Python 结构化流作业在终止时失败

我在PySpark中有MQTT使用者，但当我将数据发送到MQTT主题时，PySpark代码失败

from pyspark.sql import SparkSession

spark = SparkSession \
    .builder \
    .appName("Test") \
    .getOrCreate()

lines = (spark
             .readStream
             .format("org.apache.bahir.sql.streaming.mqtt.MQTTStreamSourceProvider")
             .option("topic","my_topic")
             .load("tcp://127.0.0.1:1883"))

query = lines.writeStream.format("console").start()

lines.printSchema()

query.awaitTermination()

我不使用Spark向MQTT发送消息

import sys
import paho.mqtt.client as mqtt
import logging


def on_connect(client, userdata, flags, rc):
    print('connected (%s)' % client._client_id)


def on_message(client, userdata, message):
    print('------------------------------')
    print('topic: %s' % message.topic)
    print('payload: %s' % message.payload)
    print('qos: %d' % message.qos)


def main(argv):
    broker = "127.0.0.1"
    port = 1883
    mqttc = mqtt.Client("Test1")
    print("connecting to broker ", broker)
    mqttc.connect(broker, port, 60)
    mqttc.on_connect = on_connect
    mqttc.on_message = on_message

    mqttc.subscribe("my_topic")

    #mqttc.loop_start()

    print("publishing ")
    mqttc.publish("my_topic","{\"messageeeee\"}")

    mqttc.loop_forever()


if __name__ == '__main__':
    main(sys.argv[1:])

这是每次在MQTT中生成新消息时停止PySpark流作业的错误消息：

Logical Plan:
org.apache.bahir.sql.streaming.mqtt.MQTTTextStreamSource@72a6d759
    at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runStream(StreamExecution.scala:295)
    at org.apache.spark.sql.execution.streaming.StreamExecution$$anon$1.run(StreamExecution.scala:189)
Caused by: java.lang.AssertionError: assertion failed: DataFrame returned by getBatch from org.apache.bahir.sql.streaming.mqtt.MQTTTextStreamSource@72a6d759 did not have isStreaming=true
Project [_1#10 AS value#13, _2#11 AS timestamp#14]
+- AnalysisBarrier
      +- LocalRelation [_1#10, _2#11]

    at scala.Predef$.assert(Predef.scala:170)
    at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$1$$anonfun$apply$9.apply(MicroBatchExecution.scala:395)
    at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$1$$anonfun$apply$9.apply(MicroBatchExecution.scala:390)
    at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
    at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
    at scala.collection.Iterator$class.foreach(Iterator.scala:893)
    at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
    at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
    at org.apache.spark.sql.execution.streaming.StreamProgress.foreach(StreamProgress.scala:25)
    at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)
    at org.apache.spark.sql.execution.streaming.StreamProgress.flatMap(StreamProgress.scala:25)
    at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$1.apply(MicroBatchExecution.scala:390)
    at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$1.apply(MicroBatchExecution.scala:390)
    at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:271)
    at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58)
    at org.apache.spark.sql.execution.streaming.MicroBatchExecution.org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch(MicroBatchExecution.scala:389)
    at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply$mcV$sp(MicroBatchExecution.scala:133)
    at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply(MicroBatchExecution.scala:121)
    at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply(MicroBatchExecution.scala:121)
    at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:271)
    at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58)
    at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1.apply$mcZ$sp(MicroBatchExecution.scala:121)
    at org.apache.spark.sql.execution.streaming.ProcessingTimeExecutor.execute(TriggerExecutor.scala:56)
    at org.apache.spark.sql.execution.streaming.MicroBatchExecution.runActivatedStream(MicroBatchExecution.scala:117)
    at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runStream(StreamExecution.scala:279)
    ... 1 more


During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/Users/mimi/test/InitiatorSpark.py", line 41, in <module>
    query.awaitTermination()

