Hickle

Hickle is an HDF5 based clone of pickle, with a twist: instead of serializing to a pickle file, Hickle dumps to an HDF5 file (Hierarchical Data Format). It is designed to be a "drop-in" replacement for pickle (for common data objects), but is really an amalgam of h5py and pickle with extended functionality.

That is: hickle is a neat little way of dumping python variables to HDF5 files that can be read in most programming languages, not just Python. Hickle is fast, and allows for transparent compression of your data (LZF / GZIP).

Why use Hickle?

While hickle is designed to be a drop-in replacement for pickle (or something like json), it works very differently. Instead of serializing / json-izing, it instead stores the data using the excellent h5py module.

The main reasons to use hickle are:

It's faster than pickle and cPickle.
It stores data in HDF5.
You can easily compress your data.

The main reasons not to use hickle are:

You don't want to store your data in HDF5. While hickle can serialize arbitrary python objects, this functionality is provided only for convenience, and you're probably better off just using the pickle module.
You want to convert your data in human-readable JSON/YAML, in which case, you should do that instead.

So, if you want your data in HDF5, or if your pickling is taking too long, give hickle a try. Hickle is particularly good at storing large numpy arrays, thanks to h5py running under the hood.

Documentation

Documentation for hickle can be found at telegraphic.github.io/hickle/.

Usage example

Hickle is nice and easy to use, and should look very familiar to those of you who have pickled before.

In short, hickle provides two methods: a hickle.load method, for loading hickle files, and a hickle.dump method, for dumping data into HDF5. Here's a complete example:

import os
import hickle as hkl
import numpy as np

# Create a numpy array of data
array_obj = np.ones(32768, dtype='float32')

# Dump to file
hkl.dump(array_obj, 'test.hkl', mode='w')

# Dump data, with compression
hkl.dump(array_obj, 'test_gzip.hkl', mode='w', compression='gzip')

# Compare filesizes
print('uncompressed: %i bytes' % os.path.getsize('test.hkl'))
print('compressed:   %i bytes' % os.path.getsize('test_gzip.hkl'))

# Load data
array_hkl = hkl.load('test_gzip.hkl')

# Check the two are the same file
assert array_hkl.dtype == array_obj.dtype
assert np.all((array_hkl, array_obj))

HDF5 compression options

A major benefit of hickle over pickle is that it allows fancy HDF5 features to be applied, by passing on keyword arguments on to h5py. So, you can do things like:

  hkl.dump(array_obj, 'test_lzf.hkl', mode='w', compression='lzf', scaleoffset=0,
           chunks=(100, 100), shuffle=True, fletcher32=True)

A detailed explanation of these keywords is given at http://docs.h5py.org/en/latest/high/dataset.html, but we give a quick rundown below.

In HDF5, datasets are stored as B-trees, a tree data structure that has speed benefits over contiguous blocks of data. In the B-tree, data are split into chunks, which is leveraged to allow dataset resizing and compression via filter pipelines. Filters such as shuffle and scaleoffset move your data around to improve compression ratios, and fletcher32 computes a checksum. These file-level options are abstracted away from the data model.

Dumping custom objects

Hickle provides several options to store objects of custom python classes. Objects of classes derived from built in classes, numpy, scipy, pandas and astropy objects will be stored using the corresponding loader provided by hickle. Any other classes per default will be stored as binary pickle string. Starting with version 4.x hickle offers the possibility to define dedicated loader functions for custom classes and starting with hickle 5.x these can be collected in module, package and application specific loader modules.

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

To create a loader for MyClass the create_MyClass_dataset and either the load_MyClass or the MyClassContainer class have to be defined.

import hdf5
from hickle.helpers 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
    ##       then 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

For dumping content of complex objects consisting of multiple sub-items which have to be stored as individual h5py.Dataset or h5py.Group objects than define create_MyClass_dataset using create_group method instead of create_dataset and define the corresponding MyClassContainer class.

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

In a last step the loader for MyClass has to be registered with hickle. This is done by calling hickle.lookup.LoaderManager.register_class method

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

For complex python modules, packages and applications defining several classes to be dumped and handled by hickle calling hickle.lookup.LoaderManager.register_class explicitly very quickly becomes tedious and confusing. Therefore hickle offers from hickle 5.x on the possibility to collect all loaders for classes and objects defined by your module, package or application within dedicated loader modules and install them along with your module, package and application.

For packages and application packages the load_MyPackage.py loader module has to be stored within hickle_loaders directory of the package directory (the first which contains a init.py file) and should be structured as follows.

