Avoiding the Distributed Monolith Trap in Microservices

Introduction

Microservices architecture is widely regarded as a game-changer for modern software development. It promises scalability, flexibility, and independent deployment, allowing teams to innovate faster and adapt to business needs with greater agility.

However, not all microservices architectures deliver on these promises. Many teams, despite their best intentions, end up creating a distributed monolith—a system that is technically distributed but still suffers from the same tight coupling, coordination challenges, and deployment bottlenecks as a traditional monolith.

A true microservices architecture should allow teams to deploy services independently, scale services individually, and avoid cascading failures when changes are introduced. But when services are overly dependent on each other—requiring synchronous communication, sharing a database, or needing coordinated releases—the system becomes fragile, difficult to manage, and increasingly expensive to maintain.

In this post, I’ll break down five common pitfalls that turn a microservices architecture into a distributed monolith. More importantly, I’ll share practical solutions, recommended tools, and real-world examples to ensure your microservices architecture remains scalable, maintainable, and resilient.

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1. Beware of Synchronous Chaining

Why is this a problem?

One of the biggest advantages of microservices is that they can be independently scalable and resilient. However, if every service relies on synchronous calls to other services, you’ve effectively built a system where a single failure can cascade through the entire architecture.

Think of it this way: If Service A depends on Service B, which depends on Service C, and a request has to travel through all three synchronously before responding to the user, you’ve introduced tight coupling.

This can lead to:
❌ Increased latency – The more services involved, the slower the request.
❌ Single points of failure – If any service in the chain fails, the entire request fails.
❌ Scalability issues – If one service gets overloaded, it drags down the entire system.

Example: A Checkout System Gone Wrong

Imagine an e-commerce platform where the Checkout Service makes an API call to the Inventory Service, which then calls the Payment Service before finally confirming the order.

If the Inventory Service experiences high traffic and slows down, the entire checkout process becomes unresponsive. Worse, if it fails, the user can’t complete their purchase at all.

Solution: Embrace Asynchronous Communication

Instead of making direct API calls that block execution, services should communicate asynchronously using event-driven messaging. This allows each service to operate independently, reducing dependencies and failure impact.

Recommended Tool: Apache Kafka

Why? Kafka is a high-performance event streaming platform that allows services to publish and subscribe to events without being tightly coupled. It ensures event persistence, so if a service is down, it can consume messages later when it recovers.

Implementation Example: Event-Driven Checkout Flow

Instead of synchronous API calls, the Checkout Service publishes an event:

{
  "event": "OrderPlaced",
  "orderId": "1234",
  "items": ["item1", "item2"]
}

The Inventory Service subscribes to this event and processes it asynchronously. If it fails or is experiencing high traffic, the event remains in Kafka until it can be processed, ensuring resiliency.

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2. Keep Your Data Decentralized

Why is this a problem?

A major pitfall in microservices adoption is sharing a single database across services. While this might seem convenient at first, it leads to:
❌ Tight coupling – Services are no longer independent because changes in the database schema can break multiple services.
❌ Deployment bottlenecks – A schema change requires coordinated releases across multiple teams.
❌ Scalability limitations – A single database can become a performance bottleneck, preventing services from scaling independently.

Example: The Travel Booking Dilemma

Consider a travel booking platform with Flights, Hotels, and Payments Services all relying on the same relational database.

If the Flights team decides to update a table structure to optimize queries, this can unexpectedly break queries in the Payments Service, leading to system-wide failures.

Solution: Database Per Service

Each microservice should have its own database, and data should be accessed via APIs rather than direct database queries.

Recommended Tool: PostgreSQL (per service) + Debezium for Change Data Capture

Why? PostgreSQL is reliable, scalable, and works well with microservices. Debezium provides Change Data Capture (CDC), allowing services to synchronize data without directly accessing each other's databases.

Implementation Example: Decoupled Flights & Payments Services

Instead of querying the Flights database directly, the Payments Service can retrieve flight information through a public API:

GET /api/flights/{flightId}

Now, the Flights team can change their database schema without breaking Payments, ensuring true service independence.

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3. Minimize Cross-Service Coordination

Why is this a problem?

Microservices should allow teams to deploy independently. If multiple teams need to coordinate releases because of inter-service dependencies, you’ve built a distributed monolith.

Example: A Retail Platform with Too Many Dependencies

A retailer has separate Cart, Order, and Shipping Services. However, every time the Cart Service changes its data format, the Order Service must also be updated. This requires synchronized deployments, slowing down feature releases.

Solution: Define Clear Service Boundaries with an API Gateway

An API Gateway ensures services can evolve independently by acting as an abstraction layer between services.

Recommended Tool: Kong API Gateway or AWS API Gateway

Why? These tools provide API management, request routing, rate limiting, and caching—reducing inter-service dependencies.

Implementation Example: Stable APIs for Loose Coupling

Instead of directly integrating with the Cart Service, the Order Service calls:

GET /api/cart/{cartId}

Even if the Cart Service’s internal logic changes, as long as the API remains stable, no other service breaks.

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4. Implement Versioning & Backward Compatibility

Why is this a problem?

One of the biggest indicators that you’re dealing with a distributed monolith is when a change in one microservice forces updates across multiple services.

A microservices architecture should enable teams to work independently. However, if introducing a new field in an API, updating a response format, or changing a database schema requires immediate updates to multiple dependent services, you’ve introduced tight coupling between services.

