我想用python创建一个redis缓存,作为一个自尊的科学家,我做了一个基准来测试性能。
有趣的是,redis的表现并不那么好。要么是Python在做一些神奇的事情(存储文件),要么是我的redis版本太慢了。
我不知道这是不是因为我的代码的结构,或者什么,但是我希望redis比它做得更好。
为了创建redis缓存,我将二进制数据(在本例中是HTML页面)设置为从文件名派生的密钥,有效期为5分钟。
在所有情况下,文件处理都是用f.read()完成的(这比f.readlines()快约3倍,我需要二进制blob)。
在我的比较中有没有遗漏什么,或者Redis真的不适合磁盘?Python是否在某个地方缓存文件,并且每次都重新访问它?为什么这比访问redis要快得多?
我在64位的Ubuntu系统上使用redis 2.8、python2.7和redis py。
我不认为Python做了什么特别神奇的事情,因为我制作了一个函数,它将文件数据存储在Python对象中,并永远生成它。
我对四个函数调用进行了分组:
读取文件X次
一个函数,用于查看redis对象是否仍在内存中、加载它或缓存新文件(单个和多个redis实例)。
一个创建生成器的函数,该生成器从redis数据库(包含单个和多个redis实例)生成结果。
最后,将文件存储在内存中,并将其永久输出。
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)
现在结果是:
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)
结果很有趣,a)生成器每次都比调用函数快,b)redis比从磁盘读取慢,c)从python对象读取快得离谱。
为什么从磁盘读取要比从redis读取内存文件快得多?
编辑: 更多的信息和测试。
我将函数替换为
data = r.get(fpKey)
if data:
return r.get(fpKey)
结果与
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)
在每个函数调用上创建一个新的redis实例实际上对读取速度没有明显的影响,从测试到测试的可变性大于增益。
Sripathi Krishnan建议实现随机文件读取。从这些结果中我们可以看到,缓存开始真正起到帮助作用。
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)
文件读取中存在大量的变化,因此百分比差异不是一个很好的加速指标。
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)
我使用random.choice(fileList)在每次传递函数时随机选择一个新文件。
如果有人想试试的话,全部要点就在这里-https://gist.github.com/3885957
编辑编辑: 没有意识到我正在为生成器调用一个文件(尽管函数调用和生成器的性能非常相似)。这也是来自生成器的不同文件的结果。
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)
这是一个苹果对桔子的比较。 见http://redis.io/topics/benchmarks
Redis是一种高效的远程数据存储。每次在Redis上执行命令时,都会向Redis服务器发送一条消息,如果客户端是同步的,则会阻止等待回复。因此,除了命令本身的成本之外,您还将支付网络往返或IPC的费用。
在现代硬件上,与其他操作相比,网络往返或ipc的成本高得惊人。这是由以下几个因素造成的:
现在,让我们回顾一下结果。
比较使用生成器和使用函数调用的实现,它们生成的Redis往返次数不同。使用发电机,您只需:
每次迭代一次往返。使用该功能,您可以:
每次迭代2次往返。难怪发电机更快。
当然,您应该重用相同的Redis连接以获得最佳性能。没有必要运行系统连接/断开连接的基准。
最后,关于Redis调用和文件读取之间的性能差异,您只需将本地调用与远程调用进行比较。文件读取由OS文件系统缓存,因此它们是内核和Python之间的快速内存传输操作。这里不涉及磁盘I/O。使用Redis,您必须支付往返的费用,因此速度要慢得多。
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