recordclass 0.22.1

Mutable variant of namedtuple -- recordclass, which support assignments, compact dataclasses and other memory saving variants.

# Recordclass library

Recordclass is MIT Licensed python library. It was started as a "proof of concept" for the problem of fast "mutable" alternative of namedtuple (see question on stackoverflow). It was evolved further in order to provide more memory saving, fast and flexible types.

Recordclass library provide record/data-like classes that do not participate in cyclic garbage collection (GC) mechanism by default, but support only reference counting for garbage collection. The instances of such classes havn't PyGC_Head prefix in the memory, which decrease their size and have a little faster path for the instance creation and deallocation. This may make sense in cases where it is necessary to limit the size of the objects as much as possible, provided that they will never be part of references cycles in the application. For example, when an object represents a record with fields with values of simple types by convention (int, float, str, date/time/datetime, timedelta, etc.).

In order to illustrate this, consider a simple class with type hints:

class Point:
    x: int
    y: int

By tacit agreement instances of the class Point is supposed to have attributes x and y with values of int type. Assigning other types of values, which are not subclass of int, should be considered as a violation of the agreement.

Other examples are non-recursive data structures in which all leaf elements represent a value of an atomic type. Of course, in python, nothing prevent you from “shooting yourself in the foot" by creating the reference cycle in the script or application code. But in many cases, this can still be avoided provided that the developer understands what he is doing and uses such classes in the codebase with care. Another option is to use static code analyzers along with type annotations to monitor compliance with typehints.

The library is built on top of the base class dataobject. The type of dataobject is special metaclass datatype. It control creation of subclasses, which will not participate in cyclic GC and do not contain PyGC_Head-prefix, __dict__ and __weakref__ by default. As the result the instance of such class need less memory. It's memory footprint is similar to memory footprint of instances of the classes with __slots__ but without PyGC_Head. So the difference in memory size is equal to the size of PyGC_Head. It also tunes basicsize of the instances, creates descriptors for the fields and etc. All subclasses of dataobject created by class statement support attrs/dataclasses-like API. For example:

    from recordclass import dataobject, astuple, asdict
    class Point(dataobject):
        x:int
        y:int

    >>> p = Point(1, 2)
    >>> astuple(p)
    (1, 2)
    >>> asdict(p)
    {'x':1, 'y':2}

The recordclass factory create dataobject-based subclass with specified fields and the support of namedtuple-like API. By default it will not participate in cyclic GC too.

    >>> from recordclass import recordclass
    >>> Point = recordclass('Point', 'x y')
    >>> p = Point(1, 2)
    >>> p.y = -1
    >>> print(p._astuple)
    (1, -1)
    >>> x, y = p
    >>> print(p._asdict)
    {'x':1, 'y':-1}

It also provide a factory function make_dataclass for creation of subclasses of dataobject with the specified field names. These subclasses support attrs/dataclasses-like API. It's equivalent to creating subclasses of dataobject using class statement. For example:

    >>> Point = make_dataclass('Point', 'x y')
    >>> p = Point(1, 2)
    >>> p.y = -1
    >>> print(p.x, p.y)
    1 -1

If one want to use some sequence for initialization then:

    >>> p = Point(*sequence)

There is also a factory function make_arrayclass for creation of the subclass of dataobject, which can be considered as a compact array of simple objects. For example:

    >>> Pair = make_arrayclass(2)
    >>> p = Pair(2, 3)
    >>> p[1] = -1
    >>> print(p)
    Pair(2, -1)

The library provide in addition the classes lightlist (immutable) and litetuple, which considers as list-like and tuple-like light containers in order to save memory. They do not supposed to participate in cyclic GC too. Mutable variant of litetuple is called by mutabletuple. For example:

    >>> lt = litetuple(1, 2, 3)
    >>> mt = mutabletuple(1, 2, 3)
    >>> lt == mt
    True
    >>> mt[-1] = -3
    >>> lt == mt
    False
    >>> print(sys.getsizeof((1,2,3)), sys.getsizeof(litetuple(1,2,3)))
    64 48

Note if one like create litetuple or mutabletuple from some iterable then:

