How to use Scala Case Class to map Kafka source in Spark Structured Streaming

Question

I am trying to use structured streaming in spark as it fits my use case well. However I cant seem to find a way to map the incoming data from Kafka into a case class. This is how far I could go based on official documentation.

import sparkSession.sqlContext.implicits._                          
val kafkaDF:DataFrame = sparkSession
                                          .readStream
                                          .format("kafka")
                                          .option("kafka.bootstrap.servers", bootstrapServers_CML)
                                          .option("subscribe", topics_ME)
                                          .option("startingOffsets", "latest")
                                          .load()
                                          .selectExpr("cast (value as string) as json") //Kakfa sends data in a specific schema (key, value, topic, offset, timestamp etc)    

val schema_ME = StructType(Seq(
    StructField("Parm1", StringType, true),
    StructField("Parm2", StringType, true),
    StructField("Parm3", TimestampType, true)))  

val mobEventDF:DataFrame = kafkaDF
                         .select(from_json($"json", schema_ME).as("mobEvent")) //Using a StructType to convert to application specific schema. Cant seem to use a case class for schema directly yet. Perhaps with later API??
                         .na.drop()

mobEventDF has a schema such as this

root
 |-- appEvent: struct (nullable = true)
 |    |-- Parm1: string (nullable = true)
 |    |-- Parm2: string (nullable = true)
 |    |-- Parm3: string (nullable = true)

Is there a better way to do this? How can I map this into a Scala Case class like the one below directly?

case class ME(name: String, 
                 factory: String,
                 delay: Timestamp)

Answer 1

选择并重命名所有字段，然后as方法调用

kafkaDF.select($"mobEvent.*").toDF("name", "factory", "delay").as[ME]