Python bindings for NodeTrie, a trie data structure library
pip install nodetrie
Motivation, design goals
NodeTrie is a Python extension to a native C library written for this purpose.
It came about from a lack of viable alternatives for Python. While other trie library implementations exist, they suffer from severe limitations such as
- Read only structures, no insertions
- High memory use for large trees
- Lack of searching, particularly file mask or wild card style searching
- Slow inserts
NodeTrie’s C library is designed to minimize memory use as much as possible and still allow arbitrary length trees that can be searched.
Each node has a name associated with it as its data, along with children list and number of children.
Features and design notes
- NodeTrie is an n-ary tree, meaning any one node can have any number of children
- Node children arrays are dynamically resized as needed on insertion on a per node basis. No fixed minimum nor maximum size
- Node names can be of arbitrary length, available memory allowing
- Node names from Node.name are always unicode in either Python 2/3
- Any python string type may be used on insertion
- Node names are implicitly decoded from unicode on insertion, if needed, with nodetrie.ENCODING (utf-8) default encoding which can be overridden
- New Python Node objects are created from the underlying C pointers every time Node.children is called. There is overhead on the Python interpreter to create these objects. It is safe and better performing to keep and re-use children references instead, see examples below
- Deletions are not implemented
- The C library implementation uses pointer arrays for children to reduce search space complexity and character pointers for names to allow for arbitrary name lengths. This may lead to memory fragmentation
- Node objects in python are read only. It is not possible to override the name of an existing Node object nor modify its attributes
- Character encodings that allow for null characters such as UCS-2 should not be used
from nodetrie import Node # This is the root of the tree, keep a reference to it. # Deleting or letting the root node go out of scope will de-allocate # the entire tree node = Node() # Insert a linked tree so that a->b->c->d where -> means 'has child node' node.insert_split_path(['a', 'b', 'c', 'd']) node.children.name == 'a' # Sub-trees can be referred to by child nodes a_node = node.children a_node.name == 'a' a_node.children.name == 'b' a_node.is_leaf() == False # Insertions create only new nodes # Insert linked tree so that a->b->c->dd node.insert_split_path(['a', 'b', 'c', 'dd']) # Only one 'a' node node.children_size == 1 # Existing references to nodes will have correct children # after insertion without recreating the node object. # Here, a_node is an existing object prior to more nodes # being added to its sub-tree. After insertion, a's sub-tree contains newly # inserted nodes as expected # 'c' node is first child of 'b' which is first child of 'a' # 'c' node has two children, 'd' and 'dd' c_node = a_node.children.children c_node.children_size == 2 c_node.is_leaf() == False # 'd' and 'dd' are both leaf nodes leaf_nodes = [c for c in c_node.children if c.is_leaf()] len(leaf_nodes) == 2
Tree is de-allocated when and only when root node goes out of scope or is deleted. Letting sub-tree objects go out of scope or explicitly deleting them will not de-allocate that sub-tree.
Insertions on non-root nodes work as expected. However, Node.insert does not check if a node is already present, unlike Node.insert_split_path
NodeTrie supports exact name as well as file mask matching tree search.
from __future__ import print_function from nodetrie import Node node = Node() for paths in [['a', 'b', 'c1', 'd1'], ['a', 'b', 'c1', 'd2'], ['a', 'b', 'c2', 'd1'], ['a', 'b', 'c2', 'd2']]: node.insert_split_path(paths) for path, _node in node.search(node, ['a', 'b', '*', '*'], ): print(path, _node)
[u'a', u'b', u'c1', u'd1'] Node: 'd1' [u'a', u'b', u'c1', u'd2'] Node: 'd2' [u'a', u'b', u'c2', u'd1'] Node: 'd1' [u'a', u'b', u'c2', u'd2'] Node: 'd2'
Separator joined node names for a matched sub-tree are returned by the query function.
for match in node.query('a.b.*.*'): print(match) for match in node.query('a|b|*|*', separator='|'): print(match)
(u'a.b.c1.d1', Node: 'd1') (u'a.b.c1.d2', Node: 'd2') (u'a.b.c2.d1', Node: 'd1') (u'a.b.c2.d2', Node: 'd2') (u'a|b|c1|d1', Node: 'd1') (u'a|b|c1|d2', Node: 'd2') (u'a|b|c2|d1', Node: 'd1') (u'a|b|c2|d2', Node: 'd2')
Contributions are most welcome.
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