逻辑计划：
org.apache.bahir.sql.streaming.mqtt。MQTTTextStreamSource@72a6d759
在org.apache.spark.sql.execution.streaming.streamingExecution.org$apache$spark$sql$execution$streaming$streaming$streamingExecution$$runStream（StreamExecution.scala:295）
位于org.apache.spark.sql.execution.streaming.StreamExecution$$anon$1.run（StreamExecution.scala:189）
原因：java.lang.AssertionError:assertion失败：getBatch从org.apache.bahir.sql.streaming.mqtt返回的数据帧。MQTTTextStreamSource@72a6d759没有isStreaming=true
项目[_1#10作为值#13，_2#11作为时间戳#14]
+-分析屏障
+-局部关系[u 1#10，_2#11]
在scala.Predef$.assert处（Predef.scala:170）
在org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$1$$anonfun$apply$9.apply（MicroBatchExecution.scala:395）
在org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$1$$anonfun$apply$9.apply（MicroBatchExecution.scala:390）
位于scala.collection.TraversableLike$$anonfun$flatMap$1.apply（TraversableLike.scala:241）
位于scala.collection.TraversableLike$$anonfun$flatMap$1.apply（TraversableLike.scala:241）
位于scala.collection.Iterator$class.foreach（Iterator.scala:893）
位于scala.collection.AbstractIterator.foreach（迭代器.scala:1336）
位于scala.collection.IterableLike$class.foreach（IterableLike.scala:72）
位于org.apache.spark.sql.execution.streaming.StreamProgress.foreach（StreamProgress.scala:25）
位于scala.collection.TraversableLike$class.flatMap（TraversableLike.scala:241）
位于org.apache.spark.sql.execution.streaming.StreamProgress.flatMap（StreamProgress.scala:25）
位于org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$1.apply（MicroBatchExecution.scala:390）
位于org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$1.apply（MicroBatchExecution.scala:390）
位于org.apache.spark.sql.execution.streaming.ProgressReporter$class.ReportTimeTake（ProgressReporter.scala:271）
位于org.apache.spark.sql.execution.streaming.StreamExecution.ReportTimeTake（StreamExecution.scala:58）
在org.apache.spark.sql.execution.streaming.MicroBatchExecution.org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch（MicroBatchExecution.scala:389）
在org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply$mcV$sp（MicroBatchExecution.scala:133）
在org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply（MicroBatchExecution.scala:121）
在org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply（MicroBatchExecution.scala:121）
位于org.apache.spark.sql.execution.streaming.ProgressReporter$class.ReportTimeTake（ProgressReporter.scala:271）
位于org.apache.spark.sql.execution.streaming.StreamExecution.ReportTimeTake（StreamExecution.scala:58）
在org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1.apply$mcZ$sp（MicroBatchExecution.scala:121）
位于org.apache.spark.sql.execution.streaming.ProcessingTimeExecutor.execute（TriggerExecutor.scala:56）
位于org.apache.spark.sql.execution.streaming.MicroBatchExecution.runActivatedStream（MicroBatchExecution.scala:117）
在org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$streaming$streaming$StreamExecution$$runStream（StreamExecution.scala:279）
... 还有一个
在处理上述异常期间，发生了另一个异常：
回溯（最近一次呼叫最后一次）：
文件“/Users/mimi/test/InitiatorSpark.py”，第41行，在
查询

---

我不知道

query.com有什么问题

。我在MQTT中发送的消息格式是否不正确，或者这里到底发生了什么？

您找到了解决方案吗？我也面临这个问题你的pyspark版本是什么？@RakeshRakshit:是的，我部分解决了这个问题。看看：@RakeshRakshit:但问题是我仍然无法解析MQTT队列中接收到的Json字符串。我得到空值。我在上面分享的帖子中解释了这一点。我正在使用PySpark 2.2.1。我以如下方式提交作业：

~/Downloads/spark-2.2.1-bin-hadoop2.7/bin/spark-submit--jars lib/spark-streaming-mqtt_2.11-2.2.1.jar、lib/spark-sql-streaming-mqtt_2.11-2.2.1.jar、lib/org.eclipse.paho.client.mqttv3-1.2.0.jar InitiatorSpark.py