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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 for that in Scalding and I believe in Pandas in Python, but I can't find anything for the new Spark Dataframe.

I assume I can write custom function of some sort that will do this but I'm not even sure how to start, especially since I am a novice with Spark. I anyone knows how to do this with built in functionality or suggestions for how to write something in Scala, it is greatly appreciated.

As mentioned by David Anderson Spark provides pivot function since version 1.6. General syntax looks as follows:

df
 .groupBy(grouping_columns)
 .pivot(pivot_column, [values]) 
 .agg(aggregate_expressions)

Usage examples using nycflights13 and csv format:

Python:

from pyspark.sql.functions import avg

flights = (sqlContext
 .read
 .format("csv")
 .options(inferSchema="true", header="true")
 .load("flights.csv")
 .na.drop())

flights.registerTempTable("flights")
sqlContext.cacheTable("flights")

gexprs = ("origin", "dest", "carrier")
aggexpr = avg("arr_delay")

flights.count()
## 336776

%timeit -n10 flights.groupBy(*gexprs ).pivot("hour").agg(aggexpr).count()
## 10 loops, best of 3: 1.03 s per loop

Scala:

val flights = sqlContext
 .read
 .format("csv")
 .options(Map("inferSchema" -> "true", "header" -> "true"))
 .load("flights.csv")

flights
 .groupBy($"origin", $"dest", $"carrier")
 .pivot("hour")
 .agg(avg($"arr_delay"))

Java:

import static org.apache.spark.sql.functions.*;
import org.apache.spark.sql.*;

Dataset<Row> df = spark.read().format("csv")
 .option("inferSchema", "true")
 .option("header", "true")
 .load("flights.csv");

df.groupBy(col("origin"), col("dest"), col("carrier"))
 .pivot("hour")
 .agg(avg(col("arr_delay")));

R / SparkR:

library(magrittr)

flights <- read.df("flights.csv", source="csv", header=TRUE, inferSchema=TRUE)

flights %>% 
 groupBy("origin", "dest", "carrier") %>% 
 pivot("hour") %>% 
 agg(avg(column("arr_delay")))

R / sparklyr

library(dplyr)

flights <- spark_read_csv(sc, "flights", "flights.csv")

avg.arr.delay <- function(gdf) 
 expr <- invoke_static(
 sc,
 "org.apache.spark.sql.functions",
 "avg",
 "arr_delay"
 )
 gdf %>% invoke("agg", expr, list())


flights %>% 
 sdf_pivot(origin + dest + carrier ~ hour, fun.aggregate=avg.arr.delay)

SQL:

CREATE TEMPORARY VIEW flights 
USING csv 
OPTIONS (header 'true', path 'flights.csv', inferSchema 'true') ;

 SELECT * FROM (
 SELECT origin, dest, carrier, arr_delay, hour FROM flights
 ) PIVOT (
 avg(arr_delay)
 FOR hour IN (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23)
 );

Example data:

"year","month","day","dep_time","sched_dep_time","dep_delay","arr_time","sched_arr_time","arr_delay","carrier","flight","tailnum","origin","dest","air_time","distance","hour","minute","time_hour"
2013,1,1,517,515,2,830,819,11,"UA",1545,"N14228","EWR","IAH",227,1400,5,15,2013-01-01 05:00:00
2013,1,1,533,529,4,850,830,20,"UA",1714,"N24211","LGA","IAH",227,1416,5,29,2013-01-01 05:00:00
2013,1,1,542,540,2,923,850,33,"AA",1141,"N619AA","JFK","MIA",160,1089,5,40,2013-01-01 05:00:00
2013,1,1,544,545,-1,1004,1022,-18,"B6",725,"N804JB","JFK","BQN",183,1576,5,45,2013-01-01 05:00:00
2013,1,1,554,600,-6,812,837,-25,"DL",461,"N668DN","LGA","ATL",116,762,6,0,2013-01-01 06:00:00
2013,1,1,554,558,-4,740,728,12,"UA",1696,"N39463","EWR","ORD",150,719,5,58,2013-01-01 05:00:00
2013,1,1,555,600,-5,913,854,19,"B6",507,"N516JB","EWR","FLL",158,1065,6,0,2013-01-01 06:00:00
2013,1,1,557,600,-3,709,723,-14,"EV",5708,"N829AS","LGA","IAD",53,229,6,0,2013-01-01 06:00:00
2013,1,1,557,600,-3,838,846,-8,"B6",79,"N593JB","JFK","MCO",140,944,6,0,2013-01-01 06:00:00
2013,1,1,558,600,-2,753,745,8,"AA",301,"N3ALAA","LGA","ORD",138,733,6,0,2013-01-01 06:00:00

Performance considerations:

Generally speaking pivoting is an expensive operation.

