datrie 0.4.2

datrie

Super-fast, efficiently stored Trie for Python (2.x and 3.x). Uses libdatrie.

Installation

pip install datrie

Usage

Create a new trie capable of storing items with lower-case ascii keys:

>>> import string
>>> import datrie
>>> trie = datrie.Trie(string.ascii_lowercase)

trie variable is a dict-like object that can have unicode keys of certain ranges and Python objects as values.

In addition to implementing the mapping interface, tries facilitate finding the items for a given prefix, and vice versa, finding the items whose keys are prefixes of a given string. As a common special case, finding the longest-prefix item is also supported.

Add some values to it (datrie keys must be unicode; the examples are for Python 2.x):

>>> trie[u'foo'] = 5
>>> trie[u'foobar'] = 10
>>> trie[u'bar'] = 'bar value'
>>> trie.setdefault(u'foobar', 15)
10

Check if u’foo’ is in trie:

>>> u'foo' in trie
True

Get a value:

>>> trie[u'foo']
5

Find all prefixes of a word:

>>> trie.prefixes(u'foobarbaz')
[u'foo', u'foobar']

>>> trie.prefix_items(u'foobarbaz')
[(u'foo', 5), (u'foobar', 10)]

>>> trie.iter_prefixes(u'foobarbaz')
<generator object ...>

>>> trie.iter_prefix_items(u'foobarbaz')
<generator object ...>

Find the longest prefix of a word:

>>> trie.longest_prefix(u'foo')
u'foo'

>>> trie.longest_prefix(u'foobarbaz')
u'foobar'

>>> trie.longest_prefix(u'gaz')
KeyError: u'gaz'

>>> trie.longest_prefix(u'gaz', default=u'vasia')
u'vasia'

>>> trie.longest_prefix_item(u'foobarbaz')
(u'foobar', 10)

Check if the trie has keys with a given prefix:

>>> trie.has_keys_with_prefix(u'fo')
True

>>> trie.has_keys_with_prefix(u'FO')
False

Get all items with a given prefix from a trie:

>>> trie.keys(u'fo')
[u'foo', u'foobar']

>>> trie.items(u'ba')
[(u'bar', 'bar value')]

>>> trie.values(u'foob')
[10]

Save & load a trie (values must be picklable):

>>> trie.save('my.trie')
>>> trie2 = datrie.Trie.load('my.trie')

Trie and BaseTrie

There are two Trie classes in datrie package: datrie.Trie and datrie.BaseTrie. datrie.BaseTrie is slightly faster and uses less memory but it can store only integer numbers 0 <= x <= 2147483647. datrie.Trie is a bit slower but can store any Python object as a value.

If you don’t need values or integer values are OK then use datrie.BaseTrie:

import datrie
import string
trie = datrie.BaseTrie(string.ascii_lowercase)

Custom iteration

If the built-in trie methods don’t fit you can use datrie.State and datrie.Iterator to implement custom traversal.

For example, let’s find all suffixes of 'fo' for our trie and get the values:

>>> state = datrie.State(trie)
>>> state.walk(u'foo')
>>> it = datrie.Iterator(state)
>>> while it.next():
...     print(it.key())
...     print(it.data))
o
5
obar
10

Performance

Performance is measured for datrie.Trie against Python’s dict with 100k unique unicode words (English and Russian) as keys and ‘1’ numbers as values.

datrie.Trie uses about 5M memory for 100k words; Python’s dict uses about 22M for this according to my unscientific tests.

This trie implementation is 2-6 times slower than python’s dict on __getitem__. Benchmark results (macbook air i5 1.7GHz, “1.000M ops/sec” == “1 000 000 operations per second”):

Python 2.6:
dict __getitem__: 6.024M ops/sec
trie __getitem__: 2.272M ops/sec

Python 2.7:
dict __getitem__: 6.693M ops/sec
trie __getitem__: 2.357M ops/sec

Python 3.2:
dict __getitem__: 3.628M ops/sec
trie __getitem__: 1.980M ops/sec

Python 3.3b1:
dict __getitem__: 6.721M ops/sec
trie __getitem__: 2.584M ops/sec

Looking for prefixes of a given word is almost as fast as __getitem__ (results are for Python 3.2, this is the slowest supported Python, results are better for 2.6, 2.7):

