Compare Scenarios

Both architectures have comparable complexity. Observability Platform carries lower operational risk. They share 11 component(s). Streaming Media Platform has 3 unique risk(s); Observability Platform has 3.

Limited confidence

Left

Streaming Media Platform

highExperienced Backend Team

Nodes

Edges

Risks

Seeds

Strengths

Adv. Risks

Right

Observability Platform

highExperienced Backend Team

Nodes

Edges

Risks

Seeds

Strengths

Adv. Risks

Comparison Dimensions

Complexity

Tie

Streaming Media Platform

high complexity, 21 nodes, 0 edges, 6 risks, 3 simulation seeds

Observability Platform

high complexity, 21 nodes, 0 edges, 6 risks, 2 simulation seeds

Both scenarios have equivalent complexity: high operational complexity, 21 topology nodes each.

Operational Risk

Observability Platformgiven this evidence

Streaming Media Platform

6 risks (top: high), 5 high/critical, 0 confirmed by simulation

Observability Platform

6 risks (top: high), 4 high/critical, 0 confirmed by simulation

Observability Platform has lower operational risk: weighted severity score 20 vs 22 (0 vs 0 simulation-confirmed).

Scalability

Depends

Streaming Media Platform

4 scaling thresholds, 3 migration paths, 4 advisor scaling signals

Observability Platform

4 scaling thresholds, 3 migration paths, 4 advisor scaling signals

Both scenarios have comparable scaling paths. The best choice depends on your specific growth trajectory. Streaming Media Platform and Observability Platform offer similar numbers of defined evolution steps.

Operational Maturity

Tie

Streaming Media Platform

Advisor assessment: Advanced; recommended team: Experienced Backend Team; 14 operational requirements

Observability Platform

Advisor assessment: Advanced; recommended team: Experienced Backend Team; 14 operational requirements

Both scenarios require equivalent team maturity: Advanced.

Observability

Observability Platformgiven this evidence

Streaming Media Platform

8 watched metrics, 7 observability recommendations, 3 simulation seeds

Observability Platform

4 watched metrics, 5 observability recommendations, 2 simulation seeds

Observability Platform has lower observability burden: 4 watched metrics vs 8.

Generator Readiness

Depends

Streaming Media Platform

generator relevance documented; topology generation relevance noted; simulation relevance noted; 3 seeds with generator notes

Observability 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 (11)

Backpressure· architecture pattern Competing Consumers· architecture pattern Rate Limiting· architecture pattern Change Data Capture via WAL· architecture patternDisk I/O Saturation· operational riskHot Partition· operational riskSlow Consumer· operational riskApache Kafka· event stream Redis· cache Event Streaming· workload Time-Series Metrics· workload

Only in Streaming Media Platform (10)

Cache-Aside· architecture pattern Event Sourcing· architecture patternCascading Failure· operational riskQueue Backlog Accumulation· operational riskThundering Herd (Cache Stampede)· operational riskApache Cassandra· primary datastore MinIO· supporting component PostgreSQL· primary datastore Batch ETL Pipeline· workload Read-Heavy API Backend· workload

Only in Observability Platform (10)

Materialized View· architecture pattern Time Series Rollup· architecture patternFanout Amplification· operational riskWAL Saturation· operational riskWrite Amplification Cascade· operational riskClickHouse· primary datastore Elasticsearch· supporting component TimescaleDB· supporting component Analytics Heavy (OLAP)· workload Write-Heavy Transactional· workload

Operational Risks

Shared (3)

Disk I/O Saturation· high Hot Partition· high Slow Consumer· moderate

Only in Streaming Media Platform (3)

Cascading Failure· high Queue Backlog Accumulation· high Thundering Herd (Cache Stampede)· high

Only in Observability Platform (3)

Fanout Amplification· moderate WAL Saturation· high Write Amplification Cascade· high

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. Observability 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

Observability Platform

Observability Platform: 6 risks (top: high), 4 high/critical, 0 confirmed by simulation

Scaling Path

Streaming Media Platform offers 4 defined scaling thresholds. Observability 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

Observability Platform

4 scaling thresholds, 3 migration paths, 4 advisor scaling signals

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

Observability Platform

Prometheus + Grafana stack with local time-series storage → Kafka-buffered ClickHouse ingestion with Redis-backed alert evaluation

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'. Observability Platform: triggered by 'Prometheus storage limits reached at production metric volum'.

