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https://issues.apache.org/jira/browse/SPARK-21577?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16106855#comment-16106855
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Hyukjin Kwon edited comment on SPARK-21577 at 7/31/17 6:26 AM:
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Please check out https://spark.apache.org/community.html and I guess you can
start the thread.
was (Author: hyukjin.kwon):
Please check out https://spark.apache.org/community.html.
> 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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