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golang-dependency-injection

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Choose the right dependency injection approach for your Go project and get wiring code you can apply.

What it does

A Go architecture guide that picks a dependency injection approach based on your dependency graph and lifecycle needs. It walks through manual constructor injection for small codebases and compares four DI libraries — google/wire, uber-go/dig, uber-go/fx, samber/do — via a decision table covering type safety, lazy loading, health checks, and graceful shutdown. In refactor mode it runs up to three parallel agents to surface global variables, init() service setup, and service-locator anti-patterns, then proposes a migration plan. Also covers testing patterns including container cloning with samber/do.

When to use it

  • Designing a new Go service with multiple cross-cutting dependencies
  • Refactoring tightly coupled Go code into testable components
  • Choosing between manual injection and a DI library (wire, dig, fx, do)
  • Picking a lifetime strategy: singleton, transient, lazy, or scoped

The skill document

Persona: You are a Go software architect. You guide teams toward testable, loosely coupled designs — you choose the simplest DI approach that solves the problem, and you never over-engineer.

Modes:

  • Design mode (new project, new service, or adding a service to an existing DI setup): assess the existing dependency graph and lifecycle needs; recommend manual injection or a library from the decision table; then generate the wiring code.
  • Refactor mode (existing coupled code): use up to 3 parallel sub-agents — Agent 1 identifies global variables and init() service setup, Agent 2 maps concrete type dependencies that should become interfaces, Agent 3 locates service-locator anti-patterns (container passed as argument) — then consolidate findings and propose a migration plan.

Community default. A company skill that explicitly supersedes samber/cc-skills-golang@golang-dependency-injection skill takes precedence.

Dependency Injection in Go

Dependency injection (DI) means passing dependencies to a component rather than having it create or find them. In Go, this is how you build testable, loosely coupled applications — your services declare what they need, and the caller (or container) provides it.

This skill is not exhaustive. When using a DI library (google/wire, uber-go/dig, uber-go/fx, samber/do), refer to the library's official documentation and code examples for current API signatures.

For interface-based design foundations (accept interfaces, return structs), see the samber/cc-skills-golang@golang-structs-interfaces skill.

Best Practices Summary

  1. Dependencies MUST be injected via constructors — NEVER use global variables or init() for service setup
  2. Small projects (< 10 services) SHOULD use manual constructor injection — no library needed
  3. Interfaces MUST be defined where consumed, not where implemented — accept interfaces, return structs
  4. NEVER use global registries or package-level service locators
  5. The DI container MUST only exist at the composition root (main() or app startup) — NEVER pass the container as a dependency
  6. Prefer lazy initialization — only create services when first requested
  7. Use singletons for stateful services (DB connections, caches) and transients for stateless ones
  8. Mock at the interface boundary — DI makes this trivial
  9. Keep the dependency graph shallow — deep chains signal design problems
  10. Choose the right DI library for your project size and team — see the decision table below

Why Dependency Injection?

Problem without DIHow DI solves it
Functions create their own dependenciesDependencies are injected — swap implementations freely
Testing requires real databases, APIsPass mock implementations in tests
Changing one component breaks othersLoose coupling via interfaces — components don't know each other's internals
Services initialized everywhereCentralized container manages lifecycle (singleton, factory, lazy)
All services loaded at startupLazy loading — services created only when first requested
Global state and init() functionsExplicit wiring at startup — predictable, debuggable

DI shines in applications with many interconnected services — HTTP servers, microservices, CLI tools with plugins. For a small script with 2-3 functions, manual wiring is fine. Don't over-engineer.

Manual Constructor Injection (No Library)

For small projects, pass dependencies through constructors. See Manual DI examples for a complete application example.

// ✓ Good — explicit dependencies, testable
type UserService struct {
    db     UserStore
    mailer Mailer
    logger *slog.Logger
}

func NewUserService(db UserStore, mailer Mailer, logger *slog.Logger) *UserService {
    return &UserService{db: db, mailer: mailer, logger: logger}
}

// main.go — manual wiring
func main() {
    logger := slog.Default()
    db := postgres.NewUserStore(connStr)
    mailer := smtp.NewMailer(smtpAddr)
    userSvc := NewUserService(db, mailer, logger)
    orderSvc := NewOrderService(db, logger)
    api := NewAPI(userSvc, orderSvc, logger)
    api.ListenAndServe(":8080")
}
// ✗ Bad — hardcoded dependencies, untestable
type UserService struct {
    db *sql.DB
}

func NewUserService() *UserService {
    db, _ := sql.Open("postgres", os.Getenv("DATABASE_URL")) // hidden dependency
    return &UserService{db: db}
}

Manual DI breaks down when:

  • You have 15+ services with cross-dependencies
  • You need lifecycle management (health checks, graceful shutdown)
  • You want lazy initialization or scoped containers
  • Wiring order becomes fragile and hard to maintain

DI Library Comparison

Go has three main approaches to DI libraries:

  • google/wire examples — Compile-time code generation
  • uber-go/dig + fx examples — Reflection-based framework
  • samber/do examples — Generics-based, no code generation

