Redis vs Disk在缓存应用程序中的性能

Question

我想在python中创建一个redis缓存，作为任何自尊的科学家，我都做了一个基准测试性能。

有趣的是，redis的表现并不那么好。Python做一些不可思议的事情（存储文件），或者我的redis版本太慢了。

我不知道这是否是因为我的代码的结构方式或原因，但是我希望redis比它做得更好。

为了进行Redis缓存，我将二进制数据（在本例中为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，文件处理都是通过f.read（）完成的（这比f.readlines（）快约3倍，我需要二进制blob）。

我在比较中是否缺少某些东西，还是Redis确实与磁盘不匹配？Python是否将文件缓存在某个位置，然后每次都重新访问它？为什么这比访问redis快得多？

我在64位Ubuntu系统上使用redis 2.8，python 2.7和redis-py。

我认为Python并没有做任何特别神奇的事情，因为我做了一个函数，将文件数据存储在python对象中并永久产生。

我对四个函数调用进行了分组：

读取文件X次

调用该函数以查看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）。

创建生成器的函数，该生成器从redis数据库（带有redis的单个和多个实例）产生结果。

最后，将文件存储在内存中并永久保存。

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)

现在的结果是：

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，因为a）生成器比每次调用函数都快，b）redis比从磁盘上读取慢，并且c）从python对象上读取快得离谱。

为什么从磁盘读取要比从Redis读取内存文件快得多？

编辑：一些更多的信息和测试。

我将功能替换为

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)

实际上，在每个函数调用上创建一个新的redis实例实际上不会对读取速度产生明显影响，因为测试之间的差异性大于增益。

Sripathi Krishnan建议实现随机文件读取。从这些结果可以看出，缓存真正开始发挥作用。

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)

文件读取中存在巨大的可变性，因此百分比差异并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)

我使用random.choice（fileList）在每次通过函数时随机选择一个新文件。

完整的要旨在这里，如果有人想尝试一下-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)

Answer 1

这是苹果与桔子的比较。参见http://redis.io/topics/benchmarks

Redis是高效的 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，它将阻止等待答复。因此，除了命令本身的成本之外，您还需要支付网络往返费用或IPC费用。

在现代硬件上，与其他操作相比，网络往返或IPC的费用高得惊人。这是由于以下几个原因：

• 介质的原始延迟（主要用于网络）
• 操作系统调度程序的延迟（在Linux / Unix上不保证）
• 内存高速缓存未命中的代价很高，并且在客户端和服务器进程被安排进/出时，高速缓存未命中的可能性增加。
• 在高端包装盒上，NUMA副作用

现在，让我们回顾一下结果。

比较使用生成器的实现和使用函数调用的实现，它们不会生成相同数量的Redis往返。使用生成器，您只需：

    while time.time() - t - expiry < 0:
        yield r.get(fpKey)

因此，每次迭代1次往返。使用此功能，您可以：

if r.exists(fpKey):
    return r.get(fpKey)

因此，每次迭代2次往返。难怪发电机会更快。

当然，您应该重用相同的Redis连接以获得最佳性能。运行系统地连接/断开连接的基准毫无意义。

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。此处不涉及磁盘I
/ O。使用Redis，您必须支付往返的费用，因此速度要慢得多。