pysafe-pickle

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

📖 Documentation: https://abdullah-masood-05.github.io/pygraph/

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 pysafe-pickle

For tensor support:

pip install pysafe-pickle[numpy]
pip install pysafe-pickle[torch]
pip install pysafe-pickle[all]

Quick Start

import pysafe_pickle as psp

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

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

Schema Evolution

Define versioned dataclasses and register migrations:

from dataclasses import dataclass
import pysafe_pickle as psp

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

@psp.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["__pysafe_pickle_version__"] = 2
    return state

How it works

• Set __pysafe_pickle_version__ as a class attribute on your dataclass (legacy __pygraph_version__ is also fully supported)
• Register migration functions with @psp.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 pysafe_pickle as psp
import io

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

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

Security

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

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

Allowlisted types

By default, pysafe-pickle supports:

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

Integrity verification

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

Migration from v1.0.x (pygraph)

In v1.0.x, the package was imported as import pygraph. In v1.1.0:

• The canonical import is now import pysafe_pickle as psp.
• Zero code breaks: Existing code using import pygraph or from pygraph.migrations import migrate continues working identically via a backward-compatible shim, emitting a FutureWarning.
• Binary compatibility: Files serialized with v1.0.x (magic PYGR) continue to deserialize seamlessly with zero warnings. New files are encoded with the PSPK magic header.
• The pygraph shim will remain supported through v1.x and will be removed in v2.0.0.

# Legacy (deprecated in v1.1.0, emits warning, still works)
import pygraph
pygraph.dumps(data)

# Recommended
import pysafe_pickle as psp
psp.dumps(data)

Benchmarks

Run the benchmark suite to compare pysafe-pickle vs pickle:

uv run pytest benchmarks/ -v --benchmark-only

Development

# Install and build with uv / maturin
uv run maturin develop

# Run tests
cargo nextest run
uv run pytest tests/ -v

License

AGPL-3.0-only