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How to get the nested stack trace of nested Py4JJavaError.java_exception

I am using pyspark and when a task failure occurs such as jdbc ConnectionReset in a task that retries 4 times and then the stage fails and then the job fails with SparkException. Looking at the stack trace I will see a SparkException listed and with the python printing of the exception the task failure is never even seen. I have to go to the spark ui logs to even find out what the actual error is.
This seems to be how to do it in scala spark: Spark Launcher: Can't see the complete stack trace for failed SQL query
How do you get the nested stack trace in pyspark?
Example full log message in cloudwatch logs which is where all our driver and executor logs goes but they are seperated and we don't always know which executor failed. This job has 20 executors so 21 logs in cloudwatch the driver + execetors. The driver log here does not print the executor failures and it is are truncated no idea how to change the truncation. This is also in AWS Glue but that should not matter:
Traceback (most recent call last):
File "/tmp/extractor.zip/database.py", line 173, in write_dynamic_frame_to_s3
transformation_ctx=tr_ctx,
File "/opt/amazon/lib/python3.6/site-packages/awsglue/dynamicframe.py", line 653, in from_options
format_options, transformation_ctx)
File "/opt/amazon/lib/python3.6/site-packages/awsglue/context.py", line 279, in write_dynamic_frame_from_options
format, format_options, transformation_ctx)
File "/opt/amazon/lib/python3.6/site-packages/awsglue/context.py", line 302, in write_from_options
return sink.write(frame_or_dfc)
File "/opt/amazon/lib/python3.6/site-packages/awsglue/data_sink.py", line 35, in write
return self.writeFrame(dynamic_frame_or_dfc, info)
File "/opt/amazon/lib/python3.6/site-packages/awsglue/data_sink.py", line 31, in writeFrame
return DynamicFrame(self._jsink.pyWriteDynamicFrame(dynamic_frame._jdf, callsite(), info), dynamic_frame.glue_ctx, dynamic_frame.name + "_errors")
File "/opt/amazon/spark/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 "/opt/amazon/spark/python/lib/pyspark.zip/pyspark/sql/utils.py", line 63, in deco
return f(*a, **kw)
File "/opt/amazon/spark/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py", line 328, in get_return_value
format(target_id, ".", name), value)
py4j.protocol.Py4JJavaError: An error occurred while calling o703.pyWriteDynamicFrame.
: org.apache.spark.SparkException: Job aborted.
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:198)
at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:159)
at org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult$lzycompute(commands.scala:104)
at org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult(commands.scala:102)
at org.apache.spark.sql.execution.command.DataWritingCommandExec.doExecute(commands.scala:122)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127)
at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:80)
at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:80)
at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:676)
at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:676)
at org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:78)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:125)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:73)
at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:676)
at org.apache.spark.sql.DataFrameWriter.saveToV1Source(DataFrameWriter.scala:285)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:271)
at com.amazonaws.services.glue.SparkSQLDataSink$$anonfun$writeDynamicFrame$1.apply(DataSink.scala:602)
at com.amazonaws.services.glue.SparkSQLDataSink$$anonfun$writeDynamicFrame$1.apply(DataSink.scala:589)
at com.amazonaws.services.glue.util.FileSchemeWrapper$$anonfun$executeWithQualifiedScheme$1.apply(FileSchemeWrapper.scala:89)
at com.amazonaws.services.glue.util.FileSchemeWrapper$$anonfun$executeWithQualifiedScheme$1.apply(FileSchemeWrapper.scala:89)
at com.amazonaws.services.glue.util.FileSchemeWrapper.executeWith(FileSchemeWrapper.scala:82)
at com.amazonaws.services.glue.util.FileSchemeWrapper.executeWithQualifiedScheme(FileSchemeWrapper.scala:89)
at com.amazonaws.services.glue.SparkSQLDataSink.writeDynamicFrame(DataSink.scala:588)
at com.amazonaws.services.glue.DataSink.pyWriteDynamicFrame(DataSink.scala:65)
