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

["Hyukjin Kwon (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-21577?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Hyukjin Kwon resolved SPARK-21577.
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    Resolution: Invalid

I don't think this JIRA describes a particular issue / suggestion but sounds rather a question or asking investigation. Let's start this on mailing list. I am resolving this.

> 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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