System Design: Design Principles and Patterns

Master Single Responsibility at system scale, Loose Coupling vs High Cohesion, Domain-Driven Design (DDD), Bounded Contexts, Anti-Corruption Layers, Eventual Consistency & CRDTs, and Read-Heavy vs Write-Heavy system design.

1. Single Responsibility at System Level & Loose Coupling vs High Cohesion

At the architecture level, the Single Responsibility Principle (SRP) dictates that each microservice must encapsulate a single distinct business domain and maintain its own private datastore. High Cohesion ensures related logic lives together, while Loose Coupling ensures services communicate asynchronously via events without fragile dependencies.

System-Level Single Responsibility Principle (SRP) & Private Datastores

System Design

Encapsulating Auth, Orders, and Payments into independent microservice boundaries

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Tight Coupling Cascading Failure Hazard vs Loose Coupling Event-Driven Mesh

System Design

Replacing synchronous HTTP chains with asynchronous Kafka/EventBridge event streams

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Rendering diagram…

flowchart TD
    subgraph Tight ["Tight Coupling (Fragile Synchronous Cascade)"]
        direction TB
        Client1[Client] --> Svc1[Order Service]
        Svc1 == "Synchronous HTTP" ==> Svc2[Payment Service]
        Svc2 == "Synchronous HTTP" ==> Svc3[Inventory Service]
        Svc3 == "Synchronous HTTP" ==> Svc4[Notification Service]
        Note1["⚠️ If Notification Service slows down,<br/>the ENTIRE checkout crashes with 504 Gateway Timeout!"]
    end

    subgraph Loose ["Loose Coupling & High Cohesion (Event-Driven Mesh)"]
        direction TB
        Client2[Client] --> SvcA[Order Service]
        SvcA --> Bus[(Kafka / EventBridge Event Bus)]
        Bus -. Async Event: OrderCreated .-> SvcB[Payment Service]
        Bus -. Async Event: OrderCreated .-> SvcC[Inventory Service]
        Bus -. Async Event: OrderCreated .-> SvcD[Notification Service]
        Note2["✅ Services scale independently.<br/>Notification failure does NOT block order placement!"]
    end

    classDef tightStyle fill:#fee2e2,stroke:#ef4444,stroke-width:2px,color:#991b1b;
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    class Client1,Svc1,Svc2,Svc3,Svc4,Note1 tightStyle;
    class Client2,SvcA,Bus,SvcB,SvcC,SvcD,Note2 looseStyle;

`Tight Coupling Hazards`

Synchronous RPC chains multiply unreliability.

Cascading Outages: If Service D has a 1% error rate, upstream services A, B, and C fail
Deployment Gridlock: Services must be deployed simultaneously due to shared models
Shared Database Anti-Pattern: Multiple services querying the same SQL database tables

`Loose Coupling & High Cohesion`

Building autonomous resilient microservices.

High Cohesion: Order calculations, discounts, and line items live in Order Service
Asynchronous Events: Order emits "OrderPlaced" event; downstream services react independently
Database Per Service: Strict zero-sharing of persistence layers

2. Domain-Driven Design (DDD) Basics & Tactical Building Blocks

Domain-Driven Design (DDD) (Eric Evans) aligns software design with complex business domains through an agreed Ubiquitous Language and clear tactical building blocks.

DDD Tactical Building Blocks & Aggregate Consistency Boundary

System Design

Aggregate Root, Entities, Value Objects, and Domain Event publishing

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`1. Entities vs Value Objects`

Identity vs Immutable Attributes.

Entity: Defined by unique identity (e.g. Order ID: "ord_123")
Value Object: Defined by attributes with no identity (e.g. Money($50, USD)); immutable

`2. Aggregate Root`

Enforcing transactional consistency.

Consistency Boundary: Encapsulates child entities and value objects
All external mutations must pass through the Root methods to guarantee business rules

`3. Domain Events`

Capturing state changes as events.

Immutable record of something that occurred (e.g. "PaymentReceivedEvent")
Published asynchronously to message brokers

Domain-Driven Design (DDD) Order Aggregate Root & Domain Event Dispatcher in TypeScript

A TypeScript aggregate root that encapsulates Order invariants and dispatches domain events on state changes.

3. Bounded Contexts & The Anti-Corruption Layer (ACL) Pattern

A Bounded Context defines the explicit boundary within which a domain model and its ubiquitous language apply. A "User" in the Auth context has a different data model than a "Customer" in the Billing context. When integrating with legacy systems, an Anti-Corruption Layer (ACL) translates foreign schemas to protect the clean domain model.

