Comparisons

Pairs that get conflated in real conversations and in real pull requests — coupling and cohesion, abstraction and indirection, refactoring and rewriting, debt and mess. Neither column wins; what decides is the requirement. Each record leads with the confusion, because the confusion is the reason the record exists.

DRY vs deliberate duplication Inheritance vs Composition Transaction Script vs Domain Model Package by layer vs Package by feature Refactor vs Rewrite Library vs Framework Monorepo vs Polyrepo Exceptions vs Result types Mocks vs Fakes Deliberate debt vs Accidental debt Encapsulation vs Information hiding Simple vs Easy

DRY vs deliberate duplication

What people get wrong about this pair

DRY is remembered as "never write the same lines twice", which is not what it says and not a useful rule. The original formulation is about knowledge — every piece of knowledge should have a single, unambiguous, authoritative representation — and knowledge is not the same thing as text. The practical consequence is that the interesting question is never "are these lines identical" but "will a single decision change both". Two functions that are character-for-character the same but belong to two teams with two roadmaps are not duplication, and merging them creates a coupling that neither team asked for: the next time one of them needs a different behaviour, the shared function grows a boolean parameter, then a second one, and eventually nobody can change it at all. The opposite error is just as real and gets less airtime lately, because the anti-DRY argument has become fashionable: a pricing rule genuinely copied into nine handlers means nine edits, a discovery phase longer than the edit, and near-certainty that one of them is already wrong. The honest rule is that premature deduplication and premature abstraction are the same mistake, and that duplication is cheaper than the *wrong* abstraction but not cheaper than the right one.

DRY — every piece of knowledge has one authoritative representation

Use it when

When the two sites encode the *same* rule, so a change to the rule must reach both. Tax calculation, an authorisation check, the definition of an active subscription: if a lawyer changing their mind must change both places, they are one place.

Deliberate duplication — two similar pieces of code kept separate on purpose

Use it when

When the code looks identical but the two sites answer to different owners and will diverge. Two report layouts that happen to have the same columns today, a validation that mirrors an external system's rules, a test fixture that resembles production code.

Dimension	DRY — every piece of knowledge has one authoritative representation	Deliberate duplication — two similar pieces of code kept separate on purpose
Unit of concern	A piece of knowledge — a rule, a format, a policy	A piece of text that happens to repeat
Right question	Would one decision change both sites?	Do these two sites have different owners or roadmaps?
Cost of getting it wrong	Nine edits per change, and drift nobody notices	A shared function accreting flags until it is unchangeable
Signal it is time	The third occurrence, or the first bug caused by drift	A boolean parameter added so one caller can behave differently
What it buys	One address per change; drift becomes impossible	Independence; either side can change without negotiation
What it costs	Coupling between every caller of the shared thing	Real edits in real places when the rule truly is shared
Reversal cost	Splitting a shared abstraction later is painful and political	Merging two duplicates later is mechanical and safe
Default when unsure	Only when you have seen the change arrive twice	Yes — duplication is the reversible choice

The same question, five structures

Layered, hexagonal, clean, vertical slice and modular monolith — compared without naming a winner, and with the block that says where the comparison stops being true.

CONTESTED

A tidy table implies an equivalence that does not exist. These are not five points on one axis: layered, hexagonal and clean are statements about dependency direction; vertical slice is a statement about directory grouping; modular monolith is a statement about deployment and module visibility. Most real systems combine several. The where this comparison misleads block on every row is the part worth reading, and it is the reason this page names no winner — none of these is mandatory, and a team that adopts one because a diagram was pretty has skipped the only question that decides it.

What it actually organises

Layered

Code by technical kind. Presentation sits above application, above domain, above data access, and each layer may call downward only.

Hexagonal

Code by whether it is inside or outside. The application defines ports it needs; adapters on the outside implement them, and the direction of the dependency is inward.

Clean

Concentric rings by rate of change and by policy versus mechanism, with a hard rule that source dependencies point inward toward entities and use cases.

Vertical slice

Code by capability. Each feature owns its whole path — entry point, rules, persistence, tests — and layering, if any, happens inside the slice.

