Apache spark Spark如何将[JSONObject]RDD到数据集

我正在从com.google.gson.JsonObject类型的元素的RDD中读取数据。试图将其转换为数据集，但不知道如何实现

import com.google.gson.{JsonParser}
import org.apache.hadoop.io.LongWritable
import org.apache.spark.sql.{SparkSession}

object tmp {
  class people(name: String, age: Long, phone: String)
  def main(args: Array[String]): Unit = {
    val spark = SparkSession.builder().master("local[*]").getOrCreate()
    val sc = spark.sparkContext

    val parser = new JsonParser();
    val jsonObject1 = parser.parse("""{"name":"abc","age":23,"phone":"0208"}""").getAsJsonObject()
    val jsonObject2 = parser.parse("""{"name":"xyz","age":33}""").getAsJsonObject()

    val PairRDD = sc.parallelize(List(
      (new LongWritable(1l), jsonObject1),
      (new LongWritable(2l), jsonObject2)
    ))

    val rdd1 =PairRDD.map(element => element._2)

    import spark.implicits._

    //How to create Dataset as schema People from rdd1?
  }
}

即使尝试打印rdd1元素也会抛出

object not serializable (class: org.apache.hadoop.io.LongWritable, value: 1)
- field (class: scala.Tuple2, name: _1, type: class java.lang.Object)
- object (class scala.Tuple2, (1,{"name":"abc","age":23,"phone":"0208"}))

基本上，我从表中得到这个RDD[LongWritable，JsonParser]，我想将它转换为Dataset，这样我就可以应用SQL进行转换

---

我在第二条记录中故意将phone保留为null，BigQuery不会为具有null值的元素返回任何内容。

感谢您的澄清。您需要在kryo中将该类注册为可序列化的。下面是我的表演作品。我在spark shell中运行，因此必须销毁旧的上下文，并使用包含注册的Kryo类的配置创建新的spark上下文

import com.google.gson.{JsonParser}
import org.apache.hadoop.io.LongWritable
import org.apache.spark.SparkContext

sc.stop()

val conf = sc.getConf
conf.registerKryoClasses( Array(classOf[LongWritable], classOf[JsonParser] ))
conf.get("spark.kryo.classesToRegister")

val sc = new SparkContext(conf)

val parser = new JsonParser();
val jsonObject1 = parser.parse("""{"name":"abc","age":23,"phone":"0208"}""").getAsJsonObject()
val jsonObject2 = parser.parse("""{"name":"xyz","age":33}""").getAsJsonObject()

val pairRDD = sc.parallelize(List(
  (new LongWritable(1l), jsonObject1),
  (new LongWritable(2l), jsonObject2)
))


val rdd = pairRDD.map(element => element._2)

rdd.collect()
// res9: Array[com.google.gson.JsonObject] = Array({"name":"abc","age":23,"phone":"0208"}, {"name":"xyz","age":33})

val jsonstrs = rdd.map(e=>e.toString).collect()
val df = spark.read.json( sc.parallelize(jsonstrs) )    
df.printSchema
// root
//  |-- age: long (nullable = true)
//  |-- name: string (nullable = true)
//  |-- phone: string (nullable = true)

---

谢谢Shoaib，我已经编辑了我的问题，如果这能提供更多的想法。