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DAWG documentation

This package provides DAWG(DAFSA)-based dictionary-like read-only objects for Python (2.x and 3.x).

String data in a DAWG may take 200x less memory than in a standard Python dict and the raw lookup speed is comparable; it also provides fast advanced methods like prefix search.

Based on dawgdic C++ library.

License

Wrapper code is licensed under MIT License. Bundled dawgdic C++ library is licensed under BSD license. Bundled libb64 is Public Domain.

Installation

From PyPI:

pip install DAWG

Usage

There are several DAWG classes in this package:

  • dawg.DAWG - basic DAWG wrapper; it can store unicode keys and do exact lookups;
  • dawg.CompletionDAWG - dawg.DAWG subclass that supports key completion and prefix lookups (but requires more memory);
  • dawg.BytesDAWG - dawg.CompletionDAWG subclass that maps unicode keys to lists of bytes objects.
  • dawg.RecordDAWG - dawg.BytesDAWG subclass that maps unicode keys to lists of data tuples. All tuples must be of the same format (the data is packed using python struct module).
  • dawg.IntDAWG - dawg.DAWG subclass that maps unicode keys to integer values.
  • dawg.IntCompletionDAWG - dawg.CompletionDAWG subclass that maps unicode keys to integer values.

DAWG and CompletionDAWG

DAWG and CompletionDAWG are useful when you need fast & memory efficient simple string storage. These classes does not support assigning values to keys.

DAWG and CompletionDAWG constructors accept an iterable with keys:

>>> import dawg
>>> words = [u'foo', u'bar', u'foobar', u'foö', u'bör']
>>> base_dawg = dawg.DAWG(words)
>>> completion_dawg = dawg.CompletionDAWG(words)

It is then possible to check if the key is in a DAWG:

>>> u'foo' in base_dawg
True
>>> u'baz' in completion_dawg
False

It is possible to find all keys that starts with a given prefix in a CompletionDAWG:

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

to test whether some key begins with a given prefix:

>>> completion_dawg.has_keys_with_prefix(u'foo')
>>> True

and to find all prefixes of a given key:

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

Iterator versions are also available:

>>> for key in completion_dawg.iterkeys(u'foo'):
...     print(key)
foo
foobar
>>> for prefix in base_dawg.iterprefixes(u'foobarz'):
...     print(prefix)
foo
foobar

It is possible to find all keys similar to a given key (using a one-way char translation table):

>>> replaces = dawg.DAWG.compile_replaces({u'o': u'ö'})
>>> base_dawg.similar_keys(u'foo', replaces)
[u'foo', u'foö']
>>> base_dawg.similar_keys(u'foö', replaces)
[u'foö']
>>> base_dawg.similar_keys(u'bor', replaces)
[u'bör']

BytesDAWG

BytesDAWG is a CompletionDAWG subclass that can store binary data for each key.

BytesDAWG constructor accepts an iterable with (unicode_key, bytes_value) tuples:

>>> data = [(u'key1', b'value1'), (u'key2', b'value2'), (u'key1', b'value3')]
>>> bytes_dawg = dawg.BytesDAWG(data)

There can be duplicate keys; all unique values are stored in this case:

>>> bytes_dawg[u'key1']
[b'value1, b'value3']

For unique keys a list with a single value is returned for consistency:

>>> bytes_dawg[u'key2']
[b'value2']

KeyError is raised for missing keys; use get method if you need a default value instead:

>>> bytes_dawg.get(u'foo', None)
None

BytesDAWG support keys, items, iterkeys and iteritems methods (they all accept optional key prefix). There is also support for similar_keys, similar_items and similar_item_values methods.

RecordDAWG

RecordDAWG is a BytesDAWG subclass that automatically packs & unpacks the binary data from/to Python objects using struct module from the standard library.

First, you have to define a format of the data. Consult Python docs (http://docs.python.org/library/struct.html#format-strings) for the format string specification.

For example, let's store 3 short unsigned numbers (in a Big-Endian byte order) as values:

>>> format = ">HHH"

RecordDAWG constructor accepts an iterable with (unicode_key, value_tuple). Let's create such iterable using zip function:

>>> keys = [u'foo', u'bar', u'foobar', u'foo']
>>> values = [(1, 2, 3), (2, 1, 0), (3, 3, 3), (2, 1, 5)]
>>> data = zip(keys, values)
>>> record_dawg = RecordDAWG(format, data)

As with BytesDAWG, there can be several values for the same key:

>>> record_dawg['foo']
[(1, 2, 3), (2, 1, 5)]
>>> record_dawg['foobar']
[(3, 3, 3)]

BytesDAWG and RecordDAWG implementation details

BytesDAWG and RecordDAWG stores data at the end of the keys:

<utf8-encoded key><separator><base64-encoded data>

Data is encoded to base64 because dawgdic C++ library doesn't allow zero bytes in keys (it uses null-terminated strings) and such keys are very likely in binary data.

