I don't think the Spark configuration is what you want to focus on. It's
hard to say without knowing the specifics of the job or the data volume,
but you should be able to accomplish this with the percent_rank function in
SparkSQL and a smart partitioning of the data. If your data has a lot of
skew
Currently, I'm using the percentile approx function with Hive.
I'm looking for a better way to run this function or another way to get the
same result with spark, but faster and not using gigantic instances..
I'm trying to optimize this job by changing the Spark configuration. If you
have any idea
If you would require higher precision, you may have to write a custom udaf.
In my case, I ended up storing the data as a key-value ordered list of
histograms.
Thanks
Muthu
On Mon, Nov 11, 2019, 20:46 Patrick McCarthy
wrote:
> Depending on your tolerance for error you could also use
> percentile
Depending on your tolerance for error you could also use
percentile_approx().
On Mon, Nov 11, 2019 at 10:14 AM Jerry Vinokurov
wrote:
> Do you mean that you are trying to compute the percent rank of some data?
> You can use the SparkSQL percent_rank function for that, but I don't think
> that's
Do you mean that you are trying to compute the percent rank of some data?
You can use the SparkSQL percent_rank function for that, but I don't think
that's going to give you any improvement over calling the percentRank
function on the data frame. Are you currently using a user-defined function
for
