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 Observability Platform
Search-Heavy Content Platform is the simpler architecture. Search-Heavy Content Platform carries lower operational risk. They share 5 component(s). Observability Platform has 5 unique risk(s); Search-Heavy Content Platform has 3. Search-Heavy Content Platform requires lower team maturity to operate.
21
Nodes
0
Edges
6
Risks
2
Seeds
0
Strengths
6
Adv. Risks
13
Nodes
6
Edges
4
Risks
1
Seeds
5
Strengths
4
Adv. Risks
Comparison Dimensions
Complexity
Observability Platform
high complexity, 21 nodes, 0 edges, 6 risks, 2 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 Observability Platform.
Operational Risk
Observability Platform
6 risks (top: high), 4 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 20 (0 vs 0 simulation-confirmed).
Scalability
Observability 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
Observability Platform has more defined scaling paths: 4 thresholds and 3 migration paths.
Operational Maturity
Observability 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 Observability Platform.
Observability
Observability Platform
4 watched metrics, 5 observability recommendations, 2 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 4.
Generator Readiness
Observability Platform
generator relevance documented; topology generation relevance noted; simulation relevance noted; 2 seeds with generator notes
Search-Heavy Content Platform
generator relevance documented; topology generation relevance noted; simulation relevance noted; 1 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 (5)
Only in Observability Platform (16)
Only in Search-Heavy Content Platform (8)
Operational Risks
Shared (1)
Only in Observability Platform (5)
Only in Search-Heavy Content 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
Observability Platform has high complexity. Search-Heavy Content Platform has high complexity. Simpler systems often carry different (not necessarily fewer) risks.
Observability Platform
Observability Platform: 6 risks (top: high), 4 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
Observability Platform offers 4 defined scaling thresholds. Search-Heavy Content Platform offers 4. More defined paths means clearer evolution steps but also more anticipated growth.
Observability 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
Observability Platform uses event stream infrastructure (Kafka/Kinesis), enabling decoupled async processing. Search-Heavy Content Platform does not, keeping the stack simpler but less decoupled.
Observability 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.
Observability Platform
0 strengths, 6 risks
Search-Heavy Content Platform
5 strengths, 4 risks
Migration Considerations
Migration Step 1
Observability Platform
Prometheus + Grafana stack with local time-series storage → Kafka-buffered ClickHouse ingestion with Redis-backed alert evaluation
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. Observability Platform: triggered by 'Prometheus storage limits reached at production metric volum'. Search-Heavy Content Platform: triggered by 'Search query p99 > 500ms on full-text queries; faceted navig'.
Migration Step 2
Observability Platform
Log shipping directly to Elasticsearch without Kafka buffer → Kafka-buffered log ingestion with backpressure and sampling controls
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. Observability Platform: triggered by 'Elasticsearch indexing pressure causing log rejection (HTTP '. Search-Heavy Content Platform: triggered by 'Application write latency increasing due to Elasticsearch in'.
Migration Step 3
Observability Platform
Direct ClickHouse queries for alert evaluation on every alert tick → TimescaleDB continuous aggregates as pre-computed alert evaluation views
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. Observability Platform: triggered by 'Alert evaluation latency > 1s causing missed alert firing wi'. 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): Disk I/O Saturation
The storage device reaches its IOPS or throughput ceiling, causing all disk- dependent database operations to queue behind I/O requests, driving latency from sub-millisecond to hundreds of milliseconds and degrading all database operations simultaneously.
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, Elasticsearch: scenario has full_text_search or log_analytics workload, Elasticsearch: scenario uses Elasticsearch as a primary datastore.
Supporting Evidence · 12 items
Coverage Warnings
- ⚠Observability 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 Observability Platform
Search-Heavy Content Platform leads on 4 weighted dimension(s): Complexity, Operational Risk, Operational Maturity. Weighted score: 6.5 vs 1.0 for Observability Platform.
Decision Intelligence
Architecture Decision Path
Structured reasoning for choosing between Observability 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 Observability Platform
Search-Heavy Content Platform leads on 4 weighted dimension(s): Complexity, Operational Risk, Operational Maturity. Weighted score: 6.5 vs 1.0 for Observability Platform. The architectures share 5 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 Observability Platform (advanced rating)?
If Yes
Your team can operate Observability 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: ClickHouse part merge frequency increasing; dashboard queries timing out on metrics with high label cardinality; ClickHouse system.metrics showing active_parts count elevated; new metric instrumentation causing sudden storage growth disproportionate to fleet size; query_log showing metrics queries scanning full column segments without pruning ?
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
Observability 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 Observability 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
Observability Platform
LeftWhen you need well-defined scaling thresholds and migration paths
HighObservability Platform has more documented scaling evolution steps (4 thresholds, 3 migration paths).
When your system requires decoupled async event processing
HighObservability 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
Observability Platform
LeftWhen your team cannot mitigate: disk i/o saturation
HighThis architecture is significantly exposed to Disk I/O Saturation. The storage device reaches its IOPS or throughput ceiling, causing all disk- dependent database operations to queue behind I/O requests, driving latency from sub-millisecond to hundreds of milliseconds and degrading all database operations simultaneously.
When 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 is early-stage or solo
HighObservability 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 8 predicted bottlenecks for Observability 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 Observability 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.
