Software Architecture & System Design

Design scalable, maintainable software systems using modern architectural patterns. From microservices to event-driven architectures, we help teams build robust software solutions that grow with your business.

Software Architecture Services

Architecture Design

System architecture design with scalability, performance, and maintainability focus.

Microservices Architecture

Microservices design, decomposition strategies, and service mesh implementation.

API Design & Strategy

RESTful API design, GraphQL implementation, and API governance frameworks.

Event-Driven Architecture

Event streaming, message queues, and asynchronous communication patterns.

Performance Optimization

System performance analysis, bottleneck identification, and optimization strategies.

Architecture Reviews

Code architecture audits, technical debt assessment, and improvement recommendations.

Microservices Architecture

Microservices Architecture

Modern distributed system design with microservices patterns and containerization

  • Domain-driven design (DDD)
  • Service decomposition strategies
  • Container orchestration
  • Service mesh implementation
  • Distributed tracing
Design Microservices
Event-Driven Systems

Event-Driven Systems

Scalable event-driven architecture with message streaming and asynchronous processing

  • Event sourcing patterns
  • CQRS implementation
  • Message broker setup
  • Event streaming platforms
  • Saga pattern orchestration
Build Event Systems

Software Architecture Patterns

Layered Architecture

Traditional N-tier architecture with presentation, business logic, and data access layers.

Hexagonal Architecture

Ports and adapters pattern for testable, maintainable applications with external dependency isolation.

Event Sourcing & CQRS

Event sourcing for audit trails and CQRS for read/write separation and performance optimization.

Serverless Architecture

Function-as-a-Service (FaaS) patterns with serverless computing and event triggers.

Software Architecture Framework

Microservices Architecture Design

Domain-Driven Design (DDD)

Bounded Contexts

bounded_contexts:
  user_management:
    entities:
      - User
      - Profile
      - Preferences
    aggregates:
      - UserAccount
    domain_events:
      - UserRegistered
      - ProfileUpdated
  
  order_management:
    entities:
      - Order
      - OrderItem
      - Payment
    aggregates:
      - OrderAggregate
    domain_events:
      - OrderPlaced
      - OrderShipped
      - PaymentProcessed

Service Decomposition Strategy

  • Business Capability Alignment: Services aligned with business functions
  • Data Ownership: Each service owns its data and database
  • Team Organization: Conway’s Law consideration for team structure
  • Communication Patterns: Synchronous vs asynchronous communication

Microservices Implementation Patterns

Service Mesh Architecture

# Example: Service mesh configuration with Istio
apiVersion: networking.istio.io/v1alpha3
kind: VirtualService
metadata:
  name: user-service
spec:
  http:
  - match:
    - headers:
        user-type:
          exact: premium
    route:
    - destination:
        host: user-service
        subset: v2
  - route:
    - destination:
        host: user-service
        subset: v1
---
apiVersion: networking.istio.io/v1alpha3
kind: DestinationRule
metadata:
  name: user-service
spec:
  host: user-service
  trafficPolicy:
    circuitBreaker:
      consecutiveErrors: 3
      interval: 30s
      baseEjectionTime: 30s
  subsets:
  - name: v1
    labels:
      version: v1
  - name: v2
    labels:
      version: v2

API Gateway Pattern

  • Request Routing: Intelligent request routing to backend services
  • Authentication: Centralized authentication and authorization
  • Rate Limiting: Request throttling and quota management
  • Response Transformation: Data format transformation and aggregation

Event-Driven Architecture

Event Sourcing Implementation

Event Store Design

interface Event {
  id: string;
  aggregateId: string;
  eventType: string;
  eventData: any;
  timestamp: Date;
  version: number;
}

class EventStore {
  async saveEvents(aggregateId: string, events: Event[], expectedVersion: number): Promise<void> {
    // Optimistic concurrency control
    const currentVersion = await this.getLastVersion(aggregateId);
    if (currentVersion !== expectedVersion) {
      throw new ConcurrencyError('Version mismatch');
    }
    
    // Save events atomically
    await this.persistEvents(events);
    
    // Publish events to message bus
    await this.publishEvents(events);
  }
  
  async getEvents(aggregateId: string, fromVersion: number = 0): Promise<Event[]> {
    return await this.loadEvents(aggregateId, fromVersion);
  }
}

class Aggregate {
  private uncommittedEvents: Event[] = [];
  
  protected addEvent(event: Event): void {
    this.applyEvent(event);
    this.uncommittedEvents.push(event);
  }
  
  getUncommittedEvents(): Event[] {
    return this.uncommittedEvents;
  }
  
  markEventsAsCommitted(): void {
    this.uncommittedEvents = [];
  }
}

CQRS Implementation

Command and Query Separation

# Command side - Write operations
class CreateUserCommand:
    def __init__(self, user_id: str, email: str, name: str):
        self.user_id = user_id
        self.email = email
        self.name = name

class CreateUserCommandHandler:
    def __init__(self, user_repository: UserRepository):
        self.user_repository = user_repository
    
    def handle(self, command: CreateUserCommand):
        user = User.create(command.user_id, command.email, command.name)
        self.user_repository.save(user)
        