Modular monolith

Code by module, in one deployable. Modules have explicit public contracts and private internals, enforced by the build rather than by convention.

Where this comparison misleads

These are not five points on one axis. Layered, hexagonal and clean are all statements about *dependency direction*; vertical slice is a statement about *directory grouping*; and modular monolith is a statement about *deployment and module visibility*. You can — and most real systems do — combine several of them: a modular monolith whose modules are vertical slices, each with a hexagonal boundary at its edges. Comparing them as alternatives is the single most common way this table is misread.

Complexity it adds

Layered

Low at first. The layer names are conventional, the rule is easy to state, and most frameworks come pre-shaped this way.

Hexagonal

Moderate: a port interface and an adapter for every outside thing the application touches, plus wiring that chooses which adapter is live.

Clean

Highest of the three dependency styles: entities, use cases, interface adapters and frameworks as distinct rings, with mapping between the types of adjacent rings.

Vertical slice

Low structurally, but it moves the difficulty into judgement — someone must decide what a feature is and where cross-cutting work lives.

Modular monolith

Moderate, and mostly in tooling: module boundaries have to be enforced somewhere, and the shared platform underneath needs an owner.

Where this comparison misleads

The word complexity is doing two jobs here. Layered and vertical slice are cheap to *set up* and can be expensive to *live in* once the codebase is large; clean and hexagonal are expensive up front and their cost is roughly flat afterwards. Any comparison made at week one inverts the ranking you would get at year three, and neither reading is dishonest — they are answering different questions.

Change locality — where one feature change lands

Layered

Across every layer: controller, service, repository, model, mapper. The feature exists in five directories and is owned by none of them.

Hexagonal

Inside the application core if the change is a rule; inside one adapter if it is about an outside system. Rule changes and integration changes are cleanly separated.

Clean

Similar to hexagonal, with an extra hop: a change to a data shape often means touching an entity, a use case and the mapping between rings.

Vertical slice

One directory, usually one team, frequently one pull request. This is the property the style exists to deliver.

Modular monolith

Inside one module, if the boundaries match the domain. When they do not, the change crosses module contracts and becomes a negotiation.

Where this comparison misleads

Locality is a property of whether the boundaries match the change history, not of the style name. A vertical slice cut along the wrong capability lines has terrible locality, and a layered codebase with only one real feature has perfect locality. The only honest way to compare these columns is to open the last thirty merged changes in your own repository and count the directories each one touched.

Testability

Layered

Depends entirely on how the layers are wired. If the service constructs its repository, the layering buys nothing; if dependencies are passed in, the domain layer tests cleanly.

Hexagonal

Strong by construction: the core has no outward dependency, so it is tested with in-memory adapters and no infrastructure at all.

Clean

Same strength as hexagonal for the inner rings, with more mapping code that itself needs tests and rarely gets them.

Vertical slice

Naturally suits testing the slice end to end at its entry point, which is closer to how the feature is actually used and further from a fast unit test.

Modular monolith

Module contract tests are the characteristic unit: exercise a module through its public surface with the others faked, and enforce that no test reaches into internals.

Where this comparison misleads

Every column here is a claim about *fast tests without infrastructure*, and any of the five achieves that as soon as dependencies are injected rather than constructed — which is a separate decision none of these styles owns. What differs is the default test boundary each one nudges you toward, and that matters more than the theoretical maximum: layered nudges toward class-level tests with mocks, vertical slice toward feature-level tests, and the difference shows up in how much your suite has to change during a refactor.

Domain complexity it repays at

Layered

Any, because it does not really engage with domain complexity. It organises files; the rules can be thin or thick and the layout is unchanged.

Hexagonal

Repays when the outside world is complex or volatile — several integrations, a provider you expect to replace, a domain that must be testable without any of them.

Clean

Repays when the domain rules are genuinely rich and long-lived enough that isolating them from delivery mechanisms pays for the mapping. Below that, it is ceremony.

Vertical slice

Repays when features are numerous and largely independent, regardless of whether the rules inside each one are deep.

Modular monolith

Repays when several teams share a codebase and need enforced independence without the operational cost of separate deployments.

