showstring error pyspark

stackoverflow.com › questions › 48504849 › pyspark-an-error-occurred-while-calling-o51-showstring-no-module-named-xxx

python - PySpark: An error occurred while calling o51.showString. No module named XXX - Stack Overflow

As it is right now, the result will depend on the working directory, where you invoke the script.

If you're in root, this will add its parent. You should use path relative to __file__ (see what does the __file__ variable mean/do?):

parentPath = os.path.join(
    os.path.abspath(os.path.dirname(__file__)), 
    os.path.pardir
)

but I'd will recommend using proper package structure.

Note:

This covers only local mode and driver path and even in local mode, worker paths, are not affected by the driver path.

To handle executor paths (after changes you get executor exceptions) you should still distribute modules to the workers How to use custom classes with Apache Spark (pyspark)?.

spark = SparkSession.builder.appName("tests").getOrCreate()
spark.sparkContext.addPyFile("/path/to/cast_to_float.py")

stackoverflow.com › questions › 47761758 › pyspark-python-issue-py4jjavaerror-an-error-occurred-while-calling-o48-showstr

PySpark python issue: Py4JJavaError: An error occurred while calling o48.showString - Stack Overflow

community.databricks.com › t5 › data-engineering › i-created-a-data-frame-but-was-not-able-to-see-the-data › td-p › 14720

1 of 3

The error is

Caused by: java.lang.OutOfMemoryError: Java heap space

You need more memory to perform the operations and avoid the OOM error.

2 of 3

You can add above your Spark Session object

import findspark
findspark.init()

Databricks Community

Solved: I created a data frame but was not able to see the... - Databricks Community - 14720

June 17, 2023 - Py4JJavaError: An error occurred while calling o48.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) (DESKTOP-N3C4AUC.attlocal.net executor driver): org.apache.spark.api.python.PythonException: Traceback (most recent call last): File "C:\pyspark\spark-3.3.1-bin-hadoop3\python\lib\pyspark.zip\pyspark\worker.py", line 668, in main ·

stackoverflow.com › questions › 70840643 › an-error-occurred-while-calling-o196-showstring

python - An error occurred while calling o196.showString - Stack Overflow

github.com › GoogleCloudDataproc › spark-bigquery-connector › issues › 225

You need to create an RDD of type RDD[Tuple[str]] but in your code, the line:

rdd = spark.sparkContext.parallelize(comments)

returns RDD[str] which then fails when you try to convert it to dataframe with that given schema.

Try modifying that line to:

rdd = spark.sparkContext.parallelize([(c,) for c in comments])

Note that you can actually pass list of tuples directly to spark.createDataFrame like this:

df = spark.createDataFrame([(c,) for c in comments], schema=schema)

df.show()
#+--------+
#|Comments|
#+--------+
#| string1|
#| string2|
#| string3|
#+--------+

GitHub

Can't show dataframe (df.show() fails) · Issue #225 · GoogleCloudDataproc/spark-bigquery-connector

July 31, 2020 - --> 328 format(target_id, ".", name), value) 329 else: 330 raise Py4JError( Py4JJavaError: An error occurred while calling o160.showString.

Author informatica92

stackoverflow.com › questions › 69927122 › error-while-i-am-using-dataframe-show-method-in-pyspark

python - Error while I am using DataFrame show method in Pyspark - Stack Overflow

reddit.com › r/apachespark › connection reset error on creating/showing dataframe directly from data, but reading from csv works

PySpark DF are lazy loading.

When you call .show() you are asking the prior steps to execute and anyone of them may not work, you just can't see it until you call .show() because they haven't executed.

I go back to earlier steps and call .collect() on each operation of the DF. This will at least allow you to isolate where the bad data was created.

Find elsewhere

Google Bing Mojeek

r/apachespark on Reddit: Connection Reset error on creating/showing DataFrame directly from data, but reading from CSV works

September 9, 2024 -

Hello, I started learning PySpark a week back and faced some issues today, which I then narrowed to create a minimal example of the problem:

    from pyspark.sql import SparkSession
    spark = SparkSession.builder \
        .appName("CreateDataFrameExample") \
        .getOrCreate()
    
    columns = ["language", "users_count"]
    data = [("Java", 20000), ("Python", 10000), ("Scala", 3000)]
    
    # fails when df.show() is called [Connection Reset error]
    df = spark.createDataFrame(data, columns)
    
    #this works as expected
    #df = spark.read.csv("data.csv", header=True)
    
    df.show()

I get a connection reset error when I show the df created directly from the data, but am able to print the dataframe created from reading the csv. For sanity check I have tried LLMs which say that the code is correct. I have tried setting the timeout and heartbeat interval to high values which hasn't helped.