- ↳Observability Platform may require additional runbook coverage and alerting investment.
Platform engineering team or SRE-equipped organisation
LeftA platform team can safely operate Observability 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. Observability Platform: triggered by 'Prometheus storage limits reached at production metric volum'. 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. Observability Platform: triggered by 'Elasticsearch indexing pressure causing log rejection (HTTP '. 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. Observability Platform: triggered by 'Alert evaluation latency > 1s causing missed alert firing wi'. Search-Heavy Content Platform: triggered by 'High write throughput (> 10k documents/min) causing segment '.
ClickHouse part merge frequency increasing; dashboard queries timing out on metrics with high label cardinality; ClickHouse system.metrics showing active_parts count elevated; new metric instrumentation causing sudden storage growth disproportionate to fleet size; query_log showing metrics queries scanning full column segments without pruning
Tier 1: Metric Cardinality Budget Exceeded: Unbounded label cardinality generating millions of distinct time series that exceed ClickHouse part merge capacity and query planner pruning effectiveness. Recommended evolution: Enforce a cardinality budget at ingestion: before a metric is accepted, evaluate the distinct value count of each label dimension against a per-dimension limit (e.g., max 100 distinct values for any single label key). Reject or rewrite metrics that exceed the budget: rewrite user_id labels to user_cohort or drop them entirely. Implement a cardinality analysis dashboard showing the top 10 highest-cardinality metric series sorted by storage cost. ClickHouse distributed table partitioning by metric name reduces the impact of a single high-cardinality metric on global query performance. .
Kafka log topic consumer lag growing > 1 million messages during incident periods; Elasticsearch indexing throughput metrics showing queue buildup; incident post-mortems noting that relevant log records were not available in the search interface during the incident; log consumer memory pressure from unbounded batch accumulation
Tier 2: Log Volume Spike Exceeding Consumer Throughput: Log Kafka consumer sized for normal throughput; unable to drain the spike volume produced during incident-driven log floods. Recommended evolution: Size the log consumer for 10x normal throughput, not 1x: observability platform capacity must be planned for the incident scenario, not the steady state. Implement consumer autoscaling triggered by consumer lag metric: when Kafka consumer lag exceeds a threshold, add consumer instances automatically. Implement log sampling at the producer side for DEBUG and INFO level messages during identified spike periods : preserve all ERROR and WARN messages, sample INFO at 10%, sample DEBUG at 1%. This bounds the worst-case log volume without sacrificing diagnostic signal. .
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.
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
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
Both4 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
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
ClickHouse: scenario has analytics_olap or event_aggregation workload
LeftBatch inserts to ClickHouse in minimum 1k-row batches; single-row inserts cause part fragmentation
Required maturity: mid_level
ClickHouse: scenario uses ClickHouse for OLTP workloads
LeftClickHouse is an OLAP engine: mutations (UPDATE/DELETE) are async and expensive; use PostgreSQL for transactional workloads
Required maturity: mid_level
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
TimescaleDB: scenario requires real-time aggregation rollups at high insert rates
LeftConfigure continuous aggregates with appropriate refresh intervals; do not use caggs for sub-second freshness requirements: use a streaming aggregation layer instead
Required maturity: mid_level
PostgreSQL: scenario includes high_write_throughput or write_heavy workload
RightDeploy PgBouncer in transaction-mode pooling before relying on vertical scaling
Required maturity: mid_level
Generator Constraints
Observability Platform
LeftGenerator relevance documented but not yet production-ready.
For observability platform product briefs, the generator must output the ClickHouse schema for raw + rollup metrics tables with the continuous materialized view pipeline, Kafka topic configuration per telemetry type (retention, partition count, consumer group strategy), Elasticsearch index template with dynamic mapping disabled and ILM policy, and Redis alert state schema as first-class generated artifacts. Cardinality budget enforcement configuration and alert grouping rules must be generated as required operational components alongside the ingestion pipeline.
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_observability_platform_vs_search_heavy_content_platform | Full comparison of Observability Platform vs Search-Heavy Content Platform: 6 dimensions, 5 shared components, 1 shared risks. |
| Advisor | advisor_observability_platform | Advisor for Observability 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 | observability_platform | Scenario 'Observability 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_time_series_metrics_risk_disk_io_saturation | Time-Series Metrics → Disk I/O Saturation |
| Risk Path | prop_workload_profile_write_heavy_transactional_risk_wal_saturation | Write-Heavy Transactional → WAL Saturation |
| Risk Path | prop_technology_profile_redis_risk_thundering_herd | Redis → Thundering Herd (Cache Stampede) |
| Risk Path | prop_workload_profile_time_series_metrics_risk_disk_io_saturation | Referenced by the operational risk comparison dimension. |
| Risk Path | prop_workload_profile_write_heavy_transactional_risk_wal_saturation | Referenced by the operational risk comparison dimension. |
Limitations
- ·Decision guidance is grounded in YAML knowledge only. Not measured from any production system.
- ·Recommendations are deterministic heuristics based on structured knowledge. Your specific workload, team profile, and business context may lead to different conclusions.
- ·Generator constraints are preliminary. No scenario should be treated as production generation-ready at this stage.
Comparison complete
Profile, topology, simulation, advisor, comparison, and decision path are ready. Your architecture decision is grounded in structured knowledge and deterministic reasoning.