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From unk1102 <>
Subject Best practices to call hiveContext in DataFrame.foreach in executor program or how to have a for loop in driver program
Date Wed, 05 Aug 2015 15:37:23 GMT
Hi I have the following code which fires hiveContext.sql() most of the time.
My task is I want to create few table and insert values into after
processing for all hive table partition. So I first fire show partitions and
using its output in a for loop I call few methods which creates table if not
exists and does insert into using hiveContext.sql. Now we cant execute
hiveContext in executor so I have to execute this for loop in driver program
and should run serially one by one. When I submit this Spark job in YARN
cluster almost all the time my executor gets lost because of shuffle not
found exception. Now this is happening because YARN is killing my executor
because of memory overload. I dont understand why I have very less data set
for each hive partition but still it causes YARN to kill my executor. Please
guide why the following code is overkill memory will the following code do
everything in parallel and try to accommodate all hive partition data in
memory at the same time? Please guide I am blocked because of this issue.

 public static void main(String[] args) throws IOException {  
      SparkConf conf = new SparkConf();
      SparkContext sc = new SparkContext(conf);
      HiveContext hc = new HiveContext(sc);

     DataFrame partitionFrame = hiveContext.sql(" show partitions dbdata
     Row[] rowArr = partitionFrame.collect();
     for(Row row : rowArr) {
      String[] splitArr = row.getString(0).split("/");
      String server = splitArr[0].split("=")[1];
      String date =  splitArr[1].split("=")[1];
      String csvPath = "hdfs:///user/db/ext/"+server+".csv";
      if(fs.exists(new Path(csvPath))) {
           hiveContext.sql("ADD FILE " + csvPath);
      createInsertIntoTableABC(hc,entity, date);
      createInsertIntoTableDEF(hc,entity, date);
      createInsertIntoTableJKL(hc,entity, date);


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