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: Job aborted due to stage failure: Task 32 in stage 39.0 failed 4 times, most recent failure: Lost task 32.3 in stage 39.0 (TID 633, 10.131.34.154, executor 4): org.apache.hadoop.fs.FileAlreadyExistsException: File already exists:s3://<bucket>/<database>/<table/<filename>.zlib.orc
at com.amazon.ws.emr.hadoop.fs.s3.upload.plan.RegularUploadPlanner.checkExistenceIfNotOverwriting(RegularUploadPlanner.java:36)
at com.amazon.ws.emr.hadoop.fs.s3.upload.plan.RegularUploadPlanner.plan(RegularUploadPlanner.java:30)
at com.amazon.ws.emr.hadoop.fs.s3.upload.plan.UploadPlannerChain.plan(UploadPlannerChain.java:37)
at com.amazon.ws.emr.hadoop.fs.s3n.S3NativeFileSystem.create(S3NativeFileSystem.java:703)
at org.apache.hadoop.fs.FileSystem.create(FileSystem.java:932)
at org.apache.hadoop.fs.FileSystem.create(FileSystem.java:913)
at com.amazon.ws.emr.hadoop.fs.EmrFileSystem.create(EmrFileSystem.java:247)
at org.apache.orc.impl.PhysicalFsWriter.<init>(PhysicalFsWriter.java:95)
at org.apache.orc.impl.WriterImpl.<init>(WriterImpl.java:177)
at org.apache.orc.OrcFile.createWriter(OrcFile.java:860)
at org.apache.orc.mapreduce.OrcOutputFormat.getRecordWriter(OrcOutputFormat.java:50)
at org.apache.spark.sql.execution.datasources.orc.OrcOutputWriter.<init>(OrcOutputWriter.scala:43)
at org.apache.spark.sql.execution.datasources.orc.OrcFileFormat$$anon$1.newInstance(OrcFileFormat.scala:121)
at org.apache.spark.sql.execution.datasources.SingleDirectoryDataWriter.newOutputWriter(FileFormatDataWriter.scala:120)
at org.apache.spark.sql.execution.datasources.SingleDirectoryDataWriter.<init>(FileFormatDataWriter.scala:108)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask(FileFormatWriter.scala:236)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:170)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:169)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:121)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1889)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1877)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1876)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1876)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:926)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2110)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2059)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2048)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:737)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:167)
... 39 more
Caused by: org.apache.hadoop.fs.FileAlreadyExistsException: File already exists:s3://<bucket>/<database>/<table/<filename>.zlib.orc
at com.amazon.ws.emr.hadoop.fs.s3.upload.plan.RegularUploadPlanner.checkExistenceIfNotOverwriting(RegularUploadPlanner.java:36)
at com.amazon.ws.emr.hadoop.fs.s3.upload.plan.RegularUploadPlanner.plan(RegularUploadPlanner.java:30)
at com.amazon.ws.emr.hadoop.fs.s3.upload.plan.UploadPlannerChain.plan(UploadPlannerChain.java:37)
at com.amazon.ws.emr.hadoop.fs.s3n.S3NativeFileSystem.create(S3NativeFileSystem.java:703)
at org.apache.hadoop.fs.FileSystem.create(FileSystem.java:932)
at org.apache.hadoop.fs.FileSystem.create(FileSystem.java:913)
at com.amazon.ws.emr.hadoop.fs.EmrFileSystem.create(EmrFileSystem.java:247)
at org.apache.orc.impl.PhysicalFsWriter.<init>(PhysicalFsWriter.java:95)
at org.apache.orc.impl.WriterImpl.<init>(WriterImpl.java:177)
at org.apache.orc.OrcFile.createWriter(OrcFile.java:860)
at org.apache.orc.mapreduce.OrcOutputFormat.getRecordWriter(OrcOutputFormat.java:50)
at org.apache.spark.sql.execution.datasources.orc.OrcOutputWriter.<init>(OrcOutputWriter.scala:43)
at org.apache.spark.sql.execution.datasources.orc.OrcFileFormat$$anon$1.newInstance(OrcFileFormat.scala:121)
at org.apache.spark.sql.execution.datasources.SingleDirectoryDataWriter.newOutputWriter(FileFormatDataWriter.scala:120)
at org.apache.spark.sql.execution.datasources.SingleDirectoryDataWriter.<init>(FileFormatDataWriter.scala:108)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask(FileFormatWriter.scala:236)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:170)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:169)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:121)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
... 1 more
During handling of the above exception, another exception occurred:
But in the spark ui logs I see the real error Caused by: com.microsoft.sqlserver.jdbc.SQLServerException: Connection reset:
..."Full Stack Trace":"org.apache.spark.SparkException: Task failed while writing rows.
