如何将 MariaDB Connector/J 与 Pyspark 一起用于 JDBC？

Question

我在 Ubuntu 18.04 上使用 Pyspark Spark 3.0.1，并希望使用 JDBC.

将数据导出到 MariaDB 服务器

我在 pyspark 命令行上指定 Connector/J jar，如下所示：

$ pyspark --jars /usr/share/java/mariadb-java-client.jar

但是，当我使用 JDBC 连接时，出现以下错误：

>>> df1 = sc.parallelize([[1,2,3], [2,3,4]]).toDF(("a", "b", "c"))
>>> df1.write.format("jdbc") \
... .mode("overwrite") \
... .option("url", "jdbc:mariadb://localhost:3306/testDatabase?user=foo&password=bar") \
... .option("dbtable", "example") \
... .save()
Traceback (most recent call last):
  File "<stdin>", line 4, in <module>
  File "/opt/spark/python/pyspark/sql/readwriter.py", line 825, in save
    self._jwrite.save()
  File "/opt/spark/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py", line 1305, in __call__
  File "/opt/spark/python/pyspark/sql/utils.py", line 128, in deco
    return f(*a, **kw)
  File "/opt/spark/python/lib/py4j-0.10.9-src.zip/py4j/protocol.py", line 328, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o60.save.
: java.sql.SQLException: No suitable driver
    at java.sql.DriverManager.getDriver(DriverManager.java:315)
    at org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions.$anonfun$driverClass(JDBCOptions.scala:105)
    at scala.Option.getOrElse(Option.scala:189)
    at org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions.<init>(JDBCOptions.scala:105)
    at org.apache.spark.sql.execution.datasources.jdbc.JdbcOptionsInWrite.<init>(JDBCOptions.scala:194)
    at org.apache.spark.sql.execution.datasources.jdbc.JdbcOptionsInWrite.<init>(JDBCOptions.scala:198)
    at org.apache.spark.sql.execution.datasources.jdbc.JdbcRelationProvider.createRelation(JdbcRelationProvider.scala:45)
    at org.apache.spark.sql.execution.datasources.SaveIntoDataSourceCommand.run(SaveIntoDataSourceCommand.scala:46)
    at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:70)
    at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:68)
    at org.apache.spark.sql.execution.command.ExecutedCommandExec.doExecute(commands.scala:90)
    at org.apache.spark.sql.execution.SparkPlan.$anonfun$execute(SparkPlan.scala:175)
    at org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery(SparkPlan.scala:213)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
    at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:210)
    at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:171)
    at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:122)
    at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:121)
    at org.apache.spark.sql.DataFrameWriter.$anonfun$runCommand(DataFrameWriter.scala:963)
    at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId(SQLExecution.scala:100)
    at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:160)
    at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId(SQLExecution.scala:87)
    at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:764)
    at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
    at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:963)
    at org.apache.spark.sql.DataFrameWriter.saveToV1Source(DataFrameWriter.scala:415)
    at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:399)
    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)

>>>

因为 java.sql.SQLException: No suitable driver 我假设我需要一些额外的配置才能调用 Connector/J。我不知道如何去做。有什么诀窍？

Answer 1

您需要在写入时使用 driver 选项指定 mariadb 驱动程序 class org.mariadb.jdbc.Driver:

df1.write.format("jdbc") \
    .mode("overwrite") \
    .option("driver", "org.mariadb.jdbc.Driver") \
    .option("url", "jdbc:mysql://localhost:3306/testDatabase?user=foo&password=bar") \
    .option("dbtable", "example") \
    .save()

请参阅文档中的 Usage。

Answer 2

对于仍然面临此错误的任何人，

在 url 中使用 mysql，而不是 mariadb。

jdbc url 应该像 jdbc:mysql:{host} ... 代替 jdbc:mariadb:{host} ...。

如何将 MariaDB Connector/J 与 Pyspark 一起用于 JDBC？

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