Microservices Architecture: Patterns and Pitfalls

Introduction

Microservices architecture has become the dominant approach for building large-scale applications, but it's also one of the most misunderstood. Many teams adopt microservices because it's trendy, only to end up with a distributed monolith—a system with all the complexity of microservices but none of the benefits. The difference between a successful microservices implementation and a failed one lies in understanding the patterns that make it work and the pitfalls that make it fail.

Microservices decompose an application into small, independently deployable services, each owning its data and business logic. This decomposition enables teams to work autonomously, deploy independently, and scale selectively. But it also introduces distributed system challenges: network latency, partial failures, data consistency, and operational complexity. This guide covers the architectural patterns that make microservices successful and the pitfalls that derail most implementations.

Understanding Microservices Architecture: Core Concepts

The fundamental principle of microservices is loose coupling with high cohesion. Each service should be self-contained, owning its data store, business logic, and API. Services communicate through well-defined interfaces—typically REST APIs, gRPC, or asynchronous messages—rather than sharing databases or internal state.

Domain-Driven Design (DDD) provides the conceptual framework for defining service boundaries. Bounded contexts—areas of the domain where a particular model applies—map naturally to microservice boundaries. An e-commerce system might have bounded contexts for orders, inventory, payments, shipping, and customer management, each becoming a separate service.

The database-per-service pattern is non-negotiable for true microservices. When services share a database, schema changes require coordination, scaling is coupled, and the database becomes a single point of failure. Each service should own its data, even if this means data duplication across services. Data synchronization happens through events or API calls, not shared tables.

Service communication falls into two categories: synchronous (request-response via REST or gRPC) and asynchronous (event-driven via message queues). Synchronous communication is simpler but creates temporal coupling—the caller must wait for the response. Asynchronous communication decouples services in time but adds complexity with eventual consistency and message ordering.

Observability is the operational foundation of microservices. With dozens or hundreds of services, you need distributed tracing to follow requests across services, centralized logging to correlate events, and metrics to detect anomalies. Without observability, debugging production issues in a microservices system is nearly impossible.

Architecture and Design Patterns

Service Communication Patterns

Synchronous Communication (REST/gRPC)

REST APIs are the most common synchronous communication pattern. They're well-understood, tooling is mature, and they work naturally with HTTP infrastructure:

// REST API client with circuit breaker
import CircuitBreaker from 'opossum';
 
class OrderService {
    private inventoryBreaker: CircuitBreaker;
 
    constructor() {
        this.inventoryBreaker = new CircuitBreaker(
            this.checkInventory.bind(this),
            {
                timeout: 3000,
                errorThresholdPercentage: 50,
                resetTimeout: 30000,
                volumeThreshold: 10,
            }
        );
 
        this.inventoryBreaker.on('open', () => {
            console.warn('Inventory service circuit breaker opened');
        });
 
        this.inventoryBreaker.fallback(() => ({
            available: false,
            reason: 'Inventory service unavailable',
        }));
    }
 
    async createOrder(order: CreateOrderRequest): Promise<Order> {
        // Check inventory through circuit breaker
        const inventory = await this.inventoryBreaker.fire(order.items);
 
        if (!inventory.available) {
            throw new Error(`Cannot fulfill order: ${inventory.reason}`);
        }
 
        // Create order in local database
        const savedOrder = await this.orderRepository.create(order);
 
        // Publish event for other services
        await this.eventBus.publish('order.created', {
            orderId: savedOrder.id,
            customerId: savedOrder.customerId,
            items: savedOrder.items,
        });
 
        return savedOrder;
    }
 
    async checkInventory(items: OrderItem[]): Promise<InventoryStatus> {
        const response = await fetch('http://inventory-service/api/inventory/check', {
            method: 'POST',
            headers: { 'Content-Type': 'application/json' },
            body: JSON.stringify({ items }),
            signal: AbortSignal.timeout(3000),
        });
 
        if (!response.ok) {
            throw new Error(`Inventory check failed: ${response.status}`);
        }
 
        return response.json();
    }
}

Asynchronous Communication (Event-Driven)