    >>> seq = [1,2,3]
    >>> lt = litetuple(*seq)
    >>> mt = mutabletuple(*seq)

Memory footprint

The following table explain memory footprints of the dataobject-based objects and litetuples:

tuple/namedtuple	class with __slots__	recordclass/dataobject	litetuple/mutabletuple
g+b+s+n×p	g+b+n×p	b+n×p	b+s+n×p

where:

• b = sizeof(PyObject)
• s = sizeof(Py_ssize_t)
• n = number of items
• p = sizeof(PyObject*)
• g = sizeof(PyGC_Head)

This is useful in that case when you absolutely sure that reference cycle isn't supposed. For example, when all field values are instances of atomic types. As a result the size of the instance is decreased by 24-32 bytes for cpython 3.4-3.7 and by 16 bytes for cpython >=3.8.

Performance counters

Here is the table with performance counters, which was measured using tools/perfcounts.py script:

• recordclass 0.21, python 3.10, debian/testing linux, x86-64:

id	size	new	getattr	setattr	getitem	setitem	getkey	setkey	iterate	copy
litetuple	48	0.18			0.2				0.33	0.19
mutabletuple	48	0.18			0.21	0.21			0.33	0.18
tuple	64	0.24			0.21				0.37	0.16
namedtuple	64	0.75	0.23		0.21				0.33	0.21
class+slots	56	0.68	0.29	0.33
dataobject	40	0.25	0.23	0.29	0.2	0.22			0.33	0.2
dataobject+gc	56	0.27	0.22	0.29	0.19	0.21			0.35	0.22
dict	232	0.32					0.2	0.24	0.35	0.25
dataobject+map	40	0.25	0.23	0.3			0.29	0.29	0.32	0.2

• recordclass 0.21, python 3.11, debian/testing linux, x86-64:

id	size	new	getattr	setattr	getitem	setitem	getkey	setkey	iterate	copy
litetuple	48	0.11			0.11				0.18	0.09
mutabletuple	48	0.11			0.11	0.12			0.18	0.08
tuple	64	0.1			0.08				0.17	0.1
namedtuple	64	0.49	0.13		0.11				0.17	0.13
class+slots	56	0.31	0.06	0.06
dataobject	40	0.13	0.06	0.06	0.11	0.12			0.16	0.12
dataobject+gc	56	0.14	0.06	0.06	0.1	0.12			0.16	0.14
dict	184	0.2					0.12	0.13	0.19	0.13
dataobject+map	40	0.12	0.07	0.06			0.15	0.16	0.16	0.12
class	56	0.35	0.06	0.06

• recordclas 0.21, python3.12, debian/testing linux, x86-64:

id	size	new	getattr	setattr	getitem	setitem	getkey	setkey	iterate	copy
litetuple	48	0.13			0.12				0.19	0.09
mutabletuple	48	0.13			0.11	0.12			0.18	0.09
tuple	64	0.11			0.09				0.16	0.09
namedtuple	64	0.52	0.13		0.11				0.16	0.12
class+slots	56	0.34	0.08	0.07
dataobject	40	0.14	0.08	0.08	0.11	0.12			0.17	0.12
dataobject+gc	56	0.15	0.08	0.07	0.12	0.12			0.17	0.13
dict	184	0.19					0.11	0.14	0.2	0.12
dataobject+map	40	0.14	0.08	0.08			0.16	0.17	0.17	0.12
class	48	0.41	0.08	0.08

Main repository for recordclass is on github.

Here is also a simple example.

More examples can be found in the folder examples.

Quick start

Installation

Installation from directory with sources

Install:

>>> python3 setup.py install

Run tests:

>>> python3 test_all.py

Installation from PyPI

Install:

>>> pip3 install recordclass

Run tests:

>>> python3 -c "from recordclass.test import *; test_all()"

Quick start with dataobject

Dataobject is the base class for creation of data classes with fast instance creation and small memory footprint. They provide dataclass-like API.

