Compare Scenarios

Social Feed Platform is the simpler architecture. Financial Ledger Platform carries lower operational risk. They share 4 component(s). Social Feed Platform has 5 unique risk(s); Financial Ledger Platform has 4.

Limited confidence

Left

Social Feed Platform

highExperienced Backend Team

Nodes

Edges

Risks

Seeds

Strengths

Adv. Risks

Right

Financial Ledger Platform

expertPlatform Engineering Team

Nodes

Edges

Risks

Seeds

Strengths

Adv. Risks

Comparison Dimensions

Complexity

Social Feed Platformgiven this evidence

Social Feed Platform

high complexity, 18 nodes, 0 edges, 5 risks, 4 simulation seeds

Financial Ledger Platform

expert complexity, 12 nodes, 9 edges, 4 risks, 2 simulation seeds

Social Feed Platform is simpler: high operational complexity with 18 topology nodes vs 12 for Financial Ledger Platform.

Operational Risk

Financial Ledger Platformgiven this evidence

Social Feed Platform

5 risks (top: high), 4 high/critical, 1 confirmed by simulation

Financial Ledger Platform

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

Financial Ledger Platform has lower operational risk: weighted severity score 16 vs 18 (0 vs 1 simulation-confirmed).

Scalability

Social Feed Platformgiven this evidence

Social Feed Platform

4 scaling thresholds, 3 migration paths, 6 advisor scaling signals

Financial Ledger Platform

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

Social Feed Platform has more defined scaling paths: 4 thresholds and 3 migration paths.

Operational Maturity

Tie

Social Feed Platform

Advisor assessment: Advanced; recommended team: Experienced Backend Team; 12 operational requirements

Financial Ledger Platform

Advisor assessment: Advanced; recommended team: Platform Engineering Team; 7 operational requirements

Both scenarios require equivalent team maturity: Advanced.

Observability

Financial Ledger Platformgiven this evidence

Social Feed Platform

12 watched metrics, 6 observability recommendations, 4 simulation seeds

Financial Ledger Platform

4 watched metrics, 5 observability recommendations, 2 simulation seeds

Financial Ledger Platform has lower observability burden: 4 watched metrics vs 12.

Generator Readiness

Social Feed Platformgiven this evidence

Social Feed Platform

generator relevance documented; topology generation relevance noted; simulation relevance noted; 4 seeds with generator notes

Financial Ledger Platform

generator relevance documented; topology generation relevance noted; simulation relevance noted; 2 seeds with generator notes

Social Feed Platform has more documented generator readiness signals. Note: this is still preliminary.

Architecture Components

Shared (4)

Transactional Outbox Pattern· architecture pattern Apache Kafka· event stream PostgreSQL· primary datastore Write-Heavy Transactional· workload

Only in Social Feed Platform (14)

Cache-Aside· architecture pattern Fan-Out on Read· architecture pattern Fan-Out on Write· architecture pattern Publisher-Subscriber· architecture pattern Read Replica· architecture patternFanout Amplification· operational riskHot Partition· operational riskQueue Backlog Accumulation· operational riskReplication Lag Cascade· operational riskThundering Herd (Cache Stampede)· operational riskRabbitMQ· event stream Redis· cache Event Streaming· workload Read-Heavy API Backend· workload

Only in Financial Ledger Platform (8)

CQRS (Command Query Responsibility Segregation)· architecture pattern Event Sourcing· architecture pattern Two-Phase Commit (2PC)· architecture patternLock Contention· operational riskSchema Migration Lock· operational riskSplit-Brain· operational riskWrite Amplification Cascade· operational riskFinancial Transaction· workload

Operational Risks

Only in Social Feed Platform (5)

Fanout Amplification· moderate Hot Partition· high Queue Backlog Accumulation· high Replication Lag Cascade· moderate Thundering Herd (Cache Stampede)· high

Only in Financial Ledger Platform (4)

Lock Contention· high Schema Migration Lock· high Split-Brain· high Write Amplification Cascade· high

Consistency Guarantees

Only Financial Ledger Platform (1)

Atomic multi-object

Moving from Financial Ledger Platform to Social Feed Platform

Atomic multi-object

Green = kept, red = lost (the target explicitly excludes it), amber = unproven (the target has made no claim, not the same as losing it).

Only 'Financial Ledger Platform' claims: atomic_multi_object.

Tradeoff Summary

Complexity vs Risk

Social Feed Platform has high complexity. Financial Ledger Platform has expert complexity. Simpler systems often carry different (not necessarily fewer) risks.