Event-driven communication decouples services in time and enables event sourcing patterns:

// Event-driven order processing
class OrderEventHandler {
    constructor(
        private eventBus: EventBus,
        private inventoryService: InventoryService,
        private paymentService: PaymentService,
        private notificationService: NotificationService,
    ) {
        this.eventBus.subscribe('order.created', this.handleOrderCreated.bind(this));
        this.eventBus.subscribe('payment.completed', this.handlePaymentCompleted.bind(this));
        this.eventBus.subscribe('inventory.reserved', this.handleInventoryReserved.bind(this));
    }
 
    async handleOrderCreated(event: OrderCreatedEvent) {
        try {
            // Reserve inventory
            await this.inventoryService.reserve(event.orderId, event.items);
            // Event published by inventory service: inventory.reserved
        } catch (error) {
            await this.eventBus.publish('order.failed', {
                orderId: event.orderId,
                reason: 'Inventory reservation failed',
                error: error.message,
            });
        }
    }
 
    async handleInventoryReserved(event: InventoryReservedEvent) {
        // Process payment
        await this.paymentService.charge(event.orderId, event.amount);
        // Event published by payment service: payment.completed
    }
 
    async handlePaymentCompleted(event: PaymentCompletedEvent) {
        // Confirm order
        await this.orderRepository.confirm(event.orderId);
        await this.eventBus.publish('order.confirmed', {
            orderId: event.orderId,
        });
        // Notify customer
        await this.notificationService.sendOrderConfirmation(event.orderId);
    }
}

API Gateway Pattern

An API Gateway provides a single entry point for clients, handling cross-cutting concerns like authentication, rate limiting, and request routing:

import express from 'express';
import { createProxyMiddleware } from 'http-proxy-middleware';
import rateLimit from 'express-rate-limit';
import jwt from 'jsonwebtoken';
 
const app = express();
 
// Rate limiting
const limiter = rateLimit({
    windowMs: 15 * 60 * 1000,
    max: 100,
    standardHeaders: true,
    legacyHeaders: false,
});
app.use(limiter);
 
// Authentication middleware
function authenticate(req: express.Request, res: express.Response, next: express.NextFunction) {
    const token = req.headers.authorization?.replace('Bearer ', '');
    if (!token) {
        return res.status(401).json({ error: 'Authentication required' });
    }
 
    try {
        const decoded = jwt.verify(token, process.env.JWT_SECRET!);
        req.user = decoded;
        next();
    } catch {
        res.status(401).json({ error: 'Invalid token' });
    }
}
 
// Route to services
app.use('/api/orders', authenticate, createProxyMiddleware({
    target: 'http://order-service:3001',
    pathRewrite: { '^/api/orders': '/api' },
    timeout: 5000,
}));
 
app.use('/api/inventory', authenticate, createProxyMiddleware({
    target: 'http://inventory-service:3002',
    pathRewrite: { '^/api/inventory': '/api' },
    timeout: 3000,
}));
 
app.use('/api/customers', authenticate, createProxyMiddleware({
    target: 'http://customer-service:3003',
    pathRewrite: { '^/api/customers': '/api' },
    timeout: 3000,
}));
 
// Health check
app.get('/health', (req, res) => {
    res.json({ status: 'healthy', timestamp: new Date().toISOString() });
});
 
app.listen(3000, () => console.log('API Gateway running on port 3000'));

Saga Pattern for Distributed Transactions

When a business process spans multiple services, the Saga pattern coordinates distributed transactions through compensating actions:

