How to sum the values of one column of a dataframe in spark/scala

Question

I have a Dataframe that I read from a CSV file with many columns like: timestamp, steps, heartrate etc.

I want to sum the values of each column, for instance the total number of steps on "steps" column.

As far as I see I want to use these kind of functions: http://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.sql.functions$

But I can understand how to use the function sum.

When I write the following:

val df = CSV.load(args(0))
val sumSteps = df.sum("steps") 

the function sum cannot be resolved.

Do I use the function sum wrongly? Do Ι need to use first the function map? and if yes how?

A simple example would be very helpful! I started writing Scala recently.

Answer 1

If you want to sum all values of one column, it's more efficient to use DataFrame's internal RDD and reduce.

import sqlContext.implicits._
import org.apache.spark.sql.functions._

val df = sc.parallelize(Array(10,2,3,4)).toDF("steps")
df.select(col("steps")).rdd.map(_(0).asInstanceOf[Int]).reduce(_+_)

//res1 Int = 19

Answer 2

You must first import the functions:

import org.apache.spark.sql.functions._

Then you can use them like this:

val df = CSV.load(args(0))
val sumSteps =  df.agg(sum("steps")).first.get(0)

You can also cast the result if needed:

val sumSteps: Long = df.agg(sum("steps").cast("long")).first.getLong(0)

Edit:

For multiple columns (e.g. "col1", "col2", ...), you could get all aggregations at once:

val sums = df.agg(sum("col1").as("sum_col1"), sum("col2").as("sum_col2"), ...).first

Edit2:

For dynamically applying the aggregations, the following options are available:

• Applying to all numeric columns at once:

df.groupBy().sum()

• Applying to a list of numeric column names:

val columnNames = List("col1", "col2")
df.groupBy().sum(columnNames: _*)

• Applying to a list of numeric column names with aliases and/or casts:

val cols = List("col1", "col2")
val sums = cols.map(colName => sum(colName).cast("double").as("sum_" + colName))
df.groupBy().agg(sums.head, sums.tail:_*).show()

Answer 3

Simply apply aggregation function, Sum on your column

df.groupby('steps').sum().show()

Follow the Documentation http://spark.apache.org/docs/2.1.0/api/python/pyspark.sql.html

Check out this link also https://www.analyticsvidhya.com/blog/2016/10/spark-dataframe-and-operations/

Answer 4

Not sure this was around when this question was asked but:

df.describe().show("columnName")

gives mean, count, stdtev stats on a column. I think it returns on all columns if you just do .show()

Answer 5

Using spark sql query..just incase if it helps anyone!

import org.apache.spark.sql.SparkSession 
import org.apache.spark.SparkConf 
import org.apache.spark.sql.functions._ 
import org.apache.spark.SparkContext 
import java.util.stream.Collectors

val conf = new SparkConf().setMaster("local[2]").setAppName("test")
val spark = SparkSession.builder.config(conf).getOrCreate()
val df = spark.sparkContext.parallelize(Seq(1, 2, 3, 4, 5, 6, 7)).toDF()

df.createOrReplaceTempView("steps")
val sum = spark.sql("select  sum(steps) as stepsSum from steps").map(row => row.getAs("stepsSum").asInstanceOf[Long]).collect()(0)
println("steps sum = " + sum) //prints 28