Social Feed Platform

Social Feed Platform: 5 risks (top: high), 4 high/critical, 1 confirmed by simulation

Financial Ledger Platform

Financial Ledger Platform: 4 risks (top: high), 4 high/critical, 0 confirmed by simulation

Scaling Path

Social Feed Platform offers 4 defined scaling thresholds. Financial Ledger Platform offers 4. More defined paths means clearer evolution steps but also more anticipated growth.

Social Feed Platform

4 scaling thresholds, 3 migration paths, 6 advisor scaling signals

Financial Ledger Platform

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

Team Maturity Requirement

Social Feed Platform can be operated by a less experienced team. Financial Ledger Platform requires deeper operational expertise.

Social Feed Platform

Advisor assessment: Advanced; recommended team: Experienced Backend Team; 12 operational requirements

Financial Ledger Platform

Advisor assessment: Advanced; recommended team: Platform Engineering Team; 7 operational requirements

Architecture Strengths vs Risks Balance

The advisor identifies strengths and risks grounded in knowledge relationships. A higher strengths-to-risks ratio suggests better mitigation coverage in the current topology.

Social Feed Platform

0 strengths, 5 risks

Financial Ledger Platform

6 strengths, 4 risks

Migration Considerations

Migration Step 1

Social Feed Platform

Monolithic feed built on PostgreSQL timeline queries → Redis pre-materialized feed with Kafka async fan-out workers

Financial Ledger Platform

Mutable account balance table with no event history → Event sourced ledger with append-only events and projected balance view

Both scenarios define a migration step at this stage. Social Feed Platform: triggered by 'PostgreSQL timeline read query p99 > 500ms; query plan for "'. Financial Ledger Platform: triggered by 'Audit requirement to reconstruct account state at any histor'.

Migration Step 2

Social Feed Platform

Uniform fan-out-on-write for all accounts → Hybrid fan-out model (fan-out-on-write for <10k followers, fan-out-on-read for high-follower accounts)

Financial Ledger Platform

Synchronous Kafka publish in transaction (dual-write pattern) → Outbox pattern with CDC relay to Kafka

Both scenarios define a migration step at this stage. Social Feed Platform: triggered by 'Fan-out worker queue lag during celebrity post events > 5 mi'. Financial Ledger Platform: triggered by 'Kafka publish failures causing financial transaction rollbac'.

Migration Step 3

Social Feed Platform

Single Redis primary for all feed data → Redis Cluster with feed keys sharded by user_id range

Financial Ledger Platform

Single PostgreSQL primary serving all reads and writes → CQRS with separate read model and write model

Both scenarios define a migration step at this stage. Social Feed Platform: triggered by 'Redis memory utilization approaching 80% of a single node; R'. Financial Ledger Platform: triggered by 'Financial dashboard query p99 > 500ms causing dashboard-driv'.

Advisor Notes

Financial Ledger Platform

Strength: The outbox pattern eliminates split-brain between a database write and a message broker publish by writing both the domain record…

The outbox pattern eliminates split-brain between a database write and a message broker publish by writing both the domain record and the outbox event in a single ACID transaction, ensuring events are published if and only if the database write committed. Key trade-off: Adds ~1ms write overhead per transaction for the outbox INSERT. Operational note: Outbox table requires a relay process: this is an additional operational component to monitor. Evidence: Atomic write to both business table and outbox table in one transaction: no window for inconsistency.

Social Feed Platform

Risk (high): 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.

Financial Ledger Platform

Risk (high): Lock Contention

Concurrent writers to the same rows serialize behind each other's row locks, so latency is set not by the work a transaction does but by how long it waits for the writers ahead of it. On a hot row the queue depth, and therefore the tail latency, grows with concurrency while throughput flattens. Blocked writers hold connections open, so a single contended row can drain the connection pool as a secondary failure.

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, Event stream operations expertise.

Supporting Evidence · 16 items

Scenario

social_feed_platformScenario 'Social Feed Platform' provides composition, operational complexity, scaling thresholds, migration paths, and team maturity requirements.

Scenario

financial_ledger_platformScenario 'Financial Ledger Platform' provides composition, operational complexity, scaling thresholds, migration paths, and team maturity requirements.

Scenario

financial_ledger_platformScenario 'Financial Ledger Platform' claims consistency guarantee(s): atomic_multi_object.

Topology

social_feed_platformTopology for 'social_feed_platform': 18 nodes, 0 edges, 5 risk nodes.

Topology

financial_ledger_platformTopology for 'financial_ledger_platform': 12 nodes, 9 edges, 4 risk nodes.

