Why does SparkContext.parallelize use memory of the driver?

Now I have to create a parallelized collection using sc.parallelize() in pyspark (Spark 2.1.0).

The collection in my driver program is big. when I parallelize it, I found it takes up a lot of memory in master node.

It seems that the collection is still being kept in spark's memory of the master node after I parallelize it to each worker node. Here's an example of my code:

# my python code
sc = SparkContext()
a = [1.0] * 1000000000
rdd_a = sc.parallelize(a, 1000000)
sum = rdd_a.reduce(lambda x, y: x+y)

I've tried

del a

to destroy it, but it didn't work. The spark which is a java process is still using a lot of memory.

After I create rdd_a, how can I destroy a to free the master node's memory?

Thanks!