Question

我已将Kafka Stream连接到Spark。我已经根据流文本训练了Apache Spark Mlib模型进行预测。我的问题是，得到一个预测，我需要传递一个DataFramework。

//kafka stream    
val stream = KafkaUtils.createDirectStream[String, String](
          ssc,
          PreferConsistent,
          Subscribe[String, String](topics, kafkaParams)
        )
//load mlib model
val model = PipelineModel.load(modelPath)
 stream.foreachRDD { rdd =>

      rdd.foreach { record =>
       //to get a prediction need to pass DF
       val toPredict = spark.createDataFrame(Seq(
          (1L, record.value())
        )).toDF("id", "review")
        val prediction = model.transform(test)
      }
}

我的问题是，Spark流不允许创建DataFrame。有没有办法做到这一点？我可以使用案例类或结构吗？

Answer 1

可以像在核心Spark中一样从RDD创建DataFrame或Dataset。为此，我们需要应用架构。在foreachRDD中，我们可以将生成的RDD转换为可以进一步与ML管道一起使用的DataFrame。

// we use a schema in the form of a case class
case class MyStructure(field:type, ....)
// and we implement our custom transformation from string to our structure
object MyStructure {
    def parse(str: String) : Option[MyStructure] = ...
}

val stream = KafkaUtils.createDirectStream... 
// give the stream a schema using a case class
val strucStream =  stream.flatMap(cr => MyStructure.parse(cr.value))

strucStream.foreachRDD { rdd =>
    import sparkSession.implicits._
    val df = rdd.toDF()
    val prediction = model.transform(df)
    // do something with df
}

在Spark Stream中创建DataFrame

1 个答案: