Details
Minor Description
Hello folks 👋
I was wondering if there is a possible update that will allow to use pub/sub DStream in PySpark, it does not seems to exist with the current version (2.4.0).
I tried to manually instantiate the pub/sub stream from Pyspark as follow:
import pyspark from pyspark.streaming import DStream, StreamingContext from pyspark.serializers import UTF8Deserializer ssc = StreamingContext(sc, 1) jlevel = ssc._sc._getJavaStorageLevel(pyspark.StorageLevel.MEMORY_AND_DISK_2) creds = sc._jvm.org.apache.spark.streaming.pubsub.SparkGCPCredentials.Builder().jsonServiceAccount("GDCP_CREDS.json").build() jstream = sc._jvm.org.apache.spark.streaming.pubsub.PubsubUtils.createStream(ssc._jssc, "PROJECT_NAME", None, "SUB_NAME", creds, jlevel) dstream = DStream(jstream, ssc, UTF8Deserializer()) ssc.start()ssc.awaitTermination() df.writeStream.foreachBatch(batch_processor).start().awaitTermination()
But I run into the following issue (org.apache.spark.SparkException: Unexpected element type class org.apache.spark.streaming.pubsub.SparkPubsubMessage)
21/02/25 10:48:59 ERROR TaskSetManager: Task 0 in stage 2.0 failed 1 times; aborting job 21/02/25 10:48:59 ERROR JobScheduler: Error running job streaming job 1614246539000 ms.0 org.apache.spark.SparkException: An exception was raised by Python: Traceback (most recent call last): File "/PATH/test_streaming_call/spark-2.4.7-bin-hadoop2.7/python/pyspark/streaming/util.py", line 68, in call r = self.func(t, *rdds) File "/PATH/test_streaming_call/spark-2.4.7-bin-hadoop2.7/python/pyspark/streaming/dstream.py", line 173, in takeAndPrint taken = rdd.take(num + 1) File "/PATH/test_streaming_call/spark-2.4.7-bin-hadoop2.7/python/pyspark/rdd.py", line 1360, in take res = self.context.runJob(self, takeUpToNumLeft, p) File "/PATH/test_streaming_call/spark-2.4.7-bin-hadoop2.7/python/pyspark/context.py", line 1069, in runJob sock_info = self._jvm.PythonRDD.runJob(self._jsc.sc(), mappedRDD._jrdd, partitions) File "/PATH/test_streaming_call/spark-2.4.7-bin-hadoop2.7/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py", line 1257, in __call__ answer, self.gateway_client, self.target_id, self.name) File "/PATH/test_streaming_call/spark-2.4.7-bin-hadoop2.7/python/pyspark/sql/utils.py", line 63, in deco return f(*a, **kw) File "/PATH/test_streaming_call/spark-2.4.7-bin-hadoop2.7/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py", line 328, in get_return_value format(target_id, ".", name), value) Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.runJob. : org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 2.0 failed 1 times, most recent failure: Lost task 0.0 in stage 2.0 (TID 2, localhost, executor driver): org.apache.spark.SparkException: Unexpected element type class org.apache.spark.streaming.pubsub.SparkPubsubMessage at org.apache.spark.api.python.PythonRDD$.org$apache$spark$api$python$PythonRDD$$write$1(PythonRDD.scala:221) at org.apache.spark.api.python.PythonRDD$$anonfun$writeIteratorToStream$1.apply(PythonRDD.scala:224) at org.apache.spark.api.python.PythonRDD$$anonfun$writeIteratorToStream$1.apply(PythonRDD.scala:224) at scala.collection.Iterator$class.foreach(Iterator.scala:891) at org.apache.spark.util.CompletionIterator.foreach(CompletionIterator.scala:25) at org.apache.spark.api.python.PythonRDD$.writeIteratorToStream(PythonRDD.scala:224) at org.apache.spark.api.python.PythonRunner$$anon$2.writeIteratorToStream(PythonRunner.scala:561) at org.apache.spark.api.python.BasePythonRunner$WriterThread$$anonfun$run$1.apply(PythonRunner.scala:346) at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1945) at org.apache.spark.api.python.BasePythonRunner$WriterThread.run(PythonRunner.scala:195)Driver stacktrace: at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1925) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1913) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1912) at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48) at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1912) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:948) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:948) at scala.Option.foreach(Option.scala:257) at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:948) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2146) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2095) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2084) at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49) at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:759) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2082) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2101) at org.apache.spark.api.python.PythonRDD$.runJob(PythonRDD.scala:153) at org.apache.spark.api.python.PythonRDD.runJob(PythonRDD.scala) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:498) at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244) at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) at py4j.Gateway.invoke(Gateway.java:282) at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) at py4j.commands.CallCommand.execute(CallCommand.java:79) at py4j.GatewayConnection.run(GatewayConnection.java:238) at java.lang.Thread.run(Thread.java:748) Caused by: org.apache.spark.SparkException: Unexpected element type class org.apache.spark.streaming.pubsub.SparkPubsubMessage at org.apache.spark.api.python.PythonRDD$.org$apache$spark$api$python$PythonRDD$$write$1(PythonRDD.scala:221) at org.apache.spark.api.python.PythonRDD$$anonfun$writeIteratorToStream$1.apply(PythonRDD.scala:224) at org.apache.spark.api.python.PythonRDD$$anonfun$writeIteratorToStream$1.apply(PythonRDD.scala:224) at scala.collection.Iterator$class.foreach(Iterator.scala:891) at org.apache.spark.util.CompletionIterator.foreach(CompletionIterator.scala:25) at org.apache.spark.api.python.PythonRDD$.writeIteratorToStream(PythonRDD.scala:224) at org.apache.spark.api.python.PythonRunner$$anon$2.writeIteratorToStream(PythonRunner.scala:561) at org.apache.spark.api.python.BasePythonRunner$WriterThread$$anonfun$run$1.apply(PythonRunner.scala:346) at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1945) at org.apache.spark.api.python.BasePythonRunner$WriterThread.run(PythonRunner.scala:195) 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:416) 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$sp(JobScheduler.scala:257) at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:257) at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:257) at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58) at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:256) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(Thread.java:748)
Seems that the receiver store data in internal format called SparkPubsubMessage format (https://github.com/apache/bahir/blob/62df1108145ee0305c5c7416a7dadeae5930aab8/streaming-pubsub/src/main/scala/org/apache/spark/streaming/pubsub/PubsubInputDStream.scala#L68)
Is there a way to make RDD in python interpret the SparkPubsubMessage object ?
Thanks a lot, feel free to ask details if needed 
!