DBRaven
Start Review →

Architecture Structural Diff

Compare two scenarios node-by-node. See exactly which components, failure modes, and connections are shared, removed (−), or added (+) when switching from the left scenario to the right.

Select Scenarios to Diff

left, removals (−)

AI Retrieval-Augmented Generation Platform
high

AI / RAG

API Gateway Platform
high

Multi-Tenant SaaS

Analytics Data Platform
Audit and Compliance Platform
high

Financial Ledger

Content Management Platform
moderate

Read-Heavy

Developer Tools Platform
high

Multi-Tenant SaaS

Distributed Job Queue Platform
moderate

Write-Heavy

E-Commerce Order Platform
high

Marketplace

Event-Driven Analytics Pipeline
high

Event-Driven

Financial Ledger Platform
expert

Financial Ledger

Gaming Backend Platform
high

Realtime Collab

Geospatial Tracking Platform
high

Realtime Collab

Healthcare Records Platform
expert

Financial Ledger

IoT Telemetry Ingestion Platform
high

Write-Heavy

ML Feature Serving Platform
expert

AI / RAG

Multi-Tenant SaaS Platform
moderate✓

Multi-Tenant SaaS

Notification Delivery Platform
moderate

Event-Driven

Observability Platform
high

Analytics

Read-Heavy SaaS API
moderate

Read-Heavy

Realtime Collaborative Editor
expert

Realtime Collab

Search-Heavy Content Platform
high

Search-Heavy

Social Feed Platform
high

Event-Driven

Streaming Media Platform
high

Event-Driven

Two-Sided Marketplace Platform
expert

Marketplace

Write-Heavy Transactional Platform
high

Write-Heavy

right, removals (−)

AI Retrieval-Augmented Generation Platform
high

AI / RAG

API Gateway Platform
high

Multi-Tenant SaaS

Analytics Data Platform
high✓

Analytics

Audit and Compliance Platform
high

Financial Ledger

Content Management Platform
moderate

Read-Heavy

Developer Tools Platform
high

Multi-Tenant SaaS

Distributed Job Queue Platform
moderate

Write-Heavy

E-Commerce Order Platform
high

Marketplace

Event-Driven Analytics Pipeline
high

Event-Driven

Financial Ledger Platform
expert

Financial Ledger

Gaming Backend Platform
high

Realtime Collab

Geospatial Tracking Platform
high

Realtime Collab

Healthcare Records Platform
expert

Financial Ledger

IoT Telemetry Ingestion Platform
high

Write-Heavy

ML Feature Serving Platform
expert

AI / RAG

Multi-Tenant SaaS Platform
Notification Delivery Platform
moderate

Event-Driven

Observability Platform
high

Analytics

Read-Heavy SaaS API
moderate

Read-Heavy

Realtime Collaborative Editor
expert

Realtime Collab

Search-Heavy Content Platform
high

Search-Heavy

Social Feed Platform
high

Event-Driven

Streaming Media Platform
high

Event-Driven

Two-Sided Marketplace Platform
expert

Marketplace

Write-Heavy Transactional Platform
high

Write-Heavy

Multi-Tenant SaaS Platform→Analytics Data Platform
−12 removed+11 added3 unchanged

Components

11

Multi-Tenant SaaS Platform

11

Analytics Data Platform

2 shared−9+9

Failure Modes

4

Multi-Tenant SaaS Platform

3

Analytics Data Platform

1 shared−3+2

Connections

7

Multi-Tenant SaaS Platform

5

Analytics Data Platform

0 shared−7+5

Components

2 shared−9 left only+9 right only
=
PostgreSQLprimary datastore
data management
=
Hot Partitionoperational risk
operational risk
−
Read-Heavy API Backendworkload
workload
−
Mixed OLTP (SaaS Core)workload
workload
−
Rediscache
acceleration
−
Connection Poolingarchitecture pattern
interface or access
−
Cache-Asidearchitecture pattern
application logic
−
Shardingarchitecture pattern
application logic
−
Connection Pool Exhaustionoperational risk
operational risk
−
N+1 Query Problemoperational risk
operational risk
−
Tenant Noisy Neighboroperational risk
operational risk
+
Analytics Heavy (OLAP)workload
workload
+
High-Throughput OLTPworkload
workload
+
Apache Kafkaevent stream
async processing
+
ClickHouseprimary datastore
data management
+
Change Data Capture via WALarchitecture pattern
application logic
+
CQRS (Command Query Responsibility Segregation)architecture pattern
application logic
+
Materialized Viewarchitecture pattern
application logic
+
Queue Backlog Accumulationoperational risk
operational risk
+
Slow Consumeroperational risk
operational risk

