py4jexception：constructor org.apache.spark.sql.sparksession（[[class org.apache.spark.spark.sparkcontext，class java.util.hashmap]）

Question

我试图通过Visual Studio代码在EC2 Linux机器上的Jupyter笔记本电脑中运行Spark会话。我的代码看起来如下：

from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("spark_app").getOrCreate()

错误是：

{
    "name": "Py4JError",
    "message": "An error occurred while calling None.org.apache.spark.sql.SparkSession. Trace:\npy4j.Py4JException: Constructor org.apache.spark.sql.SparkSession([class org.apache.spark.SparkContext, class java.util.HashMap]) does not exist\n\tat py4j.reflection.ReflectionEngine.getConstructor(ReflectionEngine.java:179)\n\tat py4j.reflection.ReflectionEngine.getConstructor(ReflectionEngine.java:196)\n\tat py4j.Gateway.invoke(Gateway.java:237)\n\tat py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80)\n\tat py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69)\n\tat py4j.GatewayConnection.run(GatewayConnection.java:238)\n\tat java.base/java.lang.Thread.run(Thread.java:829)\n\n",
    "stack": "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mPy4JError\u001b[0m                                 Traceback (most recent call last)\n\u001b[1;32mc:\\Users\\IrinaKaerkkaenen\\Projekte\\ZugPortal\\test.ipynb Cell 3'\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/c%3A/Users/IrinaKaerkkaenen/Projekte/ZugPortal/test.ipynb#ch0000002?line=0'>1</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mpyspark\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39msql\u001b[39;00m \u001b[39mimport\u001b[39;00m SparkSession\n\u001b[0;32m----> <a href='vscode-notebook-cell:/c%3A/Users/IrinaKaerkkaenen/Projekte/ZugPortal/test.ipynb#ch0000002?line=1'>2</a>\u001b[0m spark \u001b[39m=\u001b[39m SparkSession\u001b[39m.\u001b[39;49mbuilder\u001b[39m.\u001b[39;49mappName(\u001b[39m\"\u001b[39;49m\u001b[39mspark_app\u001b[39;49m\u001b[39m\"\u001b[39;49m)\u001b[39m.\u001b[39;49mgetOrCreate()\n\nFile \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pyspark/sql/session.py:272\u001b[0m, in \u001b[0;36mSparkSession.Builder.getOrCreate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    269\u001b[0m     sc \u001b[39m=\u001b[39m SparkContext\u001b[39m.\u001b[39mgetOrCreate(sparkConf)\n\u001b[1;32m    270\u001b[0m     \u001b[39m# Do not update `SparkConf` for existing `SparkContext`, as it's shared\u001b[39;00m\n\u001b[1;32m    271\u001b[0m     \u001b[39m# by all sessions.\u001b[39;00m\n\u001b[0;32m--> 272\u001b[0m     session \u001b[39m=\u001b[39m SparkSession(sc, options\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_options)\n\u001b[1;32m    273\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    274\u001b[0m     \u001b[39mgetattr\u001b[39m(\n\u001b[1;32m    275\u001b[0m         \u001b[39mgetattr\u001b[39m(session\u001b[39m.\u001b[39m_jvm, \u001b[39m\"\u001b[39m\u001b[39mSparkSession$\u001b[39m\u001b[39m\"\u001b[39m), \u001b[39m\"\u001b[39m\u001b[39mMODULE$\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    276\u001b[0m     )\u001b[39m.\u001b[39mapplyModifiableSettings(session\u001b[39m.\u001b[39m_jsparkSession, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_options)\n\nFile \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pyspark/sql/session.py:307\u001b[0m, in \u001b[0;36mSparkSession.__init__\u001b[0;34m(self, sparkContext, jsparkSession, options)\u001b[0m\n\u001b[1;32m    303\u001b[0m         \u001b[39mgetattr\u001b[39m(\u001b[39mgetattr\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_jvm, \u001b[39m\"\u001b[39m\u001b[39mSparkSession$\u001b[39m\u001b[39m\"\u001b[39m), \u001b[39m\"\u001b[39m\u001b[39mMODULE$\u001b[39m\u001b[39m\"\u001b[39m)\u001b[39m.\u001b[39mapplyModifiableSettings(\n\u001b[1;32m    304\u001b[0m             jsparkSession, options\n\u001b[1;32m    305\u001b[0m         )\n\u001b[1;32m    306\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 307\u001b[0m         jsparkSession \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_jvm\u001b[39m.\u001b[39;49mSparkSession(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_jsc\u001b[39m.