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 (−)

Event-Driven Analytics PipelineRead-Heavy SaaS API
3 removed+6 added3 unchanged

Components

5

Event-Driven Analytics Pipeline

7

Read-Heavy SaaS API

2 shared3+5

Failure Modes

1

Event-Driven Analytics Pipeline

2

Read-Heavy SaaS API

1 shared+1

Connections

0

Event-Driven Analytics Pipeline

6

Read-Heavy SaaS API

0 shared+6

Components

2 shared3 left only+5 right only
=
PostgreSQLprimary datastore
data management
=
Replication Lag Cascadeoperational risk
operational risk
High-Throughput OLTPworkload
workload
Apache Kafkaevent stream
async processing
Change Data Capture via WALarchitecture pattern
application logic
+
Read-Heavy API Backendworkload
workload
+
Rediscache
acceleration
+
Connection Poolingarchitecture pattern
interface or access
+
Read Replicaarchitecture pattern
application logic
+
Connection Pool Exhaustionoperational risk
operational risk

Failure Modes

1 shared+1 right only
=
Replication Lag Cascade0 nodes affected
moderate
+
Connection Pool Exhaustion1 nodes affected
highhigh

Connections

+6 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
+
PostgreSQL's built-in streaming replication provides the replication substrate that makes the read replica pattern operational. Physical and logical replication are both supported, enabling read scaling without data modification.
supports
+
The read replica pattern is structurally vulnerable to replication lag cascade because its value proposition: serving reads from replicas: depends on replica data being sufficiently current. Any condition that delays WAL replay degrades or invalidates the replica's usefulness.
vulnerable torisk path
+
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

Six-Dimension Assessment

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

high complexity, 5 nodes, 0 edges, 1 risks, 0 simulation seeds

Complexity

← Event-Driven

moderate complexity, 7 nodes, 6 edges, 2 risks, 2 simulation seeds

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

Operational Risk

← Event-Driven

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

3 scaling thresholds, 2 migration paths, 3 advisor scaling signals

Scalability

Read-Heavy →

4 scaling thresholds, 2 migration paths, 9 advisor scaling signals

Advisor assessment: Advanced; recommended team: Experienced Backend Team; 5 operational requirements

Operational Maturity

Read-Heavy →

Advisor assessment: Intermediate; recommended team: Experienced Backend Team; 7 operational requirements

0 watched metrics, 0 observability recommendations, 0 simulation seeds

Observability

← Event-Driven

8 watched metrics, 3 observability recommendations, 2 simulation seeds

generator relevance documented; topology generation relevance noted; simulation relevance noted

Generator Readiness

Read-Heavy →

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