class MyClass():
    def __init__(self):
        self.name = 'MyClass'
        self.value = 42

import hdf5
form hickle.helpters import no_compression


def create_MyClass_dataset(py_obj, h_group, name, **kwargs):
    """ 
    py_obj ..... the instance of MyClass to be dumped
    h_group .... the h5py.Group py_obj should be dumped into
    name ....... the name of the h5py.Dataset or h5py.Group representing py_obj
    **kwargs ... the compression keyword arguments passed to hickle.dump

    # if content of MyClass can be represented as single matrix, vector or scalar
    # values than created a dataset of appropriate size. and either set its shape and 
    # dtype parameters # to the appropriate size and tyoe . or directly pass the data
    # using the data parameter
    ds = h_group.create_dataset(name,data = py_obj.value,**kwargs)

	## NOTE: if your class represents a scalar using empty tuple for shape
    ##       than kwargs have to be filtered by no_compression
    # ds = h_group.create_dataset(name,data = py_obj.value,shape=(),**no_compression(kwargs))

	# set additional attributes providing additional specialisation of content
    ds.attrs['name'] = py_obj.name

    # when done return the new dataset object and an empty tuple or list
    return ds,()

def load_Myclass(h_node, base_type, py_obj_type):
    """
    h_node ........ the h5py.Dataset object containing the data of MyClass object to restore
    base_type ..... byte string naming the loader to be used for restoring MyClass object
    py_obj_type ... MyClass class or MyClass subclass object 
    """

    # py_obj_type should point to MyClass or any of its subclasses
    new_instance = py_obj_type()
    new_instance.name = h_node.attrs['name']
    new_instance.value = h_node[()]
    return new_instance

import h5py
from hickle.helpers import PyContainer

def create_MyClass_dataset(py_obj, h_group, name, **kwargs):
    """ 
    py_obj ..... the instance of MyClass to be dumped
    h_group .... the h5py.Group py_obj should be dumped into
    name ....... the name of the h5py.Dataset or h5py.Group representing py_obj
    **kwargs ... the compression keyword arguments passed to hickle.dump

    ds = h_group.create_group(name)

	# set additional attributes providing additional specialisation of content
    ds.attrs['name'] = py_obj.name

    # when done return the new dataset object and a tuple, list or generator function
	# providing for all subitems a tuple or list describing containgin 
    #  name ..... the name to be used storing the subitem within the h5py.Group object
    #  item ..... the subitem object to be stored
    #  attrs .... dictionary included in attrs of created h5py.Group or h5py.Dataset
    #  kwargs ... the kwargs as passed to create_MyClass_dataset function
    return ds,(('name',py_obj.name,{},kwargs),('value',py_obj.value,{'the answer':True},kwargs))



class MyClassContainer(PyContainer):
    """
    Valid container classes must be derived from hickle.helpers.PyContainer class
    """

    def __init__(self,h5_attrs,base_type,object_type):
		"""
		h5_attrs ...... the attrs dictionary attached to the group representing MyClass
    	base_type ..... byte string naming the loader to be used for restoring MyClass object
    	py_obj_type ... MyClass class or MyClass subclass object 
		"""

		# the optional protected _content parameter of the PyContainer __init__
        # method can be used to change the data structure used to store
        # the subitems passed to the append method of the PyContainer class
        # per default it is set to []
        super().__init__(h5_attrs,base_type,object_type,_content = dict())

	def filter(self,h_parent): # optional overload
        """
		generator member functoin which can be overloaded to reorganize subitems
        of h_parent h5py.Group before being restored by hickle. Its default
        implementation simply yields from h_parent.items(). 
		"""
		yield from super().filter(h_parent)

	def append(self,name,item,h5_attrs): # optional overload
        """
		in case _content parameter was explicitly set or subitems should be sored 
        in specific order or have to be preprocessed before the next item is appended
        than this can be done before storing in self._content.

        name ....... the name identifying subitem item within the parent hdf5.Group
        item ....... the object representing the subitem
        h5_attrs ... attrs dictionary attached to h5py.Dataset, h5py.Group representing item
		"""
		self._content[name] = item

	def convert(self):
        """
		called by hickle when all sub items have been appended to MyClass PyContainer
		this method must be implemented by MyClass PyContainer.
		"""

    	# py_obj_type should point to MyClass or any of its subclasses
    	new_instance = py_obj_type()
		new_instance.__dict__.update(self._content)
		return new_instance

from hickle.lookup import LoaderManager

# to register loader for object mapped to h5py.Dataset use
LoaderManager.register_class(
   MyClass,                # MyClass type object this loader handles
   b'MyClass',             # byte string representing the name of the loader 
   create_MyClass_Dataset, # the create dataset function defined in first example above
   load_MyClass,           # the load dataset function defined in first example above
   None,                   # usually None
   True,                   # Set to False to force explicit storage of MyClass instances in any case 
   'custom'                # Loader is only used when custom loaders are enabled on calling hickle.dump
)

# to register loader for object mapped to h5py.Group use
LoaderManager.register_class(
   MyClass,                # MyClass type object this loader handles
   b'MyClass',             # byte string representing the name of the loader 
   create_MyClass_Dataset, # the create dataset function defined in first example above
   None,                   # usually None
   MyClassContainer        # the PyContainer to be used to restore content of MyClass
   True,                   # Set to False to force explicit storage of MyClass instances in any case 
   None                    # if set to None loader is enabled unconditionally
)

# NOTE: in case content of MyClass instances may be mapped to h5py.Dataset or h5py.Group dependent upon
# their actual complexity than both types of loaders can be merged into one single
# using one common create_MyClass_dataset functoin and defining load_MyClass function and
# MyClassContainer class

from hickle.helpers import PyContainer

## define below all create_MyClass_dataset load_MyClass functions and MyClassContainer classes
## of the loaders serving your module, package, application package or application

....

## the class_register table and the exclude_register table are required
## by hickle to properly load and apply your loaders
## each row in the class register table will corresponds to the parameters
## of LoaderManager.register_class and has to be specified in the same order
## as above

class_register = [
   [ MyClass,                # MyClass type object this loader handles
     b'MyClass',             # byte string representing the name of the loader 
     create_MyClass_Dataset, # the create dataset function defined in first example above
     load_MyClass,           # the load dataset function defined in first example above
     None,                   # usually None
     True,                   # Set to False to force explicit storage of MyClass instances in any case 
     'custom'                # Loader is only used when custom loaders are enabled on calling hickle.dump
   ],
   [ MyClass,                # MyClass type object this loader handles
     b'MyClass',             # byte string representing the name of the loader 
     create_MyClass_Dataset, # the create dataset function defined in first example above
     None,                   # usually None
     MyClassContainer        # the PyContainer to be used to restore content of MyClass
     True,                   # Set to False to force explicit storage of MyClass instances in any case 
     None                    # if set to None loader is enabled unconditionally
   ]
]

# used by hickle 4.x legacy loaders and other special loaders
# usually an empty list
exclude_register = []

json took 2633.263 ms
uJson took 138.482 ms
hickle took 232.181 ms

Price et al., (2018). Hickle: A HDF5-based python pickle replacement. Journal of Open Source Software, 3(32), 1115, https://doi.org/10.21105/joss.01115