Like sorted but using external sorting so that large data sets can be sorted, for example:

>>> from random import random >>> from six.moves import xrange >>> from xsorted import xsorted >>> for x in xsorted(random() for _ in xrange(int(1e7))): pass

The only restriction is that the items must be pickleable (or you can provide your own serializer for externalizing partitions of items).

It is sometimes necessary to sort a dataset without having to load the entire set into memory. For example, if you want to group a very large csv file by one of it's columns. There are several ways in which this can be achieved, a common solution is to use the unix command sort. However unix sort does not offer the flexibility of the python csv module. Instead of writing a specific csv external sort I implemented xsorted, which attempts to generalize external sorting of any python iterable in a similar way in which sorted generalises the sorting of any iterable.

$ pip install xsorted

Just like sorted...

>>> from xsorted import xsorted >>> ''.join(xsorted('qwertyuiopasdfghjklzxcvbnm')) 'abcdefghijklmnopqrstuvwxyz'

With reverse...

>>> ''.join(xsorted('qwertyuiopasdfghjklzxcvbnm', reverse=True)) 'zyxwvutsrqponmlkjihgfedcba'

And a custom key...

>>> list(xsorted(('qwerty', 'uiop', 'asdfg', 'hjkl', 'zxcv', 'bnm'), key=lambda x: x[1])) ['uiop', 'hjkl', 'bnm', 'asdfg', 'qwerty', 'zxcv']

The implementation details of xsorted can be customized using the factory xsorter (in order to provide the same interface as sorted the partition_size is treated as an implementation detail):

>>> from xsorted import xsorter >>> xsorted_custom = xsorter(partition_size=4) >>> ''.join(xsorted_custom('qwertyuiopasdfghjklzxcvbnm')) 'abcdefghijklmnopqrstuvwxyz'

The main reason to use xsorted over sorted is where memory usage is a bigger concern than speed. The following chart compares the sampled memory usage of xsorted vs sorted when sorting 50000 1KB strings. As can be seen the memory usage when sorting an iterable with sorted is roughly equivalent to the memory of the data being sorted, while with xsorted the memory usage will be proportional to the number of partitions required to perform the external sort.

It should be noted that we cannot read too much into the time difference in this chart. The memory profile test is performed by running the sorting algorithm in a separate thread, sampling the memory usage in the main thread. Sampling memory usage in this way however has a detrimental effect on performance, more so for sorted than xsorted due to the additional IO required in xsorted. multiprocessing could be an option in order to reduce the performance impact, however the main point of the test is to illustrate the difference in memory usage.

The following section presents some more detailed microbenchmarks which show the difference in performance of the two sorting functions.

Again 50000 1KB strings are sorted, benchmarked using pytest-benchmark.

xsorted

sorted