What is difference between repartition / collect and collapse ...Is collapse
the same costly as collect or repartition ?
Thanks in advance ~
On Tuesday, December 22, 2015 2:24 AM, Zhan Zhang <[email protected]>
wrote:
In what situation, you have such cases? If there is no shuffle, you can
collapse all these functions into one, right? In the meantime, it is not
recommended to collectall data to driver.
Thanks.
Zhan Zhang
On Dec 21, 2015, at 3:44 AM, Zhiliang Zhu <[email protected]> wrote:
Dear All,
I need to iterator some job / rdd quite a lot of times, but just lost in the
problem of spark only accept to call around 350 number of map before it meets
one action Function , besides, dozens of action will obviously increase the run
time.Is there any proper way ...
As tested, there is piece of codes as follows:
......
83 int count = 0; 84 JavaRDD<Integer> dataSet = jsc.parallelize(list,
1).cache(); //with only 1 partition 85 int m = 350; 86
JavaRDD<Integer> r = dataSet.cache(); 87 JavaRDD<Integer> t = null; 88 89
for(int j=0; j < m; ++j) { //outer loop to temporarily convert the rdd r to t
90 if(null != t) { 91 r = t; 92 } //inner loop
to call map 350 times , if m is much more than 350 (for instance, around 400),
then the job will throw exception message "15/12/21 19:36:17
ERROR yarn.ApplicationMaster: User class threw exception:
java.lang.StackOverflowError java.lang.StackOverflowError") 93 for(int
i=0; i < m; ++i) { 94 r = r.map(new Function<Integer, Integer>() { 95
@Override 96 public Integer call(Integer integer) { 97
double x = Math.random() * 2 - 1; 98 double y = Math.random() *
2 - 1; 99 return (x * x + y * y < 1) ? 1 : 0;100 }101
});
104 }105106 List<Integer> lt = r.collect(); //then collect this rdd
to get another rdd, however, dozens of action Function as collect is VERY MUCH
COST107 t = jsc.parallelize(lt, 1).cache();108109 }110......
Thanks very much in advance!Zhiliang
