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
Streaming Media Platform vs Two-Sided Marketplace Platform: Streaming Media Platform is the simpler choice
Streaming Media Platform is the simpler architecture. Two-Sided Marketplace Platform carries lower operational risk. They share 10 component(s). Streaming Media Platform has 2 unique risk(s); Two-Sided Marketplace Platform has 1.
21
Nodes
0
Edges
6
Risks
3
Seeds
0
Strengths
6
Adv. Risks
19
Nodes
0
Edges
5
Risks
2
Seeds
0
Strengths
5
Adv. Risks
Comparison Dimensions
Complexity
Streaming Media Platform
high complexity, 21 nodes, 0 edges, 6 risks, 3 simulation seeds
Two-Sided Marketplace Platform
expert complexity, 19 nodes, 0 edges, 5 risks, 2 simulation seeds
Streaming Media Platform is simpler: high operational complexity with 21 topology nodes vs 19 for Two-Sided Marketplace Platform.
Operational Risk
Streaming Media Platform
6 risks (top: high), 5 high/critical, 0 confirmed by simulation
Two-Sided Marketplace Platform
5 risks (top: high), 5 high/critical, 1 confirmed by simulation
Two-Sided Marketplace Platform has lower operational risk: weighted severity score 20 vs 22 (1 vs 0 simulation-confirmed).
Scalability
Streaming Media Platform
4 scaling thresholds, 3 migration paths, 4 advisor scaling signals
Two-Sided Marketplace Platform
4 scaling thresholds, 3 migration paths, 6 advisor scaling signals
Two-Sided Marketplace Platform has more defined scaling paths: 4 thresholds and 3 migration paths.
Operational Maturity
Streaming Media Platform
Advisor assessment: Advanced; recommended team: Experienced Backend Team; 14 operational requirements
Two-Sided Marketplace Platform
Advisor assessment: Advanced; recommended team: Platform Engineering Team; 15 operational requirements
Both scenarios require equivalent team maturity: Advanced.
Observability
Streaming Media Platform
8 watched metrics, 7 observability recommendations, 3 simulation seeds
Two-Sided Marketplace Platform
5 watched metrics, 7 observability recommendations, 2 simulation seeds
Two-Sided Marketplace Platform has lower observability burden: 5 watched metrics vs 8.
Generator Readiness
Streaming Media Platform
generator relevance documented; topology generation relevance noted; simulation relevance noted; 3 seeds with generator notes
Two-Sided Marketplace 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 (10)
Only in Streaming Media Platform (11)
Only in Two-Sided Marketplace Platform (9)
Operational Risks
Shared (4)
Only in Streaming Media Platform (2)
Only in Two-Sided Marketplace Platform (1)
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
Streaming Media Platform has high complexity. Two-Sided Marketplace Platform has expert complexity. Simpler systems often carry different (not necessarily fewer) risks.
Streaming Media Platform
Streaming Media Platform: 6 risks (top: high), 5 high/critical, 0 confirmed by simulation
Two-Sided Marketplace Platform
Two-Sided Marketplace Platform: 5 risks (top: high), 5 high/critical, 1 confirmed by simulation
Scaling Path
Streaming Media Platform offers 4 defined scaling thresholds. Two-Sided Marketplace Platform offers 4. More defined paths means clearer evolution steps but also more anticipated growth.
Streaming Media Platform
4 scaling thresholds, 3 migration paths, 4 advisor scaling signals
Two-Sided Marketplace Platform
4 scaling thresholds, 3 migration paths, 6 advisor scaling signals
Team Maturity Requirement
Streaming Media Platform can be operated by a less experienced team. Two-Sided Marketplace Platform requires deeper operational expertise.
Streaming Media Platform
Advisor assessment: Advanced; recommended team: Experienced Backend Team; 14 operational requirements
Two-Sided Marketplace Platform
Advisor assessment: Advanced; recommended team: Platform Engineering Team; 15 operational requirements
Architecture Strengths vs Risks Balance
The advisor identifies strengths and risks grounded in knowledge relationships. A higher strengths-to-risks ratio suggests better mitigation coverage in the current topology.
Streaming Media Platform
0 strengths, 6 risks
Two-Sided Marketplace Platform
0 strengths, 5 risks
Migration Considerations
Migration Step 1
Streaming Media Platform
Synchronous transcoding in the upload request handler (blocking API response) → Async transcoding via Kafka topic with competing consumer workers
Two-Sided Marketplace Platform
Monolithic marketplace application with single database → Event-driven marketplace with Kafka + saga-based checkout flow
Both scenarios define a migration step at this stage. Streaming Media Platform: triggered by 'Upload API p99 exceeding 30 seconds due to in-process transc'. Two-Sided Marketplace Platform: triggered by 'Checkout failures from payment provider unavailability causi'.