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask(FileFormatWriter.scala:257)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:170)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:169)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:121)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Caused by: com.microsoft.sqlserver.jdbc.SQLServerException: Connection reset
at com.microsoft.sqlserver.jdbc.SQLServerConnection.terminate(SQLServerConnection.java:2392)
at com.microsoft.sqlserver.jdbc.SQLServerConnection.terminate(SQLServerConnection.java:2376)
at com.microsoft.sqlserver.jdbc.TDSChannel.read(IOBuffer.java:1900)
at com.microsoft.sqlserver.jdbc.TDSReader.readPacket(IOBuffer.java:6674)
at com.microsoft.sqlserver.jdbc.TDSReader.nextPacket(IOBuffer.java:6627)
at com.microsoft.sqlserver.jdbc.TDSReader.ensurePayload(IOBuffer.java:6603)
at com.microsoft.sqlserver.jdbc.TDSReader.readBytes(IOBuffer.java:6896)
at com.microsoft.sqlserver.jdbc.TDSReader.readWrappedBytes(IOBuffer.java:6918)
at com.microsoft.sqlserver.jdbc.TDSReader.readUnsignedShort(IOBuffer.java:6833)
at com.microsoft.sqlserver.jdbc.ServerDTVImpl.getValuePrep(dtv.java:3625)
at com.microsoft.sqlserver.jdbc.ServerDTVImpl.getValue(dtv.java:3990)
at com.microsoft.sqlserver.jdbc.DTV.getValue(dtv.java:237)
at com.microsoft.sqlserver.jdbc.Column.getValue(Column.java:162)
at com.microsoft.sqlserver.jdbc.SQLServerResultSet.getValue(SQLServerResultSet.java:2100)
at com.microsoft.sqlserver.jdbc.SQLServerResultSet.getValue(SQLServerResultSet.java:2085)
at com.microsoft.sqlserver.jdbc.SQLServerResultSet.getString(SQLServerResultSet.java:2428)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$org$apache$spark$sql$execution$datasources$jdbc$JdbcUtils$$makeGetter$10.apply(JdbcUtils.scala:444)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$org$apache$spark$sql$execution$datasources$jdbc$JdbcUtils$$makeGetter$10.apply(JdbcUtils.scala:442)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anon$1.getNext(JdbcUtils.scala:356)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anon$1.getNext(JdbcUtils.scala:338)
at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:73)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:31)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:462)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at scala.collection.Iterator$GroupedIterator.takeDestructively(Iterator.scala:1073)
at scala.collection.Iterator$GroupedIterator.go(Iterator.scala:1089)
at scala.collection.Iterator$GroupedIterator.fill(Iterator.scala:1127)
at scala.collection.Iterator$GroupedIterator.hasNext(Iterator.scala:1130)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at scala.collection.Iterator$class.foreach(Iterator.scala:891)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1334)
at org.apache.spark.api.python.PythonRDD$.writeIteratorToStream(PythonRDD.scala:224)
at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$2.writeIteratorToStream(PythonUDFRunner.scala:50)
at org.apache.spark.api.python.BasePythonRunner$WriterThread$$anonfun$run$1.apply(PythonRunner.scala:345)
at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1945)
at org.apache.spark.api.python.BasePythonRunner$WriterThread.run(PythonRunner.scala:194)
All I want is to print the nested exception so that we can quickly diagnose the errors as they occur. Since I am in AWS Glue the spark ui logs are not always up-to-date for some reason which is a problem. Sometimes the stage is pending in the ui logs, but the job failed so we cannot determine the root cause, if we could print the nested stack trace to get the root failure we would not have that problem.

How configure PySpark in PyCharm? I have a problem

I have problem to configure PySpark in PyCharm.
I use: Java JDK 1.0_311, Python 3.10.1, spark-3.2.0-bin-hadoop3.2. I followed this tutorial: https://kaizen.itversity.com/setup-spark-development-environment-pycharm-and-python/
My code is:
from pyspark.sql import SparkSession
spark=SparkSession.builder.master("local[*]").appName("SparkExamples.com").getOrCreate()
rdd=spark.sparkContext.parallelize([1,2,3,4,5,6])
print(rdd.count())
This is the error:
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
22/01/07 22:04:33 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Python not finded; Esegui senza argomenti per installare da Microsoft Store o disabilitare il collegamento da impostazioni > Gestisci app Alias di esecuzione.
