Calculate the standard deviation of grouped data in a Spark DataFrame

Question

I have user logs that I have taken from a csv and converted into a DataFrame in order to leverage the SparkSQL querying features. A single user will create numerous entries per hour, and I would like to gather some basic statistical information for each user; really just the count of the user instances, the average, and the standard deviation of numerous columns. I was able to quickly get the mean and count information by using groupBy($"user") and the aggregator with SparkSQL functions for count and avg:

val meanData = selectedData.groupBy($"user").agg(count($"logOn"), avg($"transaction"), avg($"submit"), avg($"submitsPerHour"), avg($"replies"), avg($"repliesPerHour"), avg($"duration")) 

However, I cannot seem to find an equally elegant way to calculate the standard deviation. So far I can only calculate it by mapping a string, double pair and use StatCounter().stdev utility:

val stdevduration = duration.groupByKey().mapValues(value => org.apache.spark.util.StatCounter(value).stdev) 

This returns an RDD however, and I would like to try and keep it all in a DataFrame for further queries to be possible on the returned data.

Answer 1

Spark 1.6+

You can use stddev_pop to compute population standard deviation and stddev / stddev_samp to compute unbiased sample standard deviation:

import org.apache.spark.sql.functions.{stddev_samp, stddev_pop}  selectedData.groupBy($"user").agg(stdev_pop($"duration")) 

Spark 1.5 and below (The original answer):

Not so pretty and biased (same as the value returned from describe) but using formula:

you can do something like this:

import org.apache.spark.sql.functions.sqrt  selectedData     .groupBy($"user")     .agg((sqrt(         avg($"duration" * $"duration") -         avg($"duration") * avg($"duration")      )).alias("duration_sd")) 

You can of course create a function to reduce the clutter:

import org.apache.spark.sql.Column def mySd(col: Column): Column = {     sqrt(avg(col * col) - avg(col) * avg(col)) }  df.groupBy($"user").agg(mySd($"duration").alias("duration_sd")) 

It is also possible to use Hive UDF:

df.registerTempTable("df") sqlContext.sql("""SELECT user, stddev(duration)                   FROM df                   GROUP BY user""") 

---

Source of the image: https://en.wikipedia.org/wiki/Standard_deviation