Migration Step 2
Streaming Media Platform
Viewing history in PostgreSQL → Viewing history in Cassandra
Two-Sided Marketplace Platform
PostgreSQL full-text search for listing discovery → Elasticsearch for listing search with CDC-based indexing
Both scenarios define a migration step at this stage. Streaming Media Platform: triggered by 'PostgreSQL viewing history table exceeding 500M rows; write '. Two-Sided Marketplace Platform: triggered by 'Listing search p99 > 1s; faceted navigation (category + pric'.
Migration Step 3
Streaming Media Platform
Single CDN provider with no origin rate limiting → Multi-CDN with origin request coalescing and rate limiting
Two-Sided Marketplace Platform
Monolithic PostgreSQL serving all domain writes → Domain-separated databases with event-based cross-domain data propagation
Both scenarios define a migration step at this stage. Streaming Media Platform: triggered by 'CDN provider incident causing total origin failover; CDN mis'. Two-Sided Marketplace Platform: triggered by 'Domain teams stepping on each other's schema migrations; dat'.
Advisor Notes
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): 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.
Shared Operational Requirements
Both scenarios require: Apache Kafka: scenario has team_maturity below senior, Apache Kafka: scenario uses Kafka for event streaming or CDC, Cache sizing and eviction policy configuration.
Supporting Evidence · 15 items
Coverage Warnings
- ⚠Streaming Media 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.
- ⚠Two-Sided Marketplace 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.
- ·5 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.
Two-Sided Marketplace Platform is the recommended starting point over Streaming Media Platform
Two-Sided Marketplace Platform leads on 3 weighted dimension(s): Operational Risk, Scalability, Observability. Weighted score: 5.0 vs 2.5 for Streaming Media Platform.
Decision Intelligence
Architecture Decision Path
Structured reasoning for choosing between Streaming Media Platform and Two-Sided Marketplace Platform. Every condition and trigger traces back to comparison dimensions, advisor insights, and topology evidence.
Two-Sided Marketplace Platform is the recommended starting point over Streaming Media Platform
Two-Sided Marketplace Platform leads on 3 weighted dimension(s): Operational Risk, Scalability, Observability. Weighted score: 5.0 vs 2.5 for Streaming Media Platform. The architectures share 10 component(s), reducing migration cost if you switch later. Streaming Media Platform is the operationally simpler choice.
Where to Start
Start with Streaming Media Platform
LeftStreaming Media Platform has lower operational complexity. Starting here reduces risk and cognitive load. Migrate to the more capable architecture only when you hit concrete scaling or feature limits.
Complexity: high complexity, 21 nodes, 0 edges, 6 risks, 3 simulation seeds
Migrate when:
- Kafka consumer group lag on the transcoding topic growing during peak upload hours; content availability delay > 10 minutes for newly uploaded videos; transcoding worker CPU consistently above 85% across all instances → Increase transcoding consumer instances up to the transcoding topic partition count; tune partition count to match the maximum desired worker parallelism (set this at topic creation, not after lag appears); implement per-uploader upload rate limits to smooth burst input; consider priority queuing so premium-tier content does not wait behind bulk ingest jobs
- MinIO GET request rate spikes > 10x baseline immediately after content publish or CDN invalidation; MinIO p99 latency > 500ms; CDN miss ratio > 5% on popular content → Implement origin request coalescing (single origin fetch per CDN node per object, queue subsequent requestors for the in-flight response); pre-warm CDN edges for anticipated high-traffic content before publish; add rate limiting at the origin gateway to cap per-second origin requests per content_id
- Cassandra node CPU imbalance > 40% across cluster; write latency p99 spiking on specific nodes; nodetool tpstats showing dropped mutations on hot nodes → Add a write_bucket component to the partition key (e.g., content_id + time bucket modulo N) to distribute writes across N partitions per content_id; tune N based on expected peak write rate per content item; read queries must fan out across all N buckets and merge, which increases read complexity but eliminates write hotspots
Decision Flow
Does your team have the operational maturity to run Streaming Media Platform (advanced rating)?
If Yes
Your team can operate Streaming Media 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 Two-Sided Marketplace 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: Redis cache miss spike visible in monitoring; PostgreSQL query rate spiking for listing reads despite stable write volume; p99 listing API latency > 500ms during traffic spike events ?
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
Streaming Media Platform is the simpler choice: Streaming Media Platform is simpler: high operational complexity with 21 topology nodes vs 19 for Two-Sided Marketplace 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
Streaming Media Platform
LeftWhen operational simplicity is a top priority
HighStreaming Media Platform has lower operational complexity: fewer moving parts, easier to reason about and debug.
