从python中的列表中获取哈希的最快方法

你可能会觉得这很有用.它使用我自己的自定义基准测试框架(基于timeit)来收集和打印结果.由于速度的变化主要是由于需要将r列表转换为hashlib.md5()可以使用的内容,我已经更新了测试用例套件,以显示如何将值存储在一个array.array,如@DSM在评论中建议的那样,大大加快了速度.请注意,由于列表中的整数都相对较小,因此我将它们存储在一个短(2字节)值的数组中.

from __future__ import print_function
import sys
import timeit

setup = """
import array
import random
import hashlib
import marshal
import cPickle as pickle
import struct

r = [random.randrange(1, 1000) for _ in range(0, 1000000)]
ra = array.array('h', r)   # create an array of shorts equivalent

def method1(r):
    p = pickle.dumps(r, -1)
    return hashlib.md5(p).hexdigest()

def method2(r):
    p = str(r)
    return hashlib.md5(p).hexdigest()

def method3(r):
    p = ','.join(map(str, r))
    return hashlib.md5(p).hexdigest()

def method4(r):
    fmt = '%dh' % len(r)
    buf = struct.pack(fmt, *r)
    return hashlib.md5(buf).hexdigest()

def method5(r):
    a = array.array('h', r)
    return hashlib.md5(a).hexdigest()

def method6(r):
    m = marshal.dumps(r)
    return hashlib.md5(m).hexdigest()

# using pre-built array...
def pb_method1(ra):
    p = pickle.dumps(ra, -1)
    return hashlib.md5(p).hexdigest()

def pb_method2(ra):
    p = str(ra)
    return hashlib.md5(p).hexdigest()

def pb_method3(ra):
    p = ','.join(map(str, ra))
    return hashlib.md5(p).hexdigest()

def pb_method4(ra):
    fmt = '%dh' % len(ra)
    buf = struct.pack(fmt, *ra)
    return hashlib.md5(buf).hexdigest()

def pb_method5(ra):
    return hashlib.md5(ra).hexdigest()

def pb_method6(ra):
    m = marshal.dumps(ra)
    return hashlib.md5(m).hexdigest()
"""

statements = {
    "pickle.dumps(r, -1)": """
        method1(r)
    """,
    "str(r)": """
        method2(r)
    """,
    "','.join(map(str, r))": """
        method3(r)
    """,
    "struct.pack(fmt, *r)": """
        method4(r)
    """,
    "array.array('h', r)": """
        method5(r)
    """,
    "marshal.dumps(r)": """
        method6(r)
    """,
# versions using pre-built array...
    "pickle.dumps(ra, -1)": """
        pb_method1(ra)
    """,
    "str(ra)": """
        pb_method2(ra)
    """,
    "','.join(map(str, ra))": """
        pb_method3(ra)
    """,
    "struct.pack(fmt, *ra)": """
        pb_method4(ra)
    """,
    "ra (pre-built)": """
        pb_method5(ra)
    """,
    "marshal.dumps(ra)": """
        pb_method6(ra)
    """,
}

N = 10
R = 3

timings = [(
    idea,
    min(timeit.repeat(statements[idea], setup=setup, repeat=R, number=N)),
    ) for idea in statements]

longest = max(len(t[0]) for t in timings)  # length of longest name

print('fastest to slowest timings (Python {}.{}.{})\n'.format(*sys.version_info[:3]),
      '  ({:,d} calls, best of {:d})\n'.format(N, R))

ranked = sorted(timings, key=lambda t: t[1])  # sort by speed (fastest first)
for timing in ranked:
    print("{:>{width}} : {:.6f} secs, rel speed {rel:>8.6f}x".format(
          timing[0], timing[1], rel=timing[1]/ranked[0][1], width=longest))

结果:

fastest to slowest timings (Python 2.7.6)
   (10 calls, best of 3)

        ra (pre-built) : 0.037906 secs, rel speed 1.000000x
     marshal.dumps(ra) : 0.177953 secs, rel speed 4.694626x
      marshal.dumps(r) : 0.695606 secs, rel speed 18.350932x
   pickle.dumps(r, -1) : 1.266096 secs, rel speed 33.401179x
   array.array('h', r) : 1.287884 secs, rel speed 33.975950x
  pickle.dumps(ra, -1) : 1.955048 secs, rel speed 51.576558x
  struct.pack(fmt, *r) : 2.085602 secs, rel speed 55.020743x
 struct.pack(fmt, *ra) : 2.357887 secs, rel speed 62.203962x
                str(r) : 2.918623 secs, rel speed 76.996860x
               str(ra) : 3.686666 secs, rel speed 97.258777x
 ','.join(map(str, r)) : 4.701531 secs, rel speed 124.032173x
','.join(map(str, ra)) : 4.968734 secs, rel speed 131.081303x