Query Object Pattern — Encapsulate Database Queries as Objects

In this tutorial, you'll learn how the Query Object pattern represents database queries as composable, reusable objects.

What You'll Learn

how the Query Object pattern represents database queries as composable, reusable objects.

Why It Matters

Raw SQL or query strings scattered across code are hard to test and reuse. Query Objects encapsulate query logic.

Real-World Use

JPA Criteria API, LINQ expressions, and SQLAlchemy ORM queries are Query Object implementations.

The Query Object Pattern

The Query Object 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: Query Object 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 QueryObject {
        -new: List
        -dirty: List
        -removed: List
        +registerNew()
        +registerDirty()
        +registerRemoved()
        +commit()
    }
    class Entity { id data }
    class DataMapper { +insert() +update() +delete() }
    QueryObject --> Entity : tracks
    QueryObject --> 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 QueryObjectRegistry:
    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 = QueryObjectRegistry()
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

• Query Object: Coordinates tracking and persistence of changes.
• Entity: The domain object being tracked.
• Client: Code that uses the Query Object.
• 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

• Specification

• Repository

• Data Mapper

• Design Patterns — the complete patterns catalog.

Pros and Cons

Common Mistakes

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

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

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

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

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

Practice Questions

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

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

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

4. How would you refactor legacy code to introduce Query Object?

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

Challenge

Implement a complete Query Object 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 Query Object pattern. Refactor it, write tests, and measure the improvement in testability, coupling, and cohesion.

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

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