Recently, I am struggling with performance tuning of the pivot function in Spark.
I am using a long format DataFrame for dealing with the data, and everything is working fine. The total elapsed time is less than 1 hour.
However, the new requirement is to process the DataFrame in a wide format, which means I need to transform the huge number of rows into a large number(0.5 millions) of columns. Initially, I used the pivot function. But the result indicated an “Out of Memory” (OOM) error, related to the memory heap.
I searched online, and it seems there are a few resources that point out this issue. Even after increasing the resources of the executors and drivers, it still took over 8 hours, and I had to stop the Spark job.
Does anyone have any tips or resources I can refer to? It is quite frustrating that I have spent many weeks, but I am unable to output the 0.5 million columns as a DataFrame.
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