DBRaven

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 (−)

right, removals (−)

Multi-Tenant SaaS PlatformHealthcare Records Platform
12 removed+22 added3 unchanged

Components

11

Multi-Tenant SaaS Platform

19

Healthcare Records Platform

3 shared8+16

Failure Modes

4

Multi-Tenant SaaS Platform

6

Healthcare Records Platform

0 shared4+6

Connections

7

Multi-Tenant SaaS Platform

0

Healthcare Records Platform

0 shared7

Components

3 shared8 left only+16 right only
=
Mixed OLTP (SaaS Core)workload
workload
=
PostgreSQLprimary datastore
data management
=
Rediscache
acceleration
Read-Heavy API Backendworkload
workload
Connection Poolingarchitecture pattern
interface or access
Cache-Asidearchitecture pattern
application logic
Shardingarchitecture pattern
application logic
Hot Partitionoperational risk
operational risk
Connection Pool Exhaustionoperational risk
operational risk
N+1 Query Problemoperational risk
operational risk
Tenant Noisy Neighboroperational risk
operational risk
+
Write-Heavy Transactionalworkload
workload
+
Event Streamingworkload
workload
+
Apache Kafkaevent stream
async processing
+
Event Sourcingarchitecture pattern
application logic
+
Change Data Capture via WALarchitecture pattern
application logic
+
Read Replicaarchitecture pattern
application logic
+
CQRS (Command Query Responsibility Segregation)architecture pattern
application logic
+
Index Tablearchitecture pattern
application logic
+
Transactional Outbox Patternarchitecture pattern
application logic
+
Rate Limitingarchitecture pattern
application logic
+
Replication Lag Cascadeoperational risk
operational risk
+
Lock Contentionoperational risk
operational risk
+
Schema Migration Lockoperational risk
operational risk
+
Configuration Driftoperational risk
operational risk
+
Partial Service Failureoperational risk
operational risk
+
Deadlockoperational risk
operational risk

Failure Modes

4 left only+6 right only
Hot Partition1 nodes affected
highhigh
Connection Pool Exhaustion1 nodes affected
highhigh
N+1 Query Problem1 nodes affected
moderate
Tenant Noisy Neighbor0 nodes affected
highhigh
+
Replication Lag Cascade1 nodes affected
moderate
+
Lock Contention1 nodes affected
highhigh
+
Schema Migration Lock0 nodes affected
highhigh
+
Configuration Drift0 nodes affected
moderate
+
Partial Service Failure0 nodes affected
moderate
+
Deadlock1 nodes affected
highhigh

Connections

7 left 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

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

expert complexity, 19 nodes, 0 edges, 6 risks, 3 simulation seeds

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

Operational Risk

← Multi-Tenant

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

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

Scalability

depends

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

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

Operational Maturity

← Multi-Tenant

Advisor assessment: Advanced; recommended team: Platform Engineering Team; 9 operational requirements

9 watched metrics, 6 observability recommendations, 3 simulation seeds

Observability

Healthcare →

8 watched metrics, 5 observability recommendations, 3 simulation seeds

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

Generator Readiness

depends

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