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I have this spark dataframe:

+--------------------+--------------------+------+
|                 qid|       question_text|target|
+--------------------+--------------------+------+
|403d7d49a9713e6f7caa|Do coconut trees ...|     0|
|edc9a2709785501cce09|What are 5 must-r...|     0|
|c912510d490de9e8c55c|How can I make my...|     0|
|bcd2395ccebea4d57604|Is reading biogra...|     0|
|d26dcd0879465c08ace5|Is the reason why...|     1|
|1c6398530de0b31e985a|Does John McCain ...|     1|
|69c68408690e66889e6a|Isn't the time th...|     1|
|d1a7d8a1da31041048a6|Why do people eat...|     0|
|54e96f880709e3cf9dd7|How do I get the ...|     0|
|f89c04a1c61487623ba9|What does the kno...|     0|
+--------------------+--------------------+------+

I am trying to apply this UDF:

def check_all_spelling_correct(sentence):
  
  doc=nlp(sentence)
  spell_check_performed=0
  if doc._.performed_spellCheck==True:
    spell_check_performed=1
  return spell_check_performed

Using :

check_all_spelling_correct_UDF = udf(lambda x:check_all_spelling_correct(x),IntegerType())
df_sub_2=df_sub.withColumn("Is spelling correct", check_all_spelling_correct_UDF(col("question_text")))
df_sub_2.collect()

But I am getting this error: Py4JJavaError: An error occurred while calling o235.collectToPython. : org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 100.0 failed 1 times, most recent failure: Lost task 0.0 in stage 100.0 (TID 1441) (DESKTOP-2TQTOQ2 executor driver): org.apache.spark.SparkException: Python worker failed to connect back. at org.apache.spark.api.python.PythonWorkerFactory.createSimpleWorker(PythonWorkerFactory.scala:188) at org.apache.spark.api.python.PythonWorkerFactory.create(PythonWorkerFactory.scala:108) at org.apache.spark.SparkEnv.createPythonWorker(SparkEnv.scala:121) at org.apache.spark.api.python.BasePythonRunner.compute(PythonRunner.scala:162) at org.apache.spark.sql.execution.python.BatchEvalPythonExec.evaluate(BatchEvalPythonExec.scala:81) at org.apache.spark.sql.execution.python.EvalPythonExec.$anonfun$doExecute$2(EvalPythonExec.scala:130) at org.apache.spark.rdd.RDD.$anonfun$mapPartitions$2(RDD.scala:863) at org.apache.spark.rdd.RDD.$anonfun$mapPartitions$2$adapted(RDD.scala:863) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373) at org.apache.spark.rdd.RDD.iterator(RDD.scala:337) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373) at org.apache.spark.rdd.RDD.iterator(RDD.scala:337) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373) at org.apache.spark.rdd.RDD.iterator(RDD.scala:337) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90) at org.apache.spark.scheduler.Task.run(Task.scala:131) at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:506) at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1462) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:509) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(Thread.java:750) Caused by: java.net.SocketTimeoutException: Accept timed out at java.net.DualStackPlainSocketImpl.waitForNewConnection(Native Method) at java.net.DualStackPlainSocketImpl.socketAccept(DualStackPlainSocketImpl.java:131) at java.net.AbstractPlainSocketImpl.accept(AbstractPlainSocketImpl.java:535) at java.net.PlainSocketImpl.accept(PlainSocketImpl.java:189) at java.net.ServerSocket.implAccept(ServerSocket.java:545) at java.net.ServerSocket.accept(ServerSocket.java:513) at org.apache.spark.api.python.PythonWorkerFactory.createSimpleWorker(PythonWorkerFactory.scala:175) ... 24 more

