Hi All, i just installed Pyspark in my laptop and im facing this error while trying to run the below code, These are my envionment variables:

HADOOP_HOME = C:\Programs\hadoop

JAVA_HOME = C:\Programs\Java

PYSPARK_DRIVER_PYTHON = C:\Users\Asus\AppData\Local\Programs\Python\Python313\python.exe

PYSPARK_HOME = C:\Users\Asus\AppData\Local\Programs\Python\Python313\python.exe

PYSPARK_PYTHON = C:\Users\Asus\AppData\Local\Programs\Python\Python313\python.exe

SPARK_HOME = C:\Programs\Spark

from pyspark.sql import SparkSession

spark = SparkSession.builder.master("local").appName("PySpark Installation Test").getOrCreate()
df = spark.createDataFrame([(1, "Hello"), (2, "World")], ["id", "message"])
df.show()

Error logs:

Py4JJavaError                             Traceback (most recent call last)
Cell In[1], line 5
      3 spark = SparkSession.builder.master("local").appName("PySpark Installation Test").getOrCreate()
      4 df = spark.createDataFrame([(1, "Hello"), (2, "World")], ["id", "message"])
----> 5 df.show()

File , in DataFrame.show(self, n, truncate, vertical)
    887 def show(self, n: int = 20, truncate: Union[bool, int] = True, vertical: bool = False) -> None:
    888     """Prints the first ``n`` rows to the console.
    889 
    890     .. versionadded:: 1.3.0
   (...)
    945     name | Bob
    946     """
--> 947     print(self._show_string(n, truncate, vertical))

File , in DataFrame._show_string(self, n, truncate, vertical)
    959     raise PySparkTypeError(
    960         error_class="NOT_BOOL",
    961         message_parameters={"arg_name": "vertical", "arg_type": type(vertical).__name__},
    962     )
    964 if isinstance(truncate, bool) and truncate:
--> 965     return self._jdf.showString(n, 20, vertical)
    966 else:
    967     try:

File , in JavaMember.__call__(self, *args)
   1316 command = proto.CALL_COMMAND_NAME +\
   1317     self.command_header +\
   1318     args_command +\
   1319     proto.END_COMMAND_PART
   1321 answer = self.gateway_client.send_command(command)
-> 1322 return_value = get_return_value(
   1323     answer, self.gateway_client, self.target_id, self.name)
   1325 for temp_arg in temp_args:
   1326     if hasattr(temp_arg, "_detach"):

File , in capture_sql_exception.<locals>.deco(*a, **kw)
    177 def deco(*a: Any, **kw: Any) -> Any:
    178     try:
--> 179         return f(*a, **kw)
    180     except Py4JJavaError as e:
    181         converted = convert_exception(e.java_exception)

File , in get_return_value(answer, gateway_client, target_id, name)
    324 value = OUTPUT_CONVERTER[type](answer[2:], gateway_client)
    325 if answer[1] == REFERENCE_TYPE:
--> 326     raise Py4JJavaError(
    327         "An error occurred while calling {0}{1}{2}".
    328         format(target_id, ".", name), value)
    329 else:
    330     raise Py4JError(
    331         "An error occurred while calling {0}{1}{2}. Trac{3}\n".
    332         format(target_id, ".", name, value))

Py4JJavaError: An error occurred while calling o43.showString.
: 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) (Bat-Computer executor driver): org.apache.spark.SparkException: Python worker exited unexpectedly (crashed)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator$$anonfun$1.applyOrElse(PythonRunner.scala:612)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator$$anonfun$1.applyOrElse(PythonRunner.scala:594)
at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:38)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:789)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:766)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:525)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:491)
at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:460)
at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:460)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenEvaluatorFactory$WholeStageCodegenPartitionEvaluator$$anon$1.hasNext(WholeStageCodegenEvaluatorFactory.scala:43)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:388)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:893)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:893)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:367)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:331)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:93)
at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:166)
at org.apache.spark.scheduler.Task.run(Task.scala:141)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$4(Executor.scala:620)
at org.apache.spark.util.SparkErrorUtils.tryWithSafeFinally(SparkErrorUtils.scala:64)
at org.apache.spark.util.SparkErrorUtils.tryWithSafeFinally$(SparkErrorUtils.scala:61)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:94)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:623)
at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1144)
at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:642)
at java.base/java.lang.Thread.run(Thread.java:1583)
Caused by: java.io.EOFException
at java.base/java.io.DataInputStream.readFully(DataInputStream.java:210)
at java.base/java.io.DataInputStream.readInt(DataInputStream.java:385)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:774)
... 26 more

Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2856)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2792)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2791)
at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2791)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1247)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1247)
at scala.Option.foreach(Option.scala:407)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1247)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:3060)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2994)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2983)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:989)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2393)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2414)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2433)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:530)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:483)
at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:61)
at org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:4333)
at org.apache.spark.sql.Dataset.$anonfun$head$1(Dataset.scala:3316)
at org.apache.spark.sql.Dataset.$anonfun$withAction$2(Dataset.scala:4323)
at org.apache.spark.sql.execution.QueryExecution$.withInternalError(QueryExecution.scala:546)
at org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:4321)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$6(SQLExecution.scala:125)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:201)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:108)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:900)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:66)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:4321)
at org.apache.spark.sql.Dataset.head(Dataset.scala:3316)
at org.apache.spark.sql.Dataset.take(Dataset.scala:3539)
at org.apache.spark.sql.Dataset.getRows(Dataset.scala:280)
at org.apache.spark.sql.Dataset.showString(Dataset.scala:315)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:75)
at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:52)
at java.base/java.lang.reflect.Method.invoke(Method.java:580)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:374)
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.base/java.lang.Thread.run(Thread.java:1583)
Caused by: org.apache.spark.SparkException: Python worker exited unexpectedly (crashed)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator$$anonfun$1.applyOrElse(PythonRunner.scala:612)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator$$anonfun$1.applyOrElse(PythonRunner.scala:594)
at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:38)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:789)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:766)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:525)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:491)
at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:460)
at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:460)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenEvaluatorFactory$WholeStageCodegenPartitionEvaluator$$anon$1.hasNext(WholeStageCodegenEvaluatorFactory.scala:43)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:388)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:893)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:893)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:367)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:331)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:93)
at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:166)
at org.apache.spark.scheduler.Task.run(Task.scala:141)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$4(Executor.scala:620)
at org.apache.spark.util.SparkErrorUtils.tryWithSafeFinally(SparkErrorUtils.scala:64)
at org.apache.spark.util.SparkErrorUtils.tryWithSafeFinally$(SparkErrorUtils.scala:61)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:94)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:623)
at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1144)
at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:642)
... 1 more
Caused by: java.io.EOFException
at java.base/java.io.DataInputStream.readFully(DataInputStream.java:210)
at java.base/java.io.DataInputStream.readInt(DataInputStream.java:385)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:774)
... 26 more~\Workspace\Projects\Python\PySpark\MyFirstPySpark_Proj\spark_venv\Lib\site-packages\pyspark\sql\dataframe.py:947~\Workspace\Projects\Python\PySpark\MyFirstPySpark_Proj\spark_venv\Lib\site-packages\pyspark\sql\dataframe.py:965~\Workspace\Projects\Python\PySpark\MyFirstPySpark_Proj\spark_venv\Lib\site-packages\py4j\java_gateway.py:1322~\Workspace\Projects\Python\PySpark\MyFirstPySpark_Proj\spark_venv\Lib\site-packages\pyspark\errors\exceptions\captured.py:179~\Workspace\Projects\Python\PySpark\MyFirstPySpark_Proj\spark_venv\Lib\site-packages\py4j\protocol.py:326.\ne:\n

Hey everyone!

I recently started working with Apache Spark, and its PySpark implementation in a professional environment, thus I am by no means an expert, and I am facing an error with Py4J.

In more details, I have installed Apache Spark, and already set up the SPARK_HOME, HADOOP_HOME, JAVA_HOME environment variables. As I want to run PySpark without using pip install pyspark, I have set up a PYTHONPATH environment variable, with values pointing to the python folder of Apache Spark and inside the py4j.zip.
My issue is that when I create a dataframe from scratch and use the command df.show() I get the Error

*"*Py4JJavaError: An error occurred while calling o143.showString. : org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 4.0 failed 1 times, most recent failure: Lost task 0.0 in stage 4.0 (TID 4) (xxx-yyyy.mshome.net executor driver): org.apache.spark.SparkException: Python worker failed to connect back".

However, the command works as it should when the dataframe is created, for example, by reading a csv file. Other commands that I have also tried, works as they should.

The version of the programs that I use are:
Python 3.11.9 (always using venv, so Python is not in path)
Java 11
Apache Spark 3.5.1 (and Hadoop 3.3.6 for the win.utls file and hadoop.dll)
Visual Studio Code
Windows 11

I have tried other version of Python (3.11.8, 3.12.4) and Apache Spark (3.5.2), with the same response

Any help would be greatly appreciated!

The following two pictures just show an example of the issue that I am facing.

----------- UPDATED SOLUTION -----------

In the end, also thanks to the suggestions in the comments, I figured out a way to make PySpark work with the following implementation. After running this code in a cell, PySpark is recognized as it should and the code runs without issues even for the manually created dataframe, Hopefully, it can also be helpful to others!

# Import the necessary libraries
import os, sys

# Add the necessary environment variables

os.environ["PYSPARK_PYTHON"] = sys.executable
os.environ["spark_python"] = os.getenv('SPARK_HOME') + "\\python"
os.environ["py4j"] = os.getenv('SPARK_HOME') + "\\python\lib\py4j-0.10.9.7-src.zip"

# Retrieve the values from the environment variables
spark_python_path = os.environ["spark_python"]
py4j_zip_path = os.environ["py4j"]

# Add the paths to sys.path
for path in [spark_python_path, py4j_zip_path]:
    if path not in sys.path:
        sys.path.append(path)

# Verify that the paths have been added to sys.path
print("sys.path:", sys.path)