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Python bindings to marisa-trie (unofficial)

Project description


Static memory-efficient Trie structures for Python (2.x and 3.x). Uses marisa-trie C++ library.

There are official SWIG-based Python bindings included in C++ library distribution; this package provides an alternative unofficial Cython-based pip-installable Python bindings.


pip install marisa-trie


There are several Trie classes in this package:

  • marisa_trie.Trie - read-only trie-based data structure that maps unicode keys to auto-generated unique IDs and supports exact and prefix lookups;
  • marisa_trie.RecordTrie - read-only trie-based data structure that maps unicode keys to lists of data tuples. All tuples must be of the same format (the data is packed with using python struct module). RecordTrie supports exact and prefix lookups.
  • marisa_trie.BytesTrie - read-only Trie that maps unicode keys to lists of bytes objects. BytesTrie supports exact and prefix lookups.


Create a new trie:

>>> import marisa_trie
>>> trie = marisa_trie.Trie([u'key1', u'key2', u'key12'])

Check if key is in trie:

>>> u'key1' in trie
>>> u'key20' in trie

Each key is assigned an unique ID from 0 to (n - 1), where n is the number of keys; you can use this ID to store a value in a separate structure (e.g. python list):

>>> trie.key_id(u'key2')

Key can be reconstructed from the ID:

>>> trie.restore_key(1)

Find all prefixes of a given key:

>>> trie.prefixes(u'key12')
[u'key1', u'key12']

There is also a generator version of .prefixes method called .iter_prefixes.

Find all keys from this trie that starts with a given prefix:

>> trie.keys(u'key1')
[u'key1', u'key12']

(iterator version .iterkeys(prefix) is also available).


Create a new trie:

>>> keys = [u'foo', u'bar', u'foobar', u'foo']
>>> values = [(1, 2), (2, 1), (3, 3), (2, 1)]
>>> fmt = "<HH"   # a tuple with 2 short integers
>>> trie = marisa_trie.RecordTrie(fmt, zip(keys, values))

Trie initial data must be an iterable of tuples (unicode_key, data_tuple). Data tuples will be converted to bytes with struct.pack(fmt, *data_tuple).

Take a look at for the format string specification.

Duplicate keys are allowed.

Check if key is in trie:

>>> u'foo' in trie
>>> u'spam' in trie

Get a values list:

>>> trie[u'bar']
[(2, 1)]
>>> trie[u'foo']
[(1, 2), (2, 1)]

Find all prefixes of a given key:

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

Find all keys from this trie that starts with a given prefix:

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

Find all items from this trie that starts with a given prefix:

>> trie.items(u'fo')
[(u'foo', (1, 2)), (u'foo', (2, 1), (u'foobar', (3, 3))]


Iterator version of .keys() and .items() are not implemented yet.


Trie objects supports saving/loading, pickling/unpickling and memory mapped I/O.

Write trie to a stream:

>>> with open('my_trie.marisa', 'w') as f:
...     trie.write(f)

Save trie to a file:


Read trie from stream:

>>> trie2 = marisa_trie.Trie()
>>> with open('my_trie.marisa', 'r') as f:

Load trie from file:

>>> trie2.load('my_trie.marisa')

Trie objects are picklable:

>>> import pickle
>>> data = pickle.dumps(trie)
>>> trie3 = pickle.loads(data)

You may also build a trie using marisa-build command-line utility (provided by underlying C++ library; it should be downloaded and compiled separately) and then load the trie from the resulting file using .load() method.

Memory mapped I/O

It is possible to use memory mapped file as data source:

>>> trie = marisa_trie.RecordTrie(fmt)
>>> trie.mmap('my_record_trie.marisa')

This way the whole dictionary won’t be loaded to memory; memory mapped I/O is an easy way to share dictionary data among processes.


Memory mapped trie might cause a lot of random disk accesses which considerably increase the search time.


