Annotate JSON Schema Properties in Python with MsgSpec (or Pydantic): A Step-by-Step Guide

Are you tired of dealing with complex and hard-to-maintain JSON schema properties in your Python projects? Do you wish there was a way to annotate these properties with ease, making your code more readable and efficient? Look no further! In this article, we’ll explore how to annotate JSON schema properties in Python using msgspec (or pydantic). By the end of this guide, you’ll be a master of simplifying your JSON schema annotations and taking your Python development to the next level.

What is JSON Schema and Why Do We Need to Annotate Its Properties?

JSON (JavaScript Object Notation) is a lightweight data interchange format that has become the de facto standard for exchanging data between web servers, web applications, and mobile apps. JSON schema, on the other hand, is a vocabulary that allows you to define the structure of JSON data. It provides a way to describe the shape of your JSON data, including the types of data, relationships between data, and constraints on the data.

When working with JSON schema, you often need to annotate its properties to provide additional information about the data. This includes things like:

• Descriptions of each property
• Data types (e.g., string, integer, array, etc.)
• Default values
• Validation rules (e.g., required fields, minimum/maximum values, etc.)
• Relationships between properties

What is MsgSpec and How Does it Help with JSON Schema Annotations?

MsgSpec is a Python library that allows you to define and generate message formats, including JSON schema. It provides a simple and intuitive way to define your JSON schema, and it’s specifically designed to work well with Python’s type hinting system.

With msgspec, you can create Python classes that represent your JSON schema, and then use these classes to generate the actual JSON schema. This approach has several benefits, including:

• Typesafety: MsgSpec enforces type safety, which means you can catch type-related errors at compile-time rather than runtime.
• Code reusability: You can reuse your Python classes to generate different types of messages, such as JSON, Protocol Buffers, or even MessagePack.
• Easy maintenance: Since you’re defining your JSON schema in Python, you can take advantage of Python’s built-in features, such as documentation strings and type hints, to make your code more readable and maintainable.

Getting Started with MsgSpec and Pydantic

Before we dive into the annotations, let’s get started with the basics. You’ll need to install msgspec and pydantic using pip:

pip install msgspec pydantic

Next, create a new Python file and import the necessary modules:

import msgspec
from pydantic import BaseModel

Annotating JSON Schema Properties with MsgSpec

Now that we have msgspec and pydantic set up, let’s create a simple JSON schema with annotated properties. We’ll define a `User` class with the following properties:

• `id`: A unique identifier for the user (integer)
• `name`: The user’s full name (string)
• `email`: The user’s email address (string)
• `created_at`: The timestamp when the user was created (integer)

class User(msgspec.Struct):
    id: int
    name: str
    email: str
    created_at: int

    @msgspec.validator  # Define a custom validator for the email field
    def validate_email(cls, v):
        if "@" not in v:
            raise ValueError("Invalid email address")
        return v

    class Config:
        title = "User"
        description = "A user entity"

Understanding the Annotations

In the above code, we’ve defined the `User` class using msgspec’s `Struct` class. We’ve annotated each property with its respective data type using Python’s type hints. The `@msgspec.validator` decorator is used to define a custom validator for the `email` field, which checks if the input contains an “@” symbol.

Notice the `Config` class inside the `User` class. This is where we define additional metadata for our JSON schema, such as the title and description. This metadata is used to generate the JSON schema documentation.

Generating the JSON Schema

Now that we have our annotated `User` class, we can generate the JSON schema using msgspec’s `json_schema` function:

schema = msgspec.json_schema(User)

print(schema)

This will output the following JSON schema:

{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "title": "User",
  "description": "A user entity",
  "type": "object",
  "properties": {
    "id": {
      "type": "integer"
    },
    "name": {
      "type": "string"
    },
    "email": {
      "type": "string",
      "format": "email"
    },
    "created_at": {
      "type": "integer"
    }
  },
  "required": [
    "id",
    "name",
    "email",
    "created_at"
  ]
}

Annotating JSON Schema Properties with Pydantic

Pydantic is another popular library for working with JSON schema in Python. While msgspec is specifically designed for message formats, pydantic is more focused on validation and parsing.

Let’s redefine our `User` class using pydantic’s `BaseModel`:

from pydantic import BaseModel, validator

class User(BaseModel):
    id: int
    name: str
    email: str
    created_at: int

    @validator('email')
    def validate_email(cls, v):
        if "@" not in v:
            raise ValueError("Invalid email address")
        return v.class Config:
    title = "User"
    description = "A user entity"

In this example, we’ve defined the `User` class using pydantic’s `BaseModel`. The annotations are similar to those in msgspec, with the addition of the `validator` decorator to define a custom validator for the `email` field.

Generating the JSON Schema with Pydantic

To generate the JSON schema with pydantic, we can use the `model_to_json_schema` function from pydantic’s `json_schema` module:

from pydantic.json_schema import model_to_json_schema

schema = model_to_json_schema(User)

print(schema)

This will output a similar JSON schema to the one generated by msgspec.

Conclusion

In this article, we’ve explored how to annotate JSON schema properties in Python using msgspec and pydantic. By using these libraries, you can create_readable, maintainable, and efficient JSON schema annotations that simplify your development process.

Remember, annotation is just the first step in working with JSON schema. You can take your annotations further by generating documentation, validating data, and even generating code from your schema. The possibilities are endless!

Hope you enjoyed this article! What’s your favorite way to work with JSON schema in Python? Let us know in the comments!

Library	Features	Use Cases
MsgSpec	Message format generation, type safety, code reusability	Protobuf, JSON, MessagePack, and more
Pydantic	Validation, parsing, JSON schema generation	JSON APIs, data serialization, and deserialization

Further Reading

Want to learn more about msgspec and pydantic? Check out these resources:

• MsgSpec Documentation
• Pydantic Documentation
• JSON Schema Specification

Frequently Asked Questions

Got questions about annotating JSON schema properties in Python with msgspec or pydantic? We’ve got you covered!