Scala vot是一个非常昂贵的操作，因此，建议提供列数据（如果已知）作为函数的参数，如下所示 val countries = Seq("USA","China","Canada","Mexico") val pivotDF = df.groupBy("Product").pivot("Country", countries).sum("Amount") pivotDF.show()

这一点已在中详细解释

---

快乐学习

有一种简单的旋转方法：

  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|
+---+---+---+---+

---

看到这里，我发布了一个本机Spark方法，它不需要提前知道列/类别名称。我试图复制您的示例，但我得到了一个“org.apache.Spark.sql.AnalysisException:无法解析给定输入列id、标记、值的‘US’”，这与引号有关。如果你看得到的文本字符串，你会得到'case when tag=US'，所以Spark认为那是一个列名而不是一个文本值。您真正想看到的是“case when tag=“US””。我已经编辑了上面的答案以获得正确的引号设置。但是，正如前面提到的，这是Functionality现在是使用pivot命令生成的。如果pivot数据框太大而无法容纳内存，该怎么办。如何直接在磁盘上执行此操作？应如何更改aggexpr=avg（“arr_delay”）以透视更多列，而不仅仅是SQL解决方案（不是Scala）中的1，我可以看到您使用硬编码列表（0、1、2、3、4、5、6、7、8、9、10、11、12、13、14、15、16、17、18、19、20、21、22、23）”。是否有任何方法可以使用从另一列获取的所有值？我在互联网和这个网站上搜索了一下，但什么也没找到。问题和@Windoze一样。如果需要手动提供列列表，那么SQL解决方案实际上并不等同于其他解决方案。是否可以通过select子查询获取列表？

+--+--+---+---+
|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|
+--+--+---+---+

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

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

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

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")));

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)

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)
 );

"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

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

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

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)
}

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

}

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|
+---------------+-------------+----------+---+----+------+

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; 

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

  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|
+---+---+---+---+