Spark: Convert column of string to an array

Question

How to convert a column that has been read as a string into a column of arrays? i.e. convert from below schema

scala> test.printSchema
root
 |-- a: long (         


        

Answer 1

Using a UDF would give you exact required schema. Like this:

val toArray = udf((b: String) => b.split(",").map(_.toLong))

val test1 = test.withColumn("b", toArray(col("b")))

It would give you schema as follows:

scala> test1.printSchema
root
 |-- a: long (nullable = true)
 |-- b: array (nullable = true)
 |    |-- element: long (containsNull = true)

+---+-----+
|  a|  b  |
+---+-----+
|  1|[2,3]|
+---+-----+
|  2|[4,5]|
+---+-----+

As far as applying schema on file read itself is concerned, I think that is a tough task. So, for now you can apply transformation after creating DataFrameReader of test.

I hope this helps!

Answer 2

There are various method,

The best way to do is using split function and cast to array<long>

data.withColumn("b", split(col("b"), ",").cast("array<long>"))

You can also create simple udf to convert the values

val tolong = udf((value : String) => value.split(",").map(_.toLong))

data.withColumn("newB", tolong(data("b"))).show

Hope this helps!

Answer 3

In python (pyspark) it would be:

from pyspark.sql.types import *
from pyspark.sql.functions import col, split
test = test.withColumn(
        "b",
        split(col("b"), ",\s*").cast("array<int>").alias("ev")
 )