spark2.2错误 java.lang.UnsupportedOperationException: No Encoder found for org.apache.spark.sql.Row解决

spark2.2错误 java.lang.UnsupportedOperationException: No Encoder found for org.apache.spark.sql.Row解决办

1、错误描述：

Exception in thread "main" java.lang.UnsupportedOperationException: No Encoder found for org.apache.spark.sql.Row
- field (class: "org.apache.spark.sql.Row", name: "_2")
- root class: "scala.Tuple2"
at org.apache.spark.sql.catalyst.ScalaReflection$.org$apache$spark$sql$catalyst$ScalaReflection$$serializerFor(ScalaReflection.scala:625)
at org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$10.apply(ScalaReflection.scala:619)
at org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$10.apply(ScalaReflection.scala:607)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.immutable.List.foreach(List.scala:381)
at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)
at scala.collection.immutable.List.flatMap(List.scala:344)
at org.apache.spark.sql.catalyst.ScalaReflection$.org$apache$spark$sql$catalyst$ScalaReflection$$serializerFor(ScalaReflection.scala:607)
at org.apache.spark.sql.catalyst.ScalaReflection$.serializerFor(ScalaReflection.scala:438)
at org.apache.spark.sql.catalyst.encoders.ExpressionEncoder$.apply(ExpressionEncoder.scala:71)
at org.apache.spark.sql.Encoders$.product(Encoders.scala:275)
at org.apache.spark.sql.LowPrioritySQLImplicits$class.newProductEncoder(SQLImplicits.scala:233)
at org.apache.spark.sql.SQLImplicits.newProductEncoder(SQLImplicits.scala:33)
at com.xmd.flow.test$.specifyingTheSchema(test.scala:57)
at com.xmd.flow.test$.main(test.scala:105)
at com.xmd.flow.test.main(test.scala)

2、错误代码：

people.txt的内容为：

Michael, 29, 男
Andy, 30, 女
Justin, 19, 男
Andy, 31, 女
Justin, 18, 男

 

错误代码的方法是：

/**
  * Spark SQL支持将现有RDD转换为Datasets的两种不同方法。本例子是使用编程的方式指定schema
  * 1、使用反射推理schema
  * 2、以编程方式指定schema
  *
  * @param spark
  */
def specifyingTheSchema(spark: SparkSession): Unit = {
  import org.apache.spark.sql.types._
  import spark.implicits._

  // 创建一个RDD
  val peopleRDD = spark.sparkContext.textFile("spark-2.2.0/examples/src/main/resources/people.txt")
  // schema 是一个String的编码方式
  val schemaString = "name age gender"
  // 基于String创建schema
  val fields = schemaString.split(" ")
    .map(fieldName => StructField(fieldName, StringType, nullable = true))
  val schema = StructType(fields)
  // 将RDD转换为行
  val rowRDD = peopleRDD
    .map(_.split(","))
    .map(attributes => Row(attributes(0), attributes(1).trim, attributes(2).trim))
  // 用这个schema创建RDD
  val peopleDF = spark.createDataFrame(rowRDD, schema)
  peopleDF.createOrReplaceTempView("people")

  // SQL可以在使用DataFrames创建的临时视图中运行
  val results = spark.sql("SELECT * FROM people")//.rdd

  println(results.schema)
  results.show()
    results.map{row => (row.getAs[String]("name") + "#" + row.getAs[String]("age"), row)}
      .rdd
      .groupByKey()
      .foreach(item => {
        println(item._1)
        for(row <- item._2) {
          println("------" + row.getAs[String]("gender"))
        }
      })
}

3、正确的代码：

  def specifyingTheSchema(spark: SparkSession): Unit = {
    import org.apache.spark.sql.types._
    import spark.implicits._

    // 创建一个RDD
    val peopleRDD = spark.sparkContext.textFile("/spark-2.2.0/examples/src/main/resources/people.txt")
    // schema 是一个String的编码方式
    val schemaString = "name age gender"
    // 基于String创建schema
    val fields = schemaString.split(" ")
      .map(fieldName => StructField(fieldName, StringType, nullable = true))
    val schema = StructType(fields)
    // 将RDD转换为行
    val rowRDD = peopleRDD
      .map(_.split(","))
      .map(attributes => Row(attributes(0), attributes(1).trim, attributes(2).trim))
    // 用这个schema创建RDD
    val peopleDF = spark.createDataFrame(rowRDD, schema)
    peopleDF.createOrReplaceTempView("people")

    // SQL可以在使用DataFrames创建的临时视图中运行
    val results = spark.sql("SELECT * FROM people")//.rdd

    println(results.schema)
    results.show()
    import org.apache.spark.sql.catalyst.encoders.RowEncoder
//    implicit val encoder = RowEncoder(schema)

    val encoder = Encoders.tuple(
      Encoders.STRING,
      RowEncoder(
        results.schema
        /*StructType(Seq(
          StructField("name", StringType),
          StructField("age", StringType),
          StructField("gender", StringType)))*/
      ))

    //    val conversionBaseKV: KeyValueGroupedDataset[String, (String, String)] =
      results.map{row => (row.getAs[String]("name") + "#" + row.getAs[String]("age"), row)}(encoder)
        .rdd
        .groupByKey()
        .foreach(item => {
          println(item._1)
          for(row <- item._2) {
            println("------" + row.getAs[String]("gender"))
          }
        })
  }

4、修改部分：指定了tuple2的encode

val encoder = Encoders.tuple(
  Encoders.STRING,
  RowEncoder(
    results.schema
    /*StructType(Seq(
      StructField("name", StringType),
      StructField("age", StringType),
      StructField("gender", StringType)))*/
  ))

//    val conversionBaseKV: KeyValueGroupedDataset[String, (String, String)] =
  results.map{row => (row.getAs[String]("name") + "#" + row.getAs[String]("age"), row)}(encoder)