Failure Modes

1 shared−3 left only+2 right only
=
Hot Partition1 nodes affected
high
−
Connection Pool Exhaustion1 nodes affected
highhigh
−
N+1 Query Problem1 nodes affected
moderate
−
Tenant Noisy Neighbor0 nodes affected
highhigh
+
Queue Backlog Accumulation1 nodes affected
highhigh
+
Slow Consumer0 nodes affected
moderate

Connections

−7 left only+5 right only
−
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.
mitigates
−
Read-heavy APIs benefit directly from Redis as a caching tier that absorbs repeated identical reads and provides sub-millisecond response times for hot data, reducing both latency and database load.
benefits from
−
Read-heavy APIs generate large numbers of short-lived database connections. Connection pooling reduces per-request connection overhead and allows the database to serve far more concurrent requests than its max_connections limit.
benefits from
−
A connection pool bounds the total database connections an application can open, preventing connection storms during traffic spikes and protecting the database server from exceeding its connection limit.
mitigates
−
Redis clients hold persistent TCP connections per thread or goroutine. Under connection pool misconfiguration or sudden traffic spikes, the Redis server can exhaust its maxclients limit, causing cascading cache misses that amplify load on the primary database.
introduces riskrisk path
−
Read-heavy API workloads amplify N+1 query patterns: loading a list of N entities and then issuing N individual queries for related data causes database query count to grow proportionally with response size, exhausting connection pools and causing latency spikes under load.
vulnerable torisk path
−
Sharding distributes data across partitions, but poor shard key selection concentrates traffic on a small number of shards. A hot partition receives disproportionate load, becomes a bottleneck, and degrades performance for all data on that shard.
introduces riskrisk path
+
ClickHouse's columnar storage engine, vectorized query execution, and MergeTree family of table engines are specifically designed for analytics-heavy workloads: high-throughput aggregations over billions of rows with sub-second query latency.
supports
+
Analytics-heavy workloads pre-compute expensive aggregations and joins into materialized views, reducing repeated full-scan query cost from minutes per query to milliseconds per lookup.
benefits from
+
CQRS separates the write model (normalized, ACID) from the read model; materialized views implement the read model by pre-computing the denormalized view that the query side serves. Each pattern makes the other more operationally tractable.
complements
+
Kafka is the standard downstream target for WAL-based CDC pipelines: Debezium captures database WAL records and publishes them to Kafka topics, which downstream consumers process to maintain derived data stores, caches, and event-driven services.
supports
+
A slow consumer processing messages below the producer rate causes queue backlog to accumulate. If processing speed does not recover, backlog grows unboundedly, eventually causing either message loss (if the queue has a depth limit) or indefinite processing delay.
introduces riskrisk path

Six-Dimension Assessment

Structural comparison across complexity, risk, scalability, maturity, observability, and generator readiness.

moderate complexity, 11 nodes, 7 edges, 4 risks, 3 simulation seeds

Complexity

← Multi-Tenant

high complexity, 11 nodes, 5 edges, 3 risks, 1 simulation seeds

4 risks (top: high), 3 high/critical, 2 confirmed by simulation

Operational Risk

Analytics →

3 risks (top: high), 2 high/critical, 0 confirmed by simulation

4 scaling thresholds, 3 migration paths, 9 advisor scaling signals

Scalability

← Multi-Tenant

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

Advisor assessment: Intermediate; recommended team: Small Product Team; 6 operational requirements

Operational Maturity

← Multi-Tenant

Advisor assessment: Advanced; recommended team: Experienced Backend Team; 9 operational requirements

9 watched metrics, 6 observability recommendations, 3 simulation seeds

Observability

Analytics →

4 watched metrics, 3 observability recommendations, 1 simulation seeds

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

Generator Readiness

← Multi-Tenant

generator relevance documented; topology generation relevance noted; simulation relevance noted; 1 seeds with generator notes

Full comparison + decision path →Topology: Multi-Tenant SaaS Platform →Topology: Analytics Data Platform →