Drop is a...select

Versions: Apache Spark 3.0.0

Have you ever wondered what is the relationship between drop and select operations in Apache Spark SQL? If not, I will shed some light on them in this short blog post.

Let's suppose that I want to reduce the size of the dataset and select only a subset of columns. My initial input looks that way:

  private val testSparkSession = SparkSession.builder()
    .appName("Select vs Drop").master("local[*]")
    .getOrCreate()
  import testSparkSession.implicits._

  val dataset = Seq(
    (0, "a", "A"), (1, "b", "B"), (2, "c", "C")
  ).toDF("nr", "lower_letter", "upper_letter")

To retrieve only lower_letter and upper_letter columns, we can select them explicitly:

dataset.select("lower_letter", "upper_letter").show()

But it's not the single approach. Another one uses drop(colName: String) function that removes the columns from the dataset:

dataset.drop("nr").show()

An interesting thing to notice is how the drop operation works under-the-hood. In fact, it's a select statement that is executed on the columns not matching the columns from the dropped list:

  def drop(colNames: String*): DataFrame = {
    val resolver = sparkSession.sessionState.analyzer.resolver
    val allColumns = queryExecution.analyzed.output
    val remainingCols = allColumns.filter { attribute =>
      colNames.forall(n => !resolver(attribute.name, n))
    }.map(attribute => Column(attribute))
    if (remainingCols.size == allColumns.size) {
      toDF()
    } else {
      this.select(remainingCols: _*)
    }
  }

It's even more visible when you compare the logical plans. The first one is for the select-based reduction and the second one for the drop-based one:

# select("lower_letter", "upper_letter")
== Analyzed Logical Plan ==
lower_letter: string, upper_letter: string
Project [lower_letter#11, upper_letter#12]
+- Project [_1#3 AS nr#10, _2#4 AS lower_letter#11, _3#5 AS upper_letter#12]
   +- LocalRelation [_1#3, _2#4, _3#5]

== Optimized Logical Plan ==
LocalRelation [lower_letter#11, upper_letter#12]

# drop("nr")
== Analyzed Logical Plan ==
lower_letter: string, upper_letter: string
Project [lower_letter#11, upper_letter#12]
+- Project [_1#3 AS nr#10, _2#4 AS lower_letter#11, _3#5 AS upper_letter#12]
   +- LocalRelation [_1#3, _2#4, _3#5]

== Optimized Logical Plan ==
LocalRelation [lower_letter#11, upper_letter#12]

And a short video to prove you that I'm not liying ;-):

Drop is then a select :) No need to add it then if you explicitly select some columns and want to remove the not used ones.

If you liked it, you should read:

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