quickle is a fast and small serialization format for a subset of Python types. It’s based off of Pickle, but includes several optimizations and extensions to provide improved performance and security. For supported types, serializing a message with quickle can be ~2-10x faster than using pickle.


  • Quickle is fast. Benchmarks show it’s among the fastest serialization methods for Python.

  • Quickle is safe. Unlike pickle, deserializing a user provided message doesn’t allow for arbitrary code execution.

  • Quickle is flexible. Unlike msgpack or json, Quickle natively supports a wide range of Python builtin types.

  • Quickle supports “schema evolution”. Messages can be sent between clients with different schemas without error.


Quickle can be installed via pip or conda. Note that Python >= 3.8 is required.


pip install quickle


conda install -c conda-forge quickle


Like pickle, quickle exposes two functions dumps and loads, for serializing and deserializing objects respectively.

>>> import quickle
>>> data = quickle.dumps({"hello": "world"})
>>> quickle.loads(data)
{'hello': 'world'}

Note that if you’re making multiple calls to dumps or loads, it’s more efficient to create an Encoder and Decoder once, and then use Encoder.dumps and Decoder.loads.

>>> enc = quickle.Encoder()
>>> dec = quickle.Decoder()
>>> data = enc.dumps({"hello": "world"})
>>> dec.loads(data)
{'hello': 'world'}

Structs and Enums

Quickle can serialize most builtin types, but unlike pickle, it can’t serialize arbitrary user classes. This is due to security concerns - deserializing arbitrary python objects requires executing arbitrary python code. Deserializing a malicious message could wipe your machine or steal secrets (see Why not just use pickle? for more information).

Quickle does support serializing two non-builtin types:

Structs are useful for defining structured messages. Fields are defined using python type annotations (the type annotations themselves are ignored, only the field names are used). Defaults values can also be specified for any optional arguments.

Here we define a struct representing a person, with two required fields and two optional fields.

>>> class Person(quickle.Struct):
...     """A struct describing a person"""
...     first : str
...     last : str
...     address : str = ""
...     phone : str = None

Struct types automatically generate a few methods based on the provided type annotations:

  • __init__

  • __repr__

  • __copy__

  • __eq__ & __ne__

>>> harry = Person("Harry", "Potter", address="4 Privet Drive")
>>> harry
Person(first='Harry', last='Potter', address='4 Privet Drive', phone=None)
>>> harry.first
>>> ron = Person("Ron", "Weasley", address="The Burrow")
>>> ron == harry

It is forbidden to override __init__/__new__ in a struct definition, but other methods can be overridden or added as needed. The struct fields are available via the __struct_fields__ attribute (a tuple of the fields in argument order ) if you need them. Here we add a method for converting a struct to a dict.

>>> class Point(quickle.Struct):
...     """A point in 2D space"""
...     x : float
...     y : float
...     def to_dict(self):
...         return {f: getattr(self, f) for f in self.__struct_fields__}
>>> p = Point(1.0, 2.0)
>>> p.to_dict()
{"x": 1.0, "y": 2.0}

Struct types are written in C and are quite speedy and lightweight. They’re great for defining structured messages both for serialization and for use in an application.

To add serialization support for Struct types, you need to register them with an Encoder and Decoder.

>>> enc = quickle.Encoder(registry=[Person, Point])
>>> dec = quickle.Decoder(registry=[Person, Point])
>>> data = enc.dumps(harry)
>>> dec.loads(data)
Person(first='Harry', last='Potter', address='4 Privet Drive', phone=None)

Unregistered types will fail to serialize or deserialize. Note that for deserialization to be successful the registry of the Decoder must match that of the Encoder.

Like Struct types, enum.Enum types also need to be registered before they can be serialized:

>>> import enum
>>> class Fruit(enum.IntEnum):
...     APPLE = 1
...     BANANA = 2
...     ORANGE = 3
>>> enc = quickle.Encoder(registry=[Fruit])
>>> dec = quickle.Decoder(registry=[Fruit])
>>> data = enc.dumps(Fruit.APPLE)
>>> dec.loads(data)
<Fruit.APPLE: 1>

Schema Evolution

Quickle includes support for “schema evolution”, meaning that:

  • Messages serialized with an older version of a schema will be deserializable using a newer version of the schema.

  • Messages serialized with a newer version of the schema will be deserializable using an older version of the schema.

This can be useful if, for example, you have clients and servers with mismatched versions.

For schema evolution to work smoothly, you need to follow a few guidelines when defining and registering new Struct and enum.Enum types:

  1. Any new fields on a struct must be added to the end of the struct definition, and must contain default values. Do not reorder fields in a struct.

  2. Any new Struct or enum.Enum types must be appended to the end of the registry. Do not reorder types in the registry.

For example, suppose we wanted to add a new email field to our Person struct. To do so, we add it at the end of the definition, with a default value.

>>> class Person2(quickle.Struct):
...     """A struct describing a person"""
...     first : str
...     last : str
...     address : str = ""
...     phone : str = None
...     email : str = None  # added at the end, with a default
>>> vernon = Person2("Vernon", "Dursley", address="4 Privet Drive", email="vernon@grunnings.com")

Messages serialized using the new and old schemas can still be exchanged without error.

>>> old_enc = quickle.Encoder(registry=[Person])
>>> old_dec = quickle.Decoder(registry=[Person])
>>> new_enc = quickle.Encoder(registry=[Person2])
>>> new_dec = quickle.Decoder(registry=[Person2])

>>> new_msg = new_enc.dumps(vernon)
>>> old_dec.loads(new_msg)  # deserializing a new msg with an older decoder
Person(first="Vernon", last="Dursley", address="4 Privet Drive", phone=None)

>>> old_msg = old_enc.dumps(harry)
>>> new_dec.loads(old_msg) # deserializing an old msg with a new decoder
Person2(first='Harry', last='Potter', address='4 Privet Drive', phone=None, email=None)