我想在Python中创建一个Redis的缓存，以及任何有自尊的科学家我做了一个基准点来测试性能。
有趣的是，Redis的没那么幸运。 无论是Python的是做一些魔术（存储文件）或我的Redis的版本是非常多产缓慢。
我不知道这是因为我的代码是结构化的方式，或者是什么，但我期待的Redis做的比它更好。
为了使Redis的缓存，设置我的二进制数据（在这种情况下，HTML页）从文件名派生用5分钟的过期的关键。
在所有情况下，文件处理与f.read（）来完成（这是3倍〜比f.readlines（）快了，我需要二进制BLOB）。
是否有什么我失踪在我的比较，或者是Redis的实在敌不过盘？ 是Python的缓存文件的地方，每一次reaccessing呢？ 为什么是这样的速度远远超过获得的Redis？
我使用Redis的2.8，Python 2.7版和Redis的-PY，所有64位的Ubuntu系统上。
我不认为Python是做什么特别神奇，因为我做的是存储在一个Python对象的文件数据的功能，并产生了它永远。
我有四个函数调用，我分组：
读文件X倍
被调用，以查看是否redis的对象仍处于存储器中的功能，将其加载，或缓存新的文件（单个和多个redis的实例）。
创建一个发生器，产生从redis的数据库的结果（与Redis的的单和多实例）的函数。
最后，存储在内存中的文件，并永远得到它。
import redis
import time
def load_file(fp, fpKey, r, expiry):
with open(fp, "rb") as f:
data = f.read()
p = r.pipeline()
p.set(fpKey, data)
p.expire(fpKey, expiry)
p.execute()
return data
def cache_or_get_gen(fp, expiry=300, r=redis.Redis(db=5)):
fpKey = "cached:"+fp
while True:
yield load_file(fp, fpKey, r, expiry)
t = time.time()
while time.time() - t - expiry < 0:
yield r.get(fpKey)
def cache_or_get(fp, expiry=300, r=redis.Redis(db=5)):
fpKey = "cached:"+fp
if r.exists(fpKey):
return r.get(fpKey)
else:
with open(fp, "rb") as f:
data = f.read()
p = r.pipeline()
p.set(fpKey, data)
p.expire(fpKey, expiry)
p.execute()
return data
def mem_cache(fp):
with open(fp, "rb") as f:
data = f.readlines()
while True:
yield data
def stressTest(fp, trials = 10000):
# Read the file x number of times
a = time.time()
for x in range(trials):
with open(fp, "rb") as f:
data = f.read()
b = time.time()
readAvg = trials/(b-a)
# Generator version
# Read the file, cache it, read it with a new instance each time
a = time.time()
gen = cache_or_get_gen(fp)
for x in range(trials):
data = next(gen)
b = time.time()
cachedAvgGen = trials/(b-a)
# Read file, cache it, pass in redis instance each time
a = time.time()
r = redis.Redis(db=6)
gen = cache_or_get_gen(fp, r=r)
for x in range(trials):
data = next(gen)
b = time.time()
inCachedAvgGen = trials/(b-a)
# Non generator version
# Read the file, cache it, read it with a new instance each time
a = time.time()
for x in range(trials):
data = cache_or_get(fp)
b = time.time()
cachedAvg = trials/(b-a)
# Read file, cache it, pass in redis instance each time
a = time.time()
r = redis.Redis(db=6)
for x in range(trials):
data = cache_or_get(fp, r=r)
b = time.time()
inCachedAvg = trials/(b-a)
# Read file, cache it in python object
a = time.time()
for x in range(trials):
data = mem_cache(fp)
b = time.time()
memCachedAvg = trials/(b-a)
print "\n%s file reads: %.2f reads/second\n" %(trials, readAvg)
print "Yielding from generators for data:"
print "multi redis instance: %.2f reads/second (%.2f percent)" %(cachedAvgGen, (100*(cachedAvgGen-readAvg)/(readAvg)))
print "single redis instance: %.2f reads/second (%.2f percent)" %(inCachedAvgGen, (100*(inCachedAvgGen-readAvg)/(readAvg)))
print "Function calls to get data:"
print "multi redis instance: %.2f reads/second (%.2f percent)" %(cachedAvg, (100*(cachedAvg-readAvg)/(readAvg)))