Risk Path

prop_architecture_pattern_fan_out_on_write_risk_fanout_amplificationFan-Out on Write → Fanout Amplification

Risk Path

prop_technology_profile_redis_risk_thundering_herdRedis → Thundering Herd (Cache Stampede)

Risk Path

prop_workload_profile_write_heavy_transactional_risk_lock_contentionWrite-Heavy Transactional → Lock Contention

Risk Path

prop_architecture_pattern_two_phase_commit_risk_split_brainTwo-Phase Commit (2PC) → Split-Brain

Seed

social_feed_platform__fanout_amplification__generic_risk_probeTests how Fanout Amplification manifests in Social Feed Platform under stress conditions. Involves 1 architecture component.

Seed

social_feed_platform__thundering_herd__generic_risk_probeTests how Thundering Herd (Cache Stampede) manifests in Social Feed Platform under stress conditions. Involves 1 architecture component.

Seed

financial_ledger_platform__lock_contention__generic_risk_probeTests how Lock Contention manifests in Financial Ledger Platform under stress conditions. Involves 1 architecture component.

Seed

financial_ledger_platform__split_brain__generic_risk_probeTests how Split-Brain manifests in Financial Ledger Platform under stress conditions. Involves 1 architecture component.

Execution

social_feed_platform__replication_lag_cascade__replication_lag_executionReplication lag exceeds 5s threshold at peak: stale reads reach 28%

Advisor

advisor_social_feed_platformAdvisor for 'Social Feed Platform': 0 strengths, 5 risks, maturity: advanced.

Advisor

advisor_financial_ledger_platformAdvisor for 'Financial Ledger Platform': 6 strengths, 4 risks, maturity: advanced.

Coverage Warnings

⚠Social Feed 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.

Final Architecture RecommendationLimited confidence

Decision between Social Feed Platform and Financial Ledger Platform depends on your specific context

Decision Intelligence

Architecture Decision Path

Structured reasoning for choosing between Social Feed Platform and Financial Ledger Platform. Every condition and trigger traces back to comparison dimensions, advisor insights, and topology evidence.

Decision between Social Feed Platform and Financial Ledger Platform depends on your specific context

Neither scenario is clearly better: weighted scores are Social Feed Platform 3.5 vs Financial Ledger Platform 4.0. The best choice depends on your specific workload, team profile, and growth trajectory. The architectures share 4 component(s), reducing migration cost if you switch later. Social Feed Platform is the operationally simpler choice.

Recommendation:Depends

Confidence Limited

Where to Start

→

Start with Social Feed Platform

Left

Social Feed 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, 18 nodes, 0 edges, 5 risks, 4 simulation seeds

Migrate when:

Kafka consumer group lag for fan-out worker group growing steadily; feed propagation latency (time from post write to follower feed update) exceeding 30s p95; Redis write rate on feed keys elevated but not saturated; post activity rate normal → Add fan-out worker replicas; implement fan-out cost routing: route high-follower-count fan-out events to a dedicated high-cost worker pool with separate Kafka consumer group and Redis write quota; use follower count threshold (e.g., >50k followers) as the routing decision. Monitor fan-out cost per post as a first-class metric.
Redis memory utilization > 75%; eviction rate rising; cache miss rate on feed reads increasing; cold feed read fallback queries appearing in PostgreSQL slow query log; feed read p99 > 100ms despite Redis being online → Reduce feed list cap from current value toward 100–150 items; increase Redis cluster capacity or shard feed keys by user_id range across multiple Redis primaries; implement tiered feed storage: hot recent items in Redis, older items fetched from PostgreSQL on demand with explicit product UX affordance
PostgreSQL replica lag > 10s during peak fan-out periods; follower list queries appearing in pg_stat_activity with wait_event = Lock; read replica CPU > 70%; fan-out worker follower fetch latency rising; incorrect fan-out events (missing recent followers) appearing in feed correctness monitoring → Materialize hot follower lists in Redis (TTL 60s) to absorb fan-out worker read volume; route all fan-out follower reads through Redis cache-aside before touching PostgreSQL replica; add a dedicated read replica for fan-out worker social graph reads, isolated from timeline API read replicas

Decision Flow

Does your team have the operational maturity to run Social Feed Platform (advanced rating)?

If Yes

Your team can operate Social Feed 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 Financial Ledger 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: Kafka consumer group lag for fan-out worker group growing steadily; feed propagation latency (time from post write to follower feed update) exceeding 30s p95; Redis write rate on feed keys elevated but not saturated; post activity rate normal ?

If Yes

Left scenario has more defined scaling evolution paths for this growth pattern.

Left

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

Social Feed Platform is the simpler choice: Social Feed Platform is simpler: high operational complexity with 18 topology nodes vs 12 for Financial Ledger Platform.

