Flink Scala join between two Streams doesn't seem to work

Question

I want to join two streams (json) coming from a Kafka producer. The code works if I filter the data. But it seems not working when I join them. I want to print to the console the joined stream but nothing appears. This is my code

import java.util.Properties 
import org.apache.flink.streaming.api.scala._
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer010
import org.apache.flink.streaming.util.serialization.SimpleStringSchema
import org.json4s._
import org.json4s.native.JsonMethods
import org.apache.flink.streaming.api.windowing.assigners.TumblingEventTimeWindows
import org.apache.flink.streaming.api.windowing.time.Time

object App {

def main(args : Array[String]) {

case class Data(location: String, timestamp: Long, measurement: Int, unit: String, accuracy: Double)
case class Sensor(sensor_name: String, start_date: String, end_date: String, data_schema: Array[String], data: Data, stt: Stt)


case class Datas(location: String, timestamp: Long, measurement: Int, unit: String, accuracy: Double)
case class Sensor2(sensor_name: String, start_date: String, end_date: String, data_schema: Array[String], data: Datas, stt: Stt)


val properties = new Properties();
    properties.setProperty("bootstrap.servers", "0.0.0.0:9092");
    properties.setProperty("group.id", "test");

    val env = StreamExecutionEnvironment.getExecutionEnvironment
    val consumer1 = new FlinkKafkaConsumer010[String]("topics1", new SimpleStringSchema(), properties)
   val stream1 = env
   .addSource(consumer1)

   val consumer2 = new FlinkKafkaConsumer010[String]("topics2", new SimpleStringSchema(), properties)
   val stream2 = env
   .addSource(consumer2)

   val s1 = stream1.map { x => {
     implicit val formats = DefaultFormats
     JsonMethods.parse(x).extract[Sensor]
     }
   }
   val s2 = stream2.map { x => {
     implicit val formats = DefaultFormats
     JsonMethods.parse(x).extract[Sensor2]
     }
   }

  val s1t = s1.assignAscendingTimestamps { x => x.data.timestamp }
  val s2t = s2.assignAscendingTimestamps { x => x.data.timestamp }

  val j1pre = s1t.join(s2t)
              .where(_.data.unit)
              .equalTo(_.data.unit)
              .window(TumblingEventTimeWindows.of(Time.seconds(2L)))
              .apply((g, s) => (s.sensor_name, g.sensor_name, s.data.measurement))
   env.execute()

}

}

I think the problem is on the assignment of the timestamp. I think that the assignAscendingTimestamp on the two sources is not the right function.

The json produced by the kafka producer has a field data.timestamp that should be assigned as the timestamp. But I don't know how to manage that.

I also thought that i should have to give a time window batch (as in spark) to the incoming tuples. But I'm not sure this is the right solution.

Answer 1

I think your code needs just some minor adjustments. First of all as you want to work in EventTime you should set appropriate TimeCharacteristic

env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);

Also your code that you pasted is missing a sink for the stream. If you want to print to console you should:

j1pre.print

The rest of your code seems fine.