Decision Table

CriteriaManualgoogle/wireuber-go/dig + fxsamber/do
Project sizeSmall (< 10 services)Medium-LargeLargeAny size
Type safetyCompile-timeCompile-time (codegen)Runtime (reflection)Compile-time (generics)
Code generationNoneRequired (wire_gen.go)NoneNone
ReflectionNoneNoneYesNone
API styleN/AProvider sets + build tagsStruct tags + decoratorsSimple, generic functions
Lazy loadingManualN/A (all eager)Built-in (fx)Built-in
SingletonsManualBuilt-inBuilt-inBuilt-in
Transient/factoryManualManualBuilt-inBuilt-in
Scopes/modulesManualProvider setsModule system (fx)Built-in (hierarchical)
Health checksManualManualManualBuilt-in interface
Graceful shutdownManualManualBuilt-in (fx)Built-in interface
Container cloningN/AN/AN/ABuilt-in
DebuggingPrint statementsCompile errorsfx.Visualize()ExplainInjector(), web interface
Go versionAnyAnyAny1.18+ (generics)
Learning curveNoneMediumHighLow

Quick Comparison: Same App, Four Ways

The dependency graph: Config -> Database -> UserStore -> UserService -> API

Manual:

cfg := NewConfig()
db := NewDatabase(cfg)
store := NewUserStore(db)
svc := NewUserService(store)
api := NewAPI(svc)
api.Run()
// No automatic shutdown, health checks, or lazy loading

google/wire:

// wire.go — then run: wire ./...
func InitializeAPI() (*API, error) {
    wire.Build(NewConfig, NewDatabase, NewUserStore, NewUserService, NewAPI)
    return nil, nil
}
// No lifecycle hooks (OnStart/OnStop) or health checks; cleanup via returned func() from providers

uber-go/fx:

app := fx.New(
    fx.Provide(NewConfig, NewDatabase, NewUserStore, NewUserService),
    fx.Invoke(func(api *API) { api.Run() }),
)
app.Run() // manages lifecycle, but reflection-based

samber/do:

i := do.New()
do.Provide(i, NewConfig)
do.Provide(i, NewDatabase)    // auto shutdown + health check
do.Provide(i, NewUserStore)
do.Provide(i, NewUserService)
api := do.MustInvoke[*API](i)
api.Run()
// defer i.Shutdown() — handles all cleanup automatically

Testing with DI

DI makes testing straightforward — inject mocks instead of real implementations:

// Define a mock
type MockUserStore struct {
    users map[string]*User
}

func (m *MockUserStore) FindByID(ctx context.Context, id string) (*User, error) {
    u, ok := m.users[id]
    if !ok {
        return nil, ErrNotFound
    }
    return u, nil
}

// Test with manual injection
func TestUserService_GetUser(t *testing.T) {
    mock := &MockUserStore{
        users: map[string]*User{"1": {ID: "1", Name: "Alice"}},
    }
    svc := NewUserService(mock, nil, slog.Default())

    user, err := svc.GetUser(context.Background(), "1")
    if err != nil {
        t.Fatalf("unexpected error: %v", err)
    }
    if user.Name != "Alice" {
        t.Errorf("got %q, want %q", user.Name, "Alice")
    }
}

Testing with samber/do — Clone and Override

Container cloning creates an isolated copy where you override only the services you need to mock:

func TestUserService_WithDo(t *testing.T) {
    // Create a test injector with mock implementation
    testInjector := do.New()

    // Provide the mock UserStore interface
    do.OverrideValue[UserStore](testInjector, &MockUserStore{
        users: map[string]*User{"1": {ID: "1", Name: "Alice"}},
    })

    // Provide other real services as needed
    do.Provide[*slog.Logger](testInjector, func(i *do.Injector) (*slog.Logger, error) {
        return slog.Default(), nil
    })

    svc := do.MustInvoke[*UserService](testInjector)
    user, err := svc.GetUser(context.Background(), "1")
    // ... assertions
}

This is particularly useful for integration tests where you want most services to be real but need to mock a specific boundary (database, external API, mailer).

When to Adopt a DI Library

SignalAction
< 10 services, simple dependenciesStay with manual constructor injection
10-20 services, some cross-cutting concernsConsider a DI library
20+ services, lifecycle management neededStrongly recommended
Need health checks, graceful shutdownUse a library with built-in lifecycle support
Team unfamiliar with DI conceptsStart manual, migrate incrementally

Common Mistakes

MistakeFix
Global variables as dependenciesPass through constructors or DI container
init() for service setupExplicit initialization in main() or container
Depending on concrete typesAccept interfaces at consumption boundaries
Passing the container everywhere (service locator)Inject specific dependencies, not the container
Deep dependency chains (A->B->C->D->E)Flatten — most services should depend on repositories and config directly
Creating a new container per requestOne container per application; use scopes for request-level isolation

Cross-References

  • → See samber/cc-skills-golang@golang-samber-do skill for detailed samber/do usage patterns
  • → See samber/cc-skills-golang@golang-structs-interfaces skill for interface design and composition
  • → See samber/cc-skills-golang@golang-testing skill for testing with dependency injection
  • → See samber/cc-skills-golang@golang-project-layout skill for DI initialization placement

References

Questions people ask

When should I introduce a DI library instead of manual injection?
The skill includes a decision table: under 10 services stay with manual constructor injection, 10–20 services consider a library, 20+ services strongly benefit from one. Lifecycle needs such as health checks or graceful shutdown also tip toward libraries with built-in support.
Which Go DI libraries are compared?
google/wire (compile-time codegen), uber-go/dig + fx (reflection-based, with lifecycle), and samber/do (generics-based, no codegen). The skill refers you to each library's official docs for current API signatures.
How does refactor mode work?
It runs up to three parallel agents — one for global variables and init() service setups, one for concrete-type dependencies that should become interfaces, one for service-locator anti-patterns where a container is passed as an argument — then consolidates the findings into a migration plan.

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