How Do You Prevent a Distributed Monolith?

How Do You Prevent a Distributed Monolith?

A Distributed Monolith is a system built using multiple independently deployed services that are so tightly coupled that they must coordinate changes, deployments, or runtime operations as if they were a single application.

Although the system uses microservices, it does not gain the independence, resilience, and flexibility that microservices are intended to provide.

Example of a Distributed Monolith

Consider an e-commerce application with the following services:

• Customer Service
• Order Service
• Payment Service
• Inventory Service
• Shipping Service

Suppose placing an order requires synchronous calls between all five services. If Shipping is unavailable, Order creation fails. If the Customer database schema changes, several other services must also be modified. All services must be deployed together because their contracts are tightly coupled.

This architecture has separate deployments but behaves like one tightly coupled application.

1. Identify the Warning Signs

Warning Sign	Why It Is a Problem
Multiple services must be deployed together	Services do not have meaningful deployment independence.
One business operation requires many synchronous calls	Latency and failures propagate across services.
Services directly access the same database tables	Schema changes create hidden dependencies.
One service knows the internal data model of another	Implementation changes break consumers.
Shared libraries contain business logic used by many services	Changes to shared logic require coordinated updates.
A change to one service regularly requires changes to several others	Business responsibilities and boundaries may be poorly defined.
One service outage causes many unrelated services to fail	Failure isolation is insufficient.

2. Define Clear Service Boundaries Using DDD

One of the most important ways to prevent a distributed monolith is to establish appropriate boundaries using Domain-Driven Design (DDD).

Identify bounded contexts based on:

• Business capabilities.
• Business rules and invariants.
• Domain terminology.
• Data ownership.
• Independent business change requirements.

For example, Order Management should own the order lifecycle, while Inventory Management should own stock availability and reservations.

Order Management should not directly modify Inventory tables. Instead, it should interact with Inventory through an explicit API or messaging contract.

Example of Clear Ownership

Service	Owns	Should Not Own
Customer Service	Customer profile and customer-specific rules	Order lifecycle
Order Service	Order creation, confirmation, and cancellation	Inventory reservation internals
Inventory Service	Stock levels and reservations	Payment processing
Payment Service	Payment authorization, capture, and refunds	Order persistence internals
Shipping Service	Shipment creation and delivery tracking	Customer or inventory database internals

Important: Do not create a microservice for every entity or database table. A service should represent a cohesive business capability with meaningful ownership.

3. Give Each Service Ownership of Its Data

Each service should control the data and business rules for which it is responsible.

Bad Approach: Shared Database Tables

Order Service --------\
                       \
                        Shared Database
                       /
Inventory Service ----/

If both services directly update the same tables, changing a schema or business rule can break multiple services.

Better Approach: Private Data Ownership

Order Service --------> Order Database

Inventory Service ---> Inventory Database

Payment Service -----> Payment Database

Other services access this data through published APIs or messaging contracts rather than directly querying or updating another service's private tables.

Separate physical databases are not mandatory in every architecture. A shared database server can still support service ownership if schemas, permissions, and access rules enforce clear boundaries. However, independently owned schemas alone do not guarantee independence if services remain tightly coupled through shared tables or transactions.

4. Reduce Excessive Synchronous Communication

Too many synchronous service-to-service calls create runtime dependencies. If one downstream service becomes slow or unavailable, the failure can propagate upstream.

Bad Approach: Long Synchronous Call Chain

Client
  |
  v
Order Service
  |
  v
Customer Service
  |
  v
Inventory Service
  |
  v
Payment Service
  |
  v
Shipping Service

If every operation must complete synchronously, the entire workflow may become dependent on the availability and response time of every service in the chain.

Better Approach: Use Asynchronous Messaging Where Appropriate

Client
  |
  v
Order Service
  |
  v
Order Created Event
  |
  v
Message Broker
  |
  +------> Inventory Service
  |
  +------> Payment Service
  |
  +------> Other Interested Services

Asynchronous messaging allows services to process events independently and can reduce runtime coupling.

Examples of messaging technologies include:

• Azure Service Bus
• RabbitMQ
• Apache Kafka
• Amazon SQS and SNS

Choose messaging based on the required delivery guarantees, ordering, routing, replay needs, throughput, and operational model.

Important: Do not replace every API call with an event. Use synchronous communication when an immediate response is required, and asynchronous communication when work can be completed later or when multiple consumers need to react independently.

5. Avoid Distributed Transactions Across Services

A distributed monolith often emerges when a business operation requires a single atomic transaction across multiple services.

For example, an order operation might attempt to update the Order, Inventory, and Payment databases in one distributed transaction.

This increases coordination, operational complexity, and coupling between services.

Better Approach: Use the Saga Pattern

The Saga Pattern coordinates a business transaction across multiple services using local transactions and, when necessary, compensating actions.

Example order workflow:

1. Create Order
       |
       v
2. Reserve Inventory
       |
       v
3. Authorize Payment
       |
       v
4. Confirm Order

If payment authorization fails, the workflow can release the inventory reservation and cancel or mark the order as failed, according to the business rules.

Each service commits its own local transaction. The overall workflow handles failures through explicit recovery and compensation logic rather than requiring one database transaction across all services.

Implement idempotent operations, retries with appropriate limits, timeouts, and durable workflow state. Compensation is a business operation, not necessarily a perfect reversal of every earlier action.

6. Keep Service Contracts Stable

Services should communicate through explicit, well-defined contracts rather than depending on one another's internal implementation.

For REST APIs:

• Use clear request and response models.
• Avoid exposing internal database entities directly.
• Make compatible changes whenever possible.
• Define a deprecation policy for obsolete API fields and endpoints.
• Use API versioning when a breaking change cannot be avoided.

