'How to resolve this error: Py4JJavaError: An error occurred while calling o70.showString?
Currently I'm doing PySpark and working on DataFrame.
I've created a DataFrame:
from pyspark.sql import *
import pandas as pd
spark = SparkSession.builder.appName("DataFarme").getOrCreate()
df = spark.createDataFrame([("Java", "20000"), ("Python", "100000"), ("Scala", "3000")])
df.printSchema()
#Output:-
root
|-- _1: string (nullable = true)
|-- _2: string (nullable = true)
But when I do df.show() its showing error as:
Py4JJavaError Traceback (most recent call last)
C:\Users\PRATIK~1\AppData\Local\Temp/ipykernel_20924/3726558592.py in <module>
----> 1 df.show()
C:\Spark\spark-3.2.1-bin-hadoop3.2\python\pyspark\sql\dataframe.py in show(self, n, truncate, vertical)
492
493 if isinstance(truncate, bool) and truncate:
--> 494 print(self._jdf.showString(n, 20, vertical))
495 else:
496 try:
C:\Spark\spark-3.2.1-bin-hadoop3.2\python\lib\py4j-0.10.9.3-src.zip\py4j\java_gateway.py in __call__(self, *args)
1319
1320 answer = self.gateway_client.send_command(command)
-> 1321 return_value = get_return_value(
1322 answer, self.gateway_client, self.target_id, self.name)
1323
C:\Spark\spark-3.2.1-bin-hadoop3.2\python\pyspark\sql\utils.py in deco(*a, **kw)
109 def deco(*a, **kw):
110 try:
--> 111 return f(*a, **kw)
112 except py4j.protocol.Py4JJavaError as e:
113 converted = convert_exception(e.java_exception)
C:\Spark\spark-3.2.1-bin-hadoop3.2\python\lib\py4j-0.10.9.3-src.zip\py4j\protocol.py 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}.\n".
328 format(target_id, ".", name), value)
Py4JJavaError: An error occurred while calling o70.showString.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 1.0 failed 1 times, most recent failure: Lost task 0.0 in stage 1.0 (TID 1) (KPI-PratikTrainee executor driver): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "C:\Spark\spark-3.2.1-bin-hadoop3.2\python\lib\pyspark.zip\pyspark\worker.py", line 481, in main
RuntimeError: Python in worker has different version 3.9 than that in driver 3.10, PySpark cannot run with different minor versions. Please check environment variables PYSPARK_PYTHON and PYSPARK_DRIVER_PYTHON are correctly set.
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:555)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:713)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:695)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:508)
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$$anon$10.hasNext(Iterator.scala:460)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:759)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:349)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:898)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:898)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:337)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:131)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:506)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1462)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:509)
at java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source)
at java.lang.Thread.run(Unknown Source)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2454)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2403)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2402)
at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2402)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1160)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1160)
at scala.Option.foreach(Option.scala:407)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1160)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2642)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2584)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2573)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:938)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2214)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2235)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2254)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:476)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:429)
at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:48)
at org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3715)
at org.apache.spark.sql.Dataset.$anonfun$head$1(Dataset.scala:2728)
at org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:3706)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:103)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:163)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:90)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:775)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3704)
at org.apache.spark.sql.Dataset.head(Dataset.scala:2728)
at org.apache.spark.sql.Dataset.take(Dataset.scala:2935)
at org.apache.spark.sql.Dataset.getRows(Dataset.scala:287)
at org.apache.spark.sql.Dataset.showString(Dataset.scala:326)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(Unknown Source)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(Unknown Source)
at java.lang.reflect.Method.invoke(Unknown Source)
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(Unknown Source)
Caused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "C:\Spark\spark-3.2.1-bin-hadoop3.2\python\lib\pyspark.zip\pyspark\worker.py", line 481, in main
RuntimeError: Python in worker has different version 3.9 than that in driver 3.10, PySpark cannot run with different minor versions. Please check environment variables PYSPARK_PYTHON and PYSPARK_DRIVER_PYTHON are correctly set.
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:555)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:713)
at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:695)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:508)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator$$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.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:759)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:349)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:898)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:898)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:373)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:337)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:131)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:506)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1462)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:509)
at java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source)
... 1 more
Same thing occured for df.collect().
But the same thing works perfectly fine in PyCharm once I set these 2 zip files in Project Structure: py4j-0.10.9.3-src.zip, pyspark.zip
Can anybody tell me how to set these 2 files in Jupyter so that I can run df.show() and df.collect() please?
Solution 1:[1]
The key is in this part of the error message:
RuntimeError: Python in worker has different version 3.9 than that in driver 3.10, PySpark cannot run with different minor versions. Please check environment variables PYSPARK_PYTHON and PYSPARK_DRIVER_PYTHON are correctly set.
You need to have exactly the same Python versions in driver and worker nodes.
Probably a quick solution would be to downgrade your Python version to 3.9 (assuming driver is running on the client you're using).
Sources
This article follows the attribution requirements of Stack Overflow and is licensed under CC BY-SA 3.0.
Source: Stack Overflow
| Solution | Source |
|---|---|
| Solution 1 | user2314737 |