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Comparison to Other Libraries

Choosing a dependency injection (DI) framework for Python involves considering different philosophies and feature sets. pico-ioc makes specific design choices (as outlined in Design Principles) that differentiate it from other popular libraries like dependency-injector or punq.

This comparison helps you understand pico-ioc's strengths and typical use cases relative to alternatives.

Feature Comparison Matrix

Feature pico-ioc dependency-injector punq
Primary Style Decorators + TH¹ Declarative Code² Decorators + TH¹
Type Hint Based ✅ Yes ⚠️ Partial³ ✅ Yes
Startup Validation ✅ Yes (Eager) ❌ No (Runtime) ❌ No (Runtime)
Circular Dep. Check ✅ Yes (Startup) ✅ Yes (Runtime) ✅ Yes (Runtime)
Async Support ✅ Native (aget) ✅ Yes⁴ ❌ No
AOP (Interceptors) ✅ Built-in ❌ No ❌ No
Scopes (Singleton) ✅ Yes ✅ Yes ✅ Yes
Scopes (Prototype) ✅ Yes ✅ Yes ✅ Yes
Scopes (ContextVar) ✅ Built-in ✅ Yes ❌ No
Configuration Binding ✅ Unified Tree+Flat ✅ Basic (KV) ❌ Manual
Qualifiers/Tags ✅ Yes ✅ Yes (Providers) ❌ No
List Injection ✅ Yes (Native) ✅ Yes (List) ❌ No
Dictionary Injection ✅ Yes (Native) ✅ Yes (Dict) ❌ No
Lazy Loading (lazy) ✅ Built-in ✅ Yes ❌ No
Conditional Binding ✅ Full (Profile+) ✅ Basic (Config) ❌ No
Observability (Context) ✅ Built-in ❌ Manual ❌ Manual
Observability (Stats) ✅ Built-in ❌ Manual ❌ Manual
Testing Overrides ✅ init(overrides) ✅ Yes ✅ Yes
Python Version 3.10+ 3.7+ 3.7+

Notes: ¹ Relies heavily on decorators applied directly to classes/methods and uses type hints for injection. ² Primarily uses declarative Python code (often in separate containers.py files) to define providers and wiring. ³ dependency-injector can use type hints but doesn't rely on them as the primary mechanism for resolution; its provider syntax is central. ⁴ dependency-injector supports async providers, but core resolution and lifecycle management might have nuances compared to a fully async-native design.

Philosophical Differences & Use Cases

pico-ioc

  • Focus: Startup safety, async-native, AOP, observability, advanced unified configuration, modern Python (3.10+).
  • Philosophy: Inspired by robust enterprise frameworks (like Spring/Guice), prioritizing early error detection (fail-fast validation), explicit configuration via decorators and type hints, and built-in support for complex application patterns (async, AOP, context management). Treats DI as a core architectural tool for the entire application.
  • Strengths:
    • Startup Safety: Catches most wiring errors (InvalidBindingError, CircularDependencyError) before runtime.
    • Async Native: Seamless integration with asyncio across resolution (aget), lifecycle, and AOP.
    • AOP: Built-in method interception (@intercepted_by) for cross-cutting concerns.
    • Unified Configuration: Powerful @configured binding handling both flat (ENV-like) and tree (YAML/JSON) sources via configuration(...) builder, with clear precedence and normalization rules.
    • Native Collections: Injects List[T], Set[T], Dict[str, T], and Dict[Type, T] automatically.
    • Observability: Designed for monitoring via stats(), ContainerObserver, and container_id context.
  • Weaknesses:
    • Requires Python 3.10+.
    • Feature set might be overkill for very simple scripts.
    • Relies heavily on decorators, which might not suit all style preferences.
  • Best For: Medium-to-large applications, async web services (FastAPI/Flask), microservices, systems where reliability, testability, and maintainability are crucial, applications needing AOP or complex, unified configuration patterns.

dependency-injector

  • Focus: Flexibility, declarative provider configuration, maturity, broad Python version support.
  • Philosophy: Provides a flexible, code-based declarative system for defining providers and their relationships, often centralized in dedicated container modules. Less emphasis on direct decoration of business logic classes.
  • Strengths:
    • Mature, widely adopted, and stable.
    • Supports Python 3.7+.
    • Flexible provider types (Factory, Singleton, Configuration, etc.).
    • Good support for various scopes.
  • Weaknesses:
    • Wiring errors typically occur at runtime when a dependency is first accessed.
    • Can lead to boilerplate code in container definition files.
    • Lacks built-in AOP and the unified tree/flat configuration binding found in pico-ioc post-ADR-010.
  • Best For: Projects preferring explicit, centralized wiring definitions separate from business logic, applications needing Python 3.7-3.9 support, situations where runtime error detection is acceptable.

punq

  • Focus: Simplicity, minimalism, type-hint driven.
  • Philosophy: Aims for a minimal API surface, relying almost entirely on type hints for dependency resolution with basic decorators for registration.
  • Strengths:
    • Very easy to learn and use for basic DI.
    • Clean integration with type hints.
    • Supports Python 3.7+.
  • Weaknesses:
    • Lacks many advanced features found in pico-ioc or dependency-injector (e.g., advanced scopes, async support, AOP, configuration binding, qualifiers, conditional registration, startup validation).
    • Errors occur at runtime.
  • Best For: Smaller applications or scripts where only basic constructor injection is needed and advanced container features are unnecessary.

Conclusion

pico-ioc is designed for developers building complex, modern Python applications who value startup safety, native async support, testability, and advanced features like AOP and structured, unified configuration. Its emphasis on fail-fast validation and observability makes it particularly well-suited for production-grade systems where reliability and maintainability are paramount. It requires Python 3.10+ due to its reliance on modern typing features.

Next Steps

This concludes the Architecture section. Explore the API Reference for detailed documentation on specific functions and decorators.