For events and messages:

• Define explicit event schemas.
• Use backward-compatible schema evolution.
• Distinguish business events from internal implementation details.
• Ensure consumers tolerate duplicate delivery where applicable.
• Document ownership and the meaning of each event.

For example, an event such as OrderPlaced should describe a business fact. It should not expose every internal field of the Order database just because a consumer might find it useful.

7. Avoid Excessive Shared Business Logic

Shared libraries are useful for technical concerns, but they can create tight coupling when they contain business logic that many services must adopt together.

Good Candidates for Shared Libraries

• Logging and tracing helpers.
• Authentication integration utilities.
• Standardized error response utilities.
• Telemetry configuration.
• Small, stable technical abstractions.

Potentially Risky Shared Libraries

• Order pricing rules used by unrelated services.
• Inventory business rules shared across multiple services.
• A common domain model that forces all services to change together.
• A large shared library that requires coordinated releases for every change.

Keep business rules within the bounded context that owns them. Share code only when the concept genuinely has common meaning and coordinated evolution is acceptable.

8. Design for Failure Isolation

Independent services must tolerate failures in their dependencies.

Consider these techniques:

• Timeouts: Prevent requests from waiting indefinitely.
• Circuit breakers: Temporarily stop calls to a failing dependency.
• Retries: Retry transient failures with backoff and appropriate limits.
• Bulkheads: Limit the resources consumed by one dependency or workload.
• Dead-letter queues: Isolate messages that cannot be processed successfully.
• Graceful degradation: Allow noncritical functionality to remain available when possible.

For example, if a recommendation service is unavailable, customers should generally still be able to place an order. If a payment provider is unavailable, the application may need to keep the order pending rather than pretending that payment succeeded.

Reliability patterns should reflect the business requirements. A circuit breaker cannot make an essential dependency optional, and retries alone do not solve persistent failures.

9. Allow Independent Deployment

A service is not truly independent if every release requires coordinated deployments across several services.

To improve deployment independence:

• Maintain separate build and deployment pipelines where appropriate.
• Use backward-compatible API and event changes.
• Apply the expand-and-contract approach to database schema changes.
• Use feature flags for controlled rollout when suitable.
• Run automated contract tests between service providers and consumers.
• Make each service responsible for its own configuration and deployment readiness.

For example, when adding a new optional field to an API response, deploy the provider in a backward-compatible manner before relying on the new field in consumers. Avoid requiring every consumer and provider to be upgraded at exactly the same time.

10. Monitor Coupling and Architecture Health

Do not assume the architecture is independent simply because it has multiple repositories or deployments. Measure how services interact and how often changes cross boundaries.

Metric	What It Reveals
Coordinated deployment frequency	How often multiple services must be released together.
Cross-service change frequency	How often a feature requires changes in multiple services.
Synchronous dependency depth	How many downstream calls are required for a critical operation.
Change failure rate	How frequently deployments result in failures requiring remediation.
Recovery time	How quickly the team restores service after a failure.
Cross-service incident rate	How frequently incidents spread across service boundaries.
Contract-breaking change frequency	How often service changes disrupt consumers.

These metrics are signals, not absolute rules. For example, a business workflow may legitimately involve several services, and a coordinated release may sometimes be necessary. Investigate recurring patterns rather than imposing arbitrary thresholds.

11. A Practical Architecture Checklist

Before introducing a new microservice or reviewing an existing architecture, ask the following questions:

• Does this service own a clear business capability?
• Does it have well-defined domain responsibilities?
• Does it control its own business rules and data?
• Can it be deployed without requiring all other services to deploy simultaneously?
• Are its API and event contracts explicit and stable?
• Can it tolerate temporary failures in nonessential dependencies?
• Are distributed workflows designed for retries, duplicate messages, and partial failures?
• Is there a clear reason for it to be a separate service rather than a module in a modular monolith?
• Does the operational benefit justify the additional deployment, monitoring, networking, and support complexity?

12. Distributed Monolith vs. Healthy Microservices Architecture

Area	Distributed Monolith	Healthy Microservices Architecture
Business boundaries	Overlapping responsibilities	Clear business ownership
Data	Shared tables and cross-service updates	Explicit data ownership
Communication	Long, tightly coupled call chains	Intentional synchronous and asynchronous communication
Transactions	Frequent distributed atomic transactions	Local transactions and explicit workflow coordination where appropriate
Deployment	Frequent coordinated releases	Meaningful independent deployment
Failure handling	Failures propagate across services	Appropriate isolation and recovery mechanisms
Team ownership	Shared responsibility for many components	Clear responsibility for services and business outcomes

Final Recommendation

To prevent a distributed monolith, focus on the following principles:

1. Establish clear bounded contexts using business capabilities and domain rules.
2. Give each service ownership of its data and avoid direct cross-service database access.
3. Reduce unnecessary runtime coupling by choosing synchronous calls and asynchronous messaging intentionally.
4. Avoid distributed transactions where possible and use explicit workflow patterns for multi-service business processes.
5. Keep contracts stable so services can evolve independently.
6. Build resilience with timeouts, retries, idempotency, and appropriate failure isolation.
7. Measure independence through deployment coordination, cross-service changes, reliability, and operational outcomes.

The most important principle: Microservices should be separated by business responsibility, not merely by deployment boundaries. A healthy architecture allows each service to evolve independently while collaborating with other services through explicit contracts.

If the business does not benefit from independent deployment, scaling, ownership, or failure isolation, a modular monolith may be simpler and more effective than microservices.