trie.iter_prefix_items (hits):      0.461M ops/sec
trie.prefix_items (hits):           0.743M ops/sec
trie.prefix_items loop (hits):      0.629M ops/sec
trie.iter_prefixes (hits):          0.759M ops/sec
trie.iter_prefixes (misses):        1.538M ops/sec
trie.iter_prefixes (mixed):         1.359M ops/sec
trie.has_keys_with_prefix (hits):   1.896M ops/sec
trie.has_keys_with_prefix (misses): 2.590M ops/sec
trie.longest_prefix (hits):         1.710M ops/sec
trie.longest_prefix (misses):       1.506M ops/sec
trie.longest_prefix (mixed):        1.520M ops/sec
trie.longest_prefix_item (hits):    1.276M ops/sec
trie.longest_prefix_item (misses):  1.292M ops/sec
trie.longest_prefix_item (mixed):   1.379M ops/sec

Looking for all words starting with a given prefix is mostly limited by overall result count (this can be improved in future because a lot of time is spent decoding strings from utf_32_le to Python’s unicode):

trie.items(prefix="xxx"), avg_len(res)==415:        0.721K ops/sec
trie.keys(prefix="xxx"), avg_len(res)==415:         0.723K ops/sec
trie.values(prefix="xxx"), avg_len(res)==415:       4.870K ops/sec
trie.items(prefix="xxxxx"), avg_len(res)==17:       18.084K ops/sec
trie.keys(prefix="xxxxx"), avg_len(res)==17:        18.279K ops/sec
trie.values(prefix="xxxxx"), avg_len(res)==17:      98.668K ops/sec
trie.items(prefix="xxxxxxxx"), avg_len(res)==3:     87.141K ops/sec
trie.keys(prefix="xxxxxxxx"), avg_len(res)==3:      90.251K ops/sec
trie.values(prefix="xxxxxxxx"), avg_len(res)==3:    346.981K ops/sec
trie.items(prefix="xxxxx..xx"), avg_len(res)==1.4:  202.346K ops/sec
trie.keys(prefix="xxxxx..xx"), avg_len(res)==1.4:   216.588K ops/sec
trie.values(prefix="xxxxx..xx"), avg_len(res)==1.4: 532.858K ops/sec
trie.items(prefix="xxx"), NON_EXISTING:             1864.411K ops/sec
trie.keys(prefix="xxx"), NON_EXISTING:              1857.531K ops/sec
trie.values(prefix="xxx"), NON_EXISTING:            1822.818K ops/sec

Random insert time is very slow compared to dict, this is the limitation of double-array tries; updates are quite fast. If you want to build a trie, consider sorting keys before the insertion:

dict __setitem__ (updates):         3.489M ops/sec
trie __setitem__ (updates):         1.862M ops/sec
dict __setitem__ (inserts, random): 3.628M ops/sec
trie __setitem__ (inserts, random): 0.050M ops/sec
dict __setitem__ (inserts, sorted): 3.272M ops/sec
trie __setitem__ (inserts, sorted): 0.585M ops/sec
dict setdefault (updates):          2.575M ops/sec
trie setdefault (updates):          1.600M ops/sec
dict setdefault (inserts):          2.596M ops/sec
trie setdefault (inserts):          0.050M ops/sec

Other results (note that len(trie) is currently implemented using trie traversal):

dict __contains__ (hits):   3.905M ops/sec
trie __contains__ (hits):   2.017M ops/sec
dict __contains__ (misses): 3.296M ops/sec
trie __contains__ (misses): 2.633M ops/sec
dict __len__:               199728.762 ops/sec
trie __len__:               22.007 ops/sec
dict values():              360.243 ops/sec
trie values():              19.703 ops/sec
dict keys():                180.307 ops/sec
trie keys():                3.361 ops/sec
dict items():               49.029 ops/sec
trie items():               3.172 ops/sec

Please take this benchmark results with a grain of salt; this is a very simple benchmark and may not cover your use case.

Current Limitations

• keys must be unicode (no implicit conversion for byte strings under Python 2.x, sorry);

• there are no iterator versions of keys/values/items (this is not implemented yet);

• it is painfully slow and maybe buggy under pypy;

• library is not tested with narrow Python builds.

Contributing

Development happens at github and bitbucket:

• https://github.com/kmike/datrie

• https://bitbucket.org/kmike/datrie

The main issue tracker is at github.

Feel free to submit ideas, bugs, pull requests (git or hg) or regular patches.

$ tox

$ tox -c tox-bench.ini

$ update_c.sh

License

Licensed under LGPL v2.1.

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