Snaplet

Faster JSON Mapping, the only dependency is orjson.

Features

• Just-In-Time (JIT) Codegen: Snaplet generates specialized property accessors at runtime using exec(). By eliminating getattr calls and internal dictionary lookups during access, it achieves near-native attribute access speeds.
• Zero-Copy Lazy Instantiation: Nested models and lists are only instantiated when accessed. This minimizes the performance impact when processing large JSON payloads where only specific fields are required.
• High-Throughput Bulk Loading: Bypasses the standard __init__ constructor and leverages __slots__ with __new__ to mass-produce instances. This enables high-speed loading by reducing Python-level overhead during object creation.
• Explicit Validation Control: Snaplet does not perform eager validation on load. This design allows developers to implement custom validation logic only where necessary, avoiding global performance bottlenecks.
• Transparent Alias Mapping: Supports Annotated and Field(alias=...) to map complex JSON keys to Pythonic names. JIT compilation ensures that using aliases does not incur additional runtime performance costs.
• Minimal Dependency: The only requirement is orjson. It operates as a pure Python library utilizing JIT logic, ensuring high portability across different environments without the need for complex build chains.

Installation

uv add snaplet

Usage

from typing import Annotated

from snaplet import SnapletBase
from snaplet import Field


class User(SnapletBase):
    user_id: int
    internal_id: Annotated[int, Field(alias="ID")]
    tags: Annotated[list[str], Field(alias="Tags-List-V1")]


data = {"userId": 1, "ID": 999, "Tags-List-V1": ["python", "snaplet"]}

user = User(data)
print(user.internal_id)
print(user.user_id)

You can disable JIT with jit=False but, this is not recommended as it may significantly degrade performance.

class User(SnapletBase, jit=False):
    user_id: int
    internal_id: Annotated[int, Field(alias="ID")]
    tags: Annotated[list[str], Field(alias="Tags-List-V1")]

Performance

Snaplet is designed for high-performance applications that handle large JSON datasets with minimal overhead.

Benchmark (1,000 items)

Measured on Python 3.12.

Why so fast?

• JIT Property Access: Specialized property accessors are compiled at runtime using exec(), eliminating branch overhead.
• Lazy Instantiation: Nested objects are only instantiated when accessed.
• Minimal Overhead: Bypassing __init__ during bulk loading to achieve near-native speeds.

LICENSE

MIT License