pyspark中df.show()的python问题

kb5ga3dv  于 11个月前  发布在  Spark
关注(0)|答案(1)|浏览(200)

我开始学习pyspark，但当我尝试创建df spark时，我遇到了df.show（）的问题，但当我尝试使用Colab时，没有问题发生。我有以下代码：

`from pyspark.sql import SparkSession
spark = SparkSession.builder.master("local[*]").appName("Test").getOrCreate()
df = spark.createDataFrame([
    (14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
print(df)

df.show()`

个字符
它给出了这个错误：

`Py4JJavaError                             Traceback (most recent call last)
c:\Users\taksi\OneDrive\Desktop\Python Learning\BOSS\MySql\sql.ipynb Cell 3 line 5
      1 df = spark.createDataFrame([
      2     (14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
      3 print(df)
----> 5 df.show(2)

File ~\AppData\Roaming\Python\Python312\site-packages\pyspark\sql\dataframe.py:959, in DataFrame.show(self, n, truncate, vertical)
    953     raise PySparkTypeError(
    954         error_class="NOT_BOOL",
    955         message_parameters={"arg_name": "vertical", "arg_type": type(vertical).__name__},
    956     )
    958 if isinstance(truncate, bool) and truncate:
--> 959     print(self._jdf.showString(n, 20, vertical))
    960 else:
    961     try:

File ~\AppData\Roaming\Python\Python312\site-packages\py4j\java_gateway.py:1322, 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 ~\AppData\Roaming\Python\Python312\site-packages\pyspark\errors\exceptions\captured.py:179, 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 ~\AppData\Roaming\Python\Python312\site-packages\py4j\protocol.py:326, 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)
    329 else:
    330     raise Py4JError(
    331         "An error occurred while calling {0}{1}{2}. Trace:\n{3}\n".
    332         format(target_id, ".", name, value))

Py4JJavaError: An error occurred while calling o130.showString.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 3.0 failed 1 times, most recent failure: Lost task 0.0 in stage 3.0 (TID 3) (DESKTOP-OF1LUOG executor driver): org.apache.spark.SparkException: Python worker failed to connect back.
    at org.apache.spark.api.python.PythonWorkerFactory.createSimpleWorker(PythonWorkerFactory.scala:203)
    at org.apache.spark.api.python.PythonWorkerFactory.create(PythonWorkerFactory.scala:109)
    at org.apache.spark.SparkEnv.createPythonWorker(SparkEnv.scala:124)
    at org.apache.spark.api.python.BasePythonRunner.compute(PythonRunner.scala:174)
    at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:67)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:364)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:328)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:364)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:328)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:364)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:328)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:364)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:328)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:364)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:328)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:364)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:328)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:93)
    at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:161)
    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.net.SocketTimeoutException: Accept timed out
    at java.base/sun.nio.ch.NioSocketImpl.timedAccept(NioSocketImpl.java:701)
    at java.base/sun.nio.ch.NioSocketImpl.accept(NioSocketImpl.java:745)
    at java.base/java.net.ServerSocket.implAccept(ServerSocket.java:698)
    at java.base/java.net.ServerSocket.platformImplAccept(ServerSocket.java:663)
    at java.base/java.net.ServerSocket.implAccept(ServerSocket.java:639)
    at java.base/java.net.ServerSocket.implAccept(ServerSocket.java:585)
    at java.base/java.net.ServerSocket.accept(ServerSocket.java:543)
    at org.apache.spark.api.python.PythonWorkerFactory.createSimpleWorker(PythonWorkerFactory.scala:190)
    ... 32 more

Driver stacktrace:
    at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2844)
    at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2780)
    at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2779)
    at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
    at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2779)
    at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1242)
    at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1242)
    at scala.Option.foreach(Option.scala:407)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1242)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:3048)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2982)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2971)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:984)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2398)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2419)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2438)
    at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:530)
    at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:483)
    at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:61)
    at org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:4344)
    at org.apache.spark.sql.Dataset.$anonfun$head$1(Dataset.scala:3326)
    at org.apache.spark.sql.Dataset.$anonfun$withAction$2(Dataset.scala:4334)
    at org.apache.spark.sql.execution.QueryExecution$.withInternalError(QueryExecution.scala:546)
    at org.apache.spark.sql.Dataset.$anonfun$withAction$1(Dataset.scala:4332)
    at org.apache.spark.sql.execution.SQLExecution$.$anonfun$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:4332)
    at org.apache.spark.sql.Dataset.head(Dataset.scala:3326)
    at org.apache.spark.sql.Dataset.take(Dataset.scala:3549)
    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 failed to connect back.
    at org.apache.spark.api.python.PythonWorkerFactory.createSimpleWorker(PythonWorkerFactory.scala:203)
    at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:642)
    ... 1 more
Caused by: java.net.SocketTimeoutException: Accept timed out
    at java.base/sun.nio.ch.NioSocketImpl.timedAccept(NioSocketImpl.java:701)
    at java.base/sun.nio.ch.NioSocketImpl.accept(NioSocketImpl.java:745)
    at java.base/java.net.ServerSocket.implAccept(ServerSocket.java:698)
    at java.base/java.net.ServerSocket.platformImplAccept(ServerSocket.java:663)
    at java.base/java.net.ServerSocket.implAccept(ServerSocket.java:639)
    at java.base/java.net.ServerSocket.implAccept(ServerSocket.java:585)
    at java.base/java.net.ServerSocket.accept(ServerSocket.java:543)
    at org.apache.spark.api.python.PythonWorkerFactory.createSimpleWorker(PythonWorkerFactory.scala:190)
    ... 32 more
`

型

python v. 3.12.0 pyspark V.3.5.0
可视代码
Java OpenJDK 21.0.1版

pyspark

1条答案

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lztngnrs

lztngnrs1#

以下是如何使用代码解决PySpark中的df.show()问题的示例：

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("example").getOrCreate()

data = [("Alice", 34), ("Bob", 45), ("Charlie", 28)]
df = spark.createDataFrame(data, ["name", "age"])

try:
    df.show()
except Exception as e:
    print("Error occurred when using df.show():", e)

print("Printing DataFrame using df.collect():")
for row in df.collect():
    print(row)

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