First load inventory:

>>> from recordclass import dataobject, asdict, astuple, as_dataclass, as_record

Define class one of the ways:

class Point(dataobject):
    x: int
    y: int

or

@as_dataclass()
class Point:
    x: int
    y: int

or

@as_record
def Point(x:int, y:int): pass

or

>>> Point = make_dataclass("Point", [("x",int), ("y",int)])

or

>>> Point = make_dataclass("Point", {"x":int, "y",int})

Annotations of the fields are defined as a dict in __annotations__:

>>> print(Point.__annotations__)
{'x': <class 'int'>, 'y': <class 'int'>}

There is default text representation:

>>> p = Point(1, 2)
>>> print(p)
Point(x=1, y=2)

The instances has a minimum memory footprint that is possible for CPython object, which contain only Python objects:

>>> sys.getsizeof(p) # the output below for python 3.8+ (64bit)
40
>>> p.__sizeof__() == sys.getsizeof(p) # no additional space for cyclic GC support
True

The instance is mutable by default:

>>> p_id = id(p)
>>> p.x, p.y = 10, 20
>>> id(p) == p_id
True
>>> print(p)
Point(x=10, y=20)

There are functions asdict and astuple for converting to dict and to tuple:

>>> asdict(p)
{'x':10, 'y':20}
>>> astuple(p)
(10, 20)

By default subclasses of dataobject are mutable. If one want make it immutable then there is the option readonly=True:

class Point(dataobject, readonly=True):
    x: int
    y: int

>>> p = Point(1,2)
>>> p.x = -1
. . . . . . . . . . . . .
TypeError: item is readonly

By default subclasses of dataobject are not iterable by default. If one want make it iterable then there is the option iterable=True:

class Point(dataobject, iterable=True):
    x: int
    y: int

>>> p = Point(1,2)
>>> for x in p: print(x)
1
2

Default values are also supported::

class CPoint(dataobject):
    x: int
    y: int
    color: str = 'white'

or

>>> CPoint = make_dataclass("CPoint", [("x",int), ("y",int), ("color",str)], defaults=("white",))

>>> p = CPoint(1,2)
>>> print(p)
Point(x=1, y=2, color='white')

But

class PointInvalidDefaults(dataobject):
    x:int = 0
    y:int

is not allowed. A fields without default value may not appear after a field with default value.

There is an option copy_default (starting from 0.21) in order to assign a copy of the default value when creating an instance:

 class Polygon(dataobject, copy_default=True):
    points: list = []

>>> pg1 = Polygon()
>>> pg2 = Polygon()
>>> assert pg1.points == pg2.points
True
>>> assert id(pg1.points) != id(pg2.points)
True

A Factory (starting from 0.21) allows you to setup a factory function to calculate the default value:

from recordclass import Factory

class A(dataobject, copy_default=True):
    x: tuple = Factory( lambda: (list(), dict()) )

>>> a = A()
>>> b = A()
>>> assert a.x == b.x
True
>>> assert id(a.x[0]) != id(b.x[0])
True
>>> assert id(a.x[1]) != id(b.x[1])
True

If someone wants to define a class attribute, then there is a ClassVar trick:

class Point(dataobject):
    x:int
    y:int
    color:ClassVar[int] = 0

>>> print(Point.__fields__)
('x', 'y')
>>> print(Point.color)
0

If the default value for the ClassVar-attribute is not specified, it will just be excluded from the __fields___.

Starting with python 3.10 __match_args__ is specified by default so that __match_args__ == __fields__. User can define it's own during definition:

class User(dataobject):
    first_name: str
    last_name: str
    age: int
    __match_args__ = 'first_name', 'last_name'

or

from recordclass import MATCH
class User(dataobject):
    first_name: str
    last_name: str
    _: MATCH
    age: int

or

User = make_dataclass("User", "first_name last_name * age")

Quick start with recordclass

The recordclass factory function is designed to create classes that support namedtuple's API, can be mutable and immutable, provide fast creation of the instances and have a minimum memory footprint.

