The Spark Column class defines a variety of column methods that are vital for manipulating DataFrames.

This blog post demonstrates how to instantiate Column objects and covers the commonly used Column methods.

A simple example

Let’s create a little DataFrame with superheros and their city of origin.

val df = Seq( ("thor", "new york"), ("aquaman", "atlantis"), ("wolverine", "new york") ).toDF("superhero", "city")

Let’s use the startsWith() column method to identify all cities that start with the word new :

df .withColumn("city_starts_with_new", $"city".startsWith("new")) .show()

+---------+--------+--------------------+ |superhero| city|city_starts_with_new| +---------+--------+--------------------+ | thor|new york| true| | aquaman|atlantis| false| |wolverine|new york| true| +---------+--------+--------------------+

A Column object is instantiated with the $"city" statement. Let’s look at all the different ways to create Column objects.

Instantiating Column objects

Column objects must be created to run Column methods.

A Column object corresponding with the city column can be created using the following three syntaxes:

$"city" df("city") col("city") (must run import org.apache.spark.sql.functions.col first)

Column objects are commonly passed as arguments to SQL functions (e.g. upper($"city") ).

We will create column objects in all the examples that follow.

gt()

Let’s create a DataFrame with an integer column so we can run some numerical column methods.

val df = Seq( (10, "cat"), (4, "dog"), (7, null) ).toDF("num", "word")

Let’s use the gt() method to identify all rows with a num greater than five.

df .withColumn("num_gt_5", col("num").gt(5)) .show()

+---+----+--------+ |num|word|num_gt_5| +---+----+--------+ | 10| cat| true| | 4| dog| false| | 7|null| true| +---+----+--------+

substr()

Let’s use the substr() method to create a new column with the first two letters of the word column.

df .withColumn("word_first_two", col("word").substr(0, 2)) .show()

+---+----+--------------+ |num|word|word_first_two| +---+----+--------------+ | 10| cat| ca| | 4| dog| do| | 7|null| null| +---+----+--------------+

Notice that the substr() method returns null when it’s supplied null as input. All other Column methods and SQL functions behave similarly (i.e. they return null when the input is null ).

+ operator

Let’s use the + operator to add five to the num column.

df .withColumn("num_plus_five", col("num").+(5)) .show()

+---+----+-------------+ |num|word|num_plus_five| +---+----+-------------+ | 10| cat| 15| | 4| dog| 9| | 7|null| 12| +---+----+-------------+

We can also skip the dot notation when invoking the function.

df .withColumn("num_plus_five", col("num") + 5) .show()

The syntactic sugar makes it harder to see that + is a method defined in the Column class. Take a look at the docs to convince yourself!

lit()

Let’s use the / method to take two divided by the num column.

df .withColumn("two_divided_by_num", lit(2) / col("num")) .show()

+---+----+------------------+ |num|word|two_divided_by_num| +---+----+------------------+ | 10| cat| 0.2| | 4| dog| 0.5| | 7|null|0.2857142857142857| +---+----+------------------+

Notice that the lit() function must be used to convert two into a Column object before the division can take place.

df .withColumn("two_divided_by_num", 2 / col("num")) .show()

Here is the error message:

notebook:2: error: overloaded method value / with alternatives: (x: Double)Double <and> (x: Float)Float <and> (x: Long)Long <and> (x: Int)Int <and> (x: Char)Int <and> (x: Short)Int <and> (x: Byte)Int cannot be applied to (org.apache.spark.sql.Column) .withColumn("two_divided_by_num", 2 / col("num"))

The / method is defined in both the Scala Int and Spark Column classes. We need to convert the number to a Column object, so the compiler knows to use the / method defined in the Spark Column class. Upon analyzing the error message, we can see that the compiler is mistakenly trying to use the / operator defined in the Scala Int class.

isNull

Let’s use the isNull method to identify the rows with a word of null .

df .withColumn("word_is_null", col("word").isNull) .show()

+---+----+------------+ |num|word|word_is_null| +---+----+------------+ | 10| cat| false| | 4| dog| false| | 7|null| true| +---+----+------------+

isNotNull

Let’s use the isNotNull method to filter out all rows with a word of null .

df .where(col("word").isNotNull) .show()

+---+----+ |num|word| +---+----+ | 10| cat| | 4| dog| +---+----+

when / otherwise

Let’s create a final DataFrame with word1 and word2 columns, so we can play around with the === , when() , and otherwise() methods

val df = Seq( ("bat", "bat"), ("snake", "rat"), ("cup", "phone"), ("key", null) ).toDF("word1", "word2")

Let’s write a little word comparison algorithm that analyzes the differences between the two words.

import org.apache.spark.sql.functions._ df .withColumn( "word_comparison", when($"word1" === $"word2", "same words") .when(length($"word1") > length($"word2"), "word1 is longer") .otherwise("i am confused") ).show()

+-----+-----+---------------+ |word1|word2|word_comparison| +-----+-----+---------------+ | bat| bat| same words| |snake| rat|word1 is longer| | cup|phone| i am confused| | key| null| i am confused| +-----+-----+---------------+

when() and otherwise() are how to write if / else if / else logic in Spark.

Next steps

Spark’s Colum methods are frequently used and mastery of this class is vital.

Scala syntactic sugar can make it difficult for programmers without a lot of object oriented experience to have difficulty identifying when Column methods are invoked (and differenting them from SQL functions).

Keep reading though this post till you’ve mastered all these vital concepts!