        # Publish domain events
        events = user.get_uncommitted_events()
        for event in events:
            self.event_bus.publish(event)

# Query side - Read operations
class UserQuery:
    def __init__(self, read_database):
        self.db = read_database
    
    def get_user_profile(self, user_id: str):
        return self.db.query(
            "SELECT * FROM user_profiles WHERE user_id = %s", 
            (user_id,)
        )
    
    def get_users_by_criteria(self, criteria: dict):
        # Optimized read queries with denormalized data
        pass

Message Streaming Architecture

Apache Kafka Implementation

kafka_topics:
  user_events:
    partitions: 12
    replication_factor: 3
    configs:
      retention.ms: 604800000  # 7 days
      compression.type: lz4
  
  order_events:
    partitions: 24
    replication_factor: 3
    configs:
      retention.ms: 2592000000  # 30 days
      cleanup.policy: compact

consumer_groups:
  user_profile_updater:
    topics: [user_events]
    processing_guarantee: exactly_once
  
  order_analytics:
    topics: [order_events]
    processing_guarantee: at_least_once

API Design and Strategy

RESTful API Design

Resource-Oriented Design

api_design:
  resources:
    users:
      endpoints:
        - GET /api/v1/users
        - GET /api/v1/users/{id}
        - POST /api/v1/users
        - PUT /api/v1/users/{id}
        - DELETE /api/v1/users/{id}
      
      relationships:
        - GET /api/v1/users/{id}/orders
        - GET /api/v1/users/{id}/profile
  
  hypermedia_controls:
    - self_links
    - related_resources
    - available_actions
  
  versioning_strategy:
    - header_based: "Accept: application/vnd.api.v1+json"
    - url_based: "/api/v1/"
    - parameter_based: "?version=1"

API Gateway Implementation

// Example: API Gateway with rate limiting and authentication
const express = require('express');
const httpProxy = require('http-proxy-middleware');
const rateLimit = require('express-rate-limit');
const jwt = require('jsonwebtoken');

const app = express();

// Rate limiting middleware
const limiter = rateLimit({
  windowMs: 15 * 60 * 1000, // 15 minutes
  max: 100 // limit each IP to 100 requests per windowMs
});

// Authentication middleware
const authenticate = (req, res, next) => {
  const token = req.headers.authorization?.split(' ')[1];
  if (!token) {
    return res.status(401).json({ error: 'No token provided' });
  }
  
  try {
    const decoded = jwt.verify(token, process.env.JWT_SECRET);
    req.user = decoded;
    next();
  } catch (error) {
    return res.status(401).json({ error: 'Invalid token' });
  }
};

// Service routing
app.use('/api/users', limiter, authenticate, httpProxy({
  target: 'http://user-service:3001',
  changeOrigin: true,
  pathRewrite: {
    '^/api/users': '/users'
  }
}));

app.use('/api/orders', limiter, authenticate, httpProxy({
  target: 'http://order-service:3002',
  changeOrigin: true,
  pathRewrite: {
    '^/api/orders': '/orders'
  }
}));

GraphQL Implementation

Schema Design

type User {
  id: ID!
  email: String!
  name: String!
  profile: UserProfile
  orders: [Order!]!
}

type UserProfile {
  id: ID!
  avatar: String
  bio: String
  preferences: UserPreferences
}

type Order {
  id: ID!
  userId: ID!
  items: [OrderItem!]!
  total: Float!
  status: OrderStatus!
  createdAt: DateTime!
}

type Query {
  user(id: ID!): User
  users(filter: UserFilter, pagination: Pagination): UserConnection
  order(id: ID!): Order
}

type Mutation {
  createUser(input: CreateUserInput!): User!
  updateUserProfile(userId: ID!, input: UpdateProfileInput!): UserProfile!
  createOrder(input: CreateOrderInput!): Order!
}

type Subscription {
  orderStatusChanged(userId: ID!): Order!
  userNotifications(userId: ID!): Notification!
}