This leads to:
❌ Frequent breaking changes – Every change requires coordinated releases across multiple teams.
❌ Deployment bottlenecks – Instead of deploying one service at a time, updates require multiple services to change together.
❌ Downtime risks – If an update is not deployed simultaneously across all dependent services, parts of the system can break.

Example: Breaking Changes in an eCommerce API

Imagine an eCommerce platform where the Orders Service exposes an API:

GET /orders/{id}

This API originally returns:

{
  "orderId": "12345",
  "customerName": "John Doe",
  "totalAmount": 250.00
}

Now, the team decides to rename customerName to buyerName for consistency.

Without proper versioning, any service that depends on customerName immediately breaks when this change goes live. The Payments Service, Shipping Service, and Customer Support Service all crash—leading to customer impact and a chaotic hotfix scramble.

Solution: Version APIs & Ensure Backward Compatibility

To prevent breaking changes, introduce API versioning and adopt a backward-compatible approach to evolving services.

Recommended Tools: OpenAPI & Swagger

Why? OpenAPI provides a structured way to define, document, and version APIs, making it easier for teams to track changes and prevent breaking updates.

Implementation Example: Supporting Both Old & New API Versions

Instead of modifying the existing API, introduce a new version while maintaining the old one:

Old API (Still Supported):

GET /orders/v1/{id}

{
  "orderId": "12345",
  "customerName": "John Doe",
  "totalAmount": 250.00
}

New API (New Clients Can Migrate Gradually):

GET /orders/v2/{id}

{
  "orderId": "12345",
  "buyerName": "John Doe",
  "totalAmount": 250.00
}

This allows existing services to continue using the old version while new consumers gradually migrate to v2.

💡 Additional Best Practices:
✅ Use feature flags – Instead of making breaking changes all at once, introduce new fields and phase out old ones gradually.
✅ Adopt schema evolution strategies – In GraphQL, you can deprecate fields instead of immediately removing them.
✅ Use API gateways – API Gateways (like Kong or AWS API Gateway) allow you to route requests to different versions seamlessly.

By handling API evolution gracefully, you reduce risk, avoid service disruptions, and allow teams to move at their own pace—truly embracing the microservices philosophy.

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5. Invest in Observability

Why is this a problem?

Microservices increase complexity because instead of a single, centralized application, you now have dozens or even hundreds of small, independent services.

This creates new challenges:
❌ Debugging is difficult – When a request fails, which service is responsible? Is it the Payments Service? The Inventory Service? The Database? Without proper observability, troubleshooting is a nightmare.
❌ Performance issues are hard to diagnose – If a user reports slow response times, how do you know where the bottleneck is?
❌ No single source of truth – Logs are scattered across different services, making it hard to correlate events across the system.

Example: Mystery Performance Issues in a Checkout Process

A user complains that checkout takes too long.

Without observability, an engineer might have to:

1. Look through the Checkout Service logs
2. Check the Payments Service logs
3. Review the Inventory Service logs
4. Examine the database queries

After hours of searching, the issue is finally discovered: the Inventory Service is experiencing a slowdown due to a surge in requests.

Had the team implemented proper observability, this issue would have been identified in minutes, not hours.

Solution: Centralized Logging, Distributed Tracing & Monitoring

Observability involves three key components:

1. Logging – Capturing service-level logs in a centralized location
2. Tracing – Following a request as it travels through multiple services
3. Metrics – Monitoring real-time performance and identifying anomalies

Recommended Tools: OpenTelemetry + Jaeger + Prometheus

Why?
✅ OpenTelemetry – A powerful framework for collecting logs, traces, and metrics across microservices.
✅ Jaeger – A distributed tracing tool that shows the lifecycle of a request across services.
✅ Prometheus – A monitoring system that collects metrics and triggers alerts for performance degradation.

Implementation Example: Tracing a Slow Checkout Request

Using Jaeger, we can trace a user’s checkout request across services:

TraceID: 9a8b7c6d  
→ Checkout Service: 100ms  
→ Payments Service: 120ms  
→ Inventory Service: 1.2s  🚨 (Issue Found!)  

This immediately pinpoints the Inventory Service as the bottleneck, allowing the team to fix the issue quickly.

💡 Additional Best Practices:
✅ Adopt structured logging – Use JSON-based logs to ensure they are machine-readable and searchable.
✅ Implement distributed tracing from day one – Don’t wait for an outage before setting up tracing.
✅ Set up proactive monitoring & alerts – Tools like Prometheus can detect performance issues before users report them.

With proper observability in place, teams can quickly identify and resolve issues, ensuring a reliable, high-performing microservices ecosystem.

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Conclusion

A well-architected microservices system should maximize service independence, minimize coordination overhead, and ensure resilience.

To avoid a distributed monolith, follow these best practices:
✅ Adopt event-driven architecture (Kafka) to prevent tight coupling.
✅ Ensure each service owns its data (PostgreSQL per service).
✅ Use an API Gateway (Kong) to minimize inter-service dependencies.
✅ Implement backward-compatible API versioning to avoid breaking changes.
✅ Invest in observability (OpenTelemetry, Jaeger) to debug issues efficiently.

By designing your microservices with scalability and autonomy in mind, you’ll create a system that is fast, resilient, and future-proof.

Have you encountered a distributed monolith before? What strategies worked for you?