Calculate the standard deviation of grouped data in a Spark DataFrame

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""")

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

The accepted code does not compile, as it has a typo (as pointed out by MRez). The snippet below works and is tested.

For Spark 2.0+ :

import org.apache.spark.sql.functions._
val _avg_std = df.groupBy("user").agg(
        avg(col("duration").alias("avg")),
        stddev(col("duration").alias("stdev")),
        stddev_pop(col("duration").alias("stdev_pop")),
        stddev_samp(col("duration").alias("stdev_samp"))
        )