假设我在Pypark里有这个：

def condi( x ):
    if x["age"] <= 2:
        return True
    else:
        return False

def add_count( x ):
    global aa
    aa += 1
    x["count"] += 10000
    return x

sc = pyspark.SparkContext(  master = 'spark://192.168.56.103:7077',appName = 'test' )

data = [{"age":1,"count":10},{"age":2,"count":20},{"age":3,"count":30}]

data = sc.parallelize( data )

global aa
aa = 0

k = data.map( lambda x : add_count( x ) if condi( x ) else x )

print( k.collect() )
print( aa )

输出如下：

[{'count': 10010, 'age': 1}, {'count': 10020, 'age': 2}, {'count': 30, 'age': 3}] # data
0 # aa

全局变量 aa 完全不修改。
如何使用map reduce修改全局变量？