22/01/07 22:04:46 WARN ProcfsMetricsGetter: Exception when trying to compute pagesize, as a result reporting of ProcessTree metrics is stopped
22/01/07 22:04:49 ERROR Executor: Exception in task 3.0 in stage 0.0 (TID 3)
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.api.python.PythonRDD.compute(PythonRDD.scala:65)
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:748)
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)
... 14 more
22/01/07 22:04:49 WARN TaskSetManager: Lost task 3.0 in stage 0.0 (TID 3) (Domenico-PC 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.api.python.PythonRDD.compute(PythonRDD.scala:65)
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:748)
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)
... 14 more
22/01/07 22:04:49 ERROR TaskSetManager: Task 3 in stage 0.0 failed 1 times; aborting job
Traceback (most recent call last):
File "C:\Users\domen\PycharmProjects\pythonProject16\main.py", line 7, in
print(rdd.count())
File "C:\Users\domen\spark-3.2.0-bin-hadoop3.2\python\lib\pyspark.zip\pyspark\rdd.py", line 1237, in count
File "C:\Users\domen\spark-3.2.0-bin-hadoop3.2\python\lib\pyspark.zip\pyspark\rdd.py", line 1226, in sum
File "C:\Users\domen\spark-3.2.0-bin-hadoop3.2\python\lib\pyspark.zip\pyspark\rdd.py", line 1080, in fold
File "C:\Users\domen\spark-3.2.0-bin-hadoop3.2\python\lib\pyspark.zip\pyspark\rdd.py", line 950, in collect
File "C:\Users\domen\spark-3.2.0-bin-hadoop3.2\python\lib\py4j-0.10.9.2-src.zip\py4j\java_gateway.py", line 1309, in call
File "C:\Users\domen\spark-3.2.0-bin-hadoop3.2\python\lib\pyspark.zip\pyspark\sql\utils.py", line 111, in deco
File "C:\Users\domen\spark-3.2.0-bin-hadoop3.2\python\lib\py4j-0.10.9.2-src.zip\py4j\protocol.py", line 326, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 3 in stage 0.0 failed 1 times, most recent failure: Lost task 3.0 in stage 0.0 (TID 3) (Domenico-PC 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.api.python.PythonRDD.compute(PythonRDD.scala:65)
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:748)
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)
... 14 more
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2403)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2352)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2351)
at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2351)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1109)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1109)
at scala.Option.foreach(Option.scala:407)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1109)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2591)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2533)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2522)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:898)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2214)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2235)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2254)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2279)
at org.apache.spark.rdd.RDD.$anonfun$collect$1(RDD.scala:1030)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:414)
at org.apache.spark.rdd.RDD.collect(RDD.scala:1029)
at org.apache.spark.api.python.PythonRDD$.collectAndServe(PythonRDD.scala:180)
at org.apache.spark.api.python.PythonRDD.collectAndServe(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.ClientServerConnection.waitForCommands(ClientServerConnection.java:182)
at py4j.ClientServerConnection.run(ClientServerConnection.java:106)
at java.lang.Thread.run(Thread.java:748)
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.api.python.PythonRDD.compute(PythonRDD.scala:65)
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)
... 14 more
[Stage 0:> (0 + 7) / 8]
Process finished with exit code 1
What is the problem?

It is most likely a network restriction in your environment created by anti-virus, firewall, vpn client, etc...
Try setting environment variable SPARK_LOCAL_IP to 127.0.0.1 in your PyCharm configuration and see if it helps.

Error when running simple pyspark code with cloudera CDH5.13

I am running simple code as below. Actually I am just starting to practice spark with cloudera cdh. My goal is to read 'orders' table and then print it as RDD
from pyspark import SparkContext, SparkConf
if __name__ == "__main__":
# create Spark context with Spark configuration
conf = SparkConf().setAppName("Spark Count")
sc = SparkContext(conf=conf)
data = sc.textFile("hdfs://user/cloudera/orders")
print data.collect()
But I am getting below Error
[cloudera#quickstart ~]$ spark-submit spark_ex.py Traceback (most
recent call last): File "/home/cloudera/spark_ex.py", line 7, in
print data.collect() File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/rdd.py", line 771, in
collect File
"/usr/lib/spark/python/lib/py4j-0.9-src.zip/py4j/java_gateway.py",
line 813, in call File
"/usr/lib/spark/python/lib/py4j-0.9-src.zip/py4j/protocol.py", line
308, in get_return_value py4j.protocol.Py4JJavaError: An error
occurred while calling
z:org.apache.spark.api.python.PythonRDD.collectAndServe. :
java.net.ConnectException: Call From quickstart.cloudera/10.0.2.15 to
user:8020 failed on connection exception: java.net.ConnectException:
Connection refused; For more details see:
http://wiki.apache.org/hadoop/ConnectionRefused at
sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at
sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:57)
at
sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
at java.lang.reflect.Constructor.newInstance(Constructor.java:526)
at org.apache.hadoop.net.NetUtils.wrapWithMessage(NetUtils.java:791)
at org.apache.hadoop.net.NetUtils.wrapException(NetUtils.java:731)
at org.apache.hadoop.ipc.Client.call(Client.java:1508) at
org.apache.hadoop.ipc.Client.call(Client.java:1441) at
org.apache.hadoop.ipc.ProtobufRpcEngine$Invoker.invoke(ProtobufRpcEngine.java:230)
at com.sun.proxy.$Proxy21.getFileInfo(Unknown Source) at
org.apache.hadoop.hdfs.protocolPB.ClientNamenodeProtocolTranslatorPB.getFileInfo(ClientNamenodeProtocolTranslatorPB.java:786)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606) at
org.apache.hadoop.io.retry.RetryInvocationHandler.invokeMethod(RetryInvocationHandler.java:260)
at
org.apache.hadoop.io.retry.RetryInvocationHandler.invoke(RetryInvocationHandler.java:104)
at com.sun.proxy.$Proxy22.getFileInfo(Unknown Source) at
org.apache.hadoop.hdfs.DFSClient.getFileInfo(DFSClient.java:2131) at
org.apache.hadoop.hdfs.DistributedFileSystem$20.doCall(DistributedFileSystem.java:1265)
at
org.apache.hadoop.hdfs.DistributedFileSystem$20.doCall(DistributedFileSystem.java:1261)
at
org.apache.hadoop.fs.FileSystemLinkResolver.resolve(FileSystemLinkResolver.java:81)
at
org.apache.hadoop.hdfs.DistributedFileSystem.getFileStatus(DistributedFileSystem.java:1261)
at org.apache.hadoop.fs.Globber.getFileStatus(Globber.java:64) at
org.apache.hadoop.fs.Globber.doGlob(Globber.java:272) at
org.apache.hadoop.fs.Globber.glob(Globber.java:151) at
org.apache.hadoop.fs.FileSystem.globStatus(FileSystem.java:1734) at
org.apache.hadoop.mapred.FileInputFormat.singleThreadedListStatus(FileInputFormat.java:259)
at
org.apache.hadoop.mapred.FileInputFormat.listStatus(FileInputFormat.java:229)
at
org.apache.hadoop.mapred.FileInputFormat.getSplits(FileInputFormat.java:315)
at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:202)
at
org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
at
org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
at scala.Option.getOrElse(Option.scala:120) at
org.apache.spark.rdd.RDD.partitions(RDD.scala:237) at
org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
at
org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
at
org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
at scala.Option.getOrElse(Option.scala:120) at
org.apache.spark.rdd.RDD.partitions(RDD.scala:237) at
org.apache.spark.SparkContext.runJob(SparkContext.scala:1959) at
org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:927) at
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:316) at
org.apache.spark.rdd.RDD.collect(RDD.scala:926) at
org.apache.spark.api.python.PythonRDD$.collectAndServe(PythonRDD.scala:405)
at
org.apache.spark.api.python.PythonRDD.collectAndServe(PythonRDD.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606) at
py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231) at
py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:381) at
py4j.Gateway.invoke(Gateway.java:259) at
py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
at py4j.commands.CallCommand.execute(CallCommand.java:79) at
py4j.GatewayConnection.run(GatewayConnection.java:209) at
java.lang.Thread.run(Thread.java:745) Caused by:
java.net.ConnectException: Connection refused at
sun.nio.ch.SocketChannelImpl.checkConnect(Native Method) at
sun.nio.ch.SocketChannelImpl.finishConnect(SocketChannelImpl.java:739)
at
org.apache.hadoop.net.SocketIOWithTimeout.connect(SocketIOWithTimeout.java:206)
at org.apache.hadoop.net.NetUtils.connect(NetUtils.java:530) at
org.apache.hadoop.net.NetUtils.connect(NetUtils.java:494) at
org.apache.hadoop.ipc.Client$Connection.setupConnection(Client.java:648)
at
org.apache.hadoop.ipc.Client$Connection.setupIOstreams(Client.java:744)
at
org.apache.hadoop.ipc.Client$Connection.access$3000(Client.java:396)
at org.apache.hadoop.ipc.Client.getConnection(Client.java:1557) at
org.apache.hadoop.ipc.Client.call(Client.java:1480) ... 52 more
Can anyone please Thanks

Caused by: java.io.IOException: error=13, Permission denied

I have recently installed python 3.7 on my laptop and trying to run an old program which used to work fine. Problem is that now I am getting the following exception when I try to run it:
19/08/21 13:46:53 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
19/08/21 13:46:59 ERROR Executor: Exception in task 0.0 in stage 0.0 (TID 0)
java.io.IOException: Cannot run program "/usr/local/opt/python/libexec/bin": error=13, Permission denied
at java.lang.ProcessBuilder.start(ProcessBuilder.java:1048)
