How to pivot Spark DataFrame?

Question

I am starting to use Spark DataFrames and I need to be able to pivot the data to create multiple columns out of 1 column with multiple rows. There is built in functionality

Answer 1

Initially i adopted Al M's solution. Later took the same thought and rewrote this function as a transpose function.

This method transposes any df rows to columns of any data-format with using key and value column

for input csv

id,tag,value
1,US,50a
1,UK,100
1,Can,125
2,US,75
2,UK,150
2,Can,175

ouput

+--+---+---+---+
|id| UK| US|Can|
+--+---+---+---+
| 2|150| 75|175|
| 1|100|50a|125|
+--+---+---+---+

transpose method :

def transpose(hc : HiveContext , df: DataFrame,compositeId: List[String], key: String, value: String) = {

val distinctCols =   df.select(key).distinct.map { r => r(0) }.collect().toList

val rdd = df.map { row =>
(compositeId.collect { case id => row.getAs(id).asInstanceOf[Any] },
scala.collection.mutable.Map(row.getAs(key).asInstanceOf[Any] -> row.getAs(value).asInstanceOf[Any]))
}
val pairRdd = rdd.reduceByKey(_ ++ _)
val rowRdd = pairRdd.map(r => dynamicRow(r, distinctCols))
hc.createDataFrame(rowRdd, getSchema(df.schema, compositeId, (key, distinctCols)))

}

private def dynamicRow(r: (List[Any], scala.collection.mutable.Map[Any, Any]), colNames: List[Any]) = {
val cols = colNames.collect { case col => r._2.getOrElse(col.toString(), null) }
val array = r._1 ++ cols
Row(array: _*)
}

private  def getSchema(srcSchema: StructType, idCols: List[String], distinctCols: (String, List[Any])): StructType = {
val idSchema = idCols.map { idCol => srcSchema.apply(idCol) }
val colSchema = srcSchema.apply(distinctCols._1)
val colsSchema = distinctCols._2.map { col => StructField(col.asInstanceOf[String], colSchema.dataType, colSchema.nullable) }
StructType(idSchema ++ colsSchema)
}

main snippet

import java.util.Date
import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.sql.Row
import org.apache.spark.sql.DataFrame
import org.apache.spark.sql.types.StructType
import org.apache.spark.sql.hive.HiveContext
import org.apache.spark.sql.types.StructField


...
...
def main(args: Array[String]): Unit = {

    val sc = new SparkContext(conf)
    val sqlContext = new org.apache.spark.sql.SQLContext(sc)
    val dfdata1 = sqlContext.read.format("com.databricks.spark.csv").option("header", "true").option("inferSchema", "true")
    .load("data.csv")
    dfdata1.show()  
    val dfOutput = transpose(new HiveContext(sc), dfdata1, List("id"), "tag", "value")
    dfOutput.show

}

Answer 2

There is a SIMPLE method for pivoting :

  id  tag  value
  1   US    50
  1   UK    100
  1   Can   125
  2   US    75
  2   UK    150
  2   Can   175

  import sparkSession.implicits._

  val data = Seq(
    (1,"US",50),
    (1,"UK",100),
    (1,"Can",125),
    (2,"US",75),
    (2,"UK",150),
    (2,"Can",175),
  )

  val dataFrame = data.toDF("id","tag","value")

  val df2 = dataFrame
                    .groupBy("id")
                    .pivot("tag")
                    .max("value")
  df2.show()

+---+---+---+---+
| id|Can| UK| US|
+---+---+---+---+
|  1|125|100| 50|
|  2|175|150| 75|
+---+---+---+---+

Answer 3

There are plenty of examples of pivot operation on dataset/dataframe, but I could not find many using SQL. Here is an example that worked for me.

create or replace temporary view faang 
as SELECT stock.date AS `Date`,
    stock.adj_close AS `Price`,
    stock.symbol as `Symbol` 
FROM stock  
WHERE (stock.symbol rlike '^(FB|AAPL|GOOG|AMZN)$') and year(date) > 2010;


SELECT * from faang 

PIVOT (max(price) for symbol in ('AAPL', 'FB', 'GOOG', 'AMZN')) order by date; 

Answer 4

Spark has been providing improvements to Pivoting the Spark DataFrame. A pivot function has been added to the Spark DataFrame API to Spark 1.6 version and it has a performance issue and that has been corrected in Spark 2.0

however, if you are using lower version; note that pivot is a very expensive operation hence, it is recommended to provide column data (if known) as an argument to function as shown below.

val countries = Seq("USA","China","Canada","Mexico")
val pivotDF = df.groupBy("Product").pivot("Country", countries).sum("Amount")
pivotDF.show()

This has been explained detailed at Pivoting and Unpivoting Spark DataFrame

Happy Learning !!