First load inventory:

>>> from recordclass import recordclass

Example with recordclass:

>>> Point = recordclass('Point', 'x y')
>>> p = Point(1,2)
>>> print(p)
Point(1, 2)
>>> print(p.x, p.y)
1 2
>>> p.x, p.y = 1, 2
>>> print(p)
Point(1, 2)
>>> sys.getsizeof(p) # the output below is for 64bit cpython3.8+
32

Example with class statement and typehints:

>>> from recordclass import RecordClass

class Point(RecordClass):
   x: int
   y: int

>>> print(Point.__annotations__)
{'x': <class 'int'>, 'y': <class 'int'>}
>>> p = Point(1, 2)
>>> print(p)
Point(1, 2)
>>> print(p.x, p.y)
1 2
>>> p.x, p.y = 1, 2
>>> print(p)
Point(1, 2)

By default recordclass-based class instances doesn't participate in cyclic GC and therefore they are smaller than namedtuple-based ones. If one want to use it in scenarios with reference cycles then one have to use option gc=True (gc=False by default):

>>> Node = recordclass('Node', 'root children', gc=True)

or

class Node(RecordClass, gc=True):
     root: 'Node'
     chilren: list

The recordclass factory can also specify type of the fields:

>>> Point = recordclass('Point', [('x',int), ('y',int)])

or

>>> Point = recordclass('Point', {'x':int, 'y':int})

Using dataobject-based classes with mapping protocol

class FastMapingPoint(dataobject, mapping=True):
    x: int
    y: int

or

FastMapingPoint = make_dataclass("FastMapingPoint", [("x", int), ("y", int)], mapping=True)

>>> p = FastMappingPoint(1,2)
>>> print(p['x'], p['y'])
1 2
>>> sys.getsizeof(p) # the output below for python 3.10 (64bit)
32

Using dataobject-based classes for recursive data without reference cycles

There is the option deep_dealloc (default value is False) for deallocation of recursive datastructures. Let consider simple example:

class LinkedItem(dataobject):
    val: object
    next: 'LinkedItem'

class LinkedList(dataobject, deep_dealloc=True):
    start: LinkedItem = None
    end: LinkedItem = None

    def append(self, val):
        link = LinkedItem(val, None)
        if self.start is None:
            self.start = link
        else:
            self.end.next = link
        self.end = link

Without deep_dealloc=True deallocation of the instance of LinkedList will be failed if the length of the linked list is too large. But it can be resolved with __del__ method for clearing the linked list:

def __del__(self):
    curr = self.start
    while curr is not None:
        next = curr.next
        curr.next = None
        curr = next

There is builtin more fast deallocation method using finalization mechanizm when deep_dealloc=True. In such case one don't need __del__ method for clearing the linked list.

Note that for classes with gc=True this method is disabled: the python's cyclic GC is used in these cases.

For more details see notebook example_datatypes.

Changes:

0.22.1:

• Add pyproject.toml.

• Add pytest.ini in order to run pytest:

    >>> pip3 install -e .
    >>> pytest
  

0.22.0.3:

• Rename examples\test_*.py files since they are detected by pytest as a tests.
• Fix segfault with litelist after 0.22.0.2.

0.22.0.2

• Make release on github with right tag.

0.22.0.1

• Fix regression with as_dataclass.

0.22

• Add a base class datastruct for subclasses that should behave more like simple datastructures.
• Fix bug with __match_args__ (#6).
• Start support for python 3.13.

0.21.1

• Allow to specify __match_args__. For example,

     class User(dataobject):
         first_name: str
         last_name: str
         age: int
         __match_args__ = 'first_name', 'last_name'
  

  or

      User = make_dataclass("User", "first_name last_name * age")
  

• Add @as_record adapter for def-style decalarations of dataclasses that are considered as just a struct. For example:

    @as_record()
    def Point(x:float, y:float, meta=None): pass
  
    >>> p = Point(1,2)
    >>> print(p)
    Point(x=1, y=2, meta=None)
  

  It's almost equivalent to:

    Point = make_dataclass('Point', [('x':float), ('y',float),'meta'], (None,))
  

• The option fast_new will be removed in 0.22. It will be always as fast_new=True by creation. One can specify own __new__, for example:

    class Point(dataobject):
        x:int
        y:int
  
        def __new__(cls, x=0, y=0):
             return dataobject.__new__(cls, x, y)
  

• Fix issues with _PyUnicodeWriter for python3.13.

