Skip to content

Latest commit

 

History

History
196 lines (131 loc) · 8.16 KB

README.md

File metadata and controls

196 lines (131 loc) · 8.16 KB
Raw

PyPI - Latest Release PyPI - Python Versions Travis CI - Build Status AppVeyor - Build Status CodeCov - Coverage Status JOSS Status

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 dill/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:

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

The main reasons not to use hickle are:

  1. 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.
  2. 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.

Recent changes

  • 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.

Manual install

  1. You should have Python 3.5 and above installed

  2. Install h5py (Official page: http://docs.h5py.org/en/latest/build.html)

  3. Install hdf5 (Official page: http://www.hdfgroup.org/ftp/HDF5/current/src/unpacked/release_docs/INSTALL)

  4. 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

  5. cd to your downloaded hickle directory

  6. Then run the following command in the hickle directory: python setup.py install

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