// Orchestration-based saga
class CreateOrderSaga {
    private sagaLog: SagaLog;
 
    async execute(orderRequest: CreateOrderRequest): Promise<Order> {
        const sagaId = uuid();
        const steps: SagaStep[] = [
            {
                name: 'reserve-inventory',
                execute: () => this.inventoryService.reserve(orderRequest.items),
                compensate: () => this.inventoryService.release(sagaId),
            },
            {
                name: 'process-payment',
                execute: () => this.paymentService.charge(orderRequest.customerId, orderRequest.total),
                compensate: () => this.paymentService.refund(sagaId),
            },
            {
                name: 'confirm-order',
                execute: () => this.orderService.confirm(sagaId),
                compensate: () => this.orderService.cancel(sagaId),
            },
        ];
 
        const completedSteps: SagaStep[] = [];
 
        try {
            for (const step of steps) {
                await this.sagaLog.record(sagaId, step.name, 'started');
                await step.execute();
                completedSteps.push(step);
                await this.sagaLog.record(sagaId, step.name, 'completed');
            }
 
            return await this.orderService.get(sagaId);
        } catch (error) {
            // Compensate in reverse order
            for (const step of completedSteps.reverse()) {
                try {
                    await step.compensate();
                    await this.sagaLog.record(sagaId, step.name, 'compensated');
                } catch (compensationError) {
                    // Log and alert - manual intervention required
                    console.error(`Compensation failed for ${step.name}:`, compensationError);
                    await this.sagaLog.record(sagaId, step.name, 'compensation-failed');
                }
            }
            throw new Error(`Order creation failed: ${error.message}`);
        }
    }
}

Step-by-Step Implementation

Setting Up a Microservices Project

Structure your monorepo for microservices development:

microservices-project/
├── packages/
│   ├── shared/               # Shared types and utilities
│   │   ├── src/
│   │   │   ├── types.ts
│   │   │   ├── events.ts
│   │   │   └── errors.ts
│   │   └── package.json
│   ├── order-service/
│   │   ├── src/
│   │   │   ├── api/
│   │   │   ├── domain/
│   │   │   ├── infrastructure/
│   │   │   └── index.ts
│   │   ├── Dockerfile
│   │   └── package.json
│   ├── inventory-service/
│   │   └── ...
│   └── payment-service/
│       └── ...
├── infrastructure/
│   ├── docker-compose.yml
│   ├── k8s/
│   └── terraform/
├── package.json
└── turbo.json

Implementing Service Discovery

Services need to find each other. Use DNS-based discovery with Kubernetes:

# Kubernetes service definition
apiVersion: v1
kind: Service
metadata:
  name: order-service
spec:
  selector:
    app: order-service
  ports:
  - port: 80
    targetPort: 3001
---
# Deployment
apiVersion: apps/v1
kind: Deployment
metadata:
  name: order-service
spec:
  replicas: 3
  selector:
    matchLabels:
      app: order-service
  template:
    metadata:
      labels:
        app: order-service
    spec:
      containers:
      - name: order-service
        image: order-service:latest
        ports:
        - containerPort: 3001
        env:
        - name: INVENTORY_SERVICE_URL
          value: "http://inventory-service"
        - name: PAYMENT_SERVICE_URL
          value: "http://payment-service"

Database per Service Implementation

Each service owns its database with separate connection configurations:

// Order service database configuration
class OrderDatabase {
    private prisma: PrismaClient;
 
    constructor() {
        this.prisma = new PrismaClient({
            datasources: {
                db: {
                    url: process.env.ORDER_DATABASE_URL,
                },
            },
        });
    }
 
    // Order service only accesses order-related tables
    async createOrder(data: CreateOrderData): Promise<Order> {
        return this.prisma.order.create({
            data: {
                id: data.id,
                customerId: data.customerId,
                items: {
                    create: data.items.map(item => ({
                        sku: item.sku,
                        quantity: item.quantity,
                        price: item.price,
                    })),
                },
                status: 'pending',
                total: data.items.reduce((sum, i) => sum + i.price * i.quantity, 0),
            },
            include: { items: true },
        });
    }
}

Real-World Use Cases and Case Studies

Use Case 1: E-Commerce Platform

Netflix pioneered microservices at scale, running thousands of services. Each service handles a specific domain: user profiles, recommendations, streaming, billing, and content delivery. Services communicate through a combination of synchronous APIs (for user-facing requests) and asynchronous events (for analytics and personalization). This architecture enables Netflix to deploy thousands of times per day and serve hundreds of millions of users globally.