print "single redis instance: %.2f reads/second (%.2f percent)" %(inCachedAvg, (100*(inCachedAvg-readAvg)/(readAvg)))
print "python cached object: %.2f reads/second (%.2f percent)" %(memCachedAvg, (100*(memCachedAvg-readAvg)/(readAvg)))
if __name__ == "__main__":
fileToRead = "templates/index.html"
stressTest(fileToRead)
而现在的结果：
10000 file reads: 30971.94 reads/second
Yielding from generators for data:
multi redis instance: 8489.28 reads/second (-72.59 percent)
single redis instance: 8801.73 reads/second (-71.58 percent)
Function calls to get data:
multi redis instance: 5396.81 reads/second (-82.58 percent)
single redis instance: 5419.19 reads/second (-82.50 percent)
python cached object: 1522765.03 reads/second (4816.60 percent)
结果是在一个有趣）发电机比调用函数每次，b）中的Redis比从磁盘读取速度较慢，和c）从Python对象读数是可笑快快。
从磁盘读取，为什么会有那么多的速度比从Redis的内存中的文件中读取？
编辑：一些更多的信息和测试。
我取代了功能
data = r.get(fpKey)
if data:
return r.get(fpKey)
结果没有什么不同，从多
if r.exists(fpKey):
data = r.get(fpKey)
Function calls to get data using r.exists as test
multi redis instance: 5320.51 reads/second (-82.34 percent)
single redis instance: 5308.33 reads/second (-82.38 percent)
python cached object: 1494123.68 reads/second (5348.17 percent)
Function calls to get data using if data as test
multi redis instance: 8540.91 reads/second (-71.25 percent)
single redis instance: 7888.24 reads/second (-73.45 percent)
python cached object: 1520226.17 reads/second (5132.01 percent)
创建每个函数调用一个新的Redis实例却没有对阅读速度的影响noticable，从测试到测试的变异性比收益更大。
Sripathi克里希南建议实施的随机读取文件。 这是缓存开始真正的帮助，因为我们可以从这些结果中看到的。
Total number of files: 700
10000 file reads: 274.28 reads/second
Yielding from generators for data:
multi redis instance: 15393.30 reads/second (5512.32 percent)
single redis instance: 13228.62 reads/second (4723.09 percent)
Function calls to get data:
multi redis instance: 11213.54 reads/second (3988.40 percent)
single redis instance: 14420.15 reads/second (5157.52 percent)
python cached object: 607649.98 reads/second (221446.26 percent)
有变化的文件数量巨大读取这样的百分比差别不是加速的良好指标。
Total number of files: 700
40000 file reads: 1168.23 reads/second
Yielding from generators for data:
multi redis instance: 14900.80 reads/second (1175.50 percent)
single redis instance: 14318.28 reads/second (1125.64 percent)
Function calls to get data:
multi redis instance: 13563.36 reads/second (1061.02 percent)
single redis instance: 13486.05 reads/second (1054.40 percent)
python cached object: 587785.35 reads/second (50214.25 percent)
我用random.choice（的fileList）随机地选择一个新的文件在每个通过的功能。
完整的要点是这里如果有人想尝试一下- https://gist.github.com/3885957
编辑编辑：不知道，我呼吁生成一个单一的文件（尽管函数调用和发电机的性能是非常相似）。 下面是从生成不同文件的结果也是如此。
Total number of files: 700
10000 file reads: 284.48 reads/second
Yielding from generators for data:
single redis instance: 11627.56 reads/second (3987.36 percent)
Function calls to get data:
single redis instance: 14615.83 reads/second (5037.81 percent)
python cached object: 580285.56 reads/second (203884.21 percent)
