The goal of GetPy is to provide the highest performance python dict/set that integrates into the python scientific ecosystem.
pip install getpy
Note only a linux build is currently distributed. If you would like to build the package from source you can clone the repo and run python setup.py install. Compilation will require 16gb of ram. I am working on getting that down.
GetPy is a thin binding to the Parallel Hashmap (https://github.com/greg7mdp/parallel-hashmap.git) which is the current state of the art unordered map/set with minimal memory overhead and fast runtime speed. The binding layer is supported by PyBind11 (https://github.com/pybind/pybind11.git) which is fast to compile and simple to extend.
The gp.Dict and gp.Set objects are designed to maintain a similar interface to the corresponding standard python objects. There are some key differences though, which are necessary for vectorization and other performance considerations.
gp.Dict.__init__ has three arguments key_type, value_type, and default_value. The type arguments are define which compiled data structure will be used under the hood, and the full list of preset combinations of np.dtypes is found with gp.dict_types. You can also specify a default_value at construction which must be castable to the value_type. This is the value returned by the dictionary if a key is not found.
All of getpy.Dict methods support a vectorized interface. Therefore, methods like gp.Dict.__getitem__, gp.Dict.__setitem__, and gp.Dict.__delitem__ can be performed with an np.ndarray. That allows the performance critical for-loop to happen within the compiled c++. Note that some dunder methods cannot be vectorized such as __contains__. Therefore, some keywords like in do not behave as expected. Those methods are renamed without the double underscores to note their deviation from the standard interface.
If a key does not exist, gp.Dict.__getitem__ will return the default_value. If you do not specify the default_value, it will default to the default constructor of your data type (all 0 bits). If you would like to know the difference between a key that does not exist and a key that returns the default value, you should first run gp.contains on your key/array of keys, and then retrieve values corresponding to keys that exist.
There is also a gp.MultiDict object. This object stores multiple unique values per key.
import numpy as np
import getpy as gp
key_type = np.dtype('u8')
value_type = np.dtype('u8')
keys = np.random.randint(1, 1000, size=10**2, dtype=key_type)
values = np.random.randint(1, 1000, size=10**2, dtype=value_type)
gp_dict = gp.Dict(key_type, value_type)
gp_dict[keys] = values
import numpy as np
import getpy as gp
key_type = np.dtype('u8')
value_type = np.dtype('u8')
keys = np.random.randint(1, 1000, size=10**2, dtype=key_type)