Use Case 2: Financial Trading Platform

Trading platforms use microservices to separate concerns: market data ingestion, order matching, risk calculation, settlement, and reporting. Each service has different scaling characteristics—market data needs extreme throughput, risk calculation needs low latency, and reporting needs high availability. Microservices allow each to scale independently with appropriate technology choices.

Uber's microservices architecture handles driver matching, ride requests, pricing, payments, and notifications. Real-time location updates flow through high-throughput event streams, while payment processing uses synchronous APIs with strict consistency. The architecture enables geographic scaling—ride matching services run in each city for low latency.

Best Practices for Production

Define clear service boundaries: Use Domain-Driven Design to identify bounded contexts. Each service should represent a single business capability with clear ownership.
Implement circuit breakers: Use circuit breakers for all synchronous service calls. This prevents cascading failures when one service is down.
Use asynchronous communication by default: Prefer event-driven communication for most service interactions. Reserve synchronous calls for operations that require immediate responses.
Implement distributed tracing: Use OpenTelemetry or Jaeger to trace requests across services. Every request should have a trace ID that flows through all service calls.
Centralize logging: Aggregate logs from all services into a central system (ELK, Loki, Datadog). Use structured logging with correlation IDs for cross-service debugging.
Automate deployments: Each service should have its own CI/CD pipeline. Use container orchestration (Kubernetes) for deployment management and scaling.
Version your APIs: Use semantic versioning for service APIs. Support multiple API versions simultaneously to enable gradual client migration.
Implement health checks: Every service should expose health and readiness endpoints. Use these for load balancer health checks and orchestrator probes.

Common Pitfalls and Solutions

Pitfall	Impact	Solution
Shared database	Coupled deployments, scaling bottlenecks	Enforce database-per-service; use events for data sync
Distributed monolith	All complexity, none of the benefits	Define clear boundaries; avoid synchronous chains
Too many services	Operational overhead exceeds benefits	Start with a modular monolith; extract services when needed
Missing observability	Can't debug production issues	Implement tracing, logging, and metrics from day one
Synchronous chains	Cascading failures, high latency	Use async communication; implement circuit breakers
No API versioning	Breaking changes affect all clients	Version APIs; support multiple versions simultaneously

Performance Optimization

Microservices introduce network overhead at every service boundary. Minimize synchronous calls by using the API composition pattern:

// Bad: N+1 service calls
async function getOrderDetails(orderId: string) {
    const order = await orderService.get(orderId);
    const customer = await customerService.get(order.customerId);
    const inventory = await inventoryService.check(order.items);
    const shipping = await shippingService.estimate(order.shippingAddress);
    return { order, customer, inventory, shipping };
}
 
// Good: Parallel calls with Promise.all
async function getOrderDetails(orderId: string) {
    const order = await orderService.get(orderId);
 
    const [customer, inventory, shipping] = await Promise.all([
        customerService.get(order.customerId),
        inventoryService.check(order.items),
        shippingService.estimate(order.shippingAddress),
    ]);
 
    return { order, customer, inventory, shipping };
}
 
// Better: API Gateway composition
// Let the API Gateway aggregate responses from multiple services
// and return a single composed response to the client

