[jira] [Created] (SPARK-21577) Issue is handling too many aggregations

["Kannan Subramanian (JIRA)" <ji...@apache.org>]

Kannan Subramanian created SPARK-21577:
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             Summary: Issue is handling too many aggregations 
                 Key: SPARK-21577
                 URL: https://issues.apache.org/jira/browse/SPARK-21577
             Project: Spark
          Issue Type: Bug
          Components: SQL
    Affects Versions: 1.6.0
         Environment: Cloudera CDH 1.8.3
Spark 1.6.0
            Reporter: Kannan Subramanian


 my requirement, reading the table from hive(Size - around 1.6 TB). I have to do more than 200 aggregation operations mostly avg, sum and std_dev. Spark application total execution time is take more than 12 hours. To Optimize the code I used shuffle Partitioning and memory tuning and all. But Its nothelpful for me. Please note that same query I ran in hive on map reduce. MR job completion time taken around only 5 hours.  Kindly let me know is there any way to optimize or efficient way of handling multiple aggregation operations.    val inputDataDF = hiveContext.read.parquet("/inputparquetData")    inputDataDF.groupBy("seq_no","year", "month","radius").agg(count($"Dseq"),avg($"Emp"),avg($"Ntw"),avg($"Age"),  avg($"DAll"),avg($"PAll"),avg($"DSum"),avg($"dol"),sum("sl"),sum($"PA"),sum($"DS")... like 200 columns)



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