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 (−)

Read-Heavy SaaS APIEvent-Driven Analytics Pipeline
6 removed+3 added3 unchanged

Components

7

Read-Heavy SaaS API

5

Event-Driven Analytics Pipeline

2 shared5+3

Failure Modes

2

Read-Heavy SaaS API

1

Event-Driven Analytics Pipeline

1 shared1

Connections

6

Read-Heavy SaaS API

0

Event-Driven Analytics Pipeline

0 shared6

Components

2 shared5 left only+3 right only
=
PostgreSQLprimary datastore
data management
=
Replication Lag Cascadeoperational risk
operational risk
Read-Heavy API Backendworkload
workload
Rediscache
acceleration
Connection Poolingarchitecture pattern
interface or access
Read Replicaarchitecture pattern
application logic
Connection Pool Exhaustionoperational risk
operational risk
+
High-Throughput OLTPworkload
workload
+
Apache Kafkaevent stream
async processing
+
Change Data Capture via WALarchitecture pattern
application logic

Failure Modes

1 shared1 left only
=
Replication Lag Cascade1 nodes affected
moderate
Connection Pool Exhaustion1 nodes affected
highhigh

Connections

6 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
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.

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

Complexity

Event-Driven →

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

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

Operational Risk

Event-Driven →

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

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

Scalability

← Read-Heavy

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

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

Operational Maturity

← Read-Heavy

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

8 watched metrics, 3 observability recommendations, 2 simulation seeds

Observability

Event-Driven →

0 watched metrics, 0 observability recommendations, 0 simulation seeds

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

Generator Readiness

← Read-Heavy

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