有没有一种方法可以使用模式将来自kafka和spark的avro消息转换为Dataframe？用户记录的架构文件：

{
  "fields": [
    { "name": "firstName", "type": "string" },
    { "name": "lastName", "type": "string" }
  ],
  "name": "user",
  "type": "record"
}

以及sqlnetworkwordcount示例和kafka、spark和avro的代码片段—第3部分，生成和使用avro消息以读入消息。

object Injection {
  val parser = new Schema.Parser()
  val schema = parser.parse(getClass.getResourceAsStream("/user_schema.json"))
  val injection: Injection[GenericRecord, Array[Byte]] = GenericAvroCodecs.toBinary(schema)
}

...

messages.foreachRDD((rdd: RDD[(String, Array[Byte])]) => {
  val sqlContext = SQLContextSingleton.getInstance(rdd.sparkContext)
  import sqlContext.implicits._

  val df = rdd.map(message => Injection.injection.invert(message._2).get)
    .map(record => User(record.get("firstName").toString, records.get("lastName").toString)).toDF()

  df.show()
})

case class User(firstName: String, lastName: String)

不知何故，除了使用case类将avro消息转换为dataframe之外，我找不到其他方法。有没有可能改用模式？我在用 Spark 1.6.2 以及 Kafka 0.10 .
完整的代码，如果你感兴趣的话。

import com.twitter.bijection.Injection
import com.twitter.bijection.avro.GenericAvroCodecs
import kafka.serializer.{DefaultDecoder, StringDecoder}
import org.apache.avro.Schema
import org.apache.avro.generic.GenericRecord
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.SQLContext
import org.apache.spark.streaming.kafka._
import org.apache.spark.streaming.{Seconds, StreamingContext, Time}
import org.apache.spark.{SparkConf, SparkContext}

object ReadMessagesFromKafka {
  object Injection {
    val parser = new Schema.Parser()
    val schema = parser.parse(getClass.getResourceAsStream("/user_schema.json"))
    val injection: Injection[GenericRecord, Array[Byte]] = GenericAvroCodecs.toBinary(schema)
  }

  def main(args: Array[String]) {
    val brokers = "127.0.0.1:9092"
    val topics = "test"

    // Create context with 2 second batch interval
    val sparkConf = new SparkConf().setAppName("ReadMessagesFromKafka").setMaster("local[*]")
    val ssc = new StreamingContext(sparkConf, Seconds(2))

    // Create direct kafka stream with brokers and topics
    val topicsSet = topics.split(",").toSet
    val kafkaParams = Map[String, String]("metadata.broker.list" -> brokers)
    val messages = KafkaUtils.createDirectStream[String, Array[Byte], StringDecoder, DefaultDecoder](
  ssc, kafkaParams, topicsSet)

    messages.foreachRDD((rdd: RDD[(String, Array[Byte])]) => {
      val sqlContext = SQLContextSingleton.getInstance(rdd.sparkContext)
      import sqlContext.implicits._

      val df = rdd.map(message => Injection.injection.invert(message._2).get)
    .map(record => User(record.get("firstName").toString, records.get("lastName").toString)).toDF()

      df.show()
    })

    // Start the computation
    ssc.start()
    ssc.awaitTermination()
  }
}

/**Case class for converting RDD to DataFrame */
case class User(firstName: String, lastName: String)

/**Lazily instantiated singleton instance of SQLContext */
object SQLContextSingleton {
  @transient  private var instance: SQLContext = _

  def getInstance(sparkContext: SparkContext): SQLContext = {
    if (instance == null) {
      instance = new SQLContext(sparkContext)
    }
    instance
  }
}