at org.apache.spark.api.python.PythonWorkerFactory.startDaemon(PythonWorkerFactory.scala:197)
at org.apache.spark.api.python.PythonWorkerFactory.createThroughDaemon(PythonWorkerFactory.scala:122)
at org.apache.spark.api.python.PythonWorkerFactory.create(PythonWorkerFactory.scala:95)
at org.apache.spark.SparkEnv.createPythonWorker(SparkEnv.scala:117)
at org.apache.spark.api.python.BasePythonRunner.compute(PythonRunner.scala:108)
at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:65)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:121)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:748)
Caused by: java.io.IOException: error=13, Permission denied
at java.lang.UNIXProcess.forkAndExec(Native Method)
at java.lang.UNIXProcess.<init>(UNIXProcess.java:247)
at java.lang.ProcessImpl.start(ProcessImpl.java:134)
at java.lang.ProcessBuilder.start(ProcessBuilder.java:1029)
... 16 more
19/08/21 13:46:59 WARN TaskSetManager: Lost task 0.0 in stage 0.0 (TID 0, localhost, executor driver): java.io.IOException: Cannot run program "/usr/local/opt/python/libexec/bin": error=13, Permission denied
at java.lang.ProcessBuilder.start(ProcessBuilder.java:1048)
at org.apache.spark.api.python.PythonWorkerFactory.startDaemon(PythonWorkerFactory.scala:197)
at org.apache.spark.api.python.PythonWorkerFactory.createThroughDaemon(PythonWorkerFactory.scala:122)
at org.apache.spark.api.python.PythonWorkerFactory.create(PythonWorkerFactory.scala:95)
at org.apache.spark.SparkEnv.createPythonWorker(SparkEnv.scala:117)
at org.apache.spark.api.python.BasePythonRunner.compute(PythonRunner.scala:108)
at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:65)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:121)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:748)
Caused by: java.io.IOException: error=13, Permission denied
at java.lang.UNIXProcess.forkAndExec(Native Method)
at java.lang.UNIXProcess.<init>(UNIXProcess.java:247)
at java.lang.ProcessImpl.start(ProcessImpl.java:134)
at java.lang.ProcessBuilder.start(ProcessBuilder.java:1029)
... 16 more
19/08/21 13:46:59 ERROR TaskSetManager: Task 0 in stage 0.0 failed 1 times; aborting job
Traceback (most recent call last):
File "/Applications/PyCharm CE.app/Contents/helpers/pydev/pydevd.py", line 1596, in <module>
globals = debugger.run(setup['file'], None, None, is_module)
File "/Applications/PyCharm CE.app/Contents/helpers/pydev/pydevd.py", line 1023, in run
pydev_imports.execfile(file, globals, locals) # execute the script
File "/Applications/PyCharm CE.app/Contents/helpers/pydev/_pydev_imps/_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "/Users/ahajib/clustering.py", line 343, in <module>
spark_app.run(date = '2019-01-01')
File "/Users/ahajib/clustering.py", line 75, in run
if id_rdd.isEmpty():
File "/usr/local/lib/python3.7/site-packages/pyspark/rdd.py", line 1394, in isEmpty
return self.getNumPartitions() == 0 or len(self.take(1)) == 0
File "/usr/local/lib/python3.7/site-packages/pyspark/rdd.py", line 1360, in take
res = self.context.runJob(self, takeUpToNumLeft, p)
File "/usr/local/lib/python3.7/site-packages/pyspark/context.py", line 1069, in runJob
sock_info = self._jvm.PythonRDD.runJob(self._jsc.sc(), mappedRDD._jrdd, partitions)
File "/usr/local/lib/python3.7/site-packages/py4j/java_gateway.py", line 1257, in __call__
answer, self.gateway_client, self.target_id, self.name)
File "/usr/local/lib/python3.7/site-packages/pyspark/sql/utils.py", line 63, in deco
return f(*a, **kw)
File "/usr/local/lib/python3.7/site-packages/py4j/protocol.py", line 328, in get_return_value
format(target_id, ".", name), value)
py4j.protocol.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 0.0 failed 1 times, most recent failure: Lost task 0.0 in stage 0.0 (TID 0, localhost, executor driver): java.io.IOException: Cannot run program "/usr/local/opt/python/libexec/bin": error=13, Permission denied
at java.lang.ProcessBuilder.start(ProcessBuilder.java:1048)
at org.apache.spark.api.python.PythonWorkerFactory.startDaemon(PythonWorkerFactory.scala:197)
at org.apache.spark.api.python.PythonWorkerFactory.createThroughDaemon(PythonWorkerFactory.scala:122)
at org.apache.spark.api.python.PythonWorkerFactory.create(PythonWorkerFactory.scala:95)
at org.apache.spark.SparkEnv.createPythonWorker(SparkEnv.scala:117)
at org.apache.spark.api.python.BasePythonRunner.compute(PythonRunner.scala:108)
at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:65)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:121)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:748)
Caused by: java.io.IOException: error=13, Permission denied
at java.lang.UNIXProcess.forkAndExec(Native Method)
at java.lang.UNIXProcess.<init>(UNIXProcess.java:247)
at java.lang.ProcessImpl.start(ProcessImpl.java:134)
at java.lang.ProcessBuilder.start(ProcessBuilder.java:1029)
... 16 more
