2023-06-02 23:12:37 WARN NativeCodeLoader:60 - Unable to load native-hadoop library for your platform... using builtin-java classes where applicable Setting default log level to "WARN". To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel). Traceback (most recent call last): File "mysqlTest.py", line 12, in <module> jdbcDF=spark.read.format("jdbc").option("url","jdbc:mysql://localhost:3306/sparktest?useSSL=false").option("driver","com.mysql.cj.jdbc.Driver").option("dbtable","employee").option("user", "root").option("password", "123456").load() File "/usr/local/spark/python/pyspark/sql/readwriter.py", line 172, in load return self._df(self._jreader.load()) File "/usr/local/spark/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py", line 1257, in __call__ File "/usr/local/spark/python/pyspark/sql/utils.py", line 63, in deco return f(*a, **kw) File "/usr/local/spark/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py", line 328, in get_return_value py4j.protocol.Py4JJavaError: An error occurred while calling o31.load. : java.lang.ClassNotFoundException: com.mysql.cj.jdbc.Driver at java.net.URLClassLoader.findClass(URLClassLoader.java:381) at java.lang.ClassLoader.loadClass(ClassLoader.java:424) at java.lang.ClassLoader.loadClass(ClassLoader.java:357) at org.apache.spark.sql.execution.datasources.jdbc.DriverRegistry$.register(DriverRegistry.scala:45) at org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions$$anonfun$5.apply(JDBCOptions.scala:99) at org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions$$anonfun$5.apply(JDBCOptions.scala:99) at scala.Option.foreach(Option.scala:257) at org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions.<init>(JDBCOptions.scala:99) at org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions.<init>(JDBCOptions.scala:35) at org.apache.spark.sql.execution.datasources.jdbc.JdbcRelationProvider.createRelation(JdbcRelationProvider.scala:32) at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:318) at org.apache.spark.sql.DataFrameReader.loadV1Source(DataFrameReader.scala:223) at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:211) at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:167) 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: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.GatewayConnection.run(GatewayConnection.java:238) at java.lang.Thread.run(Thread.java:748)

这段信息是一个 Python 的错误堆栈（traceback），看起来是在使用 Spark 读取 MySQL 数据库时出现了问题。错误提示为 `java.lang.ClassNotFoundException: com.mysql.cj.jdbc.Driver`，说明可能缺少 MySQL 的 JDBC 驱动，需要将对应的 jar 包加入到 Spark 的 classpath 中，或者在读取数据时指定正确的驱动类。您可以参考这篇文章进行处理：[How to resolve java.lang.ClassNotFoundException: com.mysql.jdbc.Driver error in Spark](https://kontext.tech/column/spark/441/how-to-resolve-javalangclassnotfoundexception-commysqljdbcdriver-error-in-spark)。

相关问题

2023-06-09 09:58:19,390 WARN [main] org.apache.hadoop.util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable 2023-06-09 09:58:19,406 WARN [main] org.apache.hadoop.hdfs.shortcircuit.DomainSocketFactory: The short-circuit local reads feature cannot be used because libhadoop cannot be loaded.

这是 Hadoop 在启动时打印的一些警告信息。第一条警告信息是说无法加载本机代码库，因此将使用内置的 Java 类来代替。第二条警告信息是说由于无法加载 libhadoop 库，所以无法使用短路本地读取特性。这些警告信息通常不会影响 Hadoop 的运行，但如果你遇到了其他问题，可以提供更多的信息让我来帮助你。

WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable

这个警告信息表明你正在使用 Hadoop，但是无法加载本地的 Hadoop 库。这通常不会影响到 Hadoop 的正常使用，因为它会使用 Java 类库替代。如果你需要使用到本地的 Hadoop 库，你可以查看你的系统环境变量和 Hadoop 配置是否正确设置。另外，也可以尝试重新编译 Hadoop 库，以确保它们与你的操作系统和 Java 版本匹配。