Entity Pattern — Objects with Identity and Continuity

In this tutorial, you'll learn how the Entity pattern defines objects with a distinct identity that persists across state changes.

What You'll Learn

how the Entity pattern defines objects with a distinct identity that persists across state changes.

Why It Matters

Some objects are defined by who they are, not what they look like. Entities model real-world continuity.

Real-World Use

DDD entities with @Id, Hibernate persistent entities, and database rows with primary keys.

The Entity Pattern

The Entity pattern addresses a specific recurring design problem by providing a reusable solution structure. Understanding when and how to apply it is essential for writing maintainable, scalable code.

Key Concepts

• Registry/Tracking: Entity maintains a registry of objects or operations.
• Atomicity: Changes are grouped into units that succeed or fail together.
• Isolation: Each unit operates independently.
• Consistency: The pattern ensures data integrity across operations.

Structure

The following diagram shows the structure of this pattern:

classDiagram
    class Entity {
        -new: List
        -dirty: List
        -removed: List
        +registerNew()
        +registerDirty()
        +registerRemoved()
        +commit()
    }
    class Entity { id data }
    class DataMapper { +insert() +update() +delete() }
    Entity --> Entity : tracks
    Entity --> DataMapper : persists

Implementation

from abc import ABC, abstractmethod
from dataclasses import dataclass, field
from typing import List, Dict

@dataclass
class Entity:
    id: int
    data: str = ""

class EntityRegistry:
    def __init__(self):
        self._new: List[Entity] = []
        self._dirty: List[Entity] = []
        self._removed: List[Entity] = []

    def register_new(self, e: Entity):
        self._new.append(e)

    def register_dirty(self, e: Entity):
        if e not in self._dirty:
            self._dirty.append(e)

    def register_removed(self, e: Entity):
        self._removed.append(e)

    def commit(self):
        print(f"Inserting {len(self._new)} new entities")
        print(f"Updating {len(self._dirty)} dirty entities")
        print(f"Deleting {len(self._removed)} removed entities")
        self._new.clear()
        self._dirty.clear()
        self._removed.clear()

# Usage
reg = EntityRegistry()
e1 = Entity(1, "Alice")
e2 = Entity(2, "Bob")
reg.register_new(e1)
reg.register_new(e2)
e1.data = "Alice Updated"
reg.register_dirty(e1)
reg.register_removed(e2)
reg.commit()

Expected output:

Inserting 2 new entities
Updating 1 dirty entities
Deleting 1 removed entities

Key Participants

• Entity: Coordinates tracking and persistence of changes.
• Entity: The domain object being tracked.
• Client: Code that uses the Entity.
• Data Mapper: Handles actual database operations.

Real-World Examples

• DodaTech uses this pattern internally for consistent cross-cutting concerns.
• Major frameworks and libraries implement this pattern as a core architectural element.
• Production systems at scale depend on this pattern for reliability.

Related Patterns

• Value Object

• Aggregate Root

• Repository

• Design Patterns — the complete patterns catalog.

Pros and Cons

Common Mistakes

1. **Over-engineering: Applying Entity where a simpler solution suffices, adding unnecessary complexity.

2. **Wrong granularity: Implementing Entity at the wrong level of abstraction.

3. **Thread Safety ignored: Using Entity in concurrent context without proper synchronization.

4. **Tight coupling: Violating the pattern intent by creating hidden dependencies.

5. **Premature optimization: Introducing Entity before there is evidence it is needed.

Practice Questions

1. What problem does the Entity pattern solve? Describe a real-world scenario where using it improves code quality.

2. How does Entity differ from alternative approaches? What are the trade-offs?

3. What testing Strategy would you use for code that implements Entity?

4. How would you refactor legacy code to introduce Entity?

5. When should you NOT use Entity? Describe scenarios where it adds unnecessary complexity.

Challenge

Implement a complete Entity example in Python with unit tests. Include error handling, edge cases (empty data, null values, concurrent access), and a performance comparison against a simpler alternative. Document your design decisions.

Real-World Task

Find a section of code in your current project that could benefit from the Entity pattern. Refactor it, write tests, and measure the improvement in testability, coupling, and cohesion.

Security Tip: When implementing Entity, ensure proper input validation, avoid exposing internal state, and follow Least Privilege. At DodaTech, all implementations undergo security review.

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