pygraph

Safe, fast, schema-evolvable Python object graph serialization powered by Rust.

Features

• Drop-in pickle API — dumps/loads/dump/load with the same signatures
• Zero arbitrary code execution — no __reduce__ or __setstate__ calls during deserialization
• Schema versioning — migration hooks for evolving your data models
• Zero-copy tensor support — NumPy and PyTorch integration
• Rust-native performance — PyO3 bindings for speed
• Pickle-compatible streaming — Pickler/Unpickler classes, PickleBuffer (PEP 574)
• HMAC integrity — optional tamper detection on serialized data

Installation

pip install pygraph

For tensor support:

pip install pygraph[numpy]
pip install pygraph[torch]
pip install pygraph[all]

Quick Start

import pygraph

# Serialize
data = {"key": "value", "numbers": [1, 2, 3]}
encoded = pygraph.dumps(data)

# Deserialize
decoded = pygraph.loads(encoded)
assert decoded == data

Schema Evolution

Define versioned dataclasses and register migrations:

from dataclasses import dataclass
import pygraph

@dataclass
class User:
    name: str
    age: int
    email: str = ""
    __pygraph_version__ = 2

@pygraph.migrate(from_version=1, to_version=2, type_name="User")
def migrate_v1_to_v2(state: dict) -> dict:
    """V1 had 'name' + 'age', V2 adds 'email'."""
    state["email"] = ""
    state["__pygraph_version__"] = 2
    return state

How it works

• Set __pygraph_version__ as a class attribute on your dataclass
• Register migration functions with @pygraph.migrate(from_version=N, to_version=M, type_name="ClassName")
• Migration functions receive a dict of the old state and return a dict with the new state
• Chains of migrations are resolved automatically (e.g., v1 → v2 → v3)

Pickler / Unpickler

Use pickle-compatible streaming classes:

import pygraph
import io

# Streaming dump
buf = io.BytesIO()
pickler = pygraph.Pickler(buf, protocol=5)
pickler.dump({"data": [1, 2, 3]})

# Streaming load
buf.seek(0)
unpickler = pygraph.Unpickler(buf)
result = unpickler.load()

Security

pygraph never calls __reduce__, __setstate__, or any arbitrary code during deserialization. Only allowlisted types can be loaded.

# Restrict deserialization to specific types
pygraph.loads(data, allowlist={"builtins.dict", "builtins.list"})

Allowlisted types

By default, pygraph supports:

Any type not in this list raises UnsafeTypeError unless added to the allowlist.

Integrity verification

key = b"my-secret-key"
encoded = pygraph.dumps(data, hmac_key=key)
decoded = pygraph.loads(encoded, hmac_key=key)  # raises HMACError if tampered

Benchmarks

Run the benchmark suite to compare pygraph vs pickle:

pytest benchmarks/ -v --benchmark-only

Development

# Install dev tools
pip install -e ".[dev]"

# Build the Rust extension
maturin develop

# Run tests
cargo nextest run
pytest tests/ -v

License

AGPL-3.0-only