Use caching aggressively:

class ServiceCache {
    private redis: Redis;
 
    async getOrFetch<T>(key: string, fetcher: () => Promise<T>, ttlSeconds: number): Promise<T> {
        const cached = await this.redis.get(key);
        if (cached) {
            return JSON.parse(cached);
        }
 
        const data = await fetcher();
        await this.redis.setex(key, ttlSeconds, JSON.stringify(data));
        return data;
    }
 
    async invalidate(pattern: string) {
        const keys = await this.redis.keys(pattern);
        if (keys.length > 0) {
            await this.redis.del(...keys);
        }
    }
}

Comparison with Alternatives

Feature	Microservices	Modular Monolith	Serverless	SOA
Deployment	Independent	Single	Per-function	Shared
Scaling	Per-service	Whole app	Per-request	Per-service
Complexity	High	Low	Medium	High
Team Autonomy	High	Medium	High	Medium
Technology Diversity	High	Low	Medium	Medium
Latency	Network overhead	In-process	Cold starts	Network overhead
Best For	Large teams	Small-medium teams	Event-driven	Enterprise integration

Start with a modular monolith for small teams. Extract microservices when you need independent scaling, deployment, or team autonomy. Use serverless for event-driven workloads with unpredictable traffic. Use SOA for enterprise integration with legacy systems.

Advanced Patterns and Techniques

CQRS (Command Query Responsibility Segregation)

Separate read and write models for complex domains. The write side handles business logic and persists domain events, while the read side maintains denormalized projections optimized for queries. This pattern is particularly useful when read and write workloads have fundamentally different characteristics—such as an e-commerce system where product catalogs are read-heavy but orders are write-heavy.

// Command side: Write to event store
class OrderCommandHandler {
    async handleCreateOrder(command: CreateOrderCommand) {
        const order = new Order(command);
        order.raiseEvent(new OrderCreatedEvent(order));
        await this.eventStore.append(order.id, order.uncommittedEvents);
    }
}
 
// Query side: Read from optimized read model
class OrderQueryHandler {
    async getOrderSummary(orderId: string): Promise<OrderSummary> {
        // Read from denormalized read model
        return this.readModel.getOrderSummary(orderId);
    }
 
    async getCustomerOrders(customerId: string): Promise<OrderListItem[]> {
        return this.readModel.getCustomerOrders(customerId);
    }
}
 
// Event handler updates read model
class OrderProjection {
    async handleOrderCreated(event: OrderCreatedEvent) {
        await this.readModel.insertOrderSummary({
            id: event.orderId,
            customerId: event.customerId,
            status: 'pending',
            total: event.total,
            createdAt: event.timestamp,
        });
    }
}

Event Sourcing

Event sourcing stores state changes as an immutable sequence of events rather than the current state. This provides a complete audit trail, enables temporal queries ("what was the state at time T?"), and supports event replay for rebuilding state or populating new read models.

# Python event sourcing implementation
from dataclasses import dataclass
from datetime import datetime
from typing import List, Dict, Any
import json
 
@dataclass
class Event:
    aggregate_id: str
    event_type: str
    data: Dict[str, Any]
    timestamp: datetime
    version: int
 
class EventStore:
    def __init__(self, db_connection):
        self.db = db_connection
    
    def append(self, aggregate_id: str, events: List[Event], expected_version: int):
        """Append events with optimistic concurrency check."""
        current_version = self._get_version(aggregate_id)
        if current_version != expected_version:
            raise ConcurrencyError(
                f"Expected version {expected_version}, got {current_version}"
            )
        
        for event in events:
            self.db.execute(
                "INSERT INTO events (aggregate_id, event_type, data, timestamp, version) "
                "VALUES (%s, %s, %s, %s, %s)",
                (event.aggregate_id, event.event_type, json.dumps(event.data),
                 event.timestamp, event.version)
            )
    
    def get_events(self, aggregate_id: str) -> List[Event]:
        """Retrieve all events for an aggregate."""
        rows = self.db.query(
            "SELECT * FROM events WHERE aggregate_id = %s ORDER BY version",
            (aggregate_id,)
        )
        return [Event(**row) for row in rows]
 
