Super-fast, efficiently stored Trie for Python
Project description
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 lower-case ascii letters:
>>> import string >>> import datrie >>> trie = datrie.new(string.ascii_lowercase)
trie variable is a dict-like object that can have unicode keys of certain ranges and integer 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'] = 20
Check if u’foo’ is in trie:
>>> u'foo' in trie True
Get a value:
>>> trie[u'foo'] 5
Save a trie to disk:
>>> trie.save('my.trie')
Load a trie:
>>> trie2 = datrie.load('my.trie')
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', 20)] >>> trie.values(u'foob') [10]
Performance
Performance is measured against Python’s dict with 100k unique unicode words (English and Russian) as keys and ‘1’ numbers as values.
datrie.Trie uses about 4.6M 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
Looking for prefixes of a given word is almost as fast as __getitem__ (results are for Python 3.2, they are even faster under Python 2.x on my machine):
trie.iter_prefix_items (hits): 0.697M ops/sec trie.prefix_items (hits): 0.856M ops/sec trie.prefix_items loop (hits): 0.708M ops/sec trie.iter_prefixes (hits): 0.854M ops/sec trie.iter_prefixes (misses): 1.585M ops/sec trie.iter_prefixes (mixed): 1.463M ops/sec trie.has_keys_with_prefix (hits): 1.896M ops/sec trie.has_keys_with_prefix (misses): 2.623M ops/sec trie.longest_prefix (hits): 1.788M ops/sec trie.longest_prefix (misses): 1.552M ops/sec trie.longest_prefix (mixed): 1.642M 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: 0.699K ops/sec trie.keys(prefix="xxx"), avg_len(res)==415: 0.708K ops/sec trie.values(prefix="xxx"), avg_len(res)==415: 2.165K ops/sec trie.items(prefix="xxxxx"), avg_len(res)==17: 16.227K ops/sec trie.keys(prefix="xxxxx"), avg_len(res)==17: 16.434K ops/sec trie.values(prefix="xxxxx"), avg_len(res)==17: 45.806K ops/sec trie.items(prefix="xxxxxxxx"), avg_len(res)==3: 74.912K ops/sec trie.keys(prefix="xxxxxxxx"), avg_len(res)==3: 73.857K ops/sec trie.values(prefix="xxxxxxxx"), avg_len(res)==3: 170.833K ops/sec trie.items(prefix="xxxxx..xx"), avg_len(res)==1.4: 124.003K ops/sec trie.keys(prefix="xxxxx..xx"), avg_len(res)==1.4: 124.709K ops/sec trie.values(prefix="xxxxx..xx"), avg_len(res)==1.4: 210.586K ops/sec trie.items(prefix="xxx"), NON_EXISTING: 1779.258K ops/sec trie.keys(prefix="xxx"), NON_EXISTING: 1827.053K ops/sec trie.values(prefix="xxx"), NON_EXISTING: 1793.204K 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);
values must be integers 0 <= x <= 2147483647;
insertion time is not benchmarked and optimized (but it shouldn’t be slow);
it doesn’t work under pypy+MacOS X (some obscure error);
the library doesn’t compile under Windows + MSVC2008 because of missing <stdint.h> header.
Contributing
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.
Running tests and benchmarks
Make sure tox is installed and run
$ tox
from the source checkout. Tests should pass under python 2.6, 2.7 and 3.2.
$ 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.
License
Licensed under LGPL v3.
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