0.21

• Add a new option copy_default (default False) to allow assigning a copy of the default value for the field. For example:

   class A(dataobject, copy_default=True):
        l: list = []
  
   a = A()
   b = A()
   assert(a.l == b.l)
   assert(id(a.l) != id(b.l))
  

• Allow to inherit the options: copy_default, gc, iterable. For example:

   class Base(dataobject, copy_default=True):
      pass
  
  class A(Base):
        l: list = []
  
   a = A()
   b = A()
   assert a.l == b.l
   assert id(a.l) != id(b.l)
  

• Add Factory to specify factory function for default values. For example:

    from recordclass import Factory
    class A(dataobject):
        x: tuple = Factory(lambda: (list(), dict()))
  
    a = A()
    b = A()
    assert a.x == ([],{})
    assert id(a.x) != id(b.x)
    assert id(a.x[0]) != id(b.x[0])
    assert id(a.x[1]) != id(b.x[1])
  
    from recordclass import Factory
    class A(dataobject, copy_default=True):
        l: list = []
        x: tuple = Factory(lambda: (list(), dict()))
  
    a = A()
    b = A()
    assert a.x == ([],{})
    assert id(a.x) != id(b.x)
    assert a.l == []
    assert id(a.l) != id(b.l)
  

  • Recordclass supports python 3.12 (tested on linux/debian 11/12 and windows via appveyor).

0.20.1

• Improve row_factory for sqlite on the ground of dataobject-based classes.
• Move recordclass repository to github from bitbucket.

0.20

• Library codebase is compatible with python 3.12 (tested for linux only, windows until python3.12 support on appveyor).
• Fix error with update of readonly attribute via update function.

0.19.2

• Exception message for Cls(**kwargs) with invalid kweyword argument is more precise (#37).
• Add parameter immutable_type for python >= 3.11. If immutable_type=True then a generated class (not an instance) will be immutable. If class do not contain user defuned __init__ and __new__ then instance creation will be faster (via vectorcall protocol).

0.19.1

• Fix regression with C.attr=value (with immutable class by default).

0.19

• Add vectorcall protocal to litetuple and mutabletuple.

• Add vectorcall protocal to dataobject.

• Now dataobject's op.__hash__ return id(op) by default. The option hashable=True make dataobject hashably by value.

• Now dataobject-based classes, litetuple and mutabletuple are support bytecode specializations since Python 3.11 for instance creation and for getattr/setattr.

• Fix make function for cases, when subclass have nontrivial __init__.

• Note for dataobject-based subclasses with non-trivial __init__ one may want define also __reduce__. For example:

  def __reduce__(self):
    from recordclass import dataobject, make
    tp, args = dataobject.__reduce__(self)
    return make, (tp, args)
  

0.18.4

• Fix a bug #35 with duplicating the field name during inheritance and mixing it with class level attributes.
• Allow use of ClassVar to define class level field.

0.18.3

• Fix bug with a tuple as default value of the field.
• Fix defaults propagtion to subclasses.
• Fix some issues with pickling in the context of dill.

0.18.2

• Slightly improve performance in the default __init__ when fields have default values or kwargs.
• Remove experimental pypy support: slow and difficult to predict memory footprint.
• Exclude experimental cython modules.

0.18.1.1

• Repackage 0.18.1 with use_cython=0

0.18.1

• Allow to initialize fields in the user defined __init__ method instead of __new__ (issue 29). If __init__ is defined by user then it's responsible for initialization of all fields. Note that this feature only work for mutable fields. Instances of the class with readonly=True must be initialized only in the default __new__. For example:

    class A(dataobject):
          x:int
          y:int
  
          def __init__(self, x, y):
              self.x = x
              self.y = y
  

• fast_new=True by default.

• Add make_row_factory for sqlite3 :

    class Planet(dataobject):
        name:str
        radius:int
  
    >>> con = sql.connect(":memory:")
    >>> cur = con.execute("SELECT 'Earth' AS name, 6378 AS radius")
    >>> cur.row_factory = make_row_factory(Planet)
    >>> row = cur.fetchone()
    >>> print(row)
    Planet(name='Earth', radius=6378)
  

0.18.0.1

• Exclude test_dataobject_match.py (for testing match statement) for python < 3.10.

