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 Streaming Media Platform
Search-Heavy Content Platform is the simpler architecture. Search-Heavy Content Platform carries lower operational risk. They share 7 component(s). Streaming Media Platform has 4 unique risk(s); Search-Heavy Content Platform has 2. Search-Heavy Content Platform requires lower team maturity to operate.
21
Nodes
0
Edges
6
Risks
3
Seeds
0
Strengths
6
Adv. Risks
13
Nodes
6
Edges
4
Risks
1
Seeds
5
Strengths
4
Adv. Risks
Comparison Dimensions
Complexity
Streaming Media Platform
high complexity, 21 nodes, 0 edges, 6 risks, 3 simulation seeds
Search-Heavy Content Platform
high complexity, 13 nodes, 6 edges, 4 risks, 1 simulation seeds
Search-Heavy Content Platform is simpler: high operational complexity with 13 topology nodes vs 21 for Streaming Media Platform.
Operational Risk
Streaming Media Platform
6 risks (top: high), 5 high/critical, 0 confirmed by simulation
Search-Heavy Content Platform
4 risks (top: high), 2 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
Streaming Media Platform
4 scaling thresholds, 3 migration paths, 4 advisor scaling signals
Search-Heavy Content Platform
4 scaling thresholds, 3 migration paths, 4 advisor scaling signals
Streaming Media 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
Search-Heavy Content Platform
Advisor assessment: Intermediate; recommended team: Experienced Backend Team; 9 operational requirements
Search-Heavy Content Platform requires lower team maturity (Intermediate) vs Advanced for Streaming Media Platform.
Observability
Streaming Media Platform
8 watched metrics, 7 observability recommendations, 3 simulation seeds
Search-Heavy Content Platform
2 watched metrics, 3 observability recommendations, 1 simulation seeds
Search-Heavy Content Platform has lower observability burden: 2 watched metrics vs 8.
Generator Readiness
Streaming Media Platform
generator relevance documented; topology generation relevance noted; simulation relevance noted; 3 seeds with generator notes
Search-Heavy Content Platform
generator relevance documented; topology generation relevance noted; simulation relevance noted; 1 seeds with generator notes
Streaming Media Platform has more documented generator readiness signals. Note: this is still preliminary.
Architecture Components
Shared (7)
Only in Streaming Media Platform (14)
Only in Search-Heavy Content Platform (6)
Operational Risks
Only in Streaming Media Platform (4)
Only in Search-Heavy Content Platform (2)
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. Search-Heavy Content Platform has high 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
Search-Heavy Content Platform
Search-Heavy Content Platform: 4 risks (top: high), 2 high/critical, 0 confirmed by simulation
Scaling Path
Streaming Media Platform offers 4 defined scaling thresholds. Search-Heavy Content 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
Search-Heavy Content Platform
4 scaling thresholds, 3 migration paths, 4 advisor scaling signals
Event-Driven vs Synchronous Processing
Streaming Media Platform uses event stream infrastructure (Kafka/Kinesis), enabling decoupled async processing. Search-Heavy Content Platform does not, keeping the stack simpler but less decoupled.
Streaming Media Platform
Event stream: async decoupling, consumer lag risk, higher ops burden
Search-Heavy Content Platform
No event stream: simpler stack, synchronous dependencies
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
Search-Heavy Content Platform
5 strengths, 4 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
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
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'. Search-Heavy Content Platform: triggered by 'Search query p99 > 500ms on full-text queries; faceted navig'.
Migration Step 2
Streaming Media Platform
Viewing history in PostgreSQL → Viewing history in Cassandra
Search-Heavy Content Platform
Synchronous dual-write (application writes to PostgreSQL then Elasticsearch) → Asynchronous CDC-based indexing pipeline (PostgreSQL → WAL CDC → Kafka → Elasticsearch)
Both scenarios define a migration step at this stage. Streaming Media Platform: triggered by 'PostgreSQL viewing history table exceeding 500M rows; write '. Search-Heavy Content Platform: triggered by 'Application write latency increasing due to Elasticsearch in'.
Migration Step 3
Streaming Media Platform
Single CDN provider with no origin rate limiting → Multi-CDN with origin request coalescing and rate limiting
Search-Heavy Content Platform
Single Elasticsearch cluster serving all query types → Separate read-optimized and write-optimized Elasticsearch indexes
Both scenarios define a migration step at this stage. Streaming Media Platform: triggered by 'CDN provider incident causing total origin failover; CDN mis'. Search-Heavy Content Platform: triggered by 'High write throughput (> 10k documents/min) causing segment '.
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): 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: Cache sizing and eviction policy configuration, Minimum team maturity: Experienced Backend Team, PostgreSQL: scenario includes high_write_throughput or write_heavy workload.
Supporting Evidence · 12 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.
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.
- ·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 Streaming Media Platform
Search-Heavy Content Platform leads on 4 weighted dimension(s): Complexity, Operational Risk, Operational Maturity. Weighted score: 6.5 vs 1.0 for Streaming Media Platform.
Decision Intelligence
Architecture Decision Path
Structured reasoning for choosing between Streaming Media Platform and Search-Heavy Content 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 Streaming Media Platform
Search-Heavy Content Platform leads on 4 weighted dimension(s): Complexity, Operational Risk, Operational Maturity. Weighted score: 6.5 vs 1.0 for Streaming Media 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
RightSearch-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 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 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: 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 ?
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.
Does your team already operate an event streaming platform (e.g., Kafka, Kinesis, Pub/Sub) in production?
If Yes
Streaming Media 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 Streaming Media 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 you need well-defined scaling thresholds and migration paths
HighStreaming Media Platform has more documented scaling evolution steps (4 thresholds, 3 migration paths).
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.
Search-Heavy Content Platform
RightWhen 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.
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.
Search-Heavy Content 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: 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.
Team Fit
Solo developer or small startup
RightSearch-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)
RightSearch-Heavy Content 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
LeftA 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. Streaming Media Platform: triggered by 'Upload API p99 exceeding 30 seconds due to in-process transc'. Search-Heavy Content Platform: triggered by 'Search query p99 > 500ms on full-text queries; faceted navig'.
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 '. Search-Heavy Content Platform: triggered by 'Application write latency increasing due to Elasticsearch in'.
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'. Search-Heavy Content Platform: triggered by 'High write throughput (> 10k documents/min) causing segment '.
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 .
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 .
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.
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.
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
Apache Kafka: scenario has team_maturity below senior
LeftKafka 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
LeftSet min.insync.replicas=2 with acks=all; monitor consumer lag as primary health signal
Required maturity: senior
Event stream operations expertise
LeftThis 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
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
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
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.
Search-Heavy Content Platform
RightGenerator 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.
Supporting Evidence
| Type | Reference | Explanation |
|---|---|---|
| Comparison | compare_streaming_media_platform_vs_search_heavy_content_platform | Full comparison of Streaming Media Platform vs Search-Heavy Content Platform: 6 dimensions, 7 shared components, 2 shared risks. |
| Advisor | advisor_streaming_media_platform | Advisor for Streaming Media Platform: 0 strengths, 6 risks, maturity: advanced. |
| Advisor | advisor_search_heavy_content_platform | Advisor for Search-Heavy Content Platform: 5 strengths, 4 risks, maturity: intermediate. |
| Scenario | streaming_media_platform | Scenario 'Streaming Media Platform': 4 scaling thresholds, 3 migration paths, complexity: high. |
| Scenario | search_heavy_content_platform | Scenario 'Search-Heavy Content Platform': 4 scaling thresholds, 3 migration paths, complexity: high. |
| 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 | 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.