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
E-Commerce Order Platform vs Streaming Media Platform: comparing architecture tradeoffs
Both architectures have comparable complexity. They share 10 component(s). E-Commerce Order Platform has 3 unique risk(s); Streaming Media Platform has 3.
21
Nodes
0
Edges
6
Risks
2
Seeds
0
Strengths
6
Adv. Risks
21
Nodes
0
Edges
6
Risks
3
Seeds
0
Strengths
6
Adv. Risks
Comparison Dimensions
Complexity
E-Commerce Order Platform
high complexity, 21 nodes, 0 edges, 6 risks, 2 simulation seeds
Streaming Media Platform
high complexity, 21 nodes, 0 edges, 6 risks, 3 simulation seeds
Both scenarios have equivalent complexity: high operational complexity, 21 topology nodes each.
Operational Risk
E-Commerce Order Platform
6 risks (top: high), 5 high/critical, 0 confirmed by simulation
Streaming Media Platform
6 risks (top: high), 5 high/critical, 0 confirmed by simulation
Both scenarios carry equivalent risk weight (22). Neither is meaningfully safer at this granularity.
Scalability
E-Commerce Order Platform
4 scaling thresholds, 3 migration paths, 4 advisor scaling signals
Streaming Media 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
E-Commerce Order Platform
Advisor assessment: Advanced; recommended team: Experienced Backend Team; 15 operational requirements
Streaming Media Platform
Advisor assessment: Advanced; recommended team: Experienced Backend Team; 14 operational requirements
Both scenarios require equivalent team maturity: Advanced.
Observability
E-Commerce Order Platform
4 watched metrics, 6 observability recommendations, 2 simulation seeds
Streaming Media Platform
8 watched metrics, 7 observability recommendations, 3 simulation seeds
E-Commerce Order Platform has lower observability burden: 4 watched metrics vs 8.
Generator Readiness
E-Commerce Order Platform
generator relevance documented; topology generation relevance noted; simulation relevance noted; 2 seeds with generator notes
Streaming Media Platform
generator relevance documented; topology generation relevance noted; simulation relevance noted; 3 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 E-Commerce Order Platform (11)
Only in Streaming Media Platform (11)
Operational Risks
Shared (3)
Only in E-Commerce Order Platform (3)
Only in Streaming Media Platform (3)
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
E-Commerce Order Platform has high complexity. Streaming Media Platform has high complexity. Simpler systems often carry different (not necessarily fewer) risks.
E-Commerce Order Platform
E-Commerce Order Platform: 6 risks (top: high), 5 high/critical, 0 confirmed by simulation
Streaming Media Platform
Streaming Media Platform: 6 risks (top: high), 5 high/critical, 0 confirmed by simulation
Scaling Path
E-Commerce Order Platform offers 4 defined scaling thresholds. Streaming Media Platform offers 4. More defined paths means clearer evolution steps but also more anticipated growth.
E-Commerce Order Platform
4 scaling thresholds, 3 migration paths, 4 advisor scaling signals
Streaming Media Platform
4 scaling thresholds, 3 migration paths, 4 advisor scaling signals
Migration Considerations
Migration Step 1
E-Commerce Order Platform
Synchronous checkout with direct database payment insert and synchronous payment API call → Saga-orchestrated checkout with outbox-based fulfillment events
Streaming Media Platform
Synchronous transcoding in the upload request handler (blocking API response) → Async transcoding via Kafka topic with competing consumer workers
Both scenarios define a migration step at this stage. E-Commerce Order Platform: triggered by 'Payment provider timeout causing full checkout rollback and '. Streaming Media Platform: triggered by 'Upload API p99 exceeding 30 seconds due to in-process transc'.
Migration Step 2
E-Commerce Order Platform
PostgreSQL full-text search for product discovery → Elasticsearch for product search with CDC-based catalog indexing
Streaming Media Platform
Viewing history in PostgreSQL → Viewing history in Cassandra
Both scenarios define a migration step at this stage. E-Commerce Order Platform: triggered by 'Product search p99 > 1s; faceted navigation (category + pric'. Streaming Media Platform: triggered by 'PostgreSQL viewing history table exceeding 500M rows; write '.
Migration Step 3
E-Commerce Order Platform
Monolithic order processing with inline notification delivery → RabbitMQ-based notification fanout with dead-letter handling
Streaming Media Platform
Single CDN provider with no origin rate limiting → Multi-CDN with origin request coalescing and rate limiting
Both scenarios define a migration step at this stage. E-Commerce Order Platform: triggered by 'Email/push notification provider timeouts causing checkout l'. Streaming Media Platform: triggered by 'CDN provider incident causing total origin failover; CDN mis'.
Advisor Notes
Risk (high): Lock Contention
Concurrent writers to the same rows serialize behind each other's row locks, so latency is set not by the work a transaction does but by how long it waits for the writers ahead of it. On a hot row the queue depth, and therefore the tail latency, grows with concurrency while throughput flattens. Blocked writers hold connections open, so a single contended row can drain the connection pool as a secondary failure.
Risk (high): 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.
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 · 14 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.
- ⚠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.
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.
E-Commerce Order Platform is the recommended starting point over Streaming Media Platform
E-Commerce Order Platform leads on 2 weighted dimension(s): Scalability, Observability. Weighted score: 4.8 vs 2.8 for Streaming Media Platform.