• 
  

  if you can try to provide values list:

  
  
  

  vs = list(range(25))
  %timeit -n10 flights.groupBy(*gexprs ).pivot("hour", vs).agg(aggexpr).count()
  ## 10 loops, best of 3: 392 ms per loop
  

  
  



• in some cases it proved to be beneficial (likely no longer worth the effort in 2.0 or later) to repartition and / or pre-aggregate the data




• for reshaping only, you can use first: How to use pivot and calculate average on a non-numeric column (facing AnalysisException "is not a numeric column")?

Related questions:

• How to melt Spark DataFrame?


• Unpivot in spark-sql/pyspark


• Transpose column to row with Spark

I overcame this by writing a for loop to dynamically create a SQL query. Say I have:

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

and I want:

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

I can create a list with the value I want to pivot and then create a string containing the SQL query I need.

val countries = List("US", "UK", "Can")
val numCountries = countries.length - 1

var query = "select *, "
for (i <- 0 to numCountries-1) 
 query += """case when tag = """" + countries(i) + """" then value else 0 end as """ + countries(i) + ", "

query += """case when tag = """" + countries.last + """" then value else 0 end as """ + countries.last + " from myTable"

myDataFrame.registerTempTable("myTable")
val myDF1 = sqlContext.sql(query)

I can create similar query to then do the aggregation. Not a very elegant solution but it works and is flexible for any list of values, which can also be passed in as an argument when your code is called.

A pivot operator has been added to the Spark dataframe API, and is part of Spark 1.6.

See https://github.com/apache/spark/pull/7841 for details.

I have solved a similar problem using dataframes with the following steps:

Create columns for all your countries, with 'value' as the value:

import org.apache.spark.sql.functions._
val countries = List("US", "UK", "Can")
val countryValue = udf(countryToCheck: String, countryInRow: String, value: Long) =>
 if(countryToCheck == countryInRow) value else 0

val countryFuncs = countries.mapcountry => (dataFrame: DataFrame) => dataFrame.withColumn(country, countryValue(lit(country), df("tag"), df("value"))) 
val dfWithCountries = Function.chain(countryFuncs)(df).drop("tag").drop("value")

Your dataframe 'dfWithCountries' will look like this:

+--+--+---+---+
|id|US| UK|Can|
+--+--+---+---+
| 1|50| 0| 0|
| 1| 0|100| 0|
| 1| 0| 0|125|
| 2|75| 0| 0|
| 2| 0|150| 0|
| 2| 0| 0|175|
+--+--+---+---+

Now you can sum together all the values for your desired result:

dfWithCountries.groupBy("id").sum(countries: _*).show

Result:

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

It's not a very elegant solution though. I had to create a chain of functions to add in all the columns. Also if I have lots of countries, I will expand my temporary data set to a very wide set with lots of zeroes.

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

There is simple and elegant solution.

scala> spark.sql("select * from k_tags limit 10").show()
+---------------+-------------+------+
| imsi| name| value|
+---------------+-------------+------+
|246021000000000| age| 37|
|246021000000000| gender|Female|
|246021000000000| arpu| 22|
|246021000000000| DeviceType| Phone|
|246021000000000|DataAllowance| 6GB|
+---------------+-------------+------+

scala> spark.sql("select * from k_tags limit 10").groupBy($"imsi").pivot("name").agg(min($"value")).show()
+---------------+-------------+----------+---+----+------+
| imsi|DataAllowance|DeviceType|age|arpu|gender|
+---------------+-------------+----------+---+----+------+
|246021000000000| 6GB| Phone| 37| 22|Female|
|246021000000001| 1GB| Phone| 72| 10| Male|
+---------------+-------------+----------+---+----+------+