When your system requires decoupled async event processing
HighStreaming Media Platform includes event stream infrastructure (e.g., Kafka/Kinesis), enabling async decoupling between producers and consumers.
Two-Sided Marketplace Platform
RightWhen stability and predictability matter most
CriticalTwo-Sided Marketplace Platform carries lower overall risk weight per the advisor's assessment.
When you need well-defined scaling thresholds and migration paths
HighTwo-Sided Marketplace Platform has more documented scaling evolution steps (4 thresholds, 3 migration paths).
When you want to minimise monitoring setup overhead
ModerateTwo-Sided Marketplace Platform has a lower observability burden: fewer watched metrics and monitoring targets.
When your system requires decoupled async event processing
HighTwo-Sided Marketplace Platform includes event stream infrastructure (e.g., Kafka/Kinesis), enabling async decoupling between producers and consumers.
When to Avoid Each Scenario
Streaming Media 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: 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 your team is early-stage or solo
HighStreaming Media 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 Streaming Media Platform. Rapid growth will surface these limitations quickly.
Two-Sided Marketplace Platform
RightWhen 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: 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
HighTwo-Sided Marketplace 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 Two-Sided Marketplace Platform. Rapid growth will surface these limitations quickly.
Team Fit
Solo developer or small startup
LeftStreaming Media 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)
LeftStreaming Media Platform suits small teams that need to move fast without deep platform tooling investment.
- ↳Consider Two-Sided Marketplace 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.
- ↳Two-Sided Marketplace Platform may require additional runbook coverage and alerting investment.
Platform engineering team or SRE-equipped organisation
RightA platform team can safely operate Two-Sided Marketplace 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. Streaming Media Platform: triggered by 'Upload API p99 exceeding 30 seconds due to in-process transc'. Two-Sided Marketplace Platform: triggered by 'Checkout failures from payment provider unavailability causi'.
Migration Step 2
Both scenarios define a migration step at this stage. Streaming Media Platform: triggered by 'PostgreSQL viewing history table exceeding 500M rows; write '. Two-Sided Marketplace Platform: triggered by 'Listing search p99 > 1s; faceted navigation (category + pric'.
Migration Step 3
Both scenarios define a migration step at this stage. Streaming Media Platform: triggered by 'CDN provider incident causing total origin failover; CDN mis'. Two-Sided Marketplace Platform: triggered by 'Domain teams stepping on each other's schema migrations; dat'.
Kafka consumer group lag on the transcoding topic growing during peak upload hours; content availability delay > 10 minutes for newly uploaded videos; transcoding worker CPU consistently above 85% across all instances
Tier 1: Transcoding Worker Throughput: Transcoding consumer group undersized relative to peak upload volume. Recommended evolution: Increase transcoding consumer instances up to the transcoding topic partition count; tune partition count to match the maximum desired worker parallelism (set this at topic creation, not after lag appears); implement per-uploader upload rate limits to smooth burst input; consider priority queuing so premium-tier content does not wait behind bulk ingest jobs .
MinIO GET request rate spikes > 10x baseline immediately after content publish or CDN invalidation; MinIO p99 latency > 500ms; CDN miss ratio > 5% on popular content
Tier 2: CDN Origin Thundering Herd: CDN cache miss storm on first-play of new or recently-updated content. Recommended evolution: Implement origin request coalescing (single origin fetch per CDN node per object, queue subsequent requestors for the in-flight response); pre-warm CDN edges for anticipated high-traffic content before publish; add rate limiting at the origin gateway to cap per-second origin requests per content_id .
Redis cache miss spike visible in monitoring; PostgreSQL query rate spiking for listing reads despite stable write volume; p99 listing API latency > 500ms during traffic spike events
Tier 1: Viral Listing Thundering Herd: Cache TTL expiry on hot listings during peak traffic: all concurrent requests bypass cache simultaneously. Recommended evolution: Implement staggered TTL jitter on listing cache entries; use probabilistic early refresh (refresh before TTL expiry when remaining TTL < 20% and request rate is high); implement single-flight/request coalescing at the application layer to collapse concurrent cache misses into a single database read .
Saga compensation events appearing in order event log; checkout p99 > 2s; pg_locks showing contended rows on inventory_reservations table; idempotency key conflicts increasing in payment service logs
Tier 2: Checkout Saga Contention: Concurrent checkout transactions competing for the same inventory rows; saga timeout thresholds too aggressive. Recommended evolution: Increase inventory reservation table partition count; tune saga step timeout to 2x the observed p99 for each step under load; implement a per-listing checkout serialization queue to prevent N concurrent sagas competing for the same inventory .
