Spark streaming does not work in a stand-alone cluster deployed to VM

Question

I wrote a Kafka thread program using Scala and running a stand-alone Spark cluster. The code works fine in my local. I set up Kafka, Kassandra and Spark in Azure VM. I opened all inbound and outbound ports to avoid port blocking.

the master has begun

SBIN> ./start-master.sh

Initial subordinate

sbin #. / start-slave.sh Spark: // vm-hostname: 7077

I checked this status in the main WEB interface.

Submit task

bin #. / spark-submit --class xyStreamJob --master spark: // vm-hostname: 7077 / home / user / appl.jar

I noticed that the application has been added and displayed in Master WEB UI.

I posted several posts on the topic, but the messages were not received and not saved in Cassandra DB.

- , Streaming .

VM ?

VM?

def main(args: Array[String]): Unit = {
    val spark = SparkHelper.getOrCreateSparkSession()
    val ssc = new StreamingContext(spark.sparkContext, Seconds(1))
    spark.sparkContext.setLogLevel("WARN")
    val kafkaStream = {
      val kafkaParams = Map[String, Object](
        "bootstrap.servers" -> 
                "vmip:9092",
        "key.deserializer" -> classOf[StringDeserializer],
        "value.deserializer" -> classOf[StringDeserializer],
        "group.id" -> "loc",
        "auto.offset.reset" -> "latest",
        "enable.auto.commit" -> (false: java.lang.Boolean)
      )

      val topics = Array("hello")
      val numPartitionsOfInputTopic = 3
      val streams = (1 to numPartitionsOfInputTopic) map {
        _ => KafkaUtils.createDirectStream[String, String]( ssc, PreferConsistent, Subscribe[String, String](topics, kafkaParams) )
      }
     streams
    }


    kafkaStream.foreach(rdd=> {
      rdd.foreachRDD(conRec=> {
        val offsetRanges = conRec.asInstanceOf[HasOffsetRanges].offsetRanges
        conRec.foreach(str=> {
          try {
            println(str.value().trim)
            CassandraHelper.saveItemEvent(str.value().trim)

          }catch {
            case ex: Exception => {
              println(ex.getMessage)
            }
          }
        })
        rdd.asInstanceOf[CanCommitOffsets].commitAsync(offsetRanges)
      })
      println("Read Msg")
    })
    println(" Spark parallel reader is ready !!!")
    ssc.start()
    ssc.awaitTermination()
  }

  def getSparkConf(): SparkConf = {
    val conf = new SparkConf(true)
      .setAppName("TestAppl")
      .set("spark.cassandra.connection.host", "vmip")
      .set("spark.streaming.stopGracefullyOnShutdown","true")
    .setMaster("spark://vm-hostname:7077")

    conf
  }

scalaVersion := "2.11.8"
val sparkVersion = "2.2.0"
val connectorVersion = "2.0.7"


libraryDependencies ++= Seq(
  "org.apache.spark" %% "spark-core" % sparkVersion %"provided",
  "org.apache.spark" %% "spark-sql" % sparkVersion  %"provided",
  "org.apache.spark" %% "spark-hive" % sparkVersion %"provided",
  "com.datastax.spark" %% "spark-cassandra-connector" % connectorVersion  ,
  "org.apache.kafka" %% "kafka" % "0.10.1.0",
  "org.apache.spark" %% "spark-streaming-kafka-0-10" % sparkVersion,
  "org.apache.spark" %% "spark-streaming" %  sparkVersion  %"provided",
)
mergeStrategy in assembly := {
  case PathList("org", "apache", "spark", "unused", "UnusedStubClass.class") => MergeStrategy.first
  case x => (mergeStrategy in assembly).value(x)
}