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From "Kannan Subramanian (JIRA)" <>
Subject [jira] [Created] (SPARK-21577) Issue is handling too many aggregations
Date Sun, 30 Jul 2017 12:47:00 GMT
Kannan Subramanian created SPARK-21577:

             Summary: Issue is handling too many aggregations 
                 Key: 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
="/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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