在 Spark Streaming 中,有没有办法检测批处理何时完成?

In Spark Streaming, is there a way to detect when a batch has finished?

我将 Spark 1.6.0 与 Cloudera 5.8.3 一起使用。
我有一个 DStream 对象并在其上定义了大量转换,

val stream = KafkaUtils.createDirectStream[...](...)
val mappedStream = stream.transform { ... }.map { ... }
mappedStream.foreachRDD { ... }
mappedStream.foreachRDD { ... }
mappedStream.map { ... }.foreachRDD { ... }

有没有办法注册最后一个 foreachRDD 保证最后执行且仅当上述 foreachRDD 完成执行时?
换句话说,当 Spark UI 显示作业已完成时 - 那就是我想要执行轻量级函数的时候。

API 中有什么东西可以让我实现这一目标吗?

谢谢

使用流式监听器应该可以解决您的问题:

(抱歉,这是一个 java 示例)

ssc.addStreamingListener(new JobListener());

// ...

class JobListener implements StreamingListener {

 @Override
    public void onBatchCompleted(StreamingListenerBatchCompleted batchCompleted) {

        System.out.println("Batch completed, Total delay :" + batchCompleted.batchInfo().totalDelay().get().toString() +  " ms");

    }

   /*

   snipped other methods

   */


}

https://gist.github.com/akhld/b10dc491aad1a2007183

https://jaceklaskowski.gitbooks.io/mastering-apache-spark/content/spark-streaming/spark-streaming-streaminglisteners.html

http://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.streaming.scheduler.StreamingListener

启动名称为 myStreamName 的流并等待它启动 -

deltaStreamingQuery = (streamingDF
  .writeStream
  .format("delta")
  .queryName(myStreamName)
  .start(writePath)
)

untilStreamIsReady(myStreamName) 

PySpark 版本等待流启动:

def getActiveStreams():
  try:
    return spark.streams.active
  except:
    print("Unable to iterate over all active streams - using an empty set instead.")
    return []

def untilStreamIsReady(name, progressions=3):
  import time
  queries = list(filter(lambda query: query.name == name, getActiveStreams()))

  while (len(queries) == 0 or len(queries[0].recentProgress) < progressions):
    time.sleep(5) # Give it a couple of seconds
    queries = list(filter(lambda query: query.name == name, getActiveStreams()))

  print("The stream {} is active and ready.".format(name))

Spark Scala 版本等待流启动:

def getActiveStreams():Seq[org.apache.spark.sql.streaming.StreamingQuery] = {
  return try {
    spark.streams.active
  } catch {
    case e:Throwable => {
      // In extream cases, this funtion may throw an ignorable error.
      println("Unable to iterate over all active streams - using an empty set instead.")
      Seq[org.apache.spark.sql.streaming.StreamingQuery]()
    }
  }
}

def untilStreamIsReady(name:String, progressions:Int = 3):Unit = {
  var queries = getActiveStreams().filter(_.name == name)

  while (queries.length == 0 || queries(0).recentProgress.length < progressions) {
    Thread.sleep(5*1000) // Give it a couple of seconds
    queries = getActiveStreams().filter(_.name == name)
  }
  println("The stream %s is active and ready.".format(name))
}

对于最初的问题.. 添加此函数的另一个版本 - 首先等待流启动,然后再等待一次(只需在等待状态上添加一个否定条件)让它完成,这样就完成了版本看起来像这样 -

untilStreamIsReady(myStreamName) 
untilStreamIsDone(myStreamName)   // reverse of untilStreamIsReady - wait when myStreamName will not be in the list