Stacktrace:

an error occurred while calling o47.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) (DESKTOP-\*\*\*\* executor driver): java.net.SocketException: Connection reset  
at java.net.SocketInputStream.read(SocketInputStream.java:210)  
at java.net.SocketInputStream.read(SocketInputStream.java:141)  
at java.io.BufferedInputStream.fill(BufferedInputStream.java:246)  
at java.io.BufferedInputStream.read(BufferedInputStream.java:265)  
at java.io.DataInputStream.readInt(DataInputStream.java:387)  
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:774)     
at org.apache.spark.api.python.PythonRunner$$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.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.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:2398)  
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2419)  
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2438)  
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:4332)  
at org.apache.spark.sql.Dataset.$anonfun$head$1(Dataset.scala:3314)  
at org.apache.spark.sql.Dataset.$anonfun$withAction$2(Dataset.scala:4322)  
at org.apache.spark.sql.execution.QueryExecution$.withInternalError(QueryExecution.scala:546)  
at org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:4320)  
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:4320)  
at org.apache.spark.sql.Dataset.head(Dataset.scala:3314)  
at org.apache.spark.sql.Dataset.take(Dataset.scala:3537)  
at org.apache.spark.sql.Dataset.getRows(Dataset.scala:280)  
at org.apache.spark.sql.Dataset.showString(Dataset.scala:315)  
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: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.lang.Thread.run(Thread.java:748)  
Caused by: java.net.SocketException: Connection reset  
at java.net.SocketInputStream.read(SocketInputStream.java:210)  
at java.net.SocketInputStream.read(SocketInputStream.java:141)  
at java.io.BufferedInputStream.fill(BufferedInputStream.java:246)  
at java.io.BufferedInputStream.read(BufferedInputStream.java:265)  
at java.io.DataInputStream.readInt(DataInputStream.java:387)  
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:774)     
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$4(Executor.scala:620)        at org.apache.spark.util.SparkErrorUtils.tryWithSafeFinally(SparkErrorUtils.scala:6        at org.apache.spark.util.SparkErrorUtils.tryWithSafeFinally$(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.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)   
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)   
... 1 more

Try this: from pyspark.sql import SparkSession # Initialize Spark session spark = SparkSession.builder.appName("example").getOrCreate() columns = ["language", "user_count"] data = [("Java", 20000), ("Python", 10000), ("Scala", 3000)] # Create DataFrame df = spark.createDataFrame(data).toDF(*columns) df.show()

1 of 2

2 of 2

from pyspark.sql.types import * schema = StructType([ StructField("language", StringType(), True), StructField("users_count", IntegerType(), True)]) spark.createDataFrame([(“java”, 20000), (“python”, 20000), (“scala”, 3000)], schema).show()

Apache JIRA

issues.apache.org › jira › browse › SPARK-20086

[SPARK-20086] issue with pyspark 2.1.0 window function - ASF JIRA

August 2, 2017 - Py4JJavaError: An error occurred while calling o2609.showString. with traceback: Py4JJavaErrorTraceback (most recent call last) <ipython-input-215-3106d06b6e49> in <module>() ----> 1 df.show() /Users/<>/spark-2.1.0-bin-hadoop2.7/python/pyspark/sql/dataframe.pyc in show(self, n, truncate) 316 """ 317 if isinstance(truncate, bool) and truncate: --> 318 print(self._jdf.showString(n, 20)) 319 else: 320 print(self._jdf.showString(n, int(truncate))) /Users/<>/.virtualenvs/<>/lib/python2.7/site-packages/py4j/java_gateway.pyc in __call__(self, *args) 1131 answer = self.gateway_client.send_command(comm

stackoverflow.com › questions › 55653609 › saving-result-of-dataframe-show-to-string-in-pyspark

python - Saving result of DataFrame show() to string in pyspark - Stack Overflow

medium.com › @yhoso › resolving-weird-spark-errors-f34324943e1c

You can build a helper function using the same approach as shown in post you linked Capturing the result of explain() in pyspark. Just examine the source code for show() and observe that it is calling self._jdf.showString().

The answer depends on which version of spark you are using, as the number of arguments to show() has changed over time.

