How to send each group at a time to the spark executors?

Question

I\'m unable to send each group of dataframe at a time to the executor.

I have a data as below in company_model_vals_df dataframe.

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Answer 1

If I understand your question correctly, you want to manipulate the data separately for each "model_id","fiscal_quarter","fiscal_year".

If that's correct, you would do it with a groupBy(), for example:

company_model_vals_df.groupBy("model_id","fiscal_quarter","fiscal_year").agg(avg($"col1") as "average")

If what you're looking for is to write each logical group into a separate folder, you can do that by writing:

company_model_vals_df.write.partitionBy("model_id","fiscal_quarter","fiscal_year").parquet("path/to/save")

Answer 2

There are few options here -

• you need to fork the dataset into several datasets and work them individually like ,

var dist_company_model_vals_list =  company_model_vals_df
  .select("model_id","fiscal_quarter","fiscal_year").distinct().collectAsList

Then filter company_model_vals_df with output of dist_company_model_vals_list list which provides several datasets that you can work independently, like

def rowList = {
import org.apache.spark.sql._
var dfList:Seq[DataFrame] = Seq()
for (data <- dist_company_model_vals_list.zipWithIndex) {
val i = data._2
val row = data.-1
val filterCol = col($"model_id").equalTo(row.get(i).getInt(0).and($"fiscal_quarter").equalTo(row.get(i).getInt(1).and($"fiscal_year").equalTo(row.get(i).getInt(2))

   val resultDf = company_model_vals_df.filter(filterCol)    
dfList +: = resultDf
      }
dfList
}

• If your objective is to write the data, you can use partitionBy("model_id","fiscal_quarter","fiscal_year") method on dataframeWriterto write them separately.