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1889)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1877)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1876)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1876)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:926)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2110)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2059)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2048)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:737)
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: java.io.IOException: Cannot run program "/usr/local/opt/python/libexec/bin": error=13, Permission denied
at java.lang.ProcessBuilder.start(ProcessBuilder.java:1048)
at org.apache.spark.api.python.PythonWorkerFactory.startDaemon(PythonWorkerFactory.scala:197)
at org.apache.spark.api.python.PythonWorkerFactory.createThroughDaemon(PythonWorkerFactory.scala:122)
at org.apache.spark.api.python.PythonWorkerFactory.create(PythonWorkerFactory.scala:95)
at org.apache.spark.SparkEnv.createPythonWorker(SparkEnv.scala:117)
at org.apache.spark.api.python.BasePythonRunner.compute(PythonRunner.scala:108)
at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:65)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:121)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
... 1 more
Caused by: java.io.IOException: error=13, Permission denied
at java.lang.UNIXProcess.forkAndExec(Native Method)
at java.lang.UNIXProcess.<init>(UNIXProcess.java:247)
at java.lang.ProcessImpl.start(ProcessImpl.java:134)
at java.lang.ProcessBuilder.start(ProcessBuilder.java:1029)
... 16 more
The code I am trying to run is a simple rdd operation on some data I read from a text file and it was working fine before when I used to only have Python 2.7 installed on my laptop. Sample code I am trying to execute:
# Create rdd for different id types
id_rdd = data.map(lambda x, y: (x, ast.literal_eval(y)))\
.map(lambda x, y: (x, y) if this_id_type in y.keys() else None)\
.filter(lambda x: x is not None)\
.map(lambda x, y: (x, len(y[this_id_type])))
# Skip this id type if there is no data
if id_rdd.isEmpty():
continue
Also, here is what my ~/.bash_profile looks like:
1 source ~/.bashrc
2 source ~/.vimrc
3 if [ -f ~/.git-completion.bash ]; then
4 . ~/.git-completion.bash
5 fi
6 export JAVA_HOME=/Library/Java/JavaVirtualMachines/jdk1.8.0_131.jdk/Contents/Home
7 export PATH=$PATH:/usr/local/opt/python/libexec/bin
8 export PATH=$PATH:/opt/yarn-v1.17.3/bin
9 if which pyspark > /dev/null; then
10 export SPARK_HOME="/usr/local/Cellar/apache-spark/2.4.3/libexec/"
11 export PYTHONPATH=$SPARK_HOME/python:$SPARK_HOME/python/build:$PYTHONPATH
12 export PYTHONPATH=$SPARK_HOME/python/lib/py4j-0.10.4-src.zip:$PYTHONPATH
13 export PYSPARK_PYTHON=/usr/local/opt/python/libexec/bin
14 export PYSPARK_DRIVER_PYTHON=python3.7
15 fi
I tried to change permission on /usr/local/opt/python/libexec/bin using:
chmod 777 /usr/local/opt/python/libexec/bin
But that did not fixed the issue. Is there anything I might be missing here?

The problem lies here.
export PYSPARK_PYTHON=/usr/local/opt/python/libexec/bin
Changing it to the your python3.7 path would solve the issue.
export PYSPARK_PYTHON=python3.7
PYSPARK_PYTHON should point to the Python executable you desire to use with spark.

I am getting error while loading my csv in spark using SQlcontext

enter image description here
sparkDF = sqlContext.read.format('com.databricks.spark.csv').options(header='true').load('PR_DATA_35.csv')
ERROR_
---------------------------------------------------------------------------------------------------------------------------------------------------- Py4JJavaError Traceback (most recent call
last) in ()
----> 1 sparkDF = sqlContext.read.format('com.databricks.spark.csv') .options(header='true').load('PR_DATA_35.csv')
/home/ec2-user/spark/python/pyspark/sql/readwriter.pyc in load(self,
path, format, schema, **options)
157 self.options(**options)
158 if isinstance(path, basestring):
--> 159 return self._df(self._jreader.load(path))
160 elif path is not None:
161 if type(path) != list:
/home/ec2-user/spark/python/lib/py4j-0.10.4-src.zip/py4j/java_gateway.py
in call(self, *args) 1131 answer =
self.gateway_client.send_command(command) 1132 return_value
= get_return_value(
-> 1133 answer, self.gateway_client, self.target_id, self.name) 1134 1135 for temp_arg in temp_args:
/home/ec2-user/spark/python/pyspark/sql/utils.pyc in deco(*a, **kw)
61 def deco(*a, **kw):
62 try:
---> 63 return f(*a, **kw)
64 except py4j.protocol.Py4JJavaError as e:
65 s = e.java_exception.toString()
/home/ec2-user/spark/python/lib/py4j-0.10.4-src.zip/py4j/protocol.py
in get_return_value(answer, gateway_client, target_id, name)
317 raise Py4JJavaError(
318 "An error occurred while calling {0}{1}{2}.\n".
--> 319 format(target_id, ".", name), value)
320 else:
321 raise Py4JError(
Py4JJavaError: An error occurred while calling o312.load. :
org.apache.spark.SparkException: Job aborted due to stage failure:
Task 0 in stage 5.0 failed 4 times, most recent failure: Lost task 0.3
in stage 5.0 (TID 23, 172.31.17.233, executor 0):
java.io.FileNotFoundException: File file:/home/ec2-user/PR_DATA_35.csv
does not exist It is possible the underlying files have been updated.