Left

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

Social Feed Platform

Left

When operational simplicity is a top priority

High

Social Feed Platform has lower operational complexity: fewer moving parts, easier to reason about and debug.

When you need well-defined scaling thresholds and migration paths

High

Social Feed Platform has more documented scaling evolution steps (4 thresholds, 3 migration paths).

When your system requires decoupled async event processing

High

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

Financial Ledger Platform

Right

When stability and predictability matter most

Critical

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

When you want to minimise monitoring setup overhead

Moderate

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

When your architecture benefits from: the outbox pattern eliminates split-brain between a database write and a message broker publish by writing both the domain record…

Moderate

When your architecture benefits from: financial transaction workloads benefit from event sourcing because the event log provides an immutable audit trail, enables…

Moderate

Financial transaction workloads benefit from event sourcing because the event log provides an immutable audit trail, enables temporal queries (balance at any past date), and makes the derivation of current state fully traceable: meeting regulatory requirements that state-mutation databases cannot satisfy. Key trade-off: Event log growth is unbounded for long-lived accounts: snapshot and archival strategy required. Operational note: Financial event logs must be retained for 7-10 years (regulatory requirement): plan storage accordingly. Evidence: PCI-DSS and SOX require immutable audit trails: event sourcing provides this structurally.

When your system requires decoupled async event processing

High

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

When to Avoid Each Scenario

Social Feed Platform

Left

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 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 is early-stage or solo

High

Social Feed 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 7 predicted bottlenecks for Social Feed Platform. Rapid growth will surface these limitations quickly.

Financial Ledger Platform

Right

When your team cannot mitigate: lock contention

High

This architecture is significantly exposed to Lock Contention. Concurrent writers to the same rows serialize behind each other's row locks, so latency is set not by the work a transaction does but by how long it waits for the writers ahead of it. On a hot row the queue depth, and therefore the tail latency, grows with concurrency while throughput flattens. Blocked writers hold connections open, so a single contended row can drain the connection pool as a secondary failure.

When your team cannot mitigate: split-brain

High

This architecture is significantly exposed to Split-Brain. A failover mechanism promotes a new leader without confirming the old one has stopped, so two nodes simultaneously believe they hold the primary role and both accept writes. The two histories diverge, and when the partition that triggered the failover heals, one set of committed transactions must be discarded.

When your team is early-stage or solo

High

Financial Ledger 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 7 predicted bottlenecks for Financial Ledger Platform. Rapid growth will surface these limitations quickly.

Team Fit

Solo developer or small startup

Left

Social Feed 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

Social Feed Platform suits small teams that need to move fast without deep platform tooling investment.

↳Consider Financial Ledger 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.
↳Financial Ledger Platform may require additional runbook coverage and alerting investment.

Platform engineering team or SRE-equipped organisation

Right

A platform team can safely operate Financial Ledger 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 for fan-out worker group growing steadily; feed propagation latency (time from post write to follower feed update) exceeding 30s p95; Redis write rate on feed keys elevated but not saturated; post activity rate normal

Tier 1: Fan-Out Worker Queue Backlog: Fan-out worker pool undersized for burst post activity or a celebrity post creating a sustained high-fan-out event. Recommended evolution: Add fan-out worker replicas; implement fan-out cost routing: route high-follower-count fan-out events to a dedicated high-cost worker pool with separate Kafka consumer group and Redis write quota; use follower count threshold (e.g., >50k followers) as the routing decision. Monitor fan-out cost per post as a first-class metric. .

Left→DependsAct Soon

Redis memory utilization > 75%; eviction rate rising; cache miss rate on feed reads increasing; cold feed read fallback queries appearing in PostgreSQL slow query log; feed read p99 > 100ms despite Redis being online

Tier 2: Redis Feed Memory Ceiling: Redis feed list storage approaching memory limit; feed items being evicted before TTL; or feed list cap set too high for available memory. Recommended evolution: Reduce feed list cap from current value toward 100–150 items; increase Redis cluster capacity or shard feed keys by user_id range across multiple Redis primaries; implement tiered feed storage: hot recent items in Redis, older items fetched from PostgreSQL on demand with explicit product UX affordance .

Right→DependsAct Soon

pg_locks shows contended rows on accounts table; write p99 > 50ms; deadlock errors in application logs; pg_stat_activity showing many transactions waiting for RowExclusiveLock on the same account rows

Tier 1: Hot Account Lock Contention: Concurrent debit/credit transactions competing for the same account row versions. Recommended evolution: Implement optimistic locking with version column and retry; or queue concurrent updates for the same account entity through an account-scoped serialization queue at the application layer; or partition the accounts table by account range .

