A Practical Guide to Architectural Quanta

Why Does Your “Microservice” Feel Like a Monolith?

You’ve embraced microservices, split your system into neat, independent services, and celebrated the promise of agility. Yet, a familiar frustration persists: every time your “Orders” team wants to deploy a small change, they find themselves coordinating with the “Inventory” team, the “Payments” team, and sometimes even the “Notifications” team. What gives?

This isn’t microservices; it’s a distributed monolith, and the culprit is hidden deployment coupling. This guide gives you the diagnostic tool to find the problem, and the architectural patterns to fix it. This post focuses on a critical concept within software architecture, building upon the foundational ideas explored in my From Patterns to Practice series.

The High Price of Hidden Coupling

This isn’t just an academic problem; it’s a tax on your team’s daily productivity. When your system’s true deployable units are poorly defined, you pay the price in:

• Agility and Release Cadence: Your release process slows to a crawl as ‘simple’ changes require complex, coordinated deployments across multiple teams.
• Fault Tolerance and Blast Radius: A failure in one small component can trigger a cascading failure across the entire system, erasing the resilience benefits of a distributed design.
• Scalability and Cost: You are forced into inefficient and costly scaling strategies, replicating large parts of the system to handle load on one tiny feature.

Understanding and managing coupling is central to building evolvable systems, a topic I explore extensively in my Modular by Design series.

A Litmus Test: Independent Deployability

Now that you understand the stakes, how do you find these hidden units? Start with a simple litmus test by asking one critical question:

“Can I deploy this service right now, completely on its own, without having to deploy anything else?”

If the answer is “yes,” you’ve found an independently deployable component. This is an Architectural Quantum: the smallest part of your architecture that can be deployed completely on its own. The term was first introduced in the book Building Evolutionary Architectures by Neal Ford, Rebecca Parsons, and Patrick Kua, and was significantly expanded upon in their subsequent book, Software Architecture: The Hard Parts.

It’s crucial to distinguish this from simple code organization. Code modularity is like having well-organized folders on your laptop; an architectural quantum is like asking, “What’s the smallest USB drive I can create that has everything it needs to run on its own?”

A Practical Guide to Finding Your Quanta

So, how do you apply this litmus test? You need to become a detective, hunting for the hidden forces that bind your services together.

Step 1: Hunt for Synchronous Calls

Your first task is to map out the synchronous, blocking network calls between your services. These are the potential “chemical bonds” that fuse multiple services into a single quantum. Consider a classic, real-world example: a DocumentService that must verify a user’s permissions with a PermissionsService before allowing access to a document.

 1// DocumentService
 2public Document getDocument(UUID documentId, UserCredentials user) {
 3    // This synchronous authorization check creates tight coupling.
 4    // A user's access rights must be confirmed before returning the document.
 5    try {
 6        if (!permissionsService.canAccess(user, documentId)) {
 7            throw new UnauthorizedAccessException("User does not have access to this document.");
 8        }
 9    } catch (PermissionServiceException e) {
10        // The permission service is down or failing.
11        throw new DocumentAccessException("Could not verify permissions; document access is unavailable.", e);
12    }
13
14    // ... proceed with fetching and returning the document content.
15}

Step 2: Analyze the Coupling with Connascence

Once you’ve identified a synchronous call, the next step is to analyze the coupling it creates. For this, we turn to connascence, which means “born together” and describes the degree to which two components must change together. We explore this in detail in our post on “Good Coupling, Bad Coupling, and Cohesion”. We are specifically looking for Synchronous Connascence.

Synchronous connascence means that if component A changes, component B must be released in the same deployment batch. You cannot have a version mismatch between them in production. A change to one forces a simultaneous release of the other.

Step 3: Analyze the Contract for Brittleness

Does every synchronous call create a single quantum? Not necessarily. The real issue is not synchronicity itself, but brittle contracts.

A synchronous call only creates deployment coupling if a change to the provider breaks the consumer. If the provider can be updated in a way that is backward-compatible, then the two components can still be deployed independently.

• Brittle Contract (Single Quantum): A GET /user/123 endpoint returns {"name": "Vijay"}. A new version changes the response to {"fullName": "Vijay Anant"}. All existing clients that expect the name field will break. They must be deployed in lock-step.
• Evolvable Contract (Separate Quanta): A GET /user/123 endpoint returns {"name": "Vijay"}. A new version returns {"name": "Vijay", "fullName": "Vijay Anant"}. The new field is additive. Old clients still work perfectly because they simply ignore the new fullName field. The components can be deployed independently despite the synchronous call.

Example: The Pricing and Auditing Quantum

Consider a Pricing Service and an Auditing Service with a critical business requirement: Every single pricing calculation must be verifiably audited, and the audit must happen as part of the same logical transaction.

The interaction is a synchronous, blocking call:

1// This synchronous coupling creates a single quantum
2Price price = pricingService.calculate(item);
3auditingService.log(price); // Must succeed for the operation to be valid

Because of the synchronous call and the business requirement, the system’s integrity depends on both components being in a consistent state at the same time. During a rolling deployment, an old Pricing Service might call a new Auditing Service that has a startup bug. The audit would fail, and therefore the entire pricing operation would have to fail. You cannot have a mix of old and new components in the wild.

They have synchronous connascence. To guarantee the business rule is never violated, you must deploy them together. They are one quantum.