values = np.random.randint(1, 1000, size=10**2, dtype=value_type)
gp_dict = gp.Dict(key_type, value_type, default_value=42)
gp_dict[keys] = values
random_keys = np.random.randint(1, 1000, size=500, dtype=key_type)
random_values = gp_dict[random_keys]
import numpy as np
import getpy as gp
key_type = np.dtype('S8')
value_type = np.dtype('S8')
keys = np.array([np.random.bytes(8) for i in range(10**2)], dtype=key_type)
values = np.array([np.random.bytes(8) for i in range(10**2)], dtype=value_type)
gp_dict = gp.Dict(key_type, value_type)
gp_dict[keys] = values
import numpy as np
import getpy as gp
key_type = np.dtype('u8')
value_type = np.dtype('u8')
keys = np.random.randint(1, 1000, size=10**2, dtype=key_type).reshape(10,10)
values = np.random.randint(1, 1000, size=10**2, dtype=value_type).reshape(10,10)
gp_dict = gp.Dict(key_type, value_type)
gp_dict[keys] = values
import numpy as np
import getpy as gp
key_type = np.dtype('u8')
value_type = np.dtype('u8')
keys = np.random.randint(1, 1000, size=10**2, dtype=np.dtype('u2')).reshape(25,4).view(key_type)
values = np.random.randint(1, 1000, size=(10**2)/2, dtype=np.dtype('u4')).reshape(25,2).view(value_type)
gp_dict = gp.Dict(key_type, value_type)
gp_dict[keys] = values
unpacked_values = gp_dict[keys].view(np.dtype('u4'))
import numpy as np
import getpy as gp
key_type = np.dtype('u8')
value_type = np.dtype('u8')
keys = np.random.randint(1, 1000, size=10**1, dtype=key_type)
values = np.random.randint(1, 1000, size=10**1, dtype=value_type)
gp_dict_1 = gp.Dict(key_type, value_type)
gp_dict_1[keys] = values
gp_dict_1.dump('test/test.hashtable.bin')
gp_dict_2 = gp.Dict(key_type, value_type)
gp_dict_2.load('test/test.hashtable.bin')
dict_types = {
(np.dtype('u4'), np.dtype('u1')) : _gp.Dict_u4_u1,
(np.dtype('u4'), np.dtype('u2')) : _gp.Dict_u4_u2,
(np.dtype('u4'), np.dtype('u4')) : _gp.Dict_u4_u4,
(np.dtype('u4'), np.dtype('u8')) : _gp.Dict_u4_u8,
(np.dtype('u4'), np.dtype('i1')) : _gp.Dict_u4_i1,
(np.dtype('u4'), np.dtype('i2')) : _gp.Dict_u4_i2,
(np.dtype('u4'), np.dtype('i4')) : _gp.Dict_u4_i4,
(np.dtype('u4'), np.dtype('i8')) : _gp.Dict_u4_i8,
(np.dtype('u4'), np.dtype('f4')) : _gp.Dict_u4_f4,
(np.dtype('u4'), np.dtype('f8')) : _gp.Dict_u4_f8,
(np.dtype('u4'), np.dtype('S8')) : _gp.Dict_u4_S8,
(np.dtype('u4'), np.dtype('S16')) : _gp.Dict_u4_S16,
(np.dtype('u8'), np.dtype('u1')) : _gp.Dict_u8_u1,
(np.dtype('u8'), np.dtype('u2')) : _gp.Dict_u8_u2,
(np.dtype('u8'), np.dtype('u4')) : _gp.Dict_u8_u4,
(np.dtype('u8'), np.dtype('u8')) : _gp.Dict_u8_u8,
(np.dtype('u8'), np.dtype('i1')) : _gp.Dict_u8_i1,
(np.dtype('u8'), np.dtype('i2')) : _gp.Dict_u8_i2,
(np.dtype('u8'), np.dtype('i4')) : _gp.Dict_u8_i4,
(np.dtype('u8'), np.dtype('i8')) : _gp.Dict_u8_i8,
(np.dtype('u8'), np.dtype('f4')) : _gp.Dict_u8_f4,
(np.dtype('u8'), np.dtype('f8')) : _gp.Dict_u8_f8,
