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There are several ways to merge multiple columns into a single Spark dataset. Here are two common methods:

1. Using the "concat" function: We can use the "concat" function to concatenate the multiple columns into a single dataset. The following code demonstrates how to use this function:

import org.apache.spark.sql.functions.concat

val df = spark.read.csv("path/to/file.csv")
val mergedColumn = df.select(concat($"col1", $"col2", $"col3").alias("new_col"))

In this example, the "concat" function concatenates the "col1", "col2", and "col3" columns from the input dataset "df" into a new column named "newcol". The resulting dataset "mergedColumn" contains only the new column "newcol".

1. Using the "withColumn" function: Another way to merge multiple columns is to use the "withColumn" function to create a new column that combines the values from the multiple columns. Here's an example:

val df = spark.read.csv("path/to/file.csv")
val mergedColumn = df.withColumn("new_col", $"col1" + $"col2" + $"col3")

In this example, the "withColumn" function creates a new column "newcol" by adding the values from the "col1", "col2", and "col3" columns. The resulting dataset "mergedColumn" contains all the original columns plus the new column "newcol".