PySpark RuntimeError: Set changed size during iteration

Question

I'm running a pyspark script and encountered an error below. It seems saying "RuntimeError: Set changed size during iteration" due to my code "if len(rdd.take(1)) > 0:". I'm not sure if that's the real reason and wonder what exactly went wrong. Any help will be greatly appreciated.

thanks!

 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", 

line 65, in call r = self.func(t, *rdds) File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/streaming/dstream.py", line 159, in func = lambda t, rdd: old_func(rdd) File "/home/richard/Documents/spark_code/with_kafka/./mongo_kafka_spark_script.py", line 96, in _compute_glb_max if len(rdd.take(1)) > 0: File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/rdd.py", line 1343, in take res = self.context.runJob(self, takeUpToNumLeft, p) File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/context.py", line 965, in runJob port = self._jvm.PythonRDD.runJob(self._jsc.sc(), mappedRDD._jrdd, partitions) File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/rdd.py", line 2439, in _jrdd self._jrdd_deserializer, profiler) File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/rdd.py", line 2372, in _wrap_function pickled_command, broadcast_vars, env, includes = _prepare_for_python_RDD(sc, command) File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/rdd.py", line 2363, in _prepare_for_python_RDD broadcast_ vars = [x._jbroadcast for x in sc._pickled_broadcast_vars] RuntimeError: Set changed size during iteration

  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", 

line 224, in ssc.awaitTermination(); File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/streaming/context.py", line 206, in awaitTermination File "/usr/lib/spark/python/lib/py4j-0.10.4-src.zip/py4j/java_gateway.py", line 1133, in call File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/sql/utils.py", line 63, in deco File "/usr/lib/spark/python/lib/py4j-0.10.4-src.zip/py4j/protocol.py", line 319, in get_return_value py4j.protocol.Py4JJavaError: An error occurred while calling o38.awaitTermination. : 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", line 65, in call r = self.func(t, *rdds) File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/streaming/dstream.py", line 159, in func = lambda t, rdd: old_func(rdd) File "/home/richard/Documents/spark_code/with_kafka/./mongo_kafka_spark_script.py", line 96, in _compute_glb_max if len(rdd.take(1)) > 0: File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/rdd.py", line 1343, in take res = self.context.runJob(self, takeUpToNumLeft, p) File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/context.py", line 965, in runJob port = self._jvm.PythonRDD.runJob(self._jsc.sc(), mappedRDD._jrdd, partitions) File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/rdd.py", line 2439, in _jrdd self._jrdd_deserializer, profiler) File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/rdd.py", line 2372, in _wrap_function pickled_command, b roadcast_vars, env, includes = _prepare_for_python_RDD(sc, command) File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/rdd.py", line 2363, in _prepare_for_python_RDD broadcast_vars = [x._jbroadcast for x in sc._pickled_broadcast_vars] RuntimeError: Set changed size during iteration

  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

It seems not to be the best practice creating broadcast variables among iterations. Always use updateStateByKey if possible when stateful data is required.

Answer 2

try

if rdd.count() <1 :

take() can give exceptions, but, if more details were available we could have pinpointed the error.