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Lucas Pereira de Souza
Lucas Pereira de Souza

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GraphQL Schema Design

#design #api #architecture #graphql

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Best Practices for Naming, Type and Input Separation, and Schema Versioning: A Practical Guide

Code quality is fundamental to the success of any software project. Clean, readable, and easily maintainable code not only accelerates development but also significantly reduces long-term costs. In this article, we will explore three crucial areas to achieve this goal: best practices for naming, type and input separation, and schema versioning.

1. Best Practices for Naming: The Key to Clarity

Naming is the art of giving meaningful names to variables, functions, classes, and other elements of your code. Well-chosen names make the code easier to understand and maintain, reducing the need for excessive comments.

Essential Tips:

  • Be Descriptive: Use names that clearly explain the purpose of the element. Avoid unnecessary abbreviations and jargon.
  • Consistency: Adopt a consistent naming pattern throughout the project. This makes the code easier to read and understand (Ex: camelCase for variables, PascalCase for classes).
  • Avoid Generic Names: Avoid names like data, value, or temp. Be specific and use names that reflect the real meaning.
  • Use Verbs for Functions: Function names should describe what the function does (Ex: calculateTax, getUser).
  • Boolean Variable Names: Use names that clearly indicate the boolean state (Ex: isActive, canSend).

Example:

Bad:

x = get_d()
if x > 10:
    print(\"OK\")
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Good:

total_value = get_discount()
if total_value > 10:
    print(\"Discount applied\")
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2. Type and Input Separation: Shielding Your Code

Type and input separation is an essential practice to ensure the security and robustness of your code. By clearly defining the expected data types and validating inputs, you avoid unexpected errors and undesirable behaviors.

Benefits:

  • Error Prevention: Detects type errors at development time, preventing them from propagating to runtime.
  • Improved Readability: Makes the code easier to understand, as it makes clear which data types are expected.
  • Data Validation: Ensures that the received inputs are within the expected limits, preventing attacks and other security problems.

Implementation:

  • Static Typing: Use languages with static typing (such as Java, C#, TypeScript) to explicitly define data types.
  • Data Validation: Validate inputs at all boundaries of your system (e.g., API, user interface). Use validation libraries (such as pydantic in Python) to simplify this process.
  • Interfaces and Contracts: Define clear interfaces or contracts for interactions between different parts of your system. This helps ensure that data is passed correctly.

Example (Python with pydantic):

from pydantic import BaseModel, validator

class User(BaseModel):
    name: str
    age: int
    email: str

    @validator('age')
    def valid_age(cls, age):
        if age < 0:
            raise ValueError('Age must be a positive number')
        return age

user = User(name=\"John\", age=30, email=\"joao@email.com\")
print(user)
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3. Schema Versioning: Controlled Data Evolution

Schema versioning is critical to ensure the compatibility and continuity of your system over time. When your data evolves, you need a mechanism to manage these changes without breaking compatibility with previous versions of your code.

Why Version?

  • Natural Evolution: Data changes over time. New information is added, fields are renamed, types are changed.
  • Maintaining Compatibility: Allows older versions of your software to continue working, even with data changes.
  • Ease of Deployment: Allows you to deploy new versions of your code without interruption.

Versioning Strategies:

  • Semantic Versioning: Use semantic versioning (SemVer) to identify changes in your APIs and schemas.
  • New Fields: Add new fields with default values to maintain compatibility.
  • Optional Fields: Make fields optional to avoid breaks in previous versions.
  • Deprecated: Mark old fields as \"deprecated\" and provide a transition period before removing them completely.
  • Tools: Use tools like Alembic (Python) or specific libraries for your database to manage schema migrations.

Example (Conceptual):

Version 1:

{
  \"id\": 123,
  \"name\": \"Product A\",
  \"price\": 10.00
}
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Version 2 (Adding a field):

{
  \"id\": 123,
  \"name\": \"Product A\",
  \"price\": 10.00,
  \"description\": \"High quality product"
}
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Conclusion:

Implementing best practices for naming, type and input separation, and schema versioning is a valuable investment in the quality of your code. By following these guidelines, you will build more robust, readable, and easily maintainable systems, saving time and resources in the long run. Adopt these practices in your projects and reap the benefits of high-quality code!

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