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Best Practices for Using msgspec in Python for High-Performance Serialization

· 3 min read
Serhii Hrekov
Serhii Hrekov
software engineer, creator, artist, programmer, projects founder

msgspec is a high-performance serialization library designed for modern Python applications. It combines type-safe data modeling, blazing-fast parsing, and flexible support for multiple serialization formats, including MessagePack, JSON, and TOML.

This article outlines the best practices for integrating msgspec into your codebase. It provides a practical, performance-oriented guide to writing cleaner, safer, and faster Python services.

Why Choose msgspec​

  • Performance: up to 10x faster than Pydantic for validation and parsing.
  • Type-safety using standard Python typing.
  • Multiple serialization formats out-of-the-box (e.g., JSON, MessagePack).
  • Lightweight and dependency-free.

Installation​

pip install msgspec

Defining Data Models​

Use msgspec.Struct to define typed models.

import msgspec

class User(msgspec.Struct):
    id: int
    username: str
    email: str
    is_active: bool = True

Best practice: Always use primitive types where possible (e.g., int, str, bool) for maximum performance.

Avoid dynamic attributes​

Keep models strict by not allowing dynamic fields. msgspec enforces this by default.

# This will raise a ValidationError
User(id=1, username="serhiix", email="me@example.com", extra_field="bad")

Validating and Parsing JSON​

msgspec offers fast JSON decoding with automatic validation.

import msgspec

class Product(msgspec.Struct):
    id: int
    name: str
    price: float

raw_json = b'{"id": 101, "name": "Laptop", "price": 1299.99}'
product = msgspec.json.decode(raw_json, type=Product)
print(product.name)  # Laptop

Serialization​

Convert your struct to JSON or MessagePack:

data = msgspec.json.encode(product)
decoded = msgspec.json.decode(data, type=Product)

For MessagePack:

data = msgspec.msgpack.encode(product)
decoded = msgspec.msgpack.decode(data, type=Product)

Use Defaults Where Needed​

class Comment(msgspec.Struct):
    body: str
    likes: int

    def __init__(self, body: str, likes: int = 0):
        self.body = body
        self.likes = likes

Dealing with Lists and Nested Models​

You can easily validate nested lists of structs.

class OrderItem(msgspec.Struct):
    name: str
    quantity: int

class Order(msgspec.Struct):
    items: list[OrderItem]
    total: float

json_data = b'''
{
  "items": [{"name": "Apple", "quantity": 4}, {"name": "Banana", "quantity": 2}],
  "total": 5.50
}
'''

order = msgspec.json.decode(json_data, type=Order)
print(order.items[0].name)  # Apple

Handling Union Types​

Support for tagged unions can simplify deserialization.

class Dog(msgspec.Struct, tag="type"):
    type: str = "dog"
    bark_volume: int

class Cat(msgspec.Struct, tag="type"):
    type: str = "cat"
    meow_pitch: float

Pet = Dog | Cat

json_input = b'{"type": "dog", "bark_volume": 10}'
pet = msgspec.json.decode(json_input, type=Pet)

Error Handling​

Use msgspec.ValidationError to catch decoding/validation errors.

try:
    data = msgspec.json.decode(b'{"id": "not-an-int"}', type=Product)
except msgspec.ValidationError as e:
    print("Validation failed:", e)

Prefer Structs over Dicts​

Avoid this:

data = msgspec.json.decode(json_bytes)

Prefer this:

data = msgspec.json.decode(json_bytes, type=User)

Use StructArray for Typed Lists (Optional)​

users = [User(id=i, name=f"User {i}", email="u@example.com") for i in range(1000)]
encoded = msgspec.json.encode(users)

Tips for Performance​

  • Prefer msgspec.Struct over dictionaries for structured data.
  • Reuse schemas as much as possible to reduce runtime overhead.
  • Use msgspec.msgpack for binary serialization if performance is critical.
  • Avoid deeply nested union types if performance matters.

When to Use msgspec​

Use msgspec if your application:

  • Needs maximum speed and minimal latency (e.g., microservices, APIs).
  • Requires tight control over data validation.
  • Can benefit from modern Python typing without runtime overhead.

Conclusion​

msgspec is a powerful alternative to traditional serialization tools in Python. It brings together the best parts of modern type-checking and high-performance computing in one small package.

Following the practices above will help ensure you're writing maintainable, fast, and type-safe Python code for your backend systems.

Further Reading​