class OrderAggregate:
    def __init__(self, order_id: str):
        self.id = order_id
        self.status = "draft"
        self.items = []
        self.total = 0
        self._version = 0
        self._pending_events = []
    
    def add_item(self, sku: str, quantity: int, price: float):
        event = Event(
            aggregate_id=self.id,
            event_type="ItemAdded",
            data={"sku": sku, "quantity": quantity, "price": price},
            timestamp=datetime.utcnow(),
            version=self._version + 1
        )
        self._apply(event)
        self._pending_events.append(event)
    
    def _apply(self, event: Event):
        if event.event_type == "ItemAdded":
            self.items.append(event.data)
            self.total += event.data["price"] * event.data["quantity"]
        self._version = event.version

Distributed Tracing with OpenTelemetry

Implementing distributed tracing is essential for debugging issues across microservices. OpenTelemetry provides vendor-neutral instrumentation that exports traces to backends like Jaeger, Zipkin, or Datadog.

// Go: OpenTelemetry instrumentation
package main
 
import (
    "context"
    "go.opentelemetry.io/otel"
    "go.opentelemetry.io/otel/exporters/otlp/otlptrace/otlptracegrpc"
    "go.opentelemetry.io/otel/sdk/resource"
    sdktrace "go.opentelemetry.io/otel/sdk/trace"
    semconv "go.opentelemetry.io/otel/semconv/v1.17.0"
    "go.opentelemetry.io/otel/trace"
)
 
func initTracer(serviceName string) (*sdktrace.TracerProvider, error) {
    exporter, err := otlptracegrpc.New(context.Background())
    if err != nil {
        return nil, err
    }
    
    tp := sdktrace.NewTracerProvider(
        sdktrace.WithBatcher(exporter),
        sdktrace.WithResource(resource.NewWithAttributes(
            semconv.SchemaURL,
            semconv.ServiceName(serviceName),
        )),
    )
    otel.SetTracerProvider(tp)
    return tp, nil
}
 
func processOrder(ctx context.Context, orderID string) error {
    tracer := otel.Tracer("order-service")
    ctx, span := tracer.Start(ctx, "processOrder",
        trace.WithAttributes(
            semconv.OrderID(orderID),
        ),
    )
    defer span.End()
    
    // Call inventory service - trace context propagates automatically
    if err := reserveInventory(ctx, orderID); err != nil {
        span.RecordError(err)
        return err
    }
    
    // Call payment service
    if err := processPayment(ctx, orderID); err != nil {
        span.RecordError(err)
        return err
    }
    
    return nil
}

Strangler Fig Pattern for Migration

Gradually migrate from a monolith to microservices using the strangler fig pattern:

// API Gateway routes traffic based on feature flags
app.use('/api/orders', (req, res, next) => {
    if (featureFlags.isEnabled('use-order-service')) {
        // Route to new microservice
        orderServiceProxy(req, res, next);
    } else {
        // Route to monolith
        monolithProxy(req, res, next);
    }
});

Testing Strategies

Test microservices at multiple levels:

// Contract testing with Pact
import { Pact } from '@pact-foundation/pact';
 
const provider = new Pact({
    consumer: 'OrderService',
    provider: 'InventoryService',
    port: 1234,
});
 
describe('Inventory Service Contract', () => {
    beforeAll(() => provider.setup());
    afterAll(() => provider.finalize());
 
    it('should check inventory availability', async () => {
        await provider.addInteraction({
            state: 'items are in stock',
            uponReceiving: 'a request to check inventory',
            withRequest: {
                method: 'POST',
                path: '/api/inventory/check',
                body: { items: [{ sku: 'ABC', quantity: 2 }] },
            },
            willRespondWith: {
                status: 200,
                body: { available: true },
            },
        });
 
        const result = await inventoryService.check([{ sku: 'ABC', quantity: 2 }]);
        expect(result.available).toBe(true);
    });
});
 