My quick tests show that memory usage is quite decent. For a list of 3000000 (3 million) Russian words memory consumption with different data structures (under Python 2.7):

  • list(unicode words) : about 300M
  • BaseTrie from datrie library: about 70M
  • marisa_trie.RecordTrie : 11M
  • marisa_trie.Trie: 7M


Lengths of words were stored as values in datrie.BaseTrie and marisa_trie.RecordTrie. RecordTrie compresses similar values and the key compression is better so it uses much less memory than datrie.BaseTrie.

marisa_trie.Trie provides auto-assigned IDs. It is not possible to store arbitrary values in marisa_trie.Trie so it uses less memory than RecordTrie.

Benchmark results (100k unicode words, integer values (lenghts of the words), Python 3.2, macbook air i5 1.8 Ghz):

dict __getitem__ (hits):            4.090M ops/sec
Trie __getitem__ (hits):            not supported
BytesTrie __getitem__ (hits):       0.469M ops/sec
RecordTrie __getitem__ (hits):      0.373M ops/sec
dict __contains__ (hits):           4.036M ops/sec
Trie __contains__ (hits):           0.910M ops/sec
BytesTrie __contains__ (hits):      0.573M ops/sec
RecordTrie __contains__ (hits):     0.591M ops/sec
dict __contains__ (misses):         3.346M ops/sec
Trie __contains__ (misses):         1.643M ops/sec
BytesTrie __contains__ (misses):    0.976M ops/sec
RecordTrie __contains__ (misses):   1.017M ops/sec
dict items():                       58.316 ops/sec
Trie items():                       not supported
BytesTrie items():                  2.456 ops/sec
RecordTrie items():                 2.254 ops/sec
dict keys():                        211.194 ops/sec
Trie keys():                        3.341 ops/sec
BytesTrie keys():                   2.308 ops/sec
RecordTrie keys():                  2.184 ops/sec
Trie.prefixes (hits):               0.176M ops/sec
Trie.prefixes (mixed):              0.956M ops/sec
Trie.prefixes (misses):             1.035M ops/sec
RecordTrie.prefixes (hits):         0.106M ops/sec
RecordTrie.prefixes (mixed):        0.451M ops/sec
RecordTrie.prefixes (misses):       0.173M ops/sec
Trie.iter_prefixes (hits):          0.170M ops/sec
Trie.iter_prefixes (mixed):         0.799M ops/sec
Trie.iter_prefixes (misses):        0.898M ops/sec
Trie.keys(prefix="xxx"), avg_len(res)==415:         0.825K ops/sec
Trie.keys(prefix="xxxxx"), avg_len(res)==17:        19.934K ops/sec
Trie.keys(prefix="xxxxxxxx"), avg_len(res)==3:      85.239K ops/sec
Trie.keys(prefix="xxxxx..xx"), avg_len(res)==1.4:   136.476K ops/sec
Trie.keys(prefix="xxx"), NON_EXISTING:              1073.719K ops/sec

Tries from marisa_trie uses less memory, tries from datrie.Trie are faster.

Please take this benchmark results with a grain of salt; this is a very simple benchmark on a single data set.


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.

If you found a bug in a C++ part please report it to the original bug tracker.

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, 3.2 and 3.3.


At the moment of writing the latest pip release (1.1) does not support Python 3.3; in order to run tox tests under Python 3.3 find the “virtualenv_support” directory in site-packages (of the env you run tox from) and place an sdist zip/tarball of the newer pip (from github) there.

In order to run benchmarks, type

$ tox -c bench.ini

Authors & Contributors

This module is based on marisa-trie C++ library by Susumu Yata & contributors.


Wrapper code is licensed under MIT License. Bundled marisa-trie C++ library is licensed under BSD license.


0.3.1 (2012-08-23)

  • .prefixes() support for RecordTrie and BytesTrie.

0.3 (2012-08-23)

  • RecordTrie and BytesTrie are introduced;
  • IntTrie class is removed (probably temporary?);
  • dumps/loads methods are renamed to tobytes/frombytes;
  • benchmark & tests improvements;
  • support for MARISA-trie config options is added.

0.2 (2012-08-19)

  • Pickling/unpickling support;
  • dumps/loads methods;
  • python 3.3 workaround;
  • improved tests;
  • benchmarks.

0.1 (2012-08-17)

Initial release.

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