DBRavenArchitecture Decision Reports

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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

AI / RAG

API Gateway Platform

Multi-Tenant SaaS

Analytics Data Platform

Analytics

Audit and Compliance Platform

Financial Ledger

Content Management Platform

Read-Heavy

Developer Tools Platform

Multi-Tenant SaaS

Distributed Job Queue Platform

Write-Heavy

E-Commerce Order Platform

Marketplace

Event-Driven Analytics Pipeline

Event-Driven

Financial Ledger Platform

Gaming Backend Platform

Realtime Collab

Geospatial Tracking Platform

Realtime Collab

Healthcare Records Platform

Financial Ledger

IoT Telemetry Ingestion Platform

Write-Heavy

ML Feature Serving Platform

AI / RAG

Multi-Tenant SaaS Platform

Multi-Tenant SaaS

Notification Delivery Platform

Event-Driven

Observability Platform

Analytics

Read-Heavy SaaS API

Read-Heavy

Realtime Collaborative Editor

Realtime Collab

Search-Heavy Content Platform

Search-Heavy

Social Feed Platform

Event-Driven

Streaming Media Platform

Event-Driven

Two-Sided Marketplace Platform

Marketplace

Write-Heavy Transactional Platform

Write-Heavy

right, removals (−)

AI Retrieval-Augmented Generation Platform

AI / RAG

API Gateway Platform

Multi-Tenant SaaS

Analytics Data Platform

Audit and Compliance Platform

Financial Ledger

Content Management Platform

Read-Heavy

Developer Tools Platform

Multi-Tenant SaaS

Distributed Job Queue Platform

Write-Heavy

E-Commerce Order Platform

Marketplace

Event-Driven Analytics Pipeline

Event-Driven

Financial Ledger Platform

Financial Ledger

Gaming Backend Platform

Realtime Collab

Geospatial Tracking Platform

Realtime Collab

Healthcare Records Platform

Financial Ledger

IoT Telemetry Ingestion Platform

Write-Heavy

ML Feature Serving Platform

AI / RAG

Multi-Tenant SaaS Platform

Multi-Tenant SaaS

Notification Delivery Platform

Event-Driven

Observability Platform

Analytics

Read-Heavy SaaS API

Read-Heavy

Realtime Collaborative Editor

Realtime Collab

Search-Heavy Content Platform

Search-Heavy

Social Feed Platform

Event-Driven

Streaming Media Platform

Event-Driven

Two-Sided Marketplace Platform

Marketplace

Write-Heavy Transactional Platform

Write-Heavy

Analytics Data Platform→Financial Ledger Platform

−11 removed+13 added3 unchanged

Components

11

Analytics Data Platform

12

Financial Ledger Platform

3 shared−8+9

Failure Modes

3

Analytics Data Platform

4

Financial Ledger Platform

0 shared−3+4

Connections

5

Analytics Data Platform

9

Financial Ledger Platform

0 shared−5+9

Components

3 shared−8 left only+9 right only

=

PostgreSQLprimary datastore

data management

=

Apache Kafkaevent stream

async processing

=

CQRS (Command Query Responsibility Segregation)architecture pattern

application logic

−

Analytics Heavy (OLAP)workload

workload

−

High-Throughput OLTPworkload

workload

−

ClickHouseprimary datastore

data management

−

Change Data Capture via WALarchitecture pattern

application logic

−

Materialized Viewarchitecture pattern

application logic

−

Queue Backlog Accumulationoperational risk

operational risk

−

Hot Partitionoperational risk

operational risk

−

Slow Consumeroperational risk

operational risk

+

Financial Transactionworkload

workload

+

Write-Heavy Transactionalworkload

workload

+

Event Sourcingarchitecture pattern

application logic

+

Transactional Outbox Patternarchitecture pattern

application logic

+

Two-Phase Commit (2PC)architecture pattern

application logic

+

Lock Contentionoperational risk

operational risk

+

Split-Brainoperational risk

operational risk

+

Write Amplification Cascadeoperational risk

operational risk

+

Schema Migration Lockoperational risk

operational risk

Failure Modes

−3 left only+4 right only

−

Queue Backlog Accumulation1 nodes affected

highhigh

−

Hot Partition0 nodes affected

highhigh

−

Slow Consumer0 nodes affected

moderate

+

Lock Contention1 nodes affected

highhigh

+

Split-Brain1 nodes affected

highhigh

+

Write Amplification Cascade0 nodes affected

highhigh

+

Schema Migration Lock0 nodes affected

highhigh

Connections

−5 left only+9 right only

−

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

+

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.

benefits from

+

PostgreSQL serves as a capable event store for moderate event volumes, leveraging JSONB payloads, UNIQUE constraints for optimistic concurrency, and WAL-based replication as a natural CDC feed for downstream projections.

supports

+

Kafka's durable, ordered, append-only log is the canonical infrastructure for an event store at scale. Topics with compaction or retention policies serve as the persistent event log that event sourcing requires.

supports

+

Event sourcing naturally produces a normalized write model (the event log) that CQRS separates from purpose-built read models (projections). Each pattern addresses what the other lacks: event sourcing provides audit and temporal query; CQRS provides fast reads without replay cost.

complements

+

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.

mitigates

+

Write-heavy transactional workloads that emit downstream events (order placed, payment captured) benefit from the outbox pattern to ensure events are published exactly when the database transaction commits: never before, never after.

benefits from

+

Two-phase commit's coordinator is a single point of failure. If the coordinator crashes after sending the prepare phase but before completing the commit phase, participants are left in an uncertain state: some may have committed and some not, creating a split-brain condition that requires manual operator intervention.

introduces riskrisk path

+

Schema migrations on write-heavy transactional tables acquire aggressive locks (AccessExclusiveLock) that block all reads and writes. On a high-traffic table receiving 5,000 writes/second, a migration lock that waits even 1 second queues 5,000 transactions behind it, causing a connection pool exhaustion cascade.

introduces riskrisk path

+

Write-heavy transactional workloads amplify lock contention: many concurrent writers contend for row-level locks on the same records (e.g., shared account balances, inventory counts), causing transactions to queue, latency to spike, and throughput to plateau well below hardware limits.

vulnerable torisk path

Six-Dimension Assessment

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

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

Complexity

← Analytics

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

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

Operational Risk

← Analytics

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

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

Scalability

depends

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

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

Operational Maturity

tie

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

4 watched metrics, 3 observability recommendations, 1 simulation seeds

Observability

← Analytics

4 watched metrics, 5 observability recommendations, 2 simulation seeds

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

Generator Readiness

depends

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

Full comparison + decision path →Topology: Analytics Data Platform →Topology: Financial Ledger Platform →