Driver stacktrace: at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2454) at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2403) at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2402) at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62) at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55) at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49) at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2402) at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1160) at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1160) at scala.Option.foreach(Option.scala:407) at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1160) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2642) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2584) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2573) at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49) at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:938) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2214) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2235) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2254) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2279) at org.apache.spark.rdd.RDD.$anonfun$collect$1(RDD.scala:1030) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112) at org.apache.spark.rdd.RDD.withScope(RDD.scala:414) at org.apache.spark.rdd.RDD.collect(RDD.scala:1029) at org.apache.spark.sql.execution.SparkPlan.executeCollect(SparkPlan.scala:394) at org.apache.spark.sql.execution.adaptive.AdaptiveSparkPlanExec.$anonfun$executeCollect$1(AdaptiveSparkPlanExec.scala:338) at org.apache.spark.sql.execution.adaptive.AdaptiveSparkPlanExec.withFinalPlanUpdate(AdaptiveSparkPlanExec.scala:366) at org.apache.spark.sql.execution.adaptive.AdaptiveSparkPlanExec.executeCollect(AdaptiveSparkPlanExec.scala:338) at org.apache.spark.sql.Dataset.$anonfun$collectToPython$1(Dataset.scala:3538) at org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3706) at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:103) at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:163) at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:90) at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:775) at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64) at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3704) at org.apache.spark.sql.Dataset.collectToPython(Dataset.scala:3535) 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.ClientServerConnection.waitForCommands(ClientServerConnection.java:182) at py4j.ClientServerConnection.run(ClientServerConnection.java:106) at java.lang.Thread.run(Thread.java:750) Caused by: org.apache.spark.SparkException: Python worker failed to connect back. at org.apache.spark.api.python.PythonWorkerFactory.createSimpleWorker(PythonWorkerFactory.scala:188) at org.apache.spark.api.python.PythonWorkerFactory.create(PythonWorkerFactory.scala:108) at org.apache.spark.SparkEnv.createPythonWorker(SparkEnv.scala:121) at org.apache.spark.api.python.BasePythonRunner.compute(PythonRunner.scala:162) at org.apache.spark.sql.execution.python.BatchEvalPythonExec.evaluate(BatchEvalPythonExec.scala:81) at org.apache.spark.sql.execution.python.EvalPythonExec.$anonfun$doExecute$2(EvalPythonExec.scala:130) at org.apache.spark.rdd.RDD.$anonfun$mapPartitions$2(RDD.scala:863) at org.apache.spark.rdd.RDD.$anonfun$mapPartitions$2$adapted(RDD.scala:863) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373) at org.apache.spark.rdd.RDD.iterator(RDD.scala:337) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373) at org.apache.spark.rdd.RDD.iterator(RDD.scala:337) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373) at org.apache.spark.rdd.RDD.iterator(RDD.scala:337) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90) at org.apache.spark.scheduler.Task.run(Task.scala:131) at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:506) at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1462) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:509) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) ... 1 more Caused by: java.net.SocketTimeoutException: Accept timed out at java.net.DualStackPlainSocketImpl.waitForNewConnection(Native Method) at java.net.DualStackPlainSocketImpl.socketAccept(DualStackPlainSocketImpl.java:131) at java.net.AbstractPlainSocketImpl.accept(AbstractPlainSocketImpl.java:535) at java.net.PlainSocketImpl.accept(PlainSocketImpl.java:189) at java.net.ServerSocket.implAccept(ServerSocket.java:545) at java.net.ServerSocket.accept(ServerSocket.java:513) at org.apache.spark.api.python.PythonWorkerFactory.createSimpleWorker(PythonWorkerFactory.scala:175) ... 24 more

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1 Answer 1

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df_sub_2.collect() will bring all the data to the driver node and when working with large datasets, it is very costly operation that results in stage failure.

Instead of df.collect() , use df.take(10) to see the first ten rows of the output dataframe.

OR if you only want to use df.collect() then you can increase the spark.driver.maxResultSize (default 1G) property like this and then use df.collect() :

spark = SparkSession.builder \
    .master('local[*]') \
    .appName('StackOverflow') \
    .config('spark.dirver.maxResultSize', '4096') \
    .getOrCreate()
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1 Comment

Tried both. Same error. I am using a Dataframe with 10 rows.

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