You can explicitly invalidate the cache in Spark by running 'REFRESH
TABLE tableName' command in SQL or by recreating the Dataset/DataFrame
involved. at
org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.org$apache$spark$sql$execution$datasources$FileScanRDD$$anon$$readCurrentFile(FileScanRDD.scala:127)
at
org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:174)
at
org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:105)
at
org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown
Source) at
org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at
org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:395)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:234)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:228)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287) at
org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87) at
org.apache.spark.scheduler.Task.run(Task.scala:108) at
org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Driver stacktrace: at
org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1499)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1487)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1486)
at
scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at
org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1486)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
at scala.Option.foreach(Option.scala:257) at
org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:814)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1714)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1669)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1658)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at
org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:630)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2022) at
org.apache.spark.SparkContext.runJob(SparkContext.scala:2043) at
org.apache.spark.SparkContext.runJob(SparkContext.scala:2062) at
org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:336)
at
org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38)
at
org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:2853)
at
org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2153)
at
org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2153)
at org.apache.spark.sql.Dataset$$anonfun$55.apply(Dataset.scala:2837)
at
org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:65)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:2836) at
org.apache.spark.sql.Dataset.head(Dataset.scala:2153) at
org.apache.spark.sql.Dataset.take(Dataset.scala:2366) at
org.apache.spark.sql.execution.datasources.csv.TextInputCSVDataSource$.infer(CSVDataSource.scala:147)
at
org.apache.spark.sql.execution.datasources.csv.CSVDataSource.inferSchema(CSVDataSource.scala:62)
at
org.apache.spark.sql.execution.datasources.csv.CSVFileFormat.inferSchema(CSVFileFormat.scala:57)
at
org.apache.spark.sql.execution.datasources.DataSource$$anonfun$7.apply(DataSource.scala:177)
at
org.apache.spark.sql.execution.datasources.DataSource$$anonfun$7.apply(DataSource.scala:177)
at scala.Option.orElse(Option.scala:289) at
org.apache.spark.sql.execution.datasources.DataSource.getOrInferFileFormatSchema(DataSource.scala:176)
at
org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:366)
at
org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:178)
at
org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:156)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498) at
py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244) at
py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) at
py4j.Gateway.invoke(Gateway.java:280) at
py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79) at
py4j.GatewayConnection.run(GatewayConnection.java:214) at
java.lang.Thread.run(Thread.java:748) Caused by:
java.io.FileNotFoundException: File file:/home/ec2-user/PR_DATA_35.csv
does not exist It is possible the underlying files have been updated.
You can explicitly invalidate the cache in Spark by running 'REFRESH
TABLE tableName' command in SQL or by recreating the Dataset/DataFrame
involved. at
org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.org$apache$spark$sql$execution$datasources$FileScanRDD$$anon$$readCurrentFile(FileScanRDD.scala:127)
at
org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:174)
at
org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:105)
at
org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown
Source) at
org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at
org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:395)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:234)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:228)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287) at
org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87) at
org.apache.spark.scheduler.Task.run(Task.scala:108) at
org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
... 1 more
Any help is appreciated. Thanks

Caused by: java.io.FileNotFoundException: File file:/home/ec2-user/PR_DATA_35.csv does not exist??

Your code is not finding the file you want to convert to a DataFrame
Py4JJavaError: An error occurred while calling o312.load.:
Job aborted due to stage failure: Task 0 in stage 5.0 failed 4 times,
most recent failure: Lost task 0.3 in stage 5.0 (TID 23, 172.31.17.233,
executor 0): java.io.FileNotFoundException: File file:/home/ec2
user/PR_DATA_35.csv does not exist It is possible the underlying files
have been updated.
I have some recommendations
Verify the file is stored in your EC2 instance and It is in the same folder of your spark code.
Following the Databricks guide, You can specify the entire path to the file, for ec2 instances it should be something like: /home/ubuntu/project_folder/csv_file.csv
You can take a look to this stackoverflow question
This is the way I read csv files
# submit to spark on my dev env
# ./bin/spark-submit /Users/estebance/Documents/Projects/tests/spark_csv.py
# Spark version 2.0 and up, spark-csv is part of core Spark functionality and doesn't require a separate library. So you could just do for example
from pyspark import SparkContext
from pyspark.sql import SparkSession
spark = SparkSession.builder.master("local").appName("CsvReader").getOrCreate()
csv_df = spark.read.format("csv").option("header", "true").load("/Users/estebance/Documents/Projects/tests/sample.csv")
print(csv_df.head(2))
Hope this is helpful, best regards

Contrary to what others have said, this error normally occurs in Spark when you are reading data from a folder, modifying it and saving on top of the data which you have initially read.
Just try saving the first set of data in a temporary folder or use the solution described in this answer.