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 = []

For single file modules and application scripts the load_MyModule.py or load_MyApp.py files have to be stored within the hickle_loaders directory located within the same directory as MyModule.py or My_App.py. For further examples of more complex loaders and on how to store bytearrays and strings such that they can be compressed when stored see default loader modules in hickle/loaders/ directory.

Note: storing complex objects in HDF5 file

The HDF5 file format is designed to store several big matrices, images and vectors efficiently and attach some metadata and to provide a convenient way access the data through a tree structure. It is not designed like python pickle format for efficiently mapping the in memory object structure to a file. Therefore mindlessly storing plenty of tiny objects and scalar values without combining them into a single datataset will cause the HDF5 and thus the file created by hickle explode. File sizes of several 10 GB are likely and possible when a pickle file would just need some 100 MB. This can be prevented by create_MyClass_dataset method combining sub-items into bigger numpy arrays or other data structures which can be mapped to h5py.Datasets and load_MyClass function and/or MyClassContainer.convert method restoring actual structure of the sub-items on load.

Recent changes

December 2021: Release of version 5, support for h5py >= 3.0 and numpy >= 1.21
June 2020: Major refactor to version 4, and removal of support for Python 2.
December 2018: Accepted to Journal of Open-Source Software (JOSS).
June 2018: Major refactor and support for Python 3.
Aug 2016: Added support for scipy sparse matrices bsr_matrix, csr_matrix and csc_matrix.

Performance comparison

Hickle runs a lot faster than pickle with its default settings, and a little faster than pickle with protocol=2 set:

In [1]: import numpy as np

In [2]: x = np.random.random((2000, 2000))

In [3]: import pickle

In [4]: f = open('foo.pkl', 'w')

In [5]: %time pickle.dump(x, f)  # slow by default
CPU times: user 2 s, sys: 274 ms, total: 2.27 s
Wall time: 2.74 s

In [6]: f = open('foo.pkl', 'w')

In [7]: %time pickle.dump(x, f, protocol=2)  # actually very fast
CPU times: user 18.8 ms, sys: 36 ms, total: 54.8 ms
Wall time: 55.6 ms

In [8]: import hickle

In [9]: f = open('foo.hkl', 'w')

In [10]: %time hickle.dump(x, f)  # a bit faster
dumping <type 'numpy.ndarray'> to file <HDF5 file "foo.hkl" (mode r+)>
CPU times: user 764 us, sys: 35.6 ms, total: 36.4 ms
Wall time: 36.2 ms

So if you do continue to use pickle, add the protocol=2 keyword (thanks @mrocklin for pointing this out).

For storing python dictionaries of lists, hickle beats the python json encoder, but is slower than uJson. For a dictionary with 64 entries, each containing a 4096 length list of random numbers, the times are:

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

It should be noted that these comparisons are of course not fair: storing in HDF5 will not help you convert something into JSON, nor will it help you serialize a string. But for quick storage of the contents of a python variable, it's a pretty good option.

Installation guidelines

Easy method

Install with pip by running pip install hickle from the command line.

Install on Windows 32 bit

Prebuilt Python wheels packages are available on PyPi until H5PY version 2.10 and Python 3.8. Any newer versions have to be built and installed Manually.

1) Install h5py 2.10 with pip by running pip install "h5py==2.10" from the commandline

2) Install with pip by running pip install hickle form the command line

Manual install

You should have Python 3.5 and above installed
Install hdf5 (Official page: http://www.hdfgroup.org/ftp/HDF5/current/src/unpacked/release_docs/INSTALL) (Binary Downloads: https://portal.hdfgroup.org/display/support/Downloads) Note: On Windows 32 bit install prebuilt binary package for libhdf5 1.10.4, which is the latest version supporting 32 bit on Windows
Install h5py (Official page: http://docs.h5py.org/en/latest/build.html)
Download hickle: via terminal: git clone https://github.com/telegraphic/hickle.git via manual download: Go to https://github.com/telegraphic/hickle and on right hand side you will find Download ZIP file
cd to your downloaded hickle directory
Then run the following command in the hickle directory: python setup.py install

Optional requirements:

dill: needed when files generated by hickle 3 and/or hickle 4 are to be loaded with hickle >= 5, for developent and testing
astropy: needed for development and testing
pandas: needed for development and testing

Testing

Once installed from source, run python setup.py test to check it's all working.

Bugs & contributing

Contributions and bugfixes are very welcome. Please check out our contribution guidelines for more details on how to contribute to development.

Referencing hickle

If you use hickle in academic research, we would be grateful if you could reference our paper in the Journal of Open-Source Software (JOSS).

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

telegraphic / hickle

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