Apache-Spark如何处理类中的方法？

Question

我在学阿帕奇-火花。在仔细阅读了Spark教程之后，我了解了如何将Python函数传递给Apache来处理RDD数据集。但是现在我仍然不知道Apache是如何与类中的方法工作的。例如，我的代码如下所示：

import numpy as np
import copy
from pyspark import SparkConf, SparkContext

class A():
    def __init__(self, n):
        self.num = n

class B(A):
    ### Copy the item of class A to B.
    def __init__(self, A):
        self.num = copy.deepcopy(A.num)

    ### Print out the item of B
    def display(self, s):
        print s.num
        return s

def main():
    ### Locally run an application "test" using Spark.
    conf = SparkConf().setAppName("test").setMaster("local[2]")

    ### Setup the Spark configuration.
    sc = SparkContext(conf = conf)

    ### "data" is a list to store a list of instances of class A. 
    data = []
    for i in np.arange(5):
        x = A(i)
        data.append(x)

    ### "lines" separate "data" in Spark.  
    lines = sc.parallelize(data)

    ### Parallelly creates a list of instances of class B using
    ### Spark "map".
    temp = lines.map(B)

    ### Now I got the error when it runs the following code:
    ### NameError: global name 'display' is not defined.
    temp1 = temp.map(display)

if __name__ == "__main__":
    main()

实际上，我使用上面的代码来使用class B并行地生成一个temp = lines.map(B)实例列表。之后，我做了temp1 = temp.map(display)，因为我希望并行地打印出class B实例列表中的每一项。但是现在这个错误出现了：NameError: global name 'display' is not defined.，我想知道如果我仍然使用Apache并行计算，我如何能够修复这个错误。我真的很感激有人能帮我。

Answer 1

结构

.
├── ab.py
└── main.py

main.py

import numpy as np
from pyspark import SparkConf, SparkContext
import os
from ab import A, B

def main():
    ### Locally run an application "test" using Spark.
    conf = SparkConf().setAppName("test").setMaster("local[2]")

    ### Setup the Spark configuration.
    sc = SparkContext(
            conf = conf, pyFiles=[
               os.path.join(os.path.abspath(os.path.dirname(__file__)), 'ab.py')]
    ) 

    data = []
    for i in np.arange(5):
        x = A(i)
        data.append(x)

    lines = sc.parallelize(data)
    temp = lines.map(B)

    temp.foreach(lambda x: x.display()) 

if __name__ == "__main__":
    main()

ab.py

import copy

class A():
    def __init__(self, n):
        self.num = n

class B(A):
    ### Copy the item of class A to B.
    def __init__(self, A):
        self.num = copy.deepcopy(A.num)

    ### Print out the item of B
    def display(self):
        print self.num

评论：

• 再一次-印刷是个坏主意。忽略星火架构有很大的机会，它将成为您的程序的瓶颈。
• 如果您需要诊断输出，请考虑记录或收集一个示例并在本地进行检查：for x in rdd.sample(False, 0.001).collect(): x.display()
• 对于副作用，请使用foreach而不是map
• 我改进了display法。我不确定在这种情况下s应该是什么