Mergesort parallelization using Spark
import random
import time
from pyspark import SparkContext
def execute_merge_sort(generated_list):
start_time = time.time()
sorted_list = merge_sort(generated_list)
elapsed = time.time() - start_time
print('Simple merge sort: %f sec' % elapsed)
return sorted_list
def generate_list(length):
N = length
generated_list = [random.random() for num in range(N)]
return generated_list
def merging(left_side, right_side):
result = []
i = j = 0
while i < len(left_side) and j < len(right_side):
if left_side[i] <= right_side[j]:
result.append(left_side[i])
i += 1
else:
result.append(right_side[j])
j += 1
if i == len(left_side):
result.extend(right_side[j:])
else:
result.extend(left_side[i:])
return result
def merge_sort(generated_list):
if len(generated_list) <= 1:
return generated_list
middle_value = len(generated_list) // 2
sorted_list = merging(merge_sort(generated_list[:middle_value]), merge_sort(generated_list[middle_value:]))
return sorted_list
def is_sorted(num_array):
for i in range(1, len(num_array)):
if num_array[i] < num_array[i - 1]:
return False
return True
generate_list = generate_list(500000)
sorted_list = execute_merge_sort(generate_list)
sc = SparkContext()
rdd = sc.parallelize(generate_list).mapPartitions(execute_merge_sort).collect()