Super-fast, efficiently stored Trie for Python (2.x and 3.x). Uses libdatrie.
pip install datrie
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.
Warning
For efficiency you must define allowed character range(s) while
creating trie. datrie doesn't check if keys are in allowed
ranges at runtime, so be careful! Invalid keys are OK at lookup time
but values won't be stored correctly for such keys.
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]
Get all suffixes of certain word starting with a given prefix from a trie:
>>> trie.suffixes() [u'pro', u'producer', u'producers', u'product', u'production', u'productivity', u'prof'] >>> trie.suffixes(u'prod') [u'ucer', u'ucers', u'uct', u'uction', u'uctivity']
Save & load a trie (values must be picklable):
>>> trie.save('my.trie')
>>> trie2 = datrie.Trie.load('my.trie')
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 -2147483648 <= 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)
If the built-in trie methods don't fit you can use datrie.State and
datrie.Iterator to implement custom traversal.
Note
If you use datrie.BaseTrie you need datrie.BaseState and
datrie.BaseIterator for 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 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 1.8GHz, "1.000M ops/sec" == "1 000 000 operations per second"):
Python 2.6: dict __getitem__: 7.107M ops/sec trie __getitem__: 2.478M ops/sec Python 2.7: dict __getitem__: 6.550M ops/sec trie __getitem__: 2.474M ops/sec Python 3.2: dict __getitem__: 8.185M ops/sec trie __getitem__: 2.684M ops/sec Python 3.3: dict __getitem__: 7.050M ops/sec trie __getitem__: 2.755M ops/sec
Looking for prefixes of a given word is almost as fast as
__getitem__ (results are for Python 3.3):
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: 0.609K ops/sec trie.keys(prefix="xxx"), avg_len(res)==415: 0.642K ops/sec trie.values(prefix="xxx"), avg_len(res)==415: 4.974K ops/sec trie.items(prefix="xxxxx"), avg_len(res)==17: 14.781K ops/sec trie.keys(prefix="xxxxx"), avg_len(res)==17: 15.766K ops/sec trie.values(prefix="xxxxx"), avg_len(res)==17: 96.456K ops/sec trie.items(prefix="xxxxxxxx"), avg_len(res)==3: 75.165K ops/sec trie.keys(prefix="xxxxxxxx"), avg_len(res)==3: 77.225K ops/sec trie.values(prefix="xxxxxxxx"), avg_len(res)==3: 320.755K ops/sec trie.items(prefix="xxxxx..xx"), avg_len(res)==1.4: 173.591K ops/sec trie.keys(prefix="xxxxx..xx"), avg_len(res)==1.4: 180.678K ops/sec trie.values(prefix="xxxxx..xx"), avg_len(res)==1.4: 503.392K ops/sec trie.items(prefix="xxx"), NON_EXISTING: 2023.647K ops/sec trie.keys(prefix="xxx"), NON_EXISTING: 1976.928K ops/sec trie.values(prefix="xxx"), NON_EXISTING: 2060.372K 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): 6.497M ops/sec trie __setitem__ (updates): 2.633M ops/sec dict __setitem__ (inserts, random): 5.808M ops/sec trie __setitem__ (inserts, random): 0.053M ops/sec dict __setitem__ (inserts, sorted): 5.749M ops/sec trie __setitem__ (inserts, sorted): 0.624M ops/sec dict setdefault (updates): 3.455M ops/sec trie setdefault (updates): 1.910M ops/sec dict setdefault (inserts): 3.466M ops/sec trie setdefault (inserts): 0.053M ops/sec
Other results (note that len(trie) is currently implemented
using trie traversal):
dict __contains__ (hits): 6.801M ops/sec trie __contains__ (hits): 2.816M ops/sec dict __contains__ (misses): 5.470M ops/sec trie __contains__ (misses): 4.224M ops/sec dict __len__: 334336.269 ops/sec trie __len__: 22.900 ops/sec dict values(): 406.507 ops/sec trie values(): 20.864 ops/sec dict keys(): 189.298 ops/sec trie keys(): 2.773 ops/sec dict items(): 48.734 ops/sec trie items(): 2.611 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.
- 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.
Development happens at github and bitbucket:
The main issue tracker is at github.
Feel free to submit ideas, bugs, pull requests (git or hg) or regular patches.
Make sure tox is installed and run
$ tox
from the source checkout. Tests should pass under Python 2.7 and 3.4+.
$ tox -c tox-bench.ini
runs benchmarks.
If you've changed anything in the source code then make sure cython is installed and run
$ update_c.sh
before each tox command.
Please note that benchmarks are not included in the release tar.gz's because benchmark data is large and this saves a lot of bandwidth; use source checkouts from github or bitbucket for the benchmarks.
- Mikhail Korobov <kmike84@gmail.com>
- Jared Suttles
- Gabi Davar
- Ahmed T. Youssef
This module is based on libdatrie C library by Theppitak Karoonboonyanan and is inspired by fast_trie Ruby bindings, PyTrie pure Python implementation and Tree::Trie Perl implementation; some docs and API ideas are borrowed from these projects.
Licensed under LGPL v2.1.