Decision Intelligence
Architecture Decision Path
Structured reasoning for choosing between E-Commerce Order Platform and Streaming Media Platform. Every condition and trigger traces back to comparison dimensions, advisor insights, and topology evidence.
E-Commerce Order Platform is the recommended starting point over Streaming Media Platform
E-Commerce Order Platform leads on 2 weighted dimension(s): Scalability, Observability. Weighted score: 4.8 vs 2.8 for Streaming Media Platform. The architectures share 10 component(s), reducing migration cost if you switch later.
Where to Start
Start with E-Commerce Order Platform
LeftE-Commerce Order 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, 2 simulation seeds
Migrate when:
- 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 → 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 → 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.
- CDC consumer lag on the Elasticsearch indexing consumer > 30s during catalog bulk updates; customer complaints about price changes not visible in search; search result prices diverging from checkout prices by more than the acceptable window; Kibana showing indexing throughput below the catalog update rate → Tune Elasticsearch bulk indexing batch size and flush interval to increase indexing throughput; add indexing consumer replicas with partition-based assignment to parallelize indexing across catalog segment partitions. Introduce a "price_as_of" timestamp in search results displayed to customers : this converts an invisible consistency gap into an explicit, auditable staleness signal that satisfies most checkout price dispute scenarios.
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: right scenario.
Is operational stability and minimising production risk your primary concern over feature richness or scalability ceiling?
If Yes
Both scenarios carry similar risk weight. Continue to Step 3.
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
Left 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
Both scenarios have equivalent complexity. Choose based on team preference.
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
E-Commerce Order Platform
LeftWhen 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 you want to minimise monitoring setup overhead
ModerateE-Commerce Order Platform has a lower observability burden: fewer watched metrics and monitoring targets.
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.
Streaming Media Platform
RightWhen 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.
When to Avoid Each Scenario
E-Commerce Order Platform
LeftWhen your team cannot mitigate: lock contention
HighThis architecture is significantly exposed to Lock Contention. Concurrent writers to the same rows serialize behind each other's row locks, so latency is set not by the work a transaction does but by how long it waits for the writers ahead of it. On a hot row the queue depth, and therefore the tail latency, grows with concurrency while throughput flattens. Blocked writers hold connections open, so a single contended row can drain the connection pool as a secondary failure.
When your team cannot mitigate: 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.
Streaming Media Platform
RightWhen 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.
Team Fit
Solo developer or small startup
LeftE-Commerce Order 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)
LeftE-Commerce Order Platform suits small teams that need to move fast without deep platform tooling investment.
- ↳Consider Streaming Media 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.
- ↳Streaming Media Platform may require additional runbook coverage and alerting investment.
Platform engineering team or SRE-equipped organisation
RightA platform team can safely operate Streaming Media 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. E-Commerce Order Platform: triggered by 'Payment provider timeout causing full checkout rollback and '. Streaming Media Platform: triggered by 'Upload API p99 exceeding 30 seconds due to in-process transc'.
Migration Step 2
Both scenarios define a migration step at this stage. E-Commerce Order Platform: triggered by 'Product search p99 > 1s; faceted navigation (category + pric'. Streaming Media Platform: triggered by 'PostgreSQL viewing history table exceeding 500M rows; write '.
Migration Step 3
Both scenarios define a migration step at this stage. E-Commerce Order Platform: triggered by 'Email/push notification provider timeouts causing checkout l'. Streaming Media Platform: triggered by 'CDN provider incident causing total origin failover; CDN mis'.
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. .
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 .
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
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
Both5 high-severity risks identified. Each requires a documented runbook, alerting threshold, and on-call response procedure before running in production.
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
RabbitMQ: scenario has high_throughput_writes exceeding 50k messages/second
LeftRabbitMQ 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
LeftRabbitMQ deletes acknowledged messages: use Kafka for replay-capable event streaming
Required maturity: mid_level
RabbitMQ: scenario uses classic mirrored queues for HA
LeftMigrate to quorum queues: classic mirrored queues have known split-brain behavior under network partition
Required maturity: mid_level
Apache Cassandra: scenario has team_maturity below staff_plus
RightCassandra 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
RightDesign 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
RightCassandra 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
RightConfigure 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)
RightMinIO'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
Generator Constraints
E-Commerce Order Platform
LeftGenerator 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.
Streaming Media Platform
RightGenerator 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.
Supporting Evidence
| Type | Reference | Explanation |
|---|---|---|
| Comparison | compare_ecommerce_order_platform_vs_streaming_media_platform | Full comparison of E-Commerce Order Platform vs Streaming Media Platform: 6 dimensions, 10 shared components, 3 shared risks. |
| Advisor | advisor_ecommerce_order_platform | Advisor for E-Commerce Order Platform: 0 strengths, 6 risks, maturity: advanced. |
| Advisor | advisor_streaming_media_platform | Advisor for Streaming Media Platform: 0 strengths, 6 risks, maturity: advanced. |
| Scenario | ecommerce_order_platform | Scenario 'E-Commerce Order Platform': 4 scaling thresholds, 3 migration paths, complexity: high. |
| Scenario | streaming_media_platform | Scenario 'Streaming Media 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_financial_transaction_workload_risk_deadlock | Financial Transaction → Deadlock. also affects: PostgreSQL |
| 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_technology_profile_redis_risk_thundering_herd | Referenced by the operational risk comparison dimension. |
| Risk Path | prop_workload_profile_financial_transaction_workload_risk_deadlock | 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.