Performance Optimization

Caching Strategies

Multi-Level Caching

class CachingService:
    def __init__(self):
        self.l1_cache = {}  # In-memory cache
        self.l2_cache = RedisClient()  # Distributed cache
        self.database = DatabaseClient()
    
    async def get_user(self, user_id: str):
        # L1 Cache check
        if user_id in self.l1_cache:
            return self.l1_cache[user_id]
        
        # L2 Cache check
        cached_user = await self.l2_cache.get(f"user:{user_id}")
        if cached_user:
            self.l1_cache[user_id] = cached_user
            return cached_user
        
        # Database query
        user = await self.database.get_user(user_id)
        if user:
            # Cache in both levels
            self.l1_cache[user_id] = user
            await self.l2_cache.set(f"user:{user_id}", user, ttl=3600)
        
        return user

# Cache invalidation strategy
class CacheInvalidation:
    def __init__(self, cache_service, event_bus):
        self.cache = cache_service
        self.event_bus = event_bus
        self.setup_event_handlers()
    
    def setup_event_handlers(self):
        self.event_bus.subscribe('UserUpdated', self.invalidate_user_cache)
        self.event_bus.subscribe('OrderPlaced', self.invalidate_user_orders_cache)
    
    async def invalidate_user_cache(self, event):
        await self.cache.delete(f"user:{event.user_id}")
        await self.cache.delete(f"user_orders:{event.user_id}")

Database Optimization

Query Optimization

  • Index Strategy: Composite indexes for complex queries
  • Query Analysis: Execution plan analysis and optimization
  • Connection Pooling: Efficient database connection management
  • Read Replicas: Read scaling with master-slave replication

Data Partitioning

-- Horizontal partitioning example
CREATE TABLE orders_2024_q1 PARTITION OF orders
FOR VALUES FROM ('2024-01-01') TO ('2024-04-01');

CREATE TABLE orders_2024_q2 PARTITION OF orders
FOR VALUES FROM ('2024-04-01') TO ('2024-07-01');

-- Vertical partitioning for hot/cold data
CREATE TABLE user_profiles_hot (
    user_id UUID PRIMARY KEY,
    email VARCHAR(255),
    last_login TIMESTAMP
);

CREATE TABLE user_profiles_cold (
    user_id UUID PRIMARY KEY,
    bio TEXT,
    preferences JSONB,
    created_at TIMESTAMP
);

Architecture Quality Attributes

Scalability Patterns

Horizontal Scaling

  • Load Balancing: Request distribution across instances
  • Auto Scaling: Dynamic instance scaling based on metrics
  • Database Sharding: Data distribution across multiple databases
  • CDN Integration: Content delivery network for static assets

Performance Monitoring

monitoring_stack:
  metrics:
    - prometheus
    - grafana
    - alertmanager
  
  tracing:
    - jaeger
    - zipkin
    - opentelemetry
  
  logging:
    - elasticsearch
    - logstash
    - kibana
  
  application_metrics:
    - response_time
    - throughput
    - error_rate
    - cpu_usage
    - memory_usage
    - database_connections

Resilience Patterns

Circuit Breaker Implementation

class CircuitBreaker {
  private failureCount = 0;
  private lastFailureTime: number | null = null;
  private state: 'CLOSED' | 'OPEN' | 'HALF_OPEN' = 'CLOSED';
  
  constructor(
    private failureThreshold: number = 5,
    private recoveryTimeout: number = 60000,
    private monitoringPeriod: number = 10000
  ) {}
  
  async call<T>(fn: () => Promise<T>): Promise<T> {
    if (this.state === 'OPEN') {
      if (this.shouldAttemptReset()) {
        this.state = 'HALF_OPEN';
      } else {
        throw new Error('Circuit breaker is OPEN');
      }
    }
    
    try {
      const result = await fn();
      this.onSuccess();
      return result;
    } catch (error) {
      this.onFailure();
      throw error;
    }
  }
  
  private shouldAttemptReset(): boolean {
    return this.lastFailureTime !== null &&
           Date.now() - this.lastFailureTime >= this.recoveryTimeout;
  }
  
  private onSuccess(): void {
    this.failureCount = 0;
    this.state = 'CLOSED';
  }
  
  private onFailure(): void {
    this.failureCount++;
    this.lastFailureTime = Date.now();
    
    if (this.failureCount >= this.failureThreshold) {
      this.state = 'OPEN';
    }
  }
}

Security Architecture

Security Patterns

  • Authentication: OAuth 2.0, OpenID Connect, JWT tokens
  • Authorization: Role-based access control (RBAC), attribute-based access control (ABAC)
  • Data Protection: Encryption at rest and in transit, secure key management
  • API Security: Rate limiting, input validation, SQL injection prevention

Architect Your Software System

Ready to build scalable, maintainable software architecture? Let’s design a system that grows with your business.