Spark Version 2.3 and above

In version 2.3, the vertical argument was added.

def getShowString(df, n=20, truncate=True, vertical=False):
    if isinstance(truncate, bool) and truncate:
        return(df._jdf.showString(n, 20, vertical))
    else:
        return(df._jdf.showString(n, int(truncate), vertical))

Spark Versions 1.5 through 2.2

As of version 1.5, the truncate argument was added.

def getShowString(df, n=20, truncate=True):
    if isinstance(truncate, bool) and truncate:
        return(df._jdf.showString(n, 20))
    else:
        return(df._jdf.showString(n, int(truncate)))

Spark Versions 1.3 through 1.4

The show function was first introduced in version 1.3.

def getShowString(df, n=20):
    return(df._jdf.showString(n))

Now use the helper function as follows:

x = getShowString(df)  # default arguments
print(x)
#+----+-------+
#| age|   name|
#+----+-------+
#|null|Michael|
#|  30|   Andy|
#|  19| Justin|
#+----+-------+

Or in your case:

logger.info(getShowString(df))

Medium

Solving 5 Mysterious Spark Errors | by yhoztak | Medium

November 28, 2018 - An error occurred while calling o206.showString.: org.apache.spark.SparkException: Job 25 cancelled because SparkContext was shut down ...

Gankrin

gankrin.org › how-to-fix-py4jjavaerror-while-calling-oxx-showstring-in-spark

An Error Occurred While Calling oxx.showString" in Spark

Py4JJavaError: An Error Occurred ... py4jjavaerror ,spark py4j.protocol.py4jjavaerror ,pyspark py4jjavaerror ,py4j/protocol py4jjavaerror an error occurred while calling o94 save ,an error occurred while calling o84.showstring ,an error occurred while calling o51.showstring ,an ...

Databricks

community.databricks.com › s › question › 0D53f00001HKHeECAX › i-am-running-simple-count-and-i-am-getting-an-error

I am running simple count and I am getting an error

October 20, 2022 - Py4JJavaError: An error occurred while calling o358.showString.

stackoverflow.com › questions › 70303535 › py4j-protocol-py4jjavaerror-an-error-occurred-while-calling-showstring

apache spark - py4j.protocol.Py4JJavaError: An error occurred while calling showString - Stack Overflow

Facing Py4JJavaError on executing PySpark Code · 2 · Spark dataframe will not show() - Py4JJavaError: An error occurred while calling o426.showString · 10 · Py4JJavaError: An error occurred while calling · 2 · Py4JJavaError running Pyspark program · 0 · Py4JJavaError: An error occurred while calling o840.showString ·

stackoverflow.com › questions › 65663877 › py4jjavaerror-an-error-occurred-while-calling-o8484-showstring

pyspark - Py4JJavaError: An error occurred while calling o8484.showString - Stack Overflow

Py4JJavaError: An error occurred while calling o8484.showString. : java.lang.IllegalStateException: Cannot call methods on a stopped SparkContext.

reddit.com › r/apachespark › error with pyspark and py4j

r/apachespark on Reddit: Error with PySpark and Py4J

September 5, 2024 -

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)

I was learning Spark and during the installation I got the error of 'Java gateway process exited'. After trying a lot of solutions I finally found a way. I changed the location of my Temp directory under my User Environment Variables. So, the problem was that I had a space in my username, so it was working properly. Don't know if this helps in your case.😅

1 of 5

2 of 5

I don't have access to windows computer, and I checked your code is correct. I have found this stack overflow question that contains many possible causes and fixes in the answers. One of them might apply to you: https://stackoverflow.com/questions/53252181/python-worker-failed-to-connect-back Hope this helps.

Cloudera Community

community.cloudera.com › t5 › Support-Questions › Py4JJavaError-An-error-occurred-while-calling-o53-showString › td-p › 292437

Py4JJavaError: An error occurred while calling o53... - Cloudera Community - 292437

February 10, 2021 - C:\spark-2.3.2-bin-hadoop2.7\python\pyspark\sql\dataframe.py in show(self, n, truncate, vertical) 348 """ 349 if isinstance(truncate, bool) and truncate: --> 350 print(self._jdf.showString(n, 20, vertical)) 351 else: 352 print(self._jdf.showString(n, int(truncate), vertical))

Cloudera Community

community.cloudera.com › t5 › Support-Questions › Py4JJavaError-An-error-occurred-while-calling-o53-showString › m-p › 311320

Re: Py4JJavaError: An error occurred while calling... - Cloudera Community - 292437

stackoverflow.com › questions › 78501378 › pyspark-unable-to-display-the-dataframe-contents-using-df-show-on-windows-11

apache spark - Pyspark - Unable to display the DataFrame contents using df.show() on Windows 11 - Stack Overflow