In DAWG versions prior to 0.5 <separator> was chr(255) byte. It was chosen because keys are stored as UTF8-encoded strings and chr(255) is guaranteed not to appear in valid UTF8, so the end of text part of the key is not ambiguous.

But chr(255) was proven to be problematic: it changes the order of the keys. Keys are naturally returned in lexicographical order by DAWG. But if chr(255) appears at the end of each text part of a key then the visible order would change. Imagine 'foo' key with some payload and 'foobar' key with some payload. 'foo' key would be greater than 'foobar' key: values compared would be 'foo<sep>' and 'foobar<sep>' and ord(<sep>)==255 is greater than ord(<any other character>).

So now the default <separator> is chr(1). This is the lowest allowed character and so it preserves the alphabetical order.

It is not strictly correct to use chr(1) as a separator because chr(1) is a valid UTF8 character. But I think in practice this won't be an issue: such control character is very unlikely in text keys, and binary keys are not supported anyway because dawgdic doesn't support keys containing chr(0).

If you can't guarantee chr(1) is not a part of keys, lexicographical order is not important to you or there is a need to read a BytesDAWG/RecordDAWG created by DAWG < 0.5 then pass payload_separator argument to the constructor:

>>> BytesDAWG(payload_separator=b'\xff').load('old.dawg')

The storage scheme has one more implication: values of BytesDAWG and RecordDAWG are also sorted lexicographically.

For RecordDAWG there is a gotcha: in order to have meaningful ordering of numeric values store them in big-endian format:

>>> data = [('foo', (3, 2, 256)), ('foo', (3, 2, 1)), ('foo', (3, 2, 3))]
>>> d = RecordDAWG("3H", data)
>>> d.items()
[(u'foo', (3, 2, 256)), (u'foo', (3, 2, 1)), (u'foo', (3, 2, 3))]

>>> d2 = RecordDAWG(">3H", data)
>>> d2.items()
[(u'foo', (3, 2, 1)), (u'foo', (3, 2, 3)), (u'foo', (3, 2, 256))]

IntDAWG and IntCompletionDAWG

IntDAWG is a {unicode -> int} mapping. It is possible to use RecordDAWG for this, but IntDAWG is natively supported by dawgdic C++ library and so __getitem__ is much faster.

Unlike BytesDAWG and RecordDAWG, IntDAWG doesn't support having several values for the same key.

IntDAWG constructor accepts an iterable with (unicode_key, integer_value) tuples:

>>> data = [ (u'foo', 1), (u'bar', 2) ]
>>> int_dawg = dawg.IntDAWG(data)

It is then possible to get a value from the IntDAWG:

>>> int_dawg[u'foo']
1

IntCompletionDAWG supports all IntDAWG and CompletionDAWG methods, plus .items() and .iteritems().

Persistence

All DAWGs support saving/loading and pickling/unpickling.

Write DAWG to a stream:

>>> with open('words.dawg', 'wb') as f:
...     d.write(f)

Save DAWG to a file:

>>> d.save('words.dawg')

Load DAWG from a file:

>>> d = dawg.DAWG()
>>> d.load('words.dawg')

Warning

Reading DAWGs from streams and unpickling are currently using 3x memory compared to loading DAWGs using load method; please avoid them until the issue is fixed.

Read DAWG from a stream:

>>> d = dawg.RecordDAWG(format_string)
>>> with open('words.record-dawg', 'rb') as f:
...     d.read(f)

DAWG objects are picklable:

>>> import pickle
>>> data = pickle.dumps(d)
>>> d2 = pickle.loads(data)

Benchmarks

For a list of 3000000 (3 million) Russian words memory consumption with different data structures (under Python 2.7):

  • dict(unicode words -> word lengths): about 600M
  • list(unicode words) : about 300M
  • marisa_trie.RecordTrie : 11M
  • marisa_trie.Trie: 7M
  • dawg.DAWG: 2M
  • dawg.CompletionDAWG: 3M
  • dawg.IntDAWG: 2.7M
  • dawg.RecordDAWG: 4M

Note

Lengths of words were not stored as values in dawg.DAWG, dawg.CompletionDAWG and marisa_trie.Trie because they don't support this.

Note

marisa-trie is often more memory efficient than DAWG (depending on data); it can also handle larger datasets and provides memory-mapped IO, so don't dismiss marisa-trie based on this README file. It is still several times slower than DAWG though.