Migration Step 2

Streaming Media Platform

Viewing history in PostgreSQL → Viewing history in Cassandra

Observability Platform

Log shipping directly to Elasticsearch without Kafka buffer → Kafka-buffered log ingestion with backpressure and sampling controls

Both scenarios define a migration step at this stage. Streaming Media Platform: triggered by 'PostgreSQL viewing history table exceeding 500M rows; write '. Observability Platform: triggered by 'Elasticsearch indexing pressure causing log rejection (HTTP '.

Migration Step 3

Streaming Media Platform

Single CDN provider with no origin rate limiting → Multi-CDN with origin request coalescing and rate limiting

Observability Platform

Direct ClickHouse queries for alert evaluation on every alert tick → TimescaleDB continuous aggregates as pre-computed alert evaluation views

Both scenarios define a migration step at this stage. Streaming Media Platform: triggered by 'CDN provider incident causing total origin failover; CDN mis'. Observability Platform: triggered by 'Alert evaluation latency > 1s causing missed alert firing wi'.

Advisor Notes

Streaming Media Platform

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.

Observability Platform

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.

Both

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

Scenario

streaming_media_platformScenario 'Streaming Media Platform' provides composition, operational complexity, scaling thresholds, migration paths, and team maturity requirements.

Scenario

observability_platformScenario 'Observability Platform' provides composition, operational complexity, scaling thresholds, migration paths, and team maturity requirements.

Topology

streaming_media_platformTopology for 'streaming_media_platform': 21 nodes, 0 edges, 6 risk nodes.

Topology

observability_platformTopology for 'observability_platform': 21 nodes, 0 edges, 6 risk nodes.

Risk Path

prop_workload_profile_event_streaming_workload_risk_queue_backlog_accumulationEvent Streaming → Queue Backlog Accumulation. also affects: Slow Consumer

Risk Path

prop_technology_profile_redis_risk_thundering_herdRedis → Thundering Herd (Cache Stampede)

Risk Path

prop_workload_profile_time_series_metrics_risk_disk_io_saturationTime-Series Metrics → Disk I/O Saturation

Risk Path

prop_workload_profile_write_heavy_transactional_risk_wal_saturationWrite-Heavy Transactional → WAL Saturation

Seed

streaming_media_platform__queue_backlog_accumulation__queue_backlogTests how Queue Backlog Accumulation manifests in Streaming Media Platform under stress conditions. Involves 2 architecture components.

Seed

streaming_media_platform__thundering_herd__generic_risk_probeTests how Thundering Herd (Cache Stampede) manifests in Streaming Media Platform under stress conditions. Involves 1 architecture component.

Seed

observability_platform__disk_io_saturation__generic_risk_probeTests how Disk I/O Saturation manifests in Observability Platform under stress conditions. Involves 1 architecture component.

Seed

observability_platform__wal_saturation__generic_risk_probeTests how WAL Saturation manifests in Observability Platform under stress conditions. Involves 1 architecture component.

Advisor

advisor_streaming_media_platformAdvisor for 'Streaming Media Platform': 0 strengths, 6 risks, maturity: advanced.

Advisor

advisor_observability_platformAdvisor for 'Observability Platform': 0 strengths, 6 risks, maturity: advanced.

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.
⚠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.
·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.

Final Architecture RecommendationPreliminary confidence

Observability Platform is the recommended starting point over Streaming Media Platform

Observability Platform leads on 2 weighted dimension(s): Operational Risk, Observability. Weighted score: 4.8 vs 1.8 for Streaming Media Platform.

View Observability Platform →

Decision Intelligence

Architecture Decision Path

Structured reasoning for choosing between Streaming Media Platform and Observability Platform. Every condition and trigger traces back to comparison dimensions, advisor insights, and topology evidence.

Observability Platform is the recommended starting point over Streaming Media Platform

Observability Platform leads on 2 weighted dimension(s): Operational Risk, Observability. Weighted score: 4.8 vs 1.8 for Streaming Media Platform. The architectures share 11 component(s), reducing migration cost if you switch later.

Recommendation:Right

Confidence Preliminary

Where to Start

→

Start with Streaming Media Platform

Left

Streaming 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.

Right

Is operational stability and minimising production risk your primary concern over feature richness or scalability ceiling?

