在Spark / Scala中將RDD轉換為Dataframe

Question

RDD已以Array[Array[String]]格式創建，並具有以下值：

val rdd : Array[Array[String]] = Array(
Array("4580056797", "0", "2015-07-29 10:38:42", "0", "1", "1"), 
Array("4580056797", "0", "2015-07-29 10:38:43", "0", "1", "1"))

我想用架構創建一個dataFrame：

val schemaString = "callId oCallId callTime duration calltype swId"

下一步：

scala> val rowRDD = rdd.map(p => Array(p(0), p(1), p(2),p(3),p(4),p(5).trim))
rowRDD: org.apache.spark.rdd.RDD[Array[String]] = MapPartitionsRDD[14] at map at <console>:39
scala> val calDF = sqlContext.createDataFrame(rowRDD, schema)

給出以下錯誤：

console:45: error: overloaded method value createDataFrame with alternatives:
     (rdd: org.apache.spark.api.java.JavaRDD[_],beanClass: Class[_])org.apache.spark.sql.DataFrame <and>
    (rdd: org.apache.spark.rdd.RDD[_],beanClass: Class[_])org.apache.spark.sql.DataFrame <and>
    (rowRDD: org.apache.spark.api.java.JavaRDD[org.apache.spark.sql.Row],schema: org.apache.spark.sql.types.StructType)org.apache.spark.sql.DataFrame <and>
    (rowRDD: org.apache.spark.rdd.RDD[org.apache.spark.sql.Row],schema: org.apache.spark.sql.types.StructType)org.apache.spark.sql.DataFrame
    cannot be applied to (org.apache.spark.rdd.RDD[Array[String]],   
    org.apache.spark.sql.types.StructType)
       val calDF = sqlContext.createDataFrame(rowRDD, schema)

Answer 1

只需粘貼到spark-shell ：

val a = 
  Array(
    Array("4580056797", "0", "2015-07-29 10:38:42", "0", "1", "1"), 
    Array("4580056797", "0", "2015-07-29 10:38:42", "0", "1", "1"))

val rdd = sc.makeRDD(a)

case class X(callId: String, oCallId: String, 
  callTime: String, duration: String, calltype: String, swId: String)

然后在RDD上map()以創建案例類的實例，然后使用toDF()創建DataFrame：

scala> val df = rdd.map { 
  case Array(s0, s1, s2, s3, s4, s5) => X(s0, s1, s2, s3, s4, s5) }.toDF()
df: org.apache.spark.sql.DataFrame = 
  [callId: string, oCallId: string, callTime: string, 
    duration: string, calltype: string, swId: string]

這推斷出案例類的架構。

然后你可以繼續：

scala> df.printSchema()
root
 |-- callId: string (nullable = true)
 |-- oCallId: string (nullable = true)
 |-- callTime: string (nullable = true)
 |-- duration: string (nullable = true)
 |-- calltype: string (nullable = true)
 |-- swId: string (nullable = true)

scala> df.show()
+----------+-------+-------------------+--------+--------+----+
|    callId|oCallId|           callTime|duration|calltype|swId|
+----------+-------+-------------------+--------+--------+----+
|4580056797|      0|2015-07-29 10:38:42|       0|       1|   1|
|4580056797|      0|2015-07-29 10:38:42|       0|       1|   1|
+----------+-------+-------------------+--------+--------+----+

如果你想在普通程序中使用toDF() （而不是在spark-shell ），請確保（引自此處 ）：

• 在創建SQLContext后立即import sqlContext.implicits._ SQLContext
• 使用toDF()在方法之外定義case類

Answer 2

您需要首先將Array轉換為Row ，然后定義架構。 我假設你的大部分領域都很Long

    val rdd: RDD[Array[String]] = ???
    val rows: RDD[Row] = rdd map {
      case Array(callId, oCallId, callTime, duration, swId) =>
        Row(callId.toLong, oCallId.toLong, callTime, duration.toLong, swId.toLong)
    }

    object schema {
      val callId = StructField("callId", LongType)
      val oCallId = StructField("oCallId", StringType)
      val callTime = StructField("callTime", StringType)
      val duration = StructField("duration", LongType)
      val swId = StructField("swId", LongType)

      val struct = StructType(Array(callId, oCallId, callTime, duration, swId))
    }

    sqlContext.createDataFrame(rows, schema.struct)

Answer 3

我假設您的schema與Spark指南一樣 ，如下所示：

val schema =
  StructType(
    schemaString.split(" ").map(fieldName => StructField(fieldName, StringType, true)))

如果你看一下createDataFrame的簽名，這里接受一個StructType作為第二個參數（對於Scala）

def createDataFrame（rowRDD：RDD [Row]，schema：StructType）：DataFrame

使用給定的模式從包含Rows的RDD創建DataFrame。

所以它接受RDD[Row]作為第一個參數。 你在rowRDD中rowRDD是RDD[Array[String]]因此存在不匹配。

你需要一個RDD[Array[String]]嗎？

否則，您可以使用以下內容創建數據幀：

val rowRDD = rdd.map(p => Row(p(0), p(1), p(2),p(3),p(4),p(5).trim))

Answer 4

使用spark 1.6.1和scala 2.10

我得到了相同的錯誤error: overloaded method value createDataFrame with alternatives:

對我來說，gotcha是createDataFrame中的簽名，我試圖使用val rdd : List[Row] ，但它失敗了，因為java.util.List[org.apache.spark.sql.Row]和scala.collection.immutable.List[org.apache.spark.sql.Row]不一樣。

我找到的工作解決方案是通過List[Array[String]]將val rdd : Array[Array[String]]轉換為RDD[Row] List[Array[String]] 。 我發現這是最接近文檔中的內容

import org.apache.spark.sql.Row
import org.apache.spark.sql.types.{StructType,StructField,StringType};
val sqlContext = new org.apache.spark.sql.SQLContext(sc)

val rdd_original : Array[Array[String]] = Array(
    Array("4580056797", "0", "2015-07-29 10:38:42", "0", "1", "1"), 
    Array("4580056797", "0", "2015-07-29 10:38:42", "0", "1", "1"))

val rdd : List[Array[String]] = rdd_original.toList

val schemaString = "callId oCallId callTime duration calltype swId"

// Generate the schema based on the string of schema
val schema =
  StructType(
    schemaString.split(" ").map(fieldName => StructField(fieldName, StringType, true)))

// Convert records of the RDD to Rows.
val rowRDD = rdd.map(p => Row(p: _*)) // using splat is easier
// val rowRDD = rdd.map(p => Row(p(0), p(1), p(2), p(3), p(4), p(5))) // this also works

val df = sqlContext.createDataFrame(sc.parallelize(rowRDD:List[Row]), schema)
df.show