Primitives: Foundational Design Patterns

This document describes the fundamental design patterns that serve as primitives in the NERV architecture - the basic building blocks from which more complex patterns and components are constructed.

Overview

While the NERV architecture defines its own higher-level patterns (Reactive Event Mesh, Temporal Versioning, etc.), these patterns are built upon well-established foundational design patterns from software engineering. These primitives form the atomic building blocks of our architecture - they are the basic elements from which all higher-level components are constructed.

Core Primitive Patterns

Pattern	Purpose	NERV Usage	Implementation Library
Observer	Enable notification of state changes	Event notification and reactive programming	Blinker
Command	Encapsulate operations as objects	Explicit side effect management	Effect
Monad	Encapsulate computation with context	Functional composition with effects	Effect
Strategy	Define family of interchangeable algorithms	Runtime algorithm selection	Python protocols
Builder	Construct complex objects step by step	Multi-stage object construction	Python dataclasses
DAG	Organize nodes with dependencies	Dependency-based scheduling	TaskMap
Factory	Delegate object creation to specialized methods	Object creation with configuration	Static methods
Decorator	Add behavior to objects dynamically	Adding capabilities without inheritance	Python decorators

Pattern Relationships to NERV Architecture

The primitives form the foundation for NERV’s higher-level architectural patterns:

NERV Pattern	Primitive Composition	Implementation Libraries
Reactive Event Mesh	Observer + Command + Factory	Blinker
Temporal Versioning	Command + Decorator + Factory	Eventsourcing
Perspective Shifting	Strategy + Decorator + Factory	Marshmallow
State Projection	Command + Strategy + Builder	Pyrsistent
Effect System	Command + Monad + Decorator	Effect
Quantum Partitioning	Builder + DAG + Factory	TaskMap

Key Primitive Implementations

Each primitive pattern is implemented using specific libraries within NERV:

• Observer Pattern: Blinker library for signal dispatch (EventBus)
• Command Pattern: Effect library for effect descriptions
• Monad Pattern: Effect library for monadic operations
• Strategy Pattern: Python protocols with Marshmallow for schema-based transformations
• Builder Pattern: Python dataclasses with builder methods in QuantumPartitioner
• DAG Pattern: TaskMap library for dependency execution
• Factory Pattern: Static factory methods on various domain classes
• Decorator Pattern: Python decorators and function wrappers for aspect-oriented programming

Implementation Guidance

When implementing NERV components, consider these primitive patterns:

1. Favor composition over inheritance by using these patterns to combine behaviors
2. Make interfaces explicit by clearly defining the pattern relationships
3. Consider pattern combinations to solve complex problems
4. Document pattern usage to explain design decisions and architecture
5. Leverage library primitives when available to reduce custom implementation

Understanding these primitives is crucial for effective implementation of the NERV architecture, as they form the foundation upon which all higher-level patterns and components are built.

Cross-Cutting Architectural Properties

The primitive patterns combined in NERV create these emergent architectural properties:

Property	Description	Enabled By	Primary Libraries
Decoupling	Components can evolve independently	Observer, Command	Blinker, Effect
Composability	Simple units combine into complex behaviors	Monad, Decorator	Effect, Python decorators
Extensibility	New behaviors without changing core code	Strategy, Factory	Marshmallow, Python protocols
Observability	Introspection of system behavior	Observer, Command	Blinker, Effect
Parallelism	Efficient concurrent execution	DAG, Builder	TaskMap
Flexibility	Runtime adaptation to changing needs	Strategy, Decorator	Marshmallow, Python decorators

Learn More

For detailed explanations and implementation examples of each primitive pattern, see their individual documentation pages:

• Observer Pattern: Event notification system (Blinker)
• Command Pattern: Encapsulated operations (Effect)
• Monad Pattern: Computation with context (Effect)
• Strategy Pattern: Interchangeable algorithms (Python protocols)
• Builder Pattern: Step-by-step construction (Python dataclasses)
• DAG Pattern: Dependency management (TaskMap)
• Factory Pattern: Delegated object creation (Static methods)
• Decorator Pattern: Dynamic behavior addition (Python decorators)