如何在 Scala 和 Apache Spark 中加入兩個 DataFrame？

Question

有兩個數據幀（Scala、Apache Spark 1.6.1）

1) 比賽

         MatchID | Player1    |  Player2 
         --------------------------------
               1 | John Wayne | John Doe
               2 | Ive Fish   | San Simon

2) 個人資料

              Player     |  BirthYear 
              --------------------------------
              John Wayne | 1986
              Ive Fish   | 1990
              San Simon  | 1974
              john Doe   | 1995

如何為兩個玩家創建一個帶有“BirthYear”的新 DataFrame

         MatchID | Player1    |  Player2  | BYear_P1 |BYear_P2 | Diff
         -------------------------------------------------------------
               1 | John Wayne | John Doe  |   1986   | 1995    |  9  
               2 | Ive Fish   | San Simon |   1990   | 1974    |  16

?

我試過了

    val df = MatchesDF.join(PersonalDF, MatchesDF("Player1") === PersonalDF("Player"))

然后再次加入第二個玩家

    val resDf = df.join(PersonalDF, df("Player2") === PersonalDF("Player"))

但這是非常耗時的操作。

可能是在 Scala 和 Apache Spark 中執行此操作的另一種方法？

Answer 1

這是使用 spark 數據框函數的解決方案：

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

val matches = sqlContext.sparkContext.parallelize(Row(1, "John Wayne", "John Doe"), Row(2, "Ive Fish", "San Simon")))

val players = sqlContext.sparkContext.parallelize(Seq(
  Row("John Wayne", 1986),
  Row("Ive Fish", 1990),
  Row("San Simon", 1974),
  Row("John Doe", 1995)
))

val matchesDf = sqlContext.createDataFrame(matches, StructType(Seq(
  StructField("matchId", IntegerType, nullable = false),
  StructField("player1", StringType, nullable = false),
  StructField("player2", StringType, nullable = false)))
).as('matches)

val playersDf = sqlContext.createDataFrame(players, StructType(Seq(
  StructField("player", StringType, nullable = false),
  StructField("birthYear", IntegerType, nullable = false)
))).as('players)

matchesDf
  .join(playersDf, $"matches.player1" === $"players.player")
  .select($"matches.matchId" as "matchId", $"matches.player1" as "player1", $"matches.player2" as "player2", $"players.birthYear" as "player1BirthYear")
  .join(playersDf, $"player2" === $"players.player")
  .select($"matchId" as "MatchID", $"player1" as "Player1", $"player2" as "Player2", $"player1BirthYear" as "BYear_P1", $"players.birthYear" as "BYear_P2")
  .withColumn("Diff", abs('BYear_P2.minus('BYear_P1)))
  .show()

+-------+----------+---------+--------+--------+----+
|MatchID|   Player1|  Player2|BYear_P1|BYear_P2|Diff|
+-------+----------+---------+--------+--------+----+
|      1|John Wayne| John Doe|    1986|    1995|   9|
|      2|  Ive Fish|San Simon|    1990|    1974|  16|
+-------+----------+---------+--------+--------+----+

Answer 2

這應該表現更好：

case class Match(matchId: Int, player1: String, player2: String)
case class Player(name: String, birthYear: Int)

val matches = Seq(
  Match(1, "John Wayne", "John Doe"),
  Match(2, "Ive Fish", "San Simon")
)

val players = Seq(
  Player("John Wayne", 1986),
  Player("Ive Fish", 1990),
  Player("San Simon", 1974),
  Player("John Doe", 1995)
)

val matchesDf = sqlContext.createDataFrame(matches)
val playersDf = sqlContext.createDataFrame(players)

matchesDf.registerTempTable("matches")
playersDf.registerTempTable("players")

sqlContext.sql(
  "select matchId, player1, player2, p1.birthYear, p2.birthYear, abs(p1.birthYear-p2.birthYear) " +
  "from matches m inner join  players p1 inner join players p2 " +
  "where m.player1 = p1.name and m.player2 = p2.name").show()

+-------+----------+---------+---------+---------+---+
|matchId|   player1|  player2|birthYear|birthYear|_c5|
+-------+----------+---------+---------+---------+---+
|      1|John Wayne| John Doe|     1986|     1995|  9|
|      2|  Ive Fish|San Simon|     1990|     1974| 16|
+-------+----------+---------+---------+---------+---+

我沒有找到在 Scala DSL 中表達 3 個表的連接的方法。

Answer 3

val df = left.join(right, Seq("name"))
display(df)

Answer 4

在 Spark 2.0 及更高版本中，Spark 提供了幾種語法來連接兩個數據幀

join(right: Dataset[_]): DataFrame
join(right: Dataset[_], usingColumn: String): DataFrame
join(right: Dataset[_], usingColumns: Seq[String]): DataFrame
join(right: Dataset[_], usingColumns: Seq[String], joinType: String): DataFrame
join(right: Dataset[_], joinExprs: Column): DataFrame
join(right: Dataset[_], joinExprs: Column, joinType: String): DataFrame

Dataset 類中可用的所有這些Spark Join方法，這些方法返回 DataFrame（注意 DataFrame = Dataset[Row]）

所有這些方法都將第一個參數作為 Dataset[_] ，這意味着它也需要 DataFrame。

為了解釋如何加入，我將采取emp和dept DataFrame

empDF.join(deptDF,empDF("emp_dept_id") ===  deptDF("dept_id"),"inner")
    .show(false)

如果您必須在兩個數據幀上連接相同的列名，您甚至可以忽略連接表達式。