我正在尝试让pyspark流处理在multiprocessing.Process中工作，这样我就可以做其他事情，例如同时向流中发送消息。这是我的代码的简化版本：
import os
from multiprocessing import Process
from kafka import KafkaProducer
from pyspark import SparkContext
from pyspark.streaming import StreamingContext
from pyspark.streaming.kafka import KafkaUtils
def runStream(stream):
stream.start()
stream.awaitTermination(30)
topic_name = "time-analyzer"
kafka_broker = "localhost:9092"
if __name__ == '__main__':
os.environ['PYSPARK_SUBMIT_ARGS'] = '--packages org.apache.spark:spark-streaming-kafka-0-8_2.11:2.0.2 pyspark-shell'
producer = KafkaProducer(bootstrap_servers = kafka_broker)
sc = SparkContext()
ssc = StreamingContext(sc, 5)
directKafkaStream = KafkaUtils.createDirectStream(ssc, [topic_name], {'metadata.broker.list': kafka_broker})
parsers = directKafkaStream.map(lambda v: v)
parsers.pprint()
p = Process(target=runStream, args=(ssc,))
p.start()
p.join()
#ssc.start()
#ssc.awaitTermination(30)
我的zookeeper和kafka服务器正在运行，当我尝试运行此代码时出现此错误：
xxx@xxx:xxx$ xx/xx/xx xx:xx:xx ERROR JobScheduler: Error generating jobs for time 1499813965000 ms
py4j.Py4JException: Error while sending a command.
at py4j.CallbackClient.sendCommand(CallbackClient.java:357)
at py4j.CallbackClient.sendCommand(CallbackClient.java:316)
at py4j.reflection.PythonProxyHandler.invoke(PythonProxyHandler.java:103)
at com.sun.proxy.$Proxy16.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:246)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:341)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:341)
at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:340)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:340)
at org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:415)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:335)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:333)
at scala.Option.orElse(Option.scala:289)
at org.apache.spark.streaming.dstream.DStream.getOrCompute(DStream.scala:330)
at org.apache.spark.streaming.api.python.PythonTransformedDStream.compute(PythonDStream.scala:244)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:341)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:341)
at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:340)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:340)
at org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:415)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:335)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:333)
at scala.Option.orElse(Option.scala:289)
at org.apache.spark.streaming.dstream.DStream.getOrCompute(DStream.scala:330)
at org.apache.spark.streaming.dstream.ForEachDStream.generateJob(ForEachDStream.scala:48)
at org.apache.spark.streaming.DStreamGraph$$anonfun$1.apply(DStreamGraph.scala:117)
at org.apache.spark.streaming.DStreamGraph$$anonfun$1.apply(DStreamGraph.scala:116)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)
at scala.collection.AbstractTraversable.flatMap(Traversable.scala:104)
at org.apache.spark.streaming.DStreamGraph.generateJobs(DStreamGraph.scala:116)
at org.apache.spark.streaming.scheduler.JobGenerator$$anonfun$3.apply(JobGenerator.scala:249)
at org.apache.spark.streaming.scheduler.JobGenerator$$anonfun$3.apply(JobGenerator.scala:247)
at scala.util.Try$.apply(Try.scala:192)
at org.apache.spark.streaming.scheduler.JobGenerator.generateJobs(JobGenerator.scala:247)
at org.apache.spark.streaming.scheduler.JobGenerator.org$apache$spark$streaming$scheduler$JobGenerator$$processEvent(JobGenerator.scala:183)
at org.apache.spark.streaming.scheduler.JobGenerator$$anon$1.onReceive(JobGenerator.scala:89)
at org.apache.spark.streaming.scheduler.JobGenerator$$anon$1.onReceive(JobGenerator.scala:88)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
Caused by: py4j.Py4JNetworkException
at py4j.CallbackConnection.sendCommand(CallbackConnection.java:138)
at py4j.CallbackClient.sendCommand(CallbackClient.java:344)
... 45 more
但是，如果我注释掉流程代码并取消注释后面的块，代码就可以正常工作。有谁知道如何在流程中使用流？