Create a DataFrame in Spark Stream - apache-spark

Question

I've connected the Kafka Stream to the Spark. As well as I've trained Apache Spark Mlib model to prediction based on a streamed text. My problem is, get a prediction I need to pass a 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)
}
}
My problem is, Spark streaming doesn't allow to create a DataFrame. Is there any way to do that? Can I use case class or struct?

Answer 1

It's possible to create a DataFrame or Dataset from an RDD as you would in core Spark. To do that, we need to apply a schema. Within the foreachRDD we can then transform the resulting RDD into a DataFrame that can be further used with an ML pipeline.
// 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
}