Question

我使用Spark结构化流API从MQTT流源读取数据。

val lines:= spark.readStream
  .format("org.apache.bahir.sql.streaming.mqtt.MQTTStreamSourceProvider")
  .option("topic", "Employee")
  .option("username", "username")
  .option("password", "passwork")
  .option("clientId", "employee11")
  .load("tcp://localhost:8000").as[(String, Timestamp)]

我将流数据转换为案例类Employee

case class Employee(Name: String, Department: String)    
val ds = lines.map {
        row =>
          implicit val format = DefaultFormats
          parse(row._1).extract[Employee]
      }
  ....some transformations
 df.writeStream
        .outputMode("append")
        .format("es")
        .option("es.resource", "spark/employee")
        .option("es.nodes", "localhost")
        .option("es.port", 9200)
        .start()
        .awaitTermination()

现在，队列中有些消息的结构与案例类Employee不同。可以说缺少一些必填列。我的流作业失败，找不到字段。

现在，我将要处理此类异常，并且还希望针对该异常发送警报通知。我尝试放入try / catch块。

case class ErrorMessage(row: String)        
catch {

      case e: Exception =>
        val ds = lines.map {
          row =>
            implicit val format = DefaultFormats
            parse(row._1).extract[ErrorMessage]
        }
        val error = lines.foreach(row => {
          sendErrorMail(row._1)
        })
    }
  }

获得了Exception in thread "main" org.apache.spark.sql.AnalysisException: Queries with streaming sources must be executed with writeStream.start();; mqtt的例外任何帮助，将不胜感激。

Answer 1

我在catch块中创建了一个foreach接收器，并且能够处理异常并发送邮件警报。

catch {
    case e: Exception =>
      val foreachWriter = new ForeachWriter[Row] {
        override def open(partitionId: Timestamp, version: Timestamp): Boolean = {
          true
        }

        override def process(value: Row): Unit = {
         code for sending mail.........
        }

        override def close(errorOrNull: Throwable): Unit = {}
      }
      val df = lines.selectExpr("cast (value as string) as json")
      df.writeStream
        .foreach(foreachWriter)
        .outputMode("append")
        .start()
        .awaitTermination()
  }

Answer 2

我认为您应该使用Spark streaming doc中所述的start()方法的返回对象。像这样：

val query = df.writeStream. ... .start()
try {
   //If the query has terminated with an exception, then the exception will be thrown.
   query.awaitTermination()
catch {
   case ex: Exception => /*code to send mail*/
}

实施自己的foreach接收器会导致频繁打开和关闭连接的开销。

Spark结构化流式异常处理

2 个答案: