Determining the length of an array column in Apache Spark can be achieved using built-in functions. The specific function we will use is `size`. In this explanation, I’ll walk you through an example using PySpark and Scala to showcase how you can determine the length of an array column in a DataFrame.
Using PySpark
First, let’s create a DataFrame with an array column:
from pyspark.sql import SparkSession
from pyspark.sql.functions import size
# Create Spark session
spark = SparkSession.builder.master("local").appName("ArrayLengthExample").getOrCreate()
# Sample Data
data = [
("John", [1, 2, 3]),
("Alice", [1, 2]),
("Bob", [5, 6, 7, 8])
]
# Create DataFrame
columns = ["name", "numbers"]
df = spark.createDataFrame(data, columns)
# Display original DataFrame
df.show()
The output of the DataFrame will be:
+-----+------------+
| name| numbers|
+-----+------------+
| John| [1, 2, 3]|
|Alice| [1, 2]|
| Bob|[5, 6, 7, 8]|
+-----+------------+
Now, to determine the length of the array column “numbers”, we can use the `size` function:
# Add a new column with the size of the array
df_with_length = df.withColumn("array_length", size(df["numbers"]))
# Display DataFrame with array length column
df_with_length.show()
The output will be:
+-----+------------+------------+
| name| numbers|array_length|
+-----+------------+------------+
| John| [1, 2, 3]| 3|
|Alice| [1, 2]| 2|
| Bob|[5, 6, 7, 8]| 4|
+-----+------------+------------+
Using Scala
Similarly, we can achieve the same result using Scala. Below is an equivalent example:
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions.size
// Create Spark session
val spark = SparkSession.builder.master("local").appName("ArrayLengthExample").getOrCreate()
// Sample Data
val data = Seq(
("John", Seq(1, 2, 3)),
("Alice", Seq(1, 2)),
("Bob", Seq(5, 6, 7, 8))
)
// Create DataFrame
import spark.implicits._
val df = data.toDF("name", "numbers")
// Display original DataFrame
df.show()
The output of the DataFrame will be:
+-----+------------+
| name| numbers|
+-----+------------+
| John| [1, 2, 3]|
|Alice| [1, 2]|
| Bob|[5, 6, 7, 8]|
+-----+------------+
Now, to determine the length of the array column “numbers”, we can use the `size` function:
// Add a new column with the size of the array
val dfWithLength = df.withColumn("array_length", size($"numbers"))
// Display DataFrame with array length column
dfWithLength.show()
The output will be:
+-----+------------+------------+
| name| numbers|array_length|
+-----+------------+------------+
| John| [1, 2, 3]| 3|
|Alice| [1, 2]| 2|
| Bob|[5, 6, 7, 8]| 4|
+-----+------------+------------+
In summary, the `size` function is used in both PySpark and Scala to determine the length of an array column in a DataFrame. As demonstrated, the function efficiently adds a new column showing the length of the arrays contained in the specified array column.