\u001b[39;49msc(), options)\n\u001b[1;32m    308\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    309\u001b[0m     \u001b[39mgetattr\u001b[39m(\u001b[39mgetattr\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_jvm, \u001b[39m\"\u001b[39m\u001b[39mSparkSession$\u001b[39m\u001b[39m\"\u001b[39m), \u001b[39m\"\u001b[39m\u001b[39mMODULE$\u001b[39m\u001b[39m\"\u001b[39m)\u001b[39m.\u001b[39mapplyModifiableSettings(\n\u001b[1;32m    310\u001b[0m         jsparkSession, options\n\u001b[1;32m    311\u001b[0m     )\n\nFile \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/py4j/java_gateway.py:1585\u001b[0m, in \u001b[0;36mJavaClass.__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m   1579\u001b[0m command \u001b[39m=\u001b[39m proto\u001b[39m.\u001b[39mCONSTRUCTOR_COMMAND_NAME \u001b[39m+\u001b[39m\\\n\u001b[1;32m   1580\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_command_header \u001b[39m+\u001b[39m\\\n\u001b[1;32m   1581\u001b[0m     args_command \u001b[39m+\u001b[39m\\\n\u001b[1;32m   1582\u001b[0m     proto\u001b[39m.\u001b[39mEND_COMMAND_PART\n\u001b[1;32m   1584\u001b[0m answer \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_gateway_client\u001b[39m.\u001b[39msend_command(command)\n\u001b[0;32m-> 1585\u001b[0m return_value \u001b[39m=\u001b[39m get_return_value(\n\u001b[1;32m   1586\u001b[0m     answer, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_gateway_client, \u001b[39mNone\u001b[39;49;00m, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_fqn)\n\u001b[1;32m   1588\u001b[0m \u001b[39mfor\u001b[39;00m temp_arg \u001b[39min\u001b[39;00m temp_args:\n\u001b[1;32m   1589\u001b[0m     temp_arg\u001b[39m.\u001b[39m_detach()\n\nFile \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/py4j/protocol.py:330\u001b[0m, in \u001b[0;36mget_return_value\u001b[0;34m(answer, gateway_client, target_id, name)\u001b[0m\n\u001b[1;32m    326\u001b[0m         \u001b[39mraise\u001b[39;00m Py4JJavaError(\n\u001b[1;32m    327\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mAn error occurred while calling \u001b[39m\u001b[39m{0}\u001b[39;00m\u001b[39m{1}\u001b[39;00m\u001b[39m{2}\u001b[39;00m\u001b[39m.\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39m\n\u001b[1;32m    328\u001b[0m             \u001b[39mformat\u001b[39m(target_id, \u001b[39m\"\u001b[39m\u001b[39m.\u001b[39m\u001b[39m\"\u001b[39m, name), value)\n\u001b[1;32m    329\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 330\u001b[0m         \u001b[39mraise\u001b[39;00m Py4JError(\n\u001b[1;32m    331\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mAn error occurred while calling \u001b[39m\u001b[39m{0}\u001b[39;00m\u001b[39m{1}\u001b[39;00m\u001b[39m{2}\u001b[39;00m\u001b[39m. Trace:\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m{3}\u001b[39;00m\u001b[39m\\n\u001b[39;00m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39m\n\u001b[1;32m    332\u001b[0m             \u001b[39mformat\u001b[39m(target_id, \u001b[39m\"\u001b[39m\u001b[39m.\u001b[39m\u001b[39m\"\u001b[39m, name, value))\n\u001b[1;32m    333\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    334\u001b[0m     \u001b[39mraise\u001b[39;00m Py4JError(\n\u001b[1;32m    335\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mAn error occurred while calling \u001b[39m\u001b[39m{0}\u001b[39;00m\u001b[39m{1}\u001b[39;00m\u001b[39m{2}\u001b[39;00m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39m\n\u001b[1;32m    336\u001b[0m         \u001b[39mformat\u001b[39m(target_id, \u001b[39m\"\u001b[39m\u001b[39m.\u001b[39m\u001b[39m\"\u001b[39m, name))\n\n\u001b[0;31mPy4JError\u001b[0m: An error occurred while calling None.org.apache.spark.sql.SparkSession. Trace:\npy4j.Py4JException: Constructor org.apache.spark.sql.SparkSession([class org.apache.spark.SparkContext, class java.util.HashMap]) does not exist\n\tat py4j.reflection.ReflectionEngine.getConstructor(ReflectionEngine.java:179)\n\tat py4j.reflection.ReflectionEngine.getConstructor(ReflectionEngine.java:196)\n\tat py4j.Gateway.invoke(Gateway.java:237)\n\tat py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80)\n\tat py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69)\n\tat py4j.GatewayConnection.run(GatewayConnection.java:238)\n\tat java.base/java.lang.Thread.run(Thread.java:829)\n\n"
}