0.18

• Python 3.11 support.
• Adapt data object to take benefit from bytecode specialization in 3.11.
• Fix issue for argument with default value in __new__, which havn't __repr__ that can be interpreted as valid python expression for creation of the default value.
• Add support for typing.ClassVar.
• Add Py_TPFLAGS_SEQUENCE and Py_TPFLAGS_MAPPING.
• Add __match_args__ to support match protocol for dataobject-based subclasses.

0.17.5

• Make to compile, to build and to test successfully for python 3.11.

0.17.4

• Fixed error with missing _PyObject_GC_Malloc in 3.11.

0.17.3

• Fix compatibility issue: restore gnu98 C-syntax.
• Fix remained issues with use of "Py_SIZE(op)" and "Py_TYPE(op)" as l-value.

0.17.2

• Add support for python 3.10.
• There are no use of "Py_SIZE(op)" and "Py_TYPE(op)" as l-value.

0.17.1

• Fix packaging issue with cython=1 in setup.py

0.17

• Now recordclass library may be compiled for pypy3, but there is still no complete runtime compatibility with pypy3.

• Slighly imporove performance of litetuple / mutabletuple.

• Slighly imporove performance of dataobject-based subclasses.

• Add adapter as_dataclass. For example:

    @as_dataclass()
    class Point:
        x:int
        y:int
  

• Module _litelist is implemented in pure C.

• Make dataobject.copy faster.

0.16.3

• Add possibility for recordclasses to assighn values by key:

    A = recordclass("A", "x y", mapping=True)
    a = A(1,2)
    a['x'] = 100
    a['y'] = 200
  

0.16.2

• Fix the packaging bug in 0.16.1.

0.16.1

• Add dictclass factory function to generate class with dict-like API and without attribute access to the fields. Features: fast instance creation, small memory footprint.

0.16

• RecordClass started to be a direct subclass of dataobject with sequence=True and support of namedtuple-like API. Insted of RecordClass(name, fields, **kw) for class creation use factory function recordclass(name, fields, **kw) (it allows to specify types).

• Add option api='dict' to make_dataclass for creating class that support dict-like API.

• Now one can't remove dataobject's property from it's class using del or builting delattr. For example:

    >>> Point = make_dataclass("Point", "x y")
    >>> del Point.x
    ...........
    AttributeError: Attribute x of the class Point can't be deleted
  

• Now one can't delete field's value using del or builting delattr. For example:

    >>> p = Point(1, 2)
    >>> del p.x
    ...........
    AttributeError: The value can't be deleted"
  

  Insted one can use assighnment to None:

    >>> p = Point(1, 2)
    >>> p.x = None
  

• Slightly improve performance of the access by index of dataobject-based classes with option sequence=True.

0.15.1

• Options readonly and iterable now can be sspecified via keyword arguments in class statement. For example:

    class Point(dataobject, readonly=True, iterable=True):
         x:int
         y:int
  

• Add update(cls, **kwargs) function to update attribute values.`

0.15

• Now library supports only Python >= 3.6
• 'gc' and 'fast_new' options now can be specified as kwargs in class statement.
• Add a function astuple(ob) for transformation dataobject instance ob to a tuple.
• Drop datatuple based classes.
• Add function make(cls, args, **kwargs) to create instance of the class cls.
• Add function clone(ob, **kwargs) to clone dataobject instance ob.
• Make structclass as alias of make_dataclass.
• Add option 'deep_dealloc' (@clsconfig(deep_dealloc=True)) for deallocation instances of dataobject-based recursive subclasses.

0.14.3:

• Subclasses of dataobject now support iterable and hashable protocols by default.

0.14.2:

• Fix compilation issue for python 3.9.

0.14.1:

• Fix issue with hash when subclassing recordclass-based classes.

0.14:

• Add doc to generated dataobject-based class in order to support inspect.signature.
• Add fast_new argument/option for fast instance creation.
• Fix refleak in litelist.
• Fix sequence protocol ability for dataobject/datatuple.
• Fix typed interface for StructClass.

0.13.2

• Fix issue #14 with deepcopy of dataobjects.

0.13.1

• Restore ``join_classesand add new functionjoin_dataclasses`.

0.13.0.1

• Remove redundant debug code.

