PySpark RuntimeError：在迭代期间设置更改的大小

Question

我正在运行 pyspark 脚本并在下面遇到错误。由于我的代码“if len(rdd.take(1)) > 0:”，似乎在说“RuntimeError：在迭代期间设置更改的大小”。我不确定这是否是真正的原因，并且想知道到底出了什么问题。任何帮助将不胜感激。

谢谢！

17/03/23 21:54:17 INFO DStreamGraph: Updated checkpoint data for time 1490320070000 ms
17/03/23 21:54:17 INFO JobScheduler: Finished job streaming job 1490320072000 ms.0 from job set of time 1490320072000 ms
17/03/23 21:54:17 INFO JobScheduler: Starting job streaming job 1490320072000 ms.1 from job set of time 1490320072000 ms
17/03/23 21:54:17 ERROR JobScheduler: Error running job streaming job 1490320072000 ms.0
org.apache.spark.SparkException: An exception was raised by Python:
Traceback (most recent call last):
  File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/streaming/util.py",

第 65 行，通话中 r = self.func(t, *rdds) 文件“/usr/lib/spark/python/lib/pyspark.zip/pyspark/streaming/dstream.py”， 第 159 行，在 func = lambda t, rdd: old_func(rdd) 文件“/home/richard/Documents/spark_code/with_kafka/./mongo_kafka_spark_script.py”， 第 96 行，在 _compute_glb_max 中 如果 len(rdd.take(1)) > 0: 文件“/usr/lib/spark/python/lib/pyspark.zip/pyspark/rdd.py”，第 1343 行，在采取 res = self.context.runJob(self, takeUpToNumLeft, p) 文件“/usr/lib/spark/python/lib/pyspark.zip/pyspark/context.py”，第 965 行，在 runJob 端口 = self._jvm.PythonRDD.runJob(self._jsc.sc(), mappedRDD._jrdd, partitions) _jrdd 中的文件“/usr/lib/spark/python/lib/pyspark.zip/pyspark/rdd.py”，第 2439 行 self._jrdd_deserializer，分析器） _wrap_function 中的文件“/usr/lib/spark/python/lib/pyspark.zip/pyspark/rdd.py”，第 2372 行 pickled_command，broadcast_vars，env，包括 = _prepare_for_python_RDD(sc, command) _prepare_for_python_RDD 中的文件“/usr/lib/spark/python/lib/pyspark.zip/pyspark/rdd.py”，第 2363 行 broadcast_vars = [x._jbroadcast for x in sc._pickled_broadcast_vars] RuntimeError：在迭代期间设置更改的大小

  at org.apache.spark.streaming.api.python.TransformFunction.callPythonTransformFunction(PythonDStream.scala:95)
  at org.apache.spark.streaming.api.python.TransformFunction.apply(PythonDStream.scala:78)
  at org.apache.spark.streaming.api.python.PythonDStream$$anonfun$callForeachRDD$1.apply(PythonDStream.scala:179)
  at org.apache.spark.streaming.api.python.PythonDStream$$anonfun$callForeachRDD$1.apply(PythonDStream.scala:179)
  at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ForEachDStream.scala:51)
  at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:51)
  at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:51)
  at org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:415)
  at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply$mcV$sp(ForEachDStream.scala:50)
  at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:50)
  at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:50)
  at scala.util.Try$.apply(Try.scala:192)
  at org.apache.spark.streaming.scheduler.Job.run(Job.scala:39)
  at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply$mcV$sp(JobScheduler.scala:254)
  at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:254)
  at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:254)
  at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58)
  at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:253)
  at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
  at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
  at java.lang.Thread.run(Thread.java:745)
Traceback (most recent call last):
  File "/home/richard/Documents/spark_code/with_kafka/./mongo_kafka_spark_script.py",

第 224 行，在 ssc.awaitTermination(); 文件“/usr/lib/spark/python/lib/pyspark.zip/pyspark/streaming/context.py”， 第 206 行，等待终止 文件“/usr/lib/spark/python/lib/py4j-0.10.4-src.zip/py4j/java_gateway.py”， 第 1133 行，在 调用 文件“/usr/lib/spark/python/lib/pyspark.zip/pyspark/sql/utils.py”，第 63 行， 在装饰中 文件“/usr/lib/spark/python/lib/py4j-0.10.4-src.zip/py4j/protocol.py”，行 319，在get_return_value中 py4j.protocol.Py4JJavaError：调用 o38.awaitTermination 时出错。 ：org.apache.spark.SparkException：Python引发了异常： 回溯（最近一次通话最后）： 文件“/usr/lib/spark/python/lib/pyspark.zip/pyspark/streaming/util.py”， 第 65 行，通话中 r = self.func(t, *rdds) 文件“/usr/lib/spark/python/lib/pyspark.zip/pyspark/streaming/dstream.py”， 第 159 行，在 func = lambda t, rdd: old_func(rdd) 文件“/home/richard/Documents/spark_code/with_kafka/./mongo_kafka_spark_script.py”， 第 96 行，在 _compute_glb_max 中 如果 len(rdd.take(1)) > 0: 文件“/usr/lib/spark/python/lib/pyspark.zip/pyspark/rdd.py”，第 1343 行，在采取 res = self.context.runJob(self, takeUpToNumLeft, p) 文件“/usr/lib/spark/python/lib/pyspark.zip/pyspark/context.py”，第 965 行，在 runJob 端口 = self._jvm.PythonRDD.runJob(self._jsc.sc(), mappedRDD._jrdd, partitions) _jrdd 中的文件“/usr/lib/spark/python/lib/pyspark.zip/pyspark/rdd.py”，第 2439 行 self._jrdd_deserializer，分析器） _wrap_function 中的文件“/usr/lib/spark/python/lib/pyspark.zip/pyspark/rdd.py”，第 2372 行 pickled_command，broadcast_vars，env，包括 = _prepare_for_python_RDD(sc, command) _prepare_for_python_RDD 中的文件“/usr/lib/spark/python/lib/pyspark.zip/pyspark/rdd.py”，第 2363 行 broadcast_vars = [x._jbroadcast for x in sc._pickled_broadcast_vars] RuntimeError：在迭代期间设置更改的大小

  at org.apache.spark.streaming.api.python.TransformFunction.callPythonTransformFunction(PythonDStream.scala:95)
  at org.apache.spark.streaming.api.python.TransformFunction.apply(PythonDStream.scala:78)
  at org.apache.spark.streaming.api.python.PythonDStream$$anonfun$callForeachRDD$1.apply(PythonDStream.scala:179)
  at org.apache.spark.streaming.api.python.PythonDStream$$anonfun$callForeachRDD$1.apply(PythonDStream.scala:179)
  at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ForEachDStream.scala:51)
  at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:51)
  at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:51)
  at org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:415)
  at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply$mcV$sp(ForEachDStream.scala:50)
  at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:50)
  at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:50)
  at scala.util.Try$.apply(Try.scala:192)
  at org.apache.spark.streaming.scheduler.Job.run(Job.scala:39)
  at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply$mcV$sp(JobScheduler.scala:254)
  at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:254)
  at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:254)
  at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58)
  at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:253)
  at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
  at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
  at java.lang.Thread.run(Thread.java:745)

Answer 1

在迭代之间创建广播变量似乎不是最佳实践。如果需要有状态数据，请始终使用 updateStateByKey。

Answer 2

试试

if rdd.count() <1 :

take() 可以给出异常，但是，如果有更多详细信息，我们可以查明错误。