Breaking Down Quanta: Patterns and Strategies

Identifying a single, large quantum is the diagnosis. Now for the cure. The goal is to enable independent evolution by breaking the synchronous connascence that creates the deployment coupling.

A Tale of Two Systems: The Quantum in Action

To make this concrete, let’s tell a story. Imagine a simple e-commerce platform. A user’s “Loyalty Status” (e.g., Bronze, Silver, Gold) is managed by a LoyaltyService and displayed on their profile page, which is handled by the UserService.

System A: The Entangled System

In this version, the UserService makes a direct, synchronous call to the LoyaltyService to get the user’s status every time a profile is loaded.

UserService -> (synchronous call) -> LoyaltyService

The LoyaltyService team wants to deploy a small, backward-incompatible change to their API renaming the status field to loyaltyTier. The change is simple, but the consequences are not. Because of the synchronous call, both services now form a single architectural quantum.

The result is deployment dread:

• Coordinated Release: Simple changes require complex, cross-team coordination and shared deployment windows.
• Increased Risk: The blast radius for any failure is now larger, forcing rollbacks of multiple services.
• Lost Autonomy: Teams can no longer innovate or release on their own schedule; they are tethered to the slowest-moving team in the quantum.

System B: The Decoupled System

In a revised architecture, the teams decide to break this synchronous dependency. Instead of a direct call, the LoyaltyService publishes an event whenever a user’s status changes. The UserService subscribes to these events and stores the current loyalty status locally.

LoyaltyService -> (asynchronous event) -> UserService

Now, the two services are separate architectural quanta. The LoyaltyService team can deploy their change, and the UserService can be updated on its own schedule. By breaking the synchronous connascence, the teams shrank their architectural quanta, reclaiming their agility.

Your Decoupling Toolkit: Beyond EDA

The story of System B used an asynchronous event, but that’s just one tool in the toolbox. Breaking synchronous connascence can be achieved with a variety of patterns, each with its own trade-offs. Choosing the right one depends on the specific context.

Here are several common patterns:

1. Asynchronous Command/Task Queues

   • How it works: Instead of Service A calling Service B directly, it sends a “command” message (e.g., ProcessPayment) to a dedicated queue. Service B listens to this queue and processes the command at its own pace.
   • Best for: Actions or operations that need to be done, but not necessarily right now. It’s a great way to handle background jobs and improve resilience.

2. Data Replication / Materialized Views

   • How it works: If UserService only calls LoyaltyService to read data, it can maintain its own local copy. When the LoyaltyService updates a user’s status, it publishes an event, and the UserService updates its local store.
   • Best for: Situations where services are highly read-dependent on each other. This pattern dramatically improves availability at the cost of eventual consistency.

3. UI Composition

   • How it works: The frontend application (e.g., a React app) makes three independent, parallel calls to the services instead of a backend gateway doing it synchronously.
   • Best for: User-facing applications where different parts of the page are owned by different backend services.

4. The Strangler Fig Pattern

   • How it works: This is a migration pattern. You place a proxy in front of your large quantum and gradually build new, independent services. The proxy routes calls to the new services over time, “strangling” the old system until it can be retired.
   • Best for: Incrementally and safely breaking down an existing monolith into smaller quanta without a high-risk rewrite.

Choosing the right pattern is a core design decision. By having this toolkit, you can move beyond a one-size-fits-all approach and select the precise decoupling strategy that fits your system’s unique needs.

Ultimately, the Architectural Quantum is the diagnostic tool that reveals the hidden coupling. The remedy you choose depends entirely on the specific trade-offs (like consistency versus availability) that your system can afford to make for that specific context.

Quanta: The Scorecard for Your Architectural Style

Many believe a “Microservice” architecture automatically grants independent deployability. This is a common and dangerous misconception. An architectural style, as explored in “The Architect’s Toolbox”, is an intent. The architectural quantum, however, is the reality of your system, serving as the ultimate scorecard for how successfully you’ve achieved that intent.

• The Monolith: When you choose a monolithic style, you are explicitly choosing to have a single architectural quantum. The architectural challenge isn’t to break it apart, but to maintain high modularity within that single deployable unit to prevent it from becoming a “big ball of mud.”

• Microservices vs. The Distributed Monolith: The goal of the microservice style is to create a system composed of many small, independent quanta. However, without vigilance in managing coupling, especially synchronous connascence, you risk creating a distributed monolith. This form of architectural debt, explored in “Bad Code is not Tech Debt”, means you incur the operational costs of a distributed system without its primary benefit: independent deployment. The quantum becomes your diagnostic tool here: if 10 services are all part of one quantum, it’s a clear signal.

• Event-Driven Architecture: EDA is a powerful style for enabling multiple, independent quanta, but it is not a magic fix. While it removes runtime blocking, it can introduce tight coupling at the contract level. If not managed carefully with schema evolution and versioning, you can still have a single quantum that is now mediated by a broker. True independence requires both asynchronous communication and evolvable contracts.

It’s crucial to remember that the goal is not to eliminate all coupling, which is impossible, but to ensure that components within the same architectural quantum share consistent operational characteristics, such as deployment cadence, reliability requirements, and scaling needs.

By learning to see your system in quanta, every engineer gains a powerful lens to analyze system structure, diagnose hidden coupling, and make better design trade-offs.

So, what is the architectural quantum of your current system? Is it one large, monolithic entity, or a collection of small, independent quanta? How does that reality impact your team’s ability to deploy and scale? Share your insights in the comments below.