How to speed up python instance initialization for millions of objects?

Question

I have defined a python class named Edge as follows:

class Edge:
    def __init__(self):
        self.node1 = 0
        self.node2 = 0
        self.weight = 0

Now I have to create approximately 10^6 to 10^7 instances of Edge using:

edges= []
for (i,j,w) in ijw:
    edge = Edge()
    edge.node1 = i
    edge.node2 = j
    edge.weight = w
    edges.append(edge)

I took me approximately 2 seconds in Desktop. Is there any faster way to do?

Answer 1

You can't make it much faster, but I certainly would use __slots__ to save on memory allocations. Also make it possible to pass in the attribute values when creating the instance:

class Edge:
    __slots__ = ('node1', 'node2', 'weight')
    def __init__(self, node1=0, node2=0, weight=0):
        self.node1 = node1
        self.node2 = node2
        self.weight = weight

With the updated __init__ you can use a list comprehension:

edges = [Edge(*args) for args in ijw]

Together these can shave off a decent amount of time creating the objects, roughly halve the time needed.

Comparison creating 1 million objects; the setup:

>>> from random import randrange
>>> ijw = [(randrange(100), randrange(100), randrange(1000)) for _ in range(10 ** 6)]
>>> class OrigEdge:
...     def __init__(self):
...         self.node1 = 0
...         self.node2 = 0
...         self.weight = 0
...
>>> origloop = '''\
... edges= []
... for (i,j,w) in ijw:
...     edge = Edge()
...     edge.node1 = i
...     edge.node2 = j
...     edge.weight = w
...     edges.append(edge)
... '''
>>> class SlotsEdge:
...     __slots__ = ('node1', 'node2', 'weight')
...     def __init__(self, node1=0, node2=0, weight=0):
...         self.node1 = node1
...         self.node2 = node2
...         self.weight = weight
...
>>> listcomploop = '''[Edge(*args) for args in ijw]'''

and the timings:

>>> from timeit import Timer
>>> count, total = Timer(origloop, 'from __main__ import OrigEdge as Edge, ijw').autorange()
>>> (total / count) * 1000 # milliseconds
722.1121070033405
>>> count, total = Timer(listcomploop, 'from __main__ import SlotsEdge as Edge, ijw').autorange()
>>> (total / count) * 1000 # milliseconds
386.6706900007557

That's nearly 2 times as fast.

Increasing the random input list to 10^7 items, and the timing difference holds:

>>> ijw = [(randrange(100), randrange(100), randrange(1000)) for _ in range(10 ** 7)]
>>> count, total = Timer(origloop, 'from __main__ import OrigEdge as Edge, ijw').autorange()
>>> (total / count)
7.183759553998243
>>> count, total = Timer(listcomploop, 'from __main__ import SlotsEdge as Edge, ijw').autorange()
>>> (total / count)
3.8709938440006226

Answer 2

There is another fast and memory saving method using recordclass library:

from recordclass import dataobject

from random import randrange
import sys
ijw = [(randrange(100), randrange(100), randrange(1000)) for _ in range(10 ** 7)]

class EdgeDO(dataobject):
    __fields__ = 'node1', 'node2', 'weight'

class EdgeSlots:
    __slots__ = 'node1', 'node2', 'weight'

    def __init__(self, node1, node2, weight):
         self.node1 = node1
         self.node2 = node2
         self.weight = weight
            
def list_size(lst):
    return sum(sys.getsizeof(o) for o in lst)

%time list_do = [EdgeDO(n1, n2, w) for n1, n2, w in ijw]
%time list_slots = [EdgeSlots(n1, n2, w) for n1, n2, w in ijw]

print('size (dataobject):', list_size(list_do))
print('size (__slots__): ', list_size(list_slots))

There is the output:

CPU times: user 2.23 s, sys: 20 ms, total: 2.25 s
Wall time: 2.25 s
CPU times: user 6.79 s, sys: 84.1 ms, total: 6.87 s
Wall time: 6.87 s
size (dataobject): 400000000
size (__slots__):  640000000

P.S. Since 0.15 there is option fast_new for faster instance creation. Below new performance counters for python 3.9, 64 bit.

from random import randrange import sys ijw = [(randrange(100), randrange(100), randrange(1000)) for _ in range(10 ** 7)]

class EdgeDO(dataobject, fast_new=True):
    __fields__ = 'node1', 'node2', 'weight'

class EdgeSlots:
    __slots__ = 'node1', 'node2', 'weight'

    def __init__(self, node1, node2, weight):
         self.node1 = node1
         self.node2 = node2
         self.weight = weight
        
def list_size(lst):
    return sum(sys.getsizeof(o) for o in lst)

print('dataobject timinig:')
%time list_do = [EdgeDO(*args) for args in ijw]
print('__slots__ timinig:')
%time list_slots = [EdgeSlots(*args) for args in ijw]

print('size (dataobject):', list_size(list_do))
print('size (__slots__): ', list_size(list_slots))
print(list_size(list_do)/list_size(list_slots)*100, "%")

Results:

dataobject timinig:
CPU times: user 804 ms, sys: 16 ms, total: 820 ms
Wall time: 819 ms
__slots__ timinig:
CPU times: user 5.54 s, sys: 23.9 ms, total: 5.56 s
Wall time: 5.56 s
size (dataobject): 400000000
size (__slots__):  560000000
71.42857142857143 %