CODEWITHSUNDEEP
scalaapache-sparkapache-spark-sql
YIWEN GONG
YIWEN GONG

Reputation: 141

Spark Scala Dataframe convert a column of Array of Struct to a column of Map

I am new to Scala. I have a Dataframe with fields

ID:string, Time:timestamp, Items:array(struct(name:string,ranking:long))

I want to convert each row of the Items field to a hashmap, with the name as the key. I am not very sure how to do this.

Upvotes: 5

Views: 7145

Answers (2)

BlueSheepToken
BlueSheepToken

Reputation: 6099

Since 2.4.0, one can use map_from_entries:

import spark.implicits._
import org.apache.spark.sql.functions._

val df = Seq(
  (Array(("n1", 4L), ("n2", 5L))),
  (Array(("n3", 6L), ("n4", 7L)))
).toDF("Items")

df.select(map_from_entries($"Items")).show

/*
+-----------------------+
|map_from_entries(Items)|
+-----------------------+
|     [n1 -> 4, n2 -> 5]|
|     [n3 -> 6, n4 -> 7]|
+-----------------------+
*/

Upvotes: 1

Tzach Zohar
Tzach Zohar

Reputation: 37822

This can be done using a UDF:

import spark.implicits._
import org.apache.spark.sql.functions._
import org.apache.spark.sql.Row

// Sample data:
val df = Seq(
  ("id1", "t1", Array(("n1", 4L), ("n2", 5L))),
  ("id2", "t2", Array(("n3", 6L), ("n4", 7L)))
).toDF("ID", "Time", "Items")

// Create UDF converting array of (String, Long) structs to Map[String, Long]
val arrayToMap = udf[Map[String, Long], Seq[Row]] {
  array => array.map { case Row(key: String, value: Long) => (key, value) }.toMap
}

// apply UDF
val result = df.withColumn("Items", arrayToMap($"Items"))

result.show(false)
// +---+----+---------------------+
// |ID |Time|Items                |
// +---+----+---------------------+
// |id1|t1  |Map(n1 -> 4, n2 -> 5)|
// |id2|t2  |Map(n3 -> 6, n4 -> 7)|
// +---+----+---------------------+

I can't see a way to do this without a UDF (using Spark's built-in functions only).

Upvotes: 9

Related Questions