pyskiplist 1.0.0

Description:

pyskiplist 1.0.0

PySkipList is a fast, pure Python implementation of an indexable skiplist. It
implements a SkipList data structure that provides an always sorted,
list-like data structure for (key, value) pairs. It efficiently supports the
following operations:

Insert a pair in the list, maintaining sorted order.
Find the value of a given key.
Remove a given pair based on a key.
Iterate over all pairs in sorted order.
Find the position of a given key.
Access a pair at a certain position.
Delete a pair at a certain position.

Since PySkipList is a pure Python implementation, it should work well on
alternative Python implementations such as PyPy and Jython.

Example
The following provides a few examples on how to use the SkipList API:
>>> from pyskiplist import SkipList
>>> sl = SkipList()
>>> sl.insert('foo', 'bar')
>>> sl.insert('baz', 'qux')
>>> sl
SkipList((('baz', 'qux'), ('foo', 'bar')))
>>> sl.search('foo')
'bar'
>>> sl[0]
('baz', 'qux')
>>> sl.remove('foo') # remove by key
>>> del sl[0] # remove by position


Asymptotic Complexity
Below are the Big-O complexities of the various operations implemented by
pyskiplist:


Operation
Complexity



insertion
O(log N)

search by key
O(log N)

removal by key
O(log N)

forward iteration
O(1)

find by position
O(log N)

access by position
O(log N)

delete by position
O(log N)





Performance
Below are the results of some performance tests. These are for Python 3.4.2 on
my Linux laptop:


Test
Operations / second



Insert @ 1k nodes
45,056

Insert @ 10k nodes
42,137

Insert @ 100k nodes
28,086

Remove @ 1k nodes
54,316

Remove @ 10k nodes
46,240

Remove @ 100k nodes
35,114

Search @ 1k nodes
137,248

Search @ 10k nodes
109,480

Search @ 100k nodes
77,939





Memory usage
PySkipList tries to be efficient with regards to memory usage. The numbers
below are for Python 3.4.2 on my Linux laptop. This specific test stores pairs
of integer keys and an integer values in a skiplist. The total size of the two
integers on this Python version is 56 bytes.


Nodes
Bytes / node
Overhead (fixed)



1k
164
108

10k
162
106

100k
162
106





Implementation notes
Reference papers on skiplists:

ftp://ftp.cs.umd.edu/pub/skipLists/skiplists.pdf (original paper)
http://drum.lib.umd.edu/bitstream/1903/544/2/CS-TR-2286.1.pdf (cookbook)

This implementation uses a novel (as far as I know) technique where it stores
just a single link width per node, and only in nodes with level > 0. The link
corresponds to the number of nodes skipped by the highest incoming link. Other
implementations that I’ve seen all store a width for every link. This approach
saves a lot of memory. The overhead should just be 1/e (0.37) integers per
node. It makes an indexable skiplist almost as memory efficient as its
non-indexable cousin.
Duplicate keys are allowed in this implementation, and insertion order is
maintained.
Skiplist nodes are implemented as plain lists instead of objects. This saves
memory. Kudos to http://pythonsweetness.tumblr.com/post/45227295342 for the
idea.
The built-in Mersenne Twister is used as the random source. This is preferable
over SystemRandom since it doesn’t require a system call and there is no need
for cryptographically secure numbers.