(np.dtype('u8'), np.dtype('S8')) : _gp.Dict_u8_S8,
(np.dtype('u8'), np.dtype('S16')) : _gp.Dict_u8_S16,
(np.dtype('i4'), np.dtype('u1')) : _gp.Dict_i4_u1,
(np.dtype('i4'), np.dtype('u2')) : _gp.Dict_i4_u2,
(np.dtype('i4'), np.dtype('u4')) : _gp.Dict_i4_u4,
(np.dtype('i4'), np.dtype('u8')) : _gp.Dict_i4_u8,
(np.dtype('i4'), np.dtype('i1')) : _gp.Dict_i4_i1,
(np.dtype('i4'), np.dtype('i2')) : _gp.Dict_i4_i2,
(np.dtype('i4'), np.dtype('i4')) : _gp.Dict_i4_i4,
(np.dtype('i4'), np.dtype('i8')) : _gp.Dict_i4_i8,
(np.dtype('i4'), np.dtype('f4')) : _gp.Dict_i4_f4,
(np.dtype('i4'), np.dtype('f8')) : _gp.Dict_i4_f8,
(np.dtype('i4'), np.dtype('S8')) : _gp.Dict_i4_S8,
(np.dtype('i4'), np.dtype('S16')) : _gp.Dict_i4_S16,
(np.dtype('i8'), np.dtype('u1')) : _gp.Dict_i8_u1,
(np.dtype('i8'), np.dtype('u2')) : _gp.Dict_i8_u2,
(np.dtype('i8'), np.dtype('u4')) : _gp.Dict_i8_u4,
(np.dtype('i8'), np.dtype('u8')) : _gp.Dict_i8_u8,
(np.dtype('i8'), np.dtype('i1')) : _gp.Dict_i8_i1,
(np.dtype('i8'), np.dtype('i2')) : _gp.Dict_i8_i2,
(np.dtype('i8'), np.dtype('i4')) : _gp.Dict_i8_i4,
(np.dtype('i8'), np.dtype('i8')) : _gp.Dict_i8_i8,
(np.dtype('i8'), np.dtype('f4')) : _gp.Dict_i8_f4,
(np.dtype('i8'), np.dtype('f8')) : _gp.Dict_i8_f8,
(np.dtype('i8'), np.dtype('S8')) : _gp.Dict_i8_S8,
(np.dtype('i8'), np.dtype('S16')) : _gp.Dict_i8_S16,
(np.dtype('S8'), np.dtype('u1')) : _gp.Dict_S8_u1,
(np.dtype('S8'), np.dtype('u2')) : _gp.Dict_S8_u2,
(np.dtype('S8'), np.dtype('u4')) : _gp.Dict_S8_u4,
(np.dtype('S8'), np.dtype('u8')) : _gp.Dict_S8_u8,
(np.dtype('S8'), np.dtype('i1')) : _gp.Dict_S8_i1,
(np.dtype('S8'), np.dtype('i2')) : _gp.Dict_S8_i2,
(np.dtype('S8'), np.dtype('i4')) : _gp.Dict_S8_i4,
(np.dtype('S8'), np.dtype('i8')) : _gp.Dict_S8_i8,
(np.dtype('S8'), np.dtype('f4')) : _gp.Dict_S8_f4,
(np.dtype('S8'), np.dtype('f8')) : _gp.Dict_S8_f8,
(np.dtype('S8'), np.dtype('S8')) : _gp.Dict_S8_S8,
(np.dtype('S8'), np.dtype('S16')) : _gp.Dict_S8_S16,
(np.dtype('S16'), np.dtype('u1')) : _gp.Dict_S16_u1,
(np.dtype('S16'), np.dtype('u2')) : _gp.Dict_S16_u2,
(np.dtype('S16'), np.dtype('u4')) : _gp.Dict_S16_u4,
(np.dtype('S16'), np.dtype('u8')) : _gp.Dict_S16_u8,
(np.dtype('S16'), np.dtype('i1')) : _gp.Dict_S16_i1,
(np.dtype('S16'), np.dtype('i2')) : _gp.Dict_S16_i2,
(np.dtype('S16'), np.dtype('i4')) : _gp.Dict_S16_i4,
(np.dtype('S16'), np.dtype('i8')) : _gp.Dict_S16_i8,
(np.dtype('S16'), np.dtype('f4')) : _gp.Dict_S16_f4,
(np.dtype('S16'), np.dtype('f8')) : _gp.Dict_S16_f8,
(np.dtype('S16'), np.dtype('S8')) : _gp.Dict_S16_S8,
(np.dtype('S16'), np.dtype('S16')) : _gp.Dict_S16_S16,
}
set_types = {
np.dtype('u4') : _gp.Set_u4,
np.dtype('u8') : _gp.Set_u8,
np.dtype('i4') : _gp.Set_i4,
np.dtype('i8') : _gp.Set_i8,
np.dtype('S8') : _gp.Set_S8,
np.dtype('S16') : _gp.Set_S16,
}
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