結合兩個rdds

Question

我是新來的火花，有人可以幫助我找到一種方法來組合兩個rdds以根據scala中的以下邏輯創建最終的rdd，最好不使用sqlcontext（dataframes）-

RDD1 = column1，column2，column3擁有362825條記錄

RDD2 = column2_distinct（與RDD1相同，但包含不同的值），column4具有2621條記錄

最終RDD =第1欄，第2欄，第3欄，第4欄

例-

RDD1 =

  userid |  progid  |  Rating
       a       001     5
       b       001     3
       b       002     4
       c       003     2

RDD2 =

   progid(distinct) |   id

   001                  1
   002                  2
   003                  3

最終RDD =

    userid  | progid  | id | rating
        a       001      1   5
        b       001      1   3
        b       002      2   4
        c       003      3   2

碼

val rawRdd1 = pairrdd1.map(x => x._1.split(",")(0) + "," + x._1.split(",")(1) + "," + x._2) //362825 records    

val rawRdd2 = pairrdd2.map(x => x._1 + "," + x._2) //2621 records    

val schemaString1 = "userid programid rating"    

val schemaString2 = "programid id"    

val fields1 = schemaString1.split(" ").map(fieldName => StructField(fieldName, StringType, nullable = true))    

val fields2 = schemaString2.split(" ").map(fieldName => StructField(fieldName, StringType, nullable = true))    

val schema1 = StructType(fields1)    
val schema2 = StructType(fields2)    


val rowRDD1 = rawRdd1.map(_.split(",")).map(attributes => Row(attributes(0), attributes(1), attributes(2)))    

val rowRDD2 = rawRdd2.map(_.split(",")).map(attributes => Row(attributes(0), attributes(1)))    

val DF1 = sparkSession.createDataFrame(rowRDD1, schema1)    

val DF2 = sparkSession.createDataFrame(rowRDD2, schema2)    


DF1.createOrReplaceTempView("df1")    

DF2.createOrReplaceTempView("df2")    


val resultDf = DF1.join(DF2, Seq("programid"))    

val DF3 = sparkSession.sql("""SELECT df1.userid, df1.programid, df2.id, df1.rating FROM  df1 JOIN df2 on df1.programid == df2.programid""")    


println(DF1.count())  //362825 records    

println(DF2.count())  //2621 records    

println(DF3.count())  //only 297 records

期望與DF1相同的記錄數，並且從DF2（id）附加了新列，並具有來自DF2的programid的對應值

Answer 1

這有點難看，但應該可以使用（Spark 2.0）：

 val rdd1 = sparkSession.sparkContext.parallelize(List("a,001,5", "b,001,3", "b,002,4","c,003,2"))
 val rdd2 = sparkSession.sparkContext.parallelize(List("001,1", "002,2", "003,3"))

 val groupedRDD1 = rdd1.map(x => (x.split(",")(1),x))
 val groupedRDD2 = rdd2.map(x => (x.split(",")(0),x))
 val joinRDD = groupedRDD1.join(groupedRDD2)
 // convert back to String
 val cleanJoinRDD = joinRDD.map(x => x._1 + "," + x._2._1.replace(x._1 + ",","") + "," + x._2._2.replace(x._1 + ",",""))
 cleanJoinRDD.collect().foreach(println)

我認為更好的選擇是使用Spark SQL

Answer 2

首先，為什么要再次拆分，連接和拆分行？ 您可以一步一步完成：

val rowRdd1 = pairrdd1.map{x => 
    val (userid, progid) = x._1.split(",") 
    val rating = x._2
    Row(userid, progid, rating) 
}

我的猜測是，您的問題可能是您的鍵中還有一些其他字符，因此聯接中的字符不匹配。 一種簡單的方法是進行left join並檢查不匹配的行。

這可能是行中多余的空間，您可以為兩個rdds這樣修復：

val rowRdd1 = pairrdd1.map{x =>  
    val (userid, progid) = x._1.split(",").map(_.trim)
    val rating = x._2
    Row(userid, progid, rating) 
}