is uninformative for this pair, not silently empty.
Search-Heavy Content Platform is the recommended starting point over E-Commerce Order Platform
Search-Heavy Content Platform leads on 4 weighted dimension(s): Complexity, Operational Risk, Operational Maturity. Weighted score: 6.5 vs 1.0 for E-Commerce Order Platform.
Decision Intelligence
Architecture Decision Path
Structured reasoning for choosing between Search-Heavy Content Platform and E-Commerce Order 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 E-Commerce Order Platform
Search-Heavy Content Platform leads on 4 weighted dimension(s): Complexity, Operational Risk, Operational Maturity. Weighted score: 6.5 vs 1.0 for E-Commerce Order Platform. The architectures share 7 component(s), reducing migration cost if you switch later. Search-Heavy Content Platform is the operationally simpler choice.
Where to Start
Start with Search-Heavy Content Platform
LeftSearch-Heavy Content 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, 13 nodes, 6 edges, 4 risks, 1 simulation seeds
Migrate when:
- 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 → 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) → 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
- Elasticsearch node stats showing one shard handling > 3x the query/index operations of others; hot-spotted shard's node CPU > 80% while others are idle → Enable shard-level routing with custom routing hash; review document routing key selection; for write-heavy scenarios, increase primary shard count and reindex with a new shard allocation
Decision Flow
Does your team have the operational maturity to run E-Commerce Order Platform (advanced rating)?
If Yes
Your team can operate E-Commerce Order 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: 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.
Does your team already operate an event streaming platform (e.g., Kafka, Kinesis, Pub/Sub) in production?
If Yes
E-Commerce Order 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
Search-Heavy Content Platform is the simpler choice: Search-Heavy Content Platform is simpler: high operational complexity with 13 topology nodes vs 21 for E-Commerce Order 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 operational simplicity is a top priority
HighSearch-Heavy Content Platform has lower operational complexity: fewer moving parts, easier to reason about and debug.
When 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.
E-Commerce Order Platform
RightWhen 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
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.
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
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 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. Search-Heavy Content Platform: triggered by 'Search query p99 > 500ms on full-text queries; faceted navig'. 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. Search-Heavy Content Platform: triggered by 'Application write latency increasing due to Elasticsearch in'. 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. Search-Heavy Content Platform: triggered by 'High write throughput (> 10k documents/min) causing segment '. E-Commerce Order Platform: triggered by 'Email/push notification provider timeouts causing checkout l'.
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 .
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
Cache sizing and eviction policy configuration
BothRedis 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
BothConfigure ILM policies from day one to prevent shard explosion as data grows
Required maturity: senior
Elasticsearch: scenario uses Elasticsearch as a primary datastore
BothElasticsearch 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
BothDefine explicit index mappings: dynamic mapping on high-cardinality fields causes mapping explosions and cluster instability
Required maturity: senior
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
Redis: scenario has read_heavy workload with high cache miss risk
BothImplement 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
BothRedis is not a durable store: add persistence layer or treat Redis as expendable cache only
Required maturity: junior
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.
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
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
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.
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_search_heavy_content_platform_vs_ecommerce_order_platform | Full comparison of Search-Heavy Content Platform vs E-Commerce Order Platform: 6 dimensions, 7 shared components, 1 shared risks. |
| Advisor | advisor_search_heavy_content_platform | Advisor for Search-Heavy Content Platform: 5 strengths, 4 risks, maturity: intermediate. |
| Advisor | advisor_ecommerce_order_platform | Advisor for E-Commerce Order Platform: 0 strengths, 6 risks, maturity: advanced. |
| Scenario | search_heavy_content_platform | Scenario 'Search-Heavy Content Platform': 4 scaling thresholds, 3 migration paths, complexity: high. |
| Scenario | ecommerce_order_platform | Scenario 'E-Commerce Order 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_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_technology_profile_redis_risk_thundering_herd | Referenced by the operational risk comparison dimension. |
| Risk Path | prop_technology_profile_redis_risk_thundering_herd | 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.