Where this comparison misleads

The columns are answering to different pressures: hexagonal responds to *external* volatility, clean to *domain* richness, vertical slice to *feature count*, and modular monolith to *team count*. A system can score high on one pressure and low on the rest, which is why picking a style from a comparison table rather than from your own pressures is how teams end up with four rings around a CRUD application.

Team fit

Layered

Suits a single team, or one where specialists own tiers. It gives everyone the same obvious place to put a new file, which has real value with junior engineers.

Hexagonal

Suits a team that agrees about where the domain boundary is. The port definitions are a design conversation that has to actually happen.

Clean

Suits teams with the discipline to maintain the ring rule under deadline pressure, and enough people that the mapping cost is amortised.

Vertical slice

Suits several teams owning different capabilities, and makes ownership files almost write themselves.

Modular monolith

Suits multiple teams who need independence in the code but do not want, or cannot yet afford, independent deployment and operation.

Where this comparison misleads

Team fit is not a tiebreaker, it is often the deciding factor, and it is the one this table cannot capture. A structurally superior design that the team will not maintain under deadline degrades into the worst version of itself — half-applied clean architecture, with some code respecting the ring rule and some not, is harder to work in than consistent layering. The right question is which of these your team will still be following in eighteen months.

Onboarding cost

Layered

Lowest. Nearly every engineer has seen it, the names are industry-standard, and a new joiner can place a file correctly on day one.

Hexagonal

Moderate. The inward dependency rule is one idea, but the vocabulary — ports, adapters, primary and secondary — is unfamiliar and inconsistently used across the industry.

Clean

Highest. Four rings, mapping between them, and a rule that feels arbitrary until someone explains which change it makes cheap.

Vertical slice

Low for reading — the whole feature is in one place — and higher for writing, because a new joiner has to know which slice owns the thing they are adding.

Modular monolith

Low per module and moderate overall: you learn one module quickly, and the map of which modules exist and what they promise takes longer.

Where this comparison misleads

This row compares familiarity, not intrinsic difficulty, and familiarity is a property of the industry at a moment in time rather than of the design. Layered wins here largely because it is what most people have seen, which is an argument for it and also the reason it is over-applied. It is also worth separating cost-to-read from cost-to-contribute-correctly: vertical slice inverts on those two, and the table's single number hides it.

Ceremony per feature

Layered

A file per layer, plus a mapper, whether or not the feature needs them. A trivial read endpoint costs the same ceremony as a complex workflow.

Hexagonal

A port plus an adapter for each new outside dependency; features that touch nothing new add none.

Clean

The most: entity, use case, request and response models, and mapping in both directions across the ring boundaries.

Vertical slice

The least, by design. A simple feature is allowed to be a single small file, and a complex one grows its own internal structure.

Modular monolith

Little inside a module; real ceremony when a feature crosses a module contract, which is intentional friction.

Where this comparison misleads

Ceremony is only waste when the feature did not need it, and every column here is right for some features and wrong for others in the same codebase. That is the actual finding of this row: a uniform ceremony level applied to every feature guarantees you are overpaying on the simple ones or underpaying on the complex ones. Allowing different features to carry different amounts of structure is more valuable than choosing which column to standardise on.

Where it degrades

Layered

Into a codebase where every feature change is a shotgun change, and the service layer becomes the god object that everything passes through.

Hexagonal

Into ports with one adapter each, defined by the vendor API rather than by the domain, so the boundary adds a hop and protects nothing.

Clean

Into mapping code that outweighs the logic, and an inner ring so guarded that adding a field means editing four types and two mappers.

Vertical slice

Into a shared/ directory that grows back under a new name, or slices that each reimplement infrastructure slightly differently.

Modular monolith

Into modules that reach into each other's tables because the contract was inconvenient once and nothing enforced it.

Where this comparison misleads

Every one of these degradations is the style's own strength taken past the point where it repays — which is why none of them can be called wrong, and why §138 forbids teaching any as mandatory. What makes a codebase bad is not the column it started in but the absence of anyone asking whether the structure still matches the changes arriving. The right comparison to make is between your current structure and your last thirty changes, not between two names on a page.