我有一个 Spark Streaming 作业，每 5 秒从 Kafka 读取一次，对传入数据进行一些转换，然后写入文件系统。

这真的不需要成为流式传输作业，实际上，我只想每天运行一次以将消息排入文件系统。不过，我不确定如何停止这项工作。

如果我将超时传递给 streamingContext.awaitTermination，它不会停止进程，它所做的只是导致进程在需要迭代流时产生错误(参见下面的错误)

完成我想做的事情的最好方法是什么

这是针对 Python 上的 Spark 1.6

编辑:

感谢@marios，解决方案是这样的:

ssc.start()
ssc.awaitTermination(10)
ssc.stop()

在停止前运行脚本 10 秒。

简化代码:

conf = SparkConf().setAppName("Vehicle Data Consolidator").set('spark.files.overwrite','true')
sc = SparkContext(conf=conf)
ssc = StreamingContext(sc, 5)
stream = KafkaUtils.createStream(
    ssc,
    kafkaParams["zookeeper.connect"],
    "vehicle-data-importer",
    topicPartitions,
    kafkaParams)

stream.saveAsTextFiles('stream-output/kafka-vehicle-data')

ssc.start()
ssc.awaitTermination(10)

错误:

16/01/29 15:05:44 INFO BlockManagerInfo: Added input-0-1454097944200 in memory on localhost:58960 (size: 3.0 MB, free: 48.1 MB)
16/01/29 15:05:44 WARN BlockManager: Block input-0-1454097944200 replicated to only 0 peer(s) instead of 1 peers
16/01/29 15:05:44 INFO BlockGenerator: Pushed block input-0-1454097944200
16/01/29 15:05:45 ERROR JobScheduler: Error generating jobs for time 1454097945000 ms
py4j.Py4JException: Cannot obtain a new communication channel
    at py4j.CallbackClient.sendCommand(CallbackClient.java:232)
    at py4j.reflection.PythonProxyHandler.invoke(PythonProxyHandler.java:111)
    at com.sun.proxy.$Proxy14.call(Unknown Source)
    at org.apache.spark.streaming.api.python.TransformFunction.callPythonTransformFunction(PythonDStream.scala:92)
    at org.apache.spark.streaming.api.python.TransformFunction.apply(PythonDStream.scala:78)
    at org.apache.spark.streaming.api.python.PythonTransformedDStream.compute(PythonDStream.scala:230)
    at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:352)
    at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:352)
    at scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)
    at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:351)
    at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:351)
    at org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:426)
    at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:346)
    at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:344)
    at scala.Option.orElse(Option.scala:257)
    at org.apache.spark.streaming.dstream.DStream.getOrCompute(DStream.scala:341)
    at org.apache.spark.streaming.dstream.ForEachDStream.generateJob(ForEachDStream.scala:47)
    at org.apache.spark.streaming.DStreamGraph$$anonfun$1.apply(DStreamGraph.scala:115)
    at org.apache.spark.streaming.DStreamGraph$$anonfun$1.apply(DStreamGraph.scala:114)
    at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:251)
    at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:251)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
    at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:251)
    at scala.collection.AbstractTraversable.flatMap(Traversable.scala:105)
    at org.apache.spark.streaming.DStreamGraph.generateJobs(DStreamGraph.scala:114)
    at org.apache.spark.streaming.scheduler.JobGenerator$$anonfun$3.apply(JobGenerator.scala:248)
    at org.apache.spark.streaming.scheduler.JobGenerator$$anonfun$3.apply(JobGenerator.scala:246)
    at scala.util.Try$.apply(Try.scala:161)
    at org.apache.spark.streaming.scheduler.JobGenerator.generateJobs(JobGenerator.scala:246)
    at org.apache.spark.streaming.scheduler.JobGenerator.org$apache$spark$streaming$scheduler$JobGenerator$$processEvent(JobGenerator.scala:181)
    at org.apache.spark.streaming.scheduler.JobGenerator$$anon$1.onReceive(JobGenerator.scala:87)
    at org.apache.spark.streaming.scheduler.JobGenerator$$anon$1.onReceive(JobGenerator.scala:86)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
16/01/29 15:05:45 INFO MemoryStore: Block input-0-1454097944800 stored as bytes in memory (estimated size 3.0 MB, free 466.1 MB)
16/01/29 15:05:45 INFO BlockManagerInfo: Added input-0-1454097944800 in memory on localhost:58960 (size: 3.0 MB, free: 45.1 MB)

最佳答案

看来正确的调用方法是awaitTerminationOrTimeout ( self ，超时)。

我不确定它是否也停止了流上下文。所以也许您可以在超时结束后立即调用 ssc.stop()。

ssc.start()
ssc.awaitTerminationOrTimeout(10)
ssc.stop()

注:看看here对于类似的问题。

关于python - 数据源用完时如何停止 Spark 流，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/35093336/