If Yes

Prefer Observability Platform: it carries lower operational risk weight per the advisor's assessment.

Right

If No

Proceed to Step 3 to evaluate based on scaling requirements.

Do you expect your load to reach: high sustained load with clear migration paths?

If Yes

Both scenarios have comparable scaling paths. Choose based on complexity preference.

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

Streaming Media Platform

Left

When your system requires decoupled async event processing

High

Streaming Media Platform includes event stream infrastructure (e.g., Kafka/Kinesis), enabling async decoupling between producers and consumers.

Observability Platform

Right

When stability and predictability matter most

Critical

Observability Platform carries lower overall risk weight per the advisor's assessment.

When you want to minimise monitoring setup overhead

Moderate

Observability Platform has a lower observability burden: fewer watched metrics and monitoring targets.

When your system requires decoupled async event processing

High

Observability Platform includes event stream infrastructure (e.g., Kafka/Kinesis), enabling async decoupling between producers and consumers.

When to Avoid Each Scenario

Streaming Media Platform

Left

When your team cannot mitigate: queue backlog accumulation

High

This 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)

High

This 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

High

Streaming 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

Moderate

The advisor identifies 9 predicted bottlenecks for Streaming Media Platform. Rapid growth will surface these limitations quickly.

Observability Platform

Right

When your team cannot mitigate: disk i/o saturation

High

This 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

High

This 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

High

Observability 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

Moderate

The advisor identifies 8 predicted bottlenecks for Observability Platform. Rapid growth will surface these limitations quickly.

Team Fit

Solo developer or small startup

Left

Streaming 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)

Left

Streaming Media 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

Depends

An 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

Right

A 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

Left→RightPlan

Migration Step 1

Left→RightPlan

Migration Step 2

Left→RightPlan

Migration Step 3

Left→DependsAct Soon

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 .

Left→DependsAct Soon

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 .

Right→DependsAct Soon

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. .

Right→DependsAct Soon

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. .

Readiness Requirements

Apache Kafka: scenario has team_maturity below senior

Both

Kafka 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

Both

Set min.insync.replicas=2 with acks=all; monitor consumer lag as primary health signal

Required maturity: senior

Cache sizing and eviction policy configuration

Both

Redis 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

Both

This 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

Both

This 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

Both

Implement 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

Both

Redis 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

Both

5 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

Left

Cassandra 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

Left

Design 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

Left

Cassandra 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

Left

Configure 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)

Left

MinIO'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

PostgreSQL: scenario includes high_write_throughput or write_heavy workload

Left

Deploy PgBouncer in transaction-mode pooling before relying on vertical scaling

Required maturity: mid_level

ClickHouse: scenario has analytics_olap or event_aggregation workload

Right

Batch 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

Right

ClickHouse is an OLAP engine: mutations (UPDATE/DELETE) are async and expensive; use PostgreSQL for transactional workloads

Required maturity: mid_level

Elasticsearch: scenario has full_text_search or log_analytics workload

Right

Configure ILM policies from day one to prevent shard explosion as data grows

Required maturity: senior

Elasticsearch: scenario uses Elasticsearch as a primary datastore

Right

Elasticsearch 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

Right

Define explicit index mappings: dynamic mapping on high-cardinality fields causes mapping explosions and cluster instability

Required maturity: senior

TimescaleDB: scenario requires real-time aggregation rollups at high insert rates

Right

Configure 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

Generator Constraints

Streaming Media Platform

Left

Generator 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.

Observability Platform

Right

Generator 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.

Supporting Evidence

Type	Reference	Explanation
Comparison	`compare_streaming_media_platform_vs_observability_platform`	Full comparison of Streaming Media Platform vs Observability Platform: 6 dimensions, 11 shared components, 3 shared risks.
Advisor	`advisor_streaming_media_platform`	Advisor for Streaming Media Platform: 0 strengths, 6 risks, maturity: advanced.
Advisor	`advisor_observability_platform`	Advisor for Observability Platform: 0 strengths, 6 risks, maturity: advanced.
Scenario	`streaming_media_platform`	Scenario 'Streaming Media Platform': 4 scaling thresholds, 3 migration paths, complexity: high.
Scenario	`observability_platform`	Scenario 'Observability 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_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_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.

View Streaming Media Platform →View Observability Platform →Compare another pair →Review a new system profile →