Benchmark results (100k unicode words, integer values (lengths of the words), Python 3.3, macbook air i5 1.8 Ghz):

dict __getitem__ (hits)           7.300M ops/sec
DAWG __getitem__ (hits)           not supported
BytesDAWG __getitem__ (hits)      1.230M ops/sec
RecordDAWG __getitem__ (hits)     0.792M ops/sec
IntDAWG __getitem__ (hits)        4.217M ops/sec
dict get() (hits)                 3.775M ops/sec
DAWG get() (hits)                 not supported
BytesDAWG get() (hits)            1.027M ops/sec
RecordDAWG get() (hits)           0.733M ops/sec
IntDAWG get() (hits)              3.162M ops/sec
dict get() (misses)               4.533M ops/sec
DAWG get() (misses)               not supported
BytesDAWG get() (misses)          3.545M ops/sec
RecordDAWG get() (misses)         3.485M ops/sec
IntDAWG get() (misses)            3.928M ops/sec

dict __contains__ (hits)          7.090M ops/sec
DAWG __contains__ (hits)          4.685M ops/sec
BytesDAWG __contains__ (hits)     3.885M ops/sec
RecordDAWG __contains__ (hits)    3.898M ops/sec
IntDAWG __contains__ (hits)       4.612M ops/sec

dict __contains__ (misses)        5.617M ops/sec
DAWG __contains__ (misses)        6.204M ops/sec
BytesDAWG __contains__ (misses)   6.026M ops/sec
RecordDAWG __contains__ (misses)  6.007M ops/sec
IntDAWG __contains__ (misses)     6.180M ops/sec

DAWG.similar_keys  (no replaces)  0.492M ops/sec
DAWG.similar_keys  (l33t)         0.413M ops/sec

dict items()                      55.032 ops/sec
DAWG items()                      not supported
BytesDAWG items()                 14.826 ops/sec
RecordDAWG items()                9.436 ops/sec
IntDAWG items()                   not supported

dict keys()                       200.788 ops/sec
DAWG keys()                       not supported
BytesDAWG keys()                  20.657 ops/sec
RecordDAWG keys()                 20.873 ops/sec
IntDAWG keys()                    not supported

DAWG.prefixes (hits)              1.552M ops/sec
DAWG.prefixes (mixed)             4.342M ops/sec
DAWG.prefixes (misses)            4.094M ops/sec
DAWG.iterprefixes (hits)          0.391M ops/sec
DAWG.iterprefixes (mixed)         0.476M ops/sec
DAWG.iterprefixes (misses)        0.498M ops/sec

RecordDAWG.keys(prefix="xxx"), avg_len(res)==415             5.562K ops/sec
RecordDAWG.keys(prefix="xxxxx"), avg_len(res)==17            104.011K ops/sec
RecordDAWG.keys(prefix="xxxxxxxx"), avg_len(res)==3          318.129K ops/sec
RecordDAWG.keys(prefix="xxxxx..xx"), avg_len(res)==1.4       462.238K ops/sec
RecordDAWG.keys(prefix="xxx"), NON_EXISTING                  4292.625K ops/sec

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

Current limitations

  • IntDAWG is currently a subclass of DAWG and so it doesn't support keys() and items() methods;
  • read() method reads the whole stream (DAWG must be the last or the only item in a stream if it is read with read() method) - pickling doesn't have this limitation;
  • DAWGs loaded with read() and unpickled DAWGs uses 3x-4x memory compared to DAWGs loaded with load() method;
  • there are keys() and items() methods but no values() method;
  • iterator versions of methods are not always implemented;
  • BytesDAWG and RecordDAWG has a limitation: values larger than 8KB are unsupported;
  • the maximum number of DAWG units is limited: number of DAWG units (and thus transitions - but not elements) should be less than 2^29; this mean that it may be impossible to build an especially huge DAWG (you may split your data into several DAWGs or try marisa-trie in this case).

Contributions are welcome!

Contributing

Development happens at github: https://github.com/pytries/DAWG

Issue tracker: https://github.com/pytries/DAWG/issues

Feel free to submit ideas, bugs or pull requests.

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

How is source code organized

There are 4 folders in repository:

  • bench - benchmarks & benchmark data;
  • lib - original unmodified dawgdic C++ library and a customized version of libb64 library. They are bundled for easier distribution; if something is have to be fixed in these libraries consider fixing it in the original repositories;
  • src - wrapper code; src/dawg.pyx is a wrapper implementation; src/*.pxd files are Cython headers for corresponding C++ headers; src/*.cpp files are the pre-built extension code and shouldn't be modified directly (they should be updated via update_cpp.sh script).
  • tests - the test suite.

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, 3.3 and 3.4.

In order to run benchmarks, type

$ tox -c bench.ini