在阅读文本编辑器中的完整错误之前，运行单元格的输出是

Output exceeds the size limit. Open the full output data in a text editor
---------------------------------------------------------------------------
Py4JError                                 Traceback (most recent call last)
/tmp/ipykernel_5260/8684085.py in <module>
      1 from pyspark.sql import SparkSession
----> 2 spark = SparkSession.builder.appName("spark_app").getOrCreate()

~/anaconda3/envs/zupo_env_test1/lib64/python3.7/site-packages/pyspark/sql/session.py in getOrCreate(self)
    270                     # Do not update `SparkConf` for existing `SparkContext`, as it's shared
    271                     # by all sessions.
--> 272                     session = SparkSession(sc, options=self._options)
    273                 else:
    274                     getattr(

~/anaconda3/envs/zupo_env_test1/lib64/python3.7/site-packages/pyspark/sql/session.py in __init__(self, sparkContext, jsparkSession, options)
    305                 )
    306             else:
--> 307                 jsparkSession = self._jvm.SparkSession(self._jsc.sc(), options)
    308         else:
    309             getattr(getattr(self._jvm, "SparkSession$"), "MODULE$").applyModifiableSettings(

~/anaconda3/envs/zupo_env_test1/lib64/python3.7/site-packages/py4j/java_gateway.py in __call__(self, *args)
   1584         answer = self._gateway_client.send_command(command)
   1585         return_value = get_return_value(
-> 1586             answer, self._gateway_client, None, self._fqn)
   1587 
...
    at py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69)
    at py4j.GatewayConnection.run(GatewayConnection.java:238)
    at java.base/java.lang.Thread.run(Thread.java:829)

我在没有成功的情况下进行了很多谷歌搜索。有人知道怎么了吗？

我将IPython内核安装了3.9 python。

错误发生之前的警告：

WARNING: An illegal reflective access operation has occurred
WARNING: Illegal reflective access by org.apache.spark.unsafe.Platform (file:/home/ec2-user/spark/spark-3.1.2-bin-hadoop2.7/jars/spark-unsafe_2.12-3.1.2.jar) to constructor java.nio.DirectByteBuffer(long,int)
WARNING: Please consider reporting this to the maintainers of org.apache.spark.unsafe.Platform
WARNING: Use --illegal-access=warn to enable warnings of further illegal reflective access operations
WARNING: All illegal access operations will be denied in a future release
22/07/05 21:06:22 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).

Answer 1

我遇到了同样的问题，我已经修复了从PIP和SPARK中安装相同版本的Pyspark。您应该检查安装版本是否相同。

Answer 2

看来，计算机中安装的spark的版本与pyspark的版本不匹配。

使用以下命令检查Spark的版本：

<path-to-spark-bin>/spark-submit --version
# example output
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_\   version 3.1.3
      /_/

现在正如示例输出所显示的那样，已安装的Spark版本为3.1.3，因此您需要通过执行同一版本安装Spark（Pyspark）的Python库（Pyspark）以下命令：

pip install pyspark==<the-version-of-your-spark>
# Example
pip install pyspark==3.1.3

Answer 3

pip install pyspark == 3.2.1

＃它也对我有用