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 collect all data to driver.
Thanks. Zhan Zhang On Dec 21, 2015, at 3:44 AM, Zhiliang Zhu <zchl.j...@yahoo.com.INVALID<mailto:zchl.j...@yahoo.com.INVALID>> 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 } 105 106 List<Integer> lt = r.collect(); //then collect this rdd to get another rdd, however, dozens of action Function as collect is VERY MUCH COST 107 t = jsc.parallelize(lt, 1).cache(); 108 109 } 110 ...... Thanks very much in advance! Zhiliang