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
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.
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
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
Both5 high-severity risks identified. Each requires a documented runbook, alerting threshold, and on-call response procedure before running in production.
Apache Cassandra: scenario has team_maturity below staff_plus
LeftCassandra has the highest operational complexity of common datastores: consider managed options (Astra DB, Keyspaces) or simpler alternatives
Required maturity: staff_plus
Apache Cassandra: scenario has time_series or iot_telemetry workload
LeftDesign partition keys with time-bucketing (e.g., date prefix) to prevent wide partitions as data grows
Required maturity: staff_plus
Apache Cassandra: scenario requires ad-hoc queries or analytics
LeftCassandra cannot efficiently query non-partition-key dimensions: pair with Elasticsearch or ClickHouse for analytics
Required maturity: staff_plus
MinIO: scenario enables versioning without lifecycle expiration policies
LeftConfigure ILM lifecycle policies with expiration rules for versioned objects; without expiration, version accumulation on high-churn objects consumes storage unboundedly
Required maturity: mid_level
MinIO: scenario stores large numbers of small objects (< 100KB average size)
LeftMinIO's per-request overhead reduces effective throughput for small objects; evaluate aggregating small objects into larger archives or using a key-value store for small object access patterns
Required maturity: mid_level
Minimum team maturity: Experienced Backend Team
LeftThis scenario has high operational complexity. It is recommended for Experienced Backend Team teams or higher.
Required maturity: experienced_backend_team
Elasticsearch: scenario has full_text_search or log_analytics workload
RightConfigure ILM policies from day one to prevent shard explosion as data grows
Required maturity: senior
Elasticsearch: scenario uses Elasticsearch as a primary datastore
RightElasticsearch is a search index, not a source of truth: add a durable primary store and sync to ES
Required maturity: senior
Elasticsearch: scenario uses dynamic mappings on high-cardinality fields
RightDefine explicit index mappings: dynamic mapping on high-cardinality fields causes mapping explosions and cluster instability
Required maturity: senior
Minimum team maturity: Platform Engineering Team
RightThis scenario has expert operational complexity. It is recommended for Platform Engineering Team teams or higher.
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
Streaming Media Platform
LeftGenerator relevance documented but not yet production-ready.
For content platform briefs with video or audio delivery requirements, the generator should output the Kafka async transcoding pipeline, MinIO object storage, and CDN-first delivery as the canonical composition. Redis playback session with TTL enforcement and Cassandra for time-ordered viewing history should be generated as separate store responsibilities. The generator must flag the partition key design decision for the Cassandra history table as a mandatory architecture decision requiring explicit access pattern enumeration before schema creation.
Two-Sided Marketplace Platform
RightGenerator relevance documented but not yet production-ready.
For marketplace product briefs, the generator must produce the full event-driven composition: API gateway → domain services → outbox → Kafka → downstream consumers. Saga orchestration templates for the checkout flow (create_order → reserve_inventory → charge_payment → notify_seller) must be generated with explicit compensation paths. The notification subsystem (RabbitMQ + dead-letter queue) must be generated as a separate deployable unit with its own operational SLA.
Supporting Evidence
| Type | Reference | Explanation |
|---|---|---|
| Comparison | compare_streaming_media_platform_vs_marketplace_platform | Full comparison of Streaming Media Platform vs Two-Sided Marketplace Platform: 6 dimensions, 10 shared components, 4 shared risks. |
| Advisor | advisor_streaming_media_platform | Advisor for Streaming Media Platform: 0 strengths, 6 risks, maturity: advanced. |
| Advisor | advisor_marketplace_platform | Advisor for Two-Sided Marketplace Platform: 0 strengths, 5 risks, maturity: advanced. |
| Scenario | streaming_media_platform | Scenario 'Streaming Media Platform': 4 scaling thresholds, 3 migration paths, complexity: high. |
| Scenario | marketplace_platform | Scenario 'Two-Sided Marketplace Platform': 4 scaling thresholds, 3 migration paths, complexity: expert. |
| Risk Path | prop_workload_profile_event_streaming_workload_risk_queue_backlog_accumulation | Event Streaming → Queue Backlog Accumulation. also affects: Slow Consumer |
| Risk Path | prop_technology_profile_redis_risk_thundering_herd | Redis → Thundering Herd (Cache Stampede) |
| Risk Path | prop_workload_profile_marketplace_mixed_workload_risk_hot_partition | Marketplace Mixed → Hot Partition |
| Risk Path | prop_technology_profile_redis_risk_thundering_herd | Redis → Thundering Herd (Cache Stampede) |
| Risk Path | prop_workload_profile_event_streaming_workload_risk_queue_backlog_accumulation | 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.