0.13

• Make recordclass compiled and work with cpython 3.8.
• Move repository to git instead of mercurial since bitbucket will drop support of mercurial repositories.
• Fix some potential reference leaks.

0.12.0.1

• Fix missing .h files.

0.12

• clsconfig now become the main decorator for tuning dataobject-based classes.
• Fix concatenation of mutabletuples (issue #10).

0.11.1:

• dataobject instances may be deallocated faster now.

0.11:

• Rename memoryslots to mutabletuple.
• mutabletuple and immutabletuple dosn't participate in cyclic garbage collection.
• Add litelist type for list-like objects, which doesn't participate in cyglic garbage collection.

0.10.3:

• Introduce DataclassStorage and RecordclassStorage. They allow cache classes and used them without creation of new one.
• Add iterable decorator and argument. Now dataobject with fields isn't iterable by default.
• Move astuple to dataobject.c.

0.10.2

• Fix error with dataobject's __copy__.
• Fix error with pickling of recordclasses and structclasses, which was appeared since 0.8.5 (Thanks to Connor Wolf).

0.10.1

• Now by default sequence protocol is not supported by default if dataobject has fields, but iteration is supported.
• By default argsonly=False for usability reasons.

0.10

• Invent new factory function make_class for creation of different kind of dataobject classes without GC support by default.
• Invent new metaclass datatype and new base class dataobject for creation dataobject class using class statement. It have disabled GC support, but could be enabled by decorator dataobject.enable_gc. It support type hints (for python >= 3.6) and default values. It may not specify sequence of field names in __fields__ when type hints are applied to all data attributes (for python >= 3.6).
• Now recordclass-based classes may not support cyclic garbage collection too. This reduces the memory footprint by the size of PyGC_Head. Now by default recordclass-based classes doesn't support cyclic garbage collection.

0.9

• Change version to 0.9 to indicate a step forward.
• Cleanup dataobject.__cinit__.

0.8.5

• Make arrayclass-based objects support setitem/getitem and structclass-based objects able to not support them. By default, as before structclass-based objects support setitem/getitem protocol.
• Now only instances of dataobject are comparable to 'arrayclass'-based and structclass-based instances.
• Now generated classes can be hashable.

0.8.4

• Improve support for readonly mode for structclass and arrayclass.
• Add tests for arrayclass.

0.8.3

• Add typehints support to structclass-based classes.

0.8.2

• Remove usedict, gc, weaklist from the class __dict__.

0.8.1

• Remove Cython dependence by default for building recordclass from the sources [Issue #7].

0.8

• Add structclass factory function. It's analog of recordclass but with less memory footprint for it's instances (same as for instances of classes with __slots__) in the camparison with recordclass and namedtuple (it currently implemented with Cython).
• Add arrayclass factory function which produce a class for creation fixed size array. The benefit of such approach is also less memory footprint (it currently currently implemented with Cython).
• structclass factory has argument gc now. If gc=False (by default) support of cyclic garbage collection will switched off for instances of the created class.
• Add function join(C1, C2) in order to join two structclass-based classes C1 and C2.
• Add sequenceproxy function for creation of immutable and hashable proxy object from class instances, which implement access by index (it currently currently implemented with Cython).
• Add support for access to recordclass object attributes by idiom: ob['attrname'] (Issue #5).
• Add argument readonly to recordclass factory to produce immutable namedtuple. In contrast to collection.namedtuple it use same descriptors as for regular recordclasses for performance increasing.

0.7

• Make mutabletuple objects creation faster. As a side effect: when number of fields >= 8 recordclass instance creation time is not biger than creation time of instaces of dataclasses with __slots__.
• Recordclass factory function now create new recordclass classes in the same way as namedtuple in 3.7 (there is no compilation of generated python source of class).

0.6

• Add support for default values in recordclass factory function in correspondence to same addition to namedtuple in python 3.7.

0.5

• Change version to 0.5

0.4.4

• Add support for default values in RecordClass (patches from Pedro von Hertwig)
• Add tests for RecorClass (adopted from python tests for NamedTuple)

0.4.3

• Add support for typing for python 3.6 (patches from Vladimir Bolshakov).
• Resolve memory leak issue.

0.4.2

• Fix memory leak in property getter/setter