Right→DependsAct Soon

Write p99 > 100ms with synchronous_commit = remote_apply; replica WAL apply lag visible in pg_stat_replication; network jitter between primary and replica causing write latency spikes correlating with replication ACK delays

Tier 2: Synchronous Replication Write Latency: Synchronous replication write-ahead wait amplifying network latency for every committed transaction. Recommended evolution: Co-locate primary and replica in the same availability zone for lowest replication RTT; tune wal_sender_timeout and recovery_min_apply_delay; evaluate whether synchronous_commit = on (durable to primary WAL only) is acceptable for your regulatory risk model .

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

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

PostgreSQL: scenario includes high_write_throughput or write_heavy workload

Both

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

Required maturity: mid_level

Runbooks and alerting for high-severity risks

Both

4 high-severity risks identified. Each requires a documented runbook, alerting threshold, and on-call response procedure before running in production.

Cache sizing and eviction policy configuration

Left

Redis 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

Left

This scenario has high operational complexity. It is recommended for Experienced Backend Team teams or higher.

Required maturity: experienced_backend_team

RabbitMQ: scenario has high_throughput_writes exceeding 50k messages/second

Left

RabbitMQ throughput ceiling may be insufficient: evaluate Kafka for sustained high-throughput event streams

Required maturity: mid_level

RabbitMQ: scenario requires event replay or consumer catch-up from historical messages

Left

RabbitMQ deletes acknowledged messages: use Kafka for replay-capable event streaming

Required maturity: mid_level

RabbitMQ: scenario uses classic mirrored queues for HA

Left

Migrate to quorum queues: classic mirrored queues have known split-brain behavior under network partition

Required maturity: mid_level

Redis: scenario has read_heavy workload with high cache miss risk

Left

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

Left

Redis is not a durable store: add persistence layer or treat Redis as expendable cache only

Required maturity: junior

Minimum team maturity: Platform Engineering Team

Right

This scenario has expert operational complexity. It is recommended for Platform Engineering Team teams or higher.

Required maturity: platform_engineering_team

Replica lag monitoring and lag-aware routing

Right

Read replicas must be monitored for replication lag. The application router must include a max_lag_ms threshold; queries above that threshold must be redirected to the primary.

Generator Constraints

Social Feed Platform

Left

Generator relevance documented but not yet production-ready.

For social product briefs with user-follows-user semantics, the generator must produce the hybrid fan-out composition: outbox → Kafka → fan-out worker → Redis feed list. The generator must include the follower count routing threshold as a first-class configuration parameter, and must generate the feed merge logic for high-follower-count accounts at read time. Redis feed list schema (LPUSH + LTRIM pattern with feed cap) must be generated with explicit cap configuration and PostgreSQL fallback handling.

Financial Ledger Platform

Right

Generator relevance documented but not yet production-ready.

For financial product briefs, the generator must output event sourcing + outbox + CQRS as mandatory components, not optional enhancements. synchronous_commit settings, replication standby configuration, and Kafka min.insync.replicas must be generated as explicit configuration, not left as defaults. Two-phase commit should be presented as a cross-service coordination option with explicit complexity warnings.

Supporting Evidence

Type	Reference	Explanation
Comparison	`compare_social_feed_platform_vs_financial_ledger_platform`	Full comparison of Social Feed Platform vs Financial Ledger Platform: 6 dimensions, 4 shared components, 0 shared risks.
Advisor	`advisor_social_feed_platform`	Advisor for Social Feed Platform: 0 strengths, 5 risks, maturity: advanced.
Advisor	`advisor_financial_ledger_platform`	Advisor for Financial Ledger Platform: 6 strengths, 4 risks, maturity: advanced.
Scenario	`social_feed_platform`	Scenario 'Social Feed Platform': 4 scaling thresholds, 3 migration paths, complexity: high.
Scenario	`financial_ledger_platform`	Scenario 'Financial Ledger Platform': 4 scaling thresholds, 3 migration paths, complexity: expert.
Risk Path	`prop_architecture_pattern_fan_out_on_write_risk_fanout_amplification`	Fan-Out on Write → Fanout Amplification
Risk Path	`prop_technology_profile_redis_risk_thundering_herd`	Redis → Thundering Herd (Cache Stampede)
Risk Path	`prop_workload_profile_write_heavy_transactional_risk_lock_contention`	Write-Heavy Transactional → Lock Contention
Risk Path	`prop_architecture_pattern_two_phase_commit_risk_split_brain`	Two-Phase Commit (2PC) → Split-Brain
Risk Path	`prop_architecture_pattern_fan_out_on_write_risk_fanout_amplification`	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.

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