如何在 pyspark 的 Jupyter notebook 中为 MySQL 设置 JDBC 驱动程序? [英] How to set up JDBC driver for MySQL in Jupyter notebook for pyspark?

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问题描述

我正在尝试将一堆 CSV 文件逐行加载到使用 pyspark 配置在 OpenShift 上运行的 mysql 实例中.我有一个可以启动并运行的 Jupyter 笔记本.

I am trying to load a bunch of CSV files row by row into mysql instance which is running on OpenShift using pyspark configuration. I have a Jupyter notebook with spark up and running.

下面是我的代码.它因特定的驱动程序错误而失败

Below is the code I have. And it fails with specific driver error

Py4JJavaError: An error occurred while calling o89.save.


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

if __name__ == '__main__':
    scSpark = SparkSession \
        .builder \
        .appName("reading csv") \
        .getOrCreate()

if __name__ == '__main__':
    scSpark = SparkSession \
        .builder \
        .appName("reading csv") \
        .getOrCreate()
    
data_file = '/opt/app-root/src/data/train.psv'
sdfData = scSpark.read.csv(data_file, header=True, sep="|").cache()
print('Total Records = {}'.format(sdfData.count()))
sdfData.show()

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

+--------+
|count(1)|
+--------+
| 1168686|
+--------+

import os
os.environ['PYSPARK_SUBMIT_ARGS'] = '--packages mysql:mysql-connector-java:jar:8.0.21 pyspark-shell'

output = scSpark.sql('SELECT * from train')
output.show()
output.write.format('jdbc').options(
    url='jdbc:mysql://mysql-1-28d85/sepsis',
    driver='com.mysql.jdbc.Driver',
    #driver='mysql-connector-java.Driver',
    #driver='org.mysql.jdbc.Driver',
    dbtable='train',
    user='sepsis',
    password='Success_2020').mode('append').save()


---------------------------------------------------------------------------
Py4JJavaError                             Traceback (most recent call last)
<ipython-input-57-114af97e0442> in <module>
     11     dbtable='train',
     12     user='sepsis',
---> 13     password='Success_2020').mode('append').save()

/opt/app-root/lib/python3.6/site-packages/pyspark/sql/readwriter.py in save(self, path, format, mode, partitionBy, **options)
    735             self.format(format)
    736         if path is None:
--> 737             self._jwrite.save()
    738         else:
    739             self._jwrite.save(path)

/opt/app-root/lib/python3.6/site-packages/py4j/java_gateway.py in __call__(self, *args)
   1255         answer = self.gateway_client.send_command(command)
   1256         return_value = get_return_value(
-> 1257             answer, self.gateway_client, self.target_id, self.name)
   1258 
   1259         for temp_arg in temp_args:

/opt/app-root/lib/python3.6/site-packages/pyspark/sql/utils.py in deco(*a, **kw)
     61     def deco(*a, **kw):
     62         try:
---> 63             return f(*a, **kw)
     64         except py4j.protocol.Py4JJavaError as e:
     65             s = e.java_exception.toString()

/opt/app-root/lib/python3.6/site-packages/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
    326                 raise Py4JJavaError(
    327                     "An error occurred while calling {0}{1}{2}.\n".
--> 328                     format(target_id, ".", name), value)
    329             else:
    330                 raise Py4JError(

Py4JJavaError: An error occurred while calling o1641.save.
: java.lang.ClassNotFoundException: com.mysql.jdbc.Driver
    at java.net.URLClassLoader.findClass(URLClassLoader.java:382)
    at java.lang.ClassLoader.loadClass(ClassLoader.java:419)
    at java.lang.ClassLoader.loadClass(ClassLoader.java:352)
    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$5.apply(JDBCOptions.scala:99)
    at scala.Option.foreach(Option.scala:257)
    at org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions.<init>(JDBCOptions.scala:99)
    at org.apache.spark.sql.execution.datasources.jdbc.JdbcOptionsInWrite.<init>(JDBCOptions.scala:190)
    at org.apache.spark.sql.execution.datasources.jdbc.JdbcOptionsInWrite.<init>(JDBCOptions.scala:194)
    at org.apache.spark.sql.execution.datasources.jdbc.JdbcRelationProvider.createRelation(JdbcRelationProvider.scala:45)
    at org.apache.spark.sql.execution.datasources.SaveIntoDataSourceCommand.run(SaveIntoDataSourceCommand.scala:45)
    at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$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:86)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
    at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
    at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127)
    at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:83)
    at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:81)
    at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:676)
    at org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:676)
    at org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:80)
    at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:127)
    at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:75)
    at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:676)
    at org.apache.spark.sql.DataFrameWriter.saveToV1Source(DataFrameWriter.scala:285)
    at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:271)
    at sun.reflect.GeneratedMethodAccessor67.invoke(Unknown Source)
    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)

使用包更改代码.

这也是 openshift ,其中所有组件都作为 pod 运行,无法访问外部环境.

Also this is openshift , where all components are running as pods with no access to outside environment.

推荐答案

java.lang.ClassNotFoundException: com.mysql.cj.jdbc.Driver

java.lang.ClassNotFoundException: com.mysql.cj.jdbc.Driver

这就说明了一切.您必须使用 --driver-class-path 或类似(特定于 Jupyter)的 MySQL 驱动程序启动 pyspark(或环境).

That says it all. You have to start pyspark (or the environment) with the JDBC driver for MySQL using --driver-class-path or similar (that will be specific to Jupyter).

复制Jupyter Notebook 中的 PySpark — 使用 Dataframe &JDBC 数据源:

如果你使用 Jupyter Notebook,你应该设置 PYSPARK_SUBMIT_ARGS 环境变量,如下:

If you use Jupyter Notebook, you should set the PYSPARK_SUBMIT_ARGS environment variable, as following:

import os
os.environ['PYSPARK_SUBMIT_ARGS'] = '--packages org.postgresql:postgresql:42.1.1 pyspark-shell'

更改 --packages 以引用 MySQL JDBC 驱动程序.

Change the --packages to reference the MySQL JDBC driver.

这篇关于如何在 pyspark 的 Jupyter notebook 中为 MySQL 设置 JDBC 驱动程序?的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持IT屋!

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