Bounded Contexts & Anti-Corruption Layer (ACL) Integration Map

System Design

Preventing legacy monolithic schema leaks into modern cloud microservices

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Rendering diagram…

flowchart LR
    subgraph ContextMap ["Bounded Context Mapping & Ubiquitous Language"]
        subgraph AuthContext ["Identity & Auth Context"]
            UserAuth["User Entity<br/>• email, passwordHash<br/>• mfaSecret, roles"]
        end

        subgraph BillingContext ["Billing Context"]
            CustomerBilling["Customer Entity<br/>• stripeCustomerId<br/>• billingAddress, taxId"]
        end

        subgraph ShippingContext ["Logistics & Shipping Context"]
            RecipientShipping["Recipient Entity<br/>• physicalAddress<br/>• deliveryInstructions"]
        end
    end

    subgraph LegacyMonolith ["Legacy Monolith / Third-Party ERP System"]
        LegacyTable[(Legacy Schema: TBL_USER_DATA_1998)]
    end

    ACL["Anti-Corruption Layer (ACL Adapter)<br/>Translates legacy relational rows into clean DDD Domain Entities"]

    LegacyTable <--> ACL
    ACL <--> CustomerBilling

    classDef ctxStyle fill:#eff6ff,stroke:#2563eb,stroke-width:2px,color:#1e40af;
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    class UserAuth,CustomerBilling,RecipientShipping ctxStyle;
    class LegacyTable legStyle;
    class ACL aclStyle;

`Context Mapping Patterns in Microservices`

Managing relationships across domain boundaries.

Anti-Corruption Layer (ACL): Adapter layer translating legacy payloads into clean domain entities
Shared Kernel: A small, mutually agreed-upon subset of the domain shared between two teams
Customer-Supplier: Upstream team provides APIs tailored to the downstream team's requirements

4. Eventual Consistency Trade-offs & CRDT Convergence

Distributed systems operating across multiple datacenters sacrifice strict immediate consistency for high availability (CAP Theorem AP / BASE model). Replicas synchronize in the background, achieving Eventual Consistency. Conflict-Free Replicated Data Types (CRDTs) guarantee deterministic convergence without expensive distributed locks.

Eventual Consistency Conflict-Free Convergence with CRDT PN-Counters

System Design

Mathematical merge functions resolving concurrent writes across multi-region replicas

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Rendering diagram…

sequenceDiagram
    autonumber
    participant NodeA as Replica Node A (US-East)
    participant NodeB as Replica Node B (EU-West)
    participant ClientA as Alice (US)
    participant ClientB as Bob (EU)

    Note over NodeA,NodeB: Initial Counter State: Value = 10
    ClientA->>NodeA: Increment(+2) (Alice Likes Post)
    ClientB->>NodeB: Increment(+3) (Bob Likes Post)
    NodeA->>NodeA: Local State: (A:12, B:10) -> Sum = 12
    NodeB->>NodeB: Local State: (A:10, B:13) -> Sum = 13

    Note over NodeA,NodeB: Asynchronous Network Sync (Gossip / Replication)
    NodeA->>NodeB: Sync PN-Counter Vector: [A: 2, B: 0]
    NodeB->>NodeA: Sync PN-Counter Vector: [A: 0, B: 3]

    Note over NodeA,NodeB: CRDT Merge Function: max(A_val, B_val)
    NodeA->>NodeA: Merged Vector: [A: 2, B: 3] -> Total = 10 + 5 = 15
    NodeB->>NodeB: Merged Vector: [A: 2, B: 3] -> Total = 10 + 5 = 15
    Note over NodeA,NodeB: Conflict-Free Convergence Achieved without Locks! (Eventual Consistency)

`BASE vs ACID in Distributed Systems`

Trading immediate consistency for global availability.

Basically Available: System remains operational despite node failures
Soft State: System state may drift temporarily between replicas
Eventual Consistency: Replicas converge once updates cease

`Conflict-Free Replicated Data Types (CRDTs)`

Lock-free mathematical consistency.

Commutative & Associative: Update order does not affect the final result
PN-Counter: Tracks positive increments and negative decrements separately
Used in: Collaborative editors (Figma, Google Docs), Redis Enterprise, and chat apps

Conflict-Free Replicated Data Type (PN-Counter CRDT) in Node.js

A PN-Counter CRDT implementation that merges concurrent increments and decrements without conflicts.

5. Read-Heavy vs Write-Heavy System Design

System design requires tailoring storage and compute engines to the expected Read-to-Write ratio of the application.

Read-Heavy vs Write-Heavy Architectural Paradigms Comparison

System Design

Contrasting CDN edge caching and read replicas against LSM-Tree sequential logs and Kafka write buffering

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`Read-Heavy Optimizations (Twitter, Wikipedia)`

Minimizing read latency across high query volumes.

Multi-Tier Caching: In-memory Redis + CDN Edge caching (CloudFront)
Read Replicas: Horizontally scaling read-only database replicas
Denormalized CQRS: Precomputing views into search engines (Elasticsearch)

`Write-Heavy Optimizations (IoT, Financial Ledgers)`

Maximizing ingestion throughput without disk I/O bottlenecks.

LSM-Trees (Cassandra/RocksDB): Appending writes sequentially to Memtable and WAL
Write Buffering: Ingesting into Kafka message queues and flushing in bulk batches
Sharding: Partitioning writes across nodes by Device ID or Hash

Knowledge Check

1. What is the definition of High Cohesion and Loose Coupling in system design?

2. What role does an Anti-Corruption Layer (ACL) play in Bounded Contexts?

3. What characterizes an Aggregate Root in Domain-Driven Design (DDD)?

4. How do Conflict-Free Replicated Data Types (CRDTs) achieve eventual consistency?

5. What architectural pattern is primary in Read-Heavy systems (e.g. Twitter Feed)?