Distributed Monolith: The Hidden Anti-Pattern in Microservices

Software teams often move from monolithic systems to microservices hoping for scalability, agility, and better maintainability. But many discover a painful reality — instead of independent services, they end up with a Distributed Monolith.

This hidden anti-pattern can cripple performance, increase complexity, and defeat the entire purpose of microservices. Let’s explore what it is, why it happens, and how to avoid it.

What Is a Distributed Monolith?

A distributed monolith is when your system looks like microservices on the surface (separate deployments, multiple services) but behaves like a tightly coupled monolith underneath.

This happens when services are so dependent on each other that:

• A single failure cascades across the system.
• Deployments require coordination across teams.
• Changes in one service break multiple others.

In short: you get the worst of both worlds — distributed complexity plus monolithic coupling.

Why Do Distributed Monoliths Happen?

Several design and organizational mistakes lead to this trap:

# Order service calling Payment and Inventory directly
def place_order(order):
    if not inventory_service.reserve(order):
        raise Exception("Out of stock")
    if not payment_service.charge(order):
        raise Exception("Payment failed")
    return "Order placed successfully"

-- Inventory and Order services using the same table
SELECT * FROM orders WHERE status = 'pending';

Distributed Monolith vs Microservices

# Order Service publishes an event instead of waiting for responses
event = {"order_id": 101, "status": "pending"}
publish_to_queue("order_events", event)

from pybreaker import CircuitBreaker

breaker = CircuitBreaker(fail_max=3, reset_timeout=60)

@breaker
def call_payment_service(order):
    # API call to payment service
    pass