1. Benjamin Saller
  2. rbtree


rbtree /

Filename Size Date modified Message
110 B
Added tag RELEASE_0_8_8 for changeset c13c2ee95613
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follow more standard package layout
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follow more standard package layout
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Port to modern Pyrex (0.9.8+)
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apparently sdist was broken.
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remove scripts
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adding rbtree.c so setuptools can include it for people w/o pyrex
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adding rbtree.c so setuptools can include it for people w/o pyrex
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Increment version to 0.9

Fast Red-Black Tree implementation for Python

Author:Benjamin Saller <bcsaller@gmail.com>

An rbtree is a fast, balanced efficient data structure with the following properties:

get O(log n) set O(log n) delete O(log n) min O(log n) max O(log n) contains O(log n)

Because the worst case timing is minimal across the range of standard dict and ordered data operations it makes sense to use this when you have volatile/dynamic sorted data.

In common usage its nearly as fast as the Python dict impl but has a slightly more expensive usage of the compare function as the keys are ordered and not hashed.


Uses distutils (and optionally make)

sudo easy_install rbtree


sudo python setup.py install


make sudo make install


rbtree.rbtree can be used as a normal Python dictionary but will keep values ordered by a key compare function.

>>> import rbtree
>>> r = rbtree.rbtree()

We can assign key/values like a dictionary using the same protocol. >>> r[2] = 3 >>> r[1] = 2 >>> r[3] = 4

Dictionary methods like keys, items and values work but the keys will come out ordered with the compare function.

>>> assert r.keys() == [1,2,3]

Protocol Extensions

A protocol is the combination of syntactic style and invocation semantic associated with the usage of an object. For example dictionary like getitem access is a protocol as is object.method. Because rbtrees support fast ordered data manipulation it is convenient to extend the iteration protocol to support its additional capabilities.

Here we create an rbtree with the key/value pairs from 1 to 9.

>>> r = rbtree.rbtree(dict(zip(range(10), range(10))))

Create an iterator >>> i = iter(r)

and jump to the key/value pair pointed at by key 5 >>> i.goto(5)

We can now verify that the next keys we see in normal iteration are as expected >>> assert i.next() == 6 >>> assert i.next() == 7

Lets finish off the iterator now >>> assert list(i) == [8, 9]

Other interesting extensions to the iterator protocol are as follows. Because keys are ordered we can support mutation (and deletion) of the tree while doing iteration.

>>> r = rbtree.rbtree(zip(range(10), range(10)))

We will now use the iternodes method of the tree to walk the key value node set. Each node has the following attributes

.key -> key .value -> value .item -> (k,v)

and a .delete() method which remove the node. The iterator will be set to the next node in the tree in the direction indicated by iter.direction which can be 1 (the default) for forwards iteration and -1 for backwards.

>>> for i in  r.iternodes():
...    i.delete()
>>> r = rbtree.rbtree(zip(range(10), range(10)))
>>> i = iter(r)
>>> i.goto(5)
>>> i.delete()
>>> assert i.item == (6, 6)

Here we will alter the direction of the iterator to point backwards, now when we do a delete the cursor will end up on the previous node. >>> i = iter(r) >>> i.direction = -1 >>> i.goto(4) >>> i.delete() >>> assert i.item == (3,3)

Using the iternodes interface and the .delete method is a rare and special case if you are treating the rbtree as an ordered dictionary. However, if you are using custom compare functions that allows the same key into the tree more than once then its very useful to take an iterator and walk sets of similar keys which the dictionary interface will not be able to return. If this use-case doesn't make sense to you it continue using the rbtree as an ordered dict.