python-3.x - 为什么我们使用 hadoop mapreduce 进行数据处理？为什么不在本地机器上做呢？

问题描述

我很困惑，我尝试像一百万个随机数一样采用概率。我尝试在谷歌 dataProc 中使用 MapReduce 两种方法，并在 spyder 上运行 python 脚本来做同样的事情。但更快的是本地机器。那我们为什么要使用 Mapreduce 呢？我使用以下代码。

#!/usr/bin/env python3
import timeit
start = timeit.default_timer()
from collections import Counter
import numpy as np
import matplotlib.pyplot as plt

#Random Number Generating
x = np.random.randint(low=1, high=100, size=1000000)

counts = Counter(x)
total = sum(counts.values())
d1 = {k:v/total for k,v in counts.items()}
grad = d1.keys()
prob = d1.values()
#print(str(grad))
#print(str(prob))
#bins = 20
plt.hist(prob,bins=20, normed=1, facecolor='blue', alpha=0.5)
#plt.plot(bins, hist, 'r--')
plt.xlabel('Probability')
plt.ylabel('Number Of Students')
plt.title('Histogram of Students Grade')
plt.subplots_adjust(left=0.15)
plt.show()

stop = timeit.default_timer()
print('Time: ', stop - start)

#!/usr/bin/env python3
"""mapper.py"""
import sys

# Get input lines from stdin
for line in sys.stdin:
    # Remove spaces from beginning and end of the line
    #line = line.strip()

    # Split it into tokens
    #tokens = line.split()

    #Get probability_mass values
    for probability_mass in line:
        print("None\t{}".format(probability_mass))
        #print(str(probability_mass)+ '\t1')
        #print('%s\t%s' % (probability_mass, None))

#!/usr/bin/env python3
"""reducer.py"""
import sys
from collections import defaultdict

counts = defaultdict(float)

# Get input from stdin
for line in sys.stdin:
    #Remove spaces from beginning and end of the line
    #line = line.strip()

    # skip empty lines
    if not line:
        continue  

    # parse the input from mapper.py
    k,v = line.split('\t', 1)
    counts[v] += 1
total = (float(sum(counts.values())))
#total = sum(counts.values())
probability_mass = {k:v/total for k,v in counts.items()}
print(probability_mass)

标签： python-3.xhadoopgoogle-cloud-platformmapreducebigdata

python-3.x - 为什么我们使用 hadoop mapreduce 进行数据处理？为什么不在本地机器上做呢？

问题描述

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