// Integration test with testcontainers
describe('Order Service Integration', () => {
    let kafka: StartedTestContainer;
    let postgres: StartedTestContainer;
 
    beforeAll(async () => {
        postgres = await new GenericContainer('postgres:15')
            .withEnvironment({ POSTGRES_DB: 'orders' })
            .withExposedPorts(5432)
            .start();
 
        kafka = await new GenericContainer('confluentinc/cp-kafka:7.5.0')
            .withExposedPorts(9092)
            .start();
    });
 
    it('should create order and publish event', async () => {
        const order = await orderService.create({ customerId: '123', items: [...] });
        expect(order.status).toBe('pending');
 
        const event = await waitForEvent('order.created', 5000);
        expect(event.orderId).toBe(order.id);
    });
});

Future Outlook

Microservices are evolving toward serverless microservices with platforms like AWS Lambda and Knative, reducing operational overhead. Service mesh technologies like Istio and Linkerd provide transparent networking, security, and observability. WebAssembly (WASM) is emerging as a lightweight alternative to containers for microservice deployment. Event-driven architectures are becoming the default, with event brokers like Apache Pulsar and cloud-native event buses simplifying inter-service communication.

Conclusion

Microservices architecture enables independent scaling, deployment, and team autonomy, but it introduces significant complexity. Success requires clear service boundaries based on Domain-Driven Design, asynchronous communication patterns, comprehensive observability, and disciplined API management. Avoid the distributed monolith by enforcing database-per-service and minimizing synchronous dependencies.

Key takeaways: start with a modular monolith and extract services when needed. Use events for inter-service communication, implement circuit breakers for resilience, and invest in observability from day one. Define clear service boundaries, version your APIs, and automate deployments. The complexity of microservices is only justified when the organizational benefits outweigh the technical costs.

For further reading, consult Sam Newman's "Building Microservices," Chris Richardson's "Microservices Patterns," and the Microsoft Cloud Architecture Center for production-ready patterns.

Minh Vo

Slaying code & making it lit fr fr 🔥 tagline

Microservices Architecture: Patterns and Pitfalls

Introduction

Understanding Microservices Architecture: Core Concepts

Architecture and Design Patterns

Service Communication Patterns

API Gateway Pattern

Saga Pattern for Distributed Transactions

Step-by-Step Implementation

Setting Up a Microservices Project

Implementing Service Discovery

Database per Service Implementation

Real-World Use Cases and Case Studies

Use Case 1: E-Commerce Platform

Use Case 2: Financial Trading Platform

Best Practices for Production

Common Pitfalls and Solutions

Performance Optimization

Comparison with Alternatives

Advanced Patterns and Techniques

CQRS (Command Query Responsibility Segregation)

Event Sourcing

Distributed Tracing with OpenTelemetry

Strangler Fig Pattern for Migration

Testing Strategies

Future Outlook

Conclusion

Minh Vo

Slaying code & making it lit fr fr 🔥 tagline

Microservices Architecture: Patterns and Pitfalls

Introduction

Understanding Microservices Architecture: Core Concepts

Architecture and Design Patterns

Service Communication Patterns

API Gateway Pattern

Saga Pattern for Distributed Transactions

Step-by-Step Implementation

Setting Up a Microservices Project

Implementing Service Discovery

Database per Service Implementation

Real-World Use Cases and Case Studies

Use Case 1: E-Commerce Platform

Use Case 2: Financial Trading Platform

Use Case 3: Ride-Sharing Application

Best Practices for Production

Common Pitfalls and Solutions

Performance Optimization

Comparison with Alternatives

Advanced Patterns and Techniques

CQRS (Command Query Responsibility Segregation)

Event Sourcing

Distributed Tracing with OpenTelemetry

Strangler Fig Pattern for Migration

Testing Strategies

Future Outlook

Conclusion