在 python 中连接到 AWS RDS MySQL 服务时出错

Question

我使用 PySpark 读取一些 Excel 并将其加载到 AWS EC2 Linux 服务器中的 AWS RDS MySQL 服务。

我的脚本：

  from pyspark.sql import SparkSession
from pyspark.sql import SQLContext

if __name__ == '__main__':
    scSpark = SparkSession \
        .builder \
        .appName("reading csv") \
        .config("spark.driver.extraClassPath", "./mysql-connector-java-8.0.16.jar") \
        .getOrCreate()


    data_file = './text.xlsx'
    sdfData = scSpark.read.csv(data_file, header=True, sep=",").cache()

    sdfData.registerTempTable("books")
    output = scSpark.sql('SELECT * from books')
    output.show()

    output.write.format('jdbc').options(
        url='XXX.rds.amazonaws.com',
        driver='com.mysql.cj.jdbc.Driver',
        dbtable='books',
        user='xxx',
        password='xxx').mode('append').save()

使用此脚本连接到 AWS RDS MySQL 服务时出现一些错误：

PuTTYTraceback (most recent call last):
  File "ETL.py", line 24, in <module>
    password='XXX').mode('append').save()
  File "/home/ec2-user/.local/lib/python3.7/site-packages/pyspark/sql/readwriter.py", line 738, in save
    self._jwrite.save()
  File "/home/ec2-user/.local/lib/python3.7/site-packages/py4j/java_gateway.py", line 1322, in __call__
    answer, self.gateway_client, self.target_id, self.name)
  File "/home/ec2-user/.local/lib/python3.7/site-packages/pyspark/sql/utils.py", line 111, in deco
    return f(*a, **kw)
  File "/home/ec2-user/.local/lib/python3.7/site-packages/py4j/protocol.py", line 328, in get_return_value
    format(target_id, ".", name), value)
py4j.protocol.Py4JJavaError: An error occurred while calling o45.save.
: java.lang.ClassNotFoundException: com.mysql.cj.jdbc.Driver
        at java.net.URLClassLoader.findClass(URLClassLoader.java:387)
        at java.lang.ClassLoader.loadClass(ClassLoader.java:418)
        at java.lang.ClassLoader.loadClass(ClassLoader.java:351)
        at org.apache.spark.sql.execution.datasources.jdbc.DriverRegistry$.register(DriverRegistry.scala:46)
        at org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions.$anonfun$driverClass$1(JDBCOptions.scala:101)
        at org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions.$anonfun$driverClass$1$adapted(JDBCOptions.scala:101)
        at scala.Option.foreach(Option.scala:407)
        at org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions.<init>(JDBCOptions.scala:101)
        at org.apache.spark.sql.execution.datasources.jdbc.JdbcOptionsInWrite.<init>(JDBCOptions.scala:218)
        at org.apache.spark.sql.execution.datasources.jdbc.JdbcOptionsInWrite.<init>(JDBCOptions.scala:222)
        at org.apache.spark.sql.execution.datasources.jdbc.JdbcRelationProvider.createRelation(JdbcRelationProvider.scala:46)
        at org.apache.spark.sql.execution.datasources.SaveIntoDataSourceCommand.run(SaveIntoDataSourceCommand.scala:45)
        at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:75)
        at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:73)
        at org.apache.spark.sql.execution.command.ExecutedCommandExec.executeCollect(commands.scala:84)
        at org.apache.spark.sql.execution.QueryExecution$$anonfun$eagerlyExecuteCommands$1.$anonfun$applyOrElse$1(QueryExecution.scala:110)
        at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:103)
        at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:163)
        at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:90)
        at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:775)
        at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
        at org.apache.spark.sql.execution.QueryExecution$$anonfun$eagerlyExecuteCommands$1.applyOrElse(QueryExecution.scala:110)
        at org.apache.spark.sql.execution.QueryExecution$$anonfun$eagerlyExecuteCommands$1.applyOrElse(QueryExecution.scala:106)
        at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDownWithPruning$1(TreeNode.scala:481)
        at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:82)
        at org.apache.spark.sql.catalyst.trees.TreeNode.transformDownWithPruning(TreeNode.scala:481)
        at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.org$apache$spark$sql$catalyst$plans$logical$AnalysisHelper$$super$transformDownWithPruning(LogicalPlan.scala:30)
        at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDownWithPruning(AnalysisHelper.scala:267)
        at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDownWithPruning$(AnalysisHelper.scala:263)
        at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDownWithPruning(LogicalPlan.scala:30)
        at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDownWithPruning(LogicalPlan.scala:30)
        at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:457)
        at org.apache.spark.sql.execution.QueryExecution.eagerlyExecuteCommands(QueryExecution.scala:106)
        at org.apache.spark.sql.execution.QueryExecution.commandExecuted$lzycompute(QueryExecution.scala:93)
        at org.apache.spark.sql.execution.QueryExecution.commandExecuted(QueryExecution.scala:91)
        at org.apache.spark.sql.execution.QueryExecution.assertCommandExecuted(QueryExecution.scala:128)
        at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:848)
        at org.apache.spark.sql.DataFrameWriter.saveToV1Source(DataFrameWriter.scala:382)
        at org.apache.spark.sql.DataFrameWriter.saveInternal(DataFrameWriter.scala:355)
        at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:247)
        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.ClientServerConnection.waitForCommands(ClientServerConnection.java:182)
        at py4j.ClientServerConnection.run(ClientServerConnection.java:106)
        at java.lang.Thread.run(Thread.java:748)

我已经下载了驱动程序mysql-connector-java-8.0.16.jar，并将其放在与脚本相同的文件夹中。 但是，当我运行脚本时，脚本的最后一行会不断抛出该错误。

我该如何解决这个问题？

Answer 1

在 jdbc 选项中将 url 值设置为：

url='XXX.rds.amazonaws.com?useSSL=FALSE&nullCatalogMeansCurrent=true&zeroDateTimeBehavior=convertToNull'

MySQL 连接器 java 8.0 需要 SSL 或显式禁用。

参考： https ://dev.mysql.com/doc/connector-j/8.0/en/connector-j-connp-props-security.html