binary-trie 0.0.9

A simple binary trie implementation

Binary Trie

Python binary trie implementation, helping with XOR distances.

Author: Guillaume Michel

Purpose

IPFS and libp2p are built upon the Kademlia DHT, which uses the XOR distance as a distance metric between keys. As explained in this blogpost, the XOR distance is tricky to understand and represent. This distance metric is odd for it is non linear. For instance, $2 \oplus 3 = 1$ but $3 \oplus 4 = 7$. Ordering $N_8$ by XOR distance to 2 gives the following result: $[2,3,0,1,6,7,4,5]$

One popular representation for XOR distance is the Binary Trie. A binary brie is a simple Trie with keyspace $\lbrace0,1\rbrace^n$ with $n$ being the size of the keyspace. The perfect visual representation of the XOR distance would be a N-dimensional binary trie. The example below displays a binary trie containing the keys $[2,3,4,6,7,9,11,13]$.

Each node in the Trie can be associated with some metadata for convenience.

Usage

Install

pip install binary-trie

Import

from binary_trie import Trie, bytes_to_bitstring, bitstring_to_bytes, int_to_bitstring, bitstring_to_int

Creating an empty trie

trie = Trie()

Adding keys

The add(key) method takes a bitstring as input. The bitstring can either be provided directly, or be computer from an int or bytes using the int_to_bitstring() and bytes_to_bitstring() functions. Note that the l parameter represent the bit length of the binary representation. It is important that all keys share the same bit length. The bit length can be omitted in bytes_to_bistring() when working with bit lengths multiple of 8.

trie.add("0010")
trie.add(4*"0")
trie.add(int_to_bitstring(3, l=4))
trie.add(bytes_to_bitstring(b'\x0e', l=4))

The add function returns True on success, and False if the key was already in the trie (not inserted).

Finding keys

The find(key) method returns True if the provided key is in the Trie, False otherwise.

trie.contains("0010") # True
trie.contains("0100") # False

Key depth

The depth(key) method returns the depth of the provided key, if it is in the Trie and -1 otherwise. The depth of a trie node is defined as the number of its direct ancestors, up to the root of the trie.

trie.depth("0")    # 1
trie.depth("0010") # 3
trie.depth("0111") # 4
trie.depth("1101") # 2
trie.depth("11")   # -1

Finding the closest keys to a target

The n_closest_keys(key, n) method returns the n closest keys to the provided key in the trie, according to the XOR distance. The keys are sorted according to the distance to the target key. Note that only leaves of the trie will be returned, not intermediary nodes.

trie.n_closest_keys("0001", 1) # ["0000"]
trie.n_closest_keys("0010", 3) # ["0010", "0011", "0000"]

Prefix matching

The prefix_match_keys(prefix) will return the list of keys of all leaves of the Trie matching the provided prefix.

trie.prefix_match_keys("00")   # ["0000", "0010", "0011"]
trie.prefix_match_keys("1111") # []

Attaching metadata to Trie nodes

It is possible to attach an object as metadata to a trie leaf node.

class MyObj(object):
    def __init__(self, key, name):
        self.key = key
        self.name = name

trie = Trie(MyObj)

obj = MyObj(int_to_bitstring(10, 4), "Node 10")
trie.add(obj.key, obj)

trie.find(obj.key).name        # "Node 10"
trie.n_closest(obj.key, 1)     # [obj]
trie.prefix_match(obj.key[:2]) # [obj]

Note that the find(key) method is similar to the contains(key) method, but returns the associated metadata if any, instead of returning a bool. The n_closest(key, n) method is similar to the n_closest_keys(key, n) method, but returns the list of metadata associated with the closest keys, instead of the list of keys.

Predicates

It is possible to assign some boolean variables to metadata objects, and make use of them using predicate in n_closest(), n_closest_keys(), prefix_match() and prefix_match_keys() methods to filter the results.

class MyObj(object):
    def __init__(self, key, name, some_bool):
        self.key = key
        self.name = name
        self.some_bool = some_bool

trie = Trie(MyObj)

nodeIDs = [0, 1, 2] # ["0000", "0001", "0010"]
for i in nodeIDs:
    obj = MyObj(int_to_bitstring(i, 4), "Node "+str(i), i % 2 == 0)
    trie.add(obj.key, obj)

trie.n_closest_keys("0001", 2, predicate=lambda n: n.some_bool) # ["0000", "0010"] 
trie.prefix_match_keys("000", predicate=lambda n: n.some_bool)  # ["0000"]

# Note that the key "0001" matched both requests, but wasn't taken into
# consideration as it doesn't satisfy the predicate

Helpers

There are 4 helpers functions to facilitate the use of this implementation with keys being not only bitstring, but also bytes or int. These helper functions help translate bytes and int to bitstring and reciprocally.

def bytes_to_bitstring(data: bytes, l: int=8*len(data)) -> bytes:
    ...
bytes_to_bitstring(b'\xff\x00') # "1111111100000000"
bytes_to_bitstring(b'\xf3',l=4) # "0011"

def bitstring_to_bytes(bs: str) -> bytes:
    ...
bitstring_to_bytes("1111111100000000") # b'\xff\x00'
bitstring_to_bytes("0011")             # b'\x03'

def int_to_bitstring(i, l: int) -> bytes:
    ...
int_to_bitstring(6, 4)  # "0110"
int_to_bitstring(6, 16) # "0000000000000110"

def bitstring_to_int(bs: str) -> int:
    ...
bitstring_to_int("0110")             # 6
bitstring_to_int("0000000000000110") # 6