5 ways to calculate max of row in Spark
5 ways to calculate max of row in Spark/PySpark In this post I tested the performance of 5 different methods to calculate a maximal value of a single row. You might encounter this situation before: you have multiple columns with some value, integer or float for example, and you want to calculate the maximum value per row for all the data frame and append it as additional column. We will test 5 different ways to calculate maximum value: udf - user define function 'array' and 'array_max' functions 'greatest' function rdd 'map' transformation data frame groupBy and max-aggregation For each of these methods will count the time it take to read a CSV file, calculate the maximal value and add it as 'max' column. For the data, we are going to generate 10 columns of integer values over 1 million rows via Python code. The values will be in the range of 1 to 1000. import random def gen_csv (): f = open ( "../data_10x1m.csv...