AWS EMR PySpark 连接到 mysql

我正在尝试使用 jdbc 通过 pyspark 连接到 mysql。我可以在 EMR 之外完成此操作。但是当我尝试使用 EMR 时，pyspark 无法正确启动。

我在我的机器上使用的命令

pyspark --conf spark.executor.extraClassPath=/home/hadoop/mysql-connector-java-5.1.38-bin.jar --driver-class-path /home/hadoop/mysql-connector-java-5.1.38-bin.jar --jars /home/hadoop/mysql-connector-java-5.1.38-bin.jar

并得到以下输出：

16/05/18 14:29:21 INFO Client: Application report for application_1463578502297_0011 (state: FAILED)
16/05/18 14:29:21 INFO Client: 
     client token: N/A
     diagnostics: Application application_1463578502297_0011 failed 2 times due to AM Container for appattempt_1463578502297_0011_000002 exited with  exitCode: 1
For more detailed output, check application tracking page:http://ip-10-24-0-75.ec2.internal:8088/cluster/app/application_1463578502297_0011Then, click on links to logs of each attempt.
Diagnostics: Exception from container-launch.
Container id: container_1463578502297_0011_02_000001
Exit code: 1
Stack trace: ExitCodeException exitCode=1: 
    at org.apache.hadoop.util.Shell.runCommand(Shell.java:545)
    at org.apache.hadoop.util.Shell.run(Shell.java:456)
    at org.apache.hadoop.util.Shell$ShellCommandExecutor.execute(Shell.java:722)
    at org.apache.hadoop.yarn.server.nodemanager.DefaultContainerExecutor.launchContainer(DefaultContainerExecutor.java:212)
    at org.apache.hadoop.yarn.server.nodemanager.containermanager.launcher.ContainerLaunch.call(ContainerLaunch.java:302)
    at org.apache.hadoop.yarn.server.nodemanager.containermanager.launcher.ContainerLaunch.call(ContainerLaunch.java:82)
    at java.util.concurrent.FutureTask.run(FutureTask.java:262)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
    at java.lang.Thread.run(Thread.java:745)


Container exited with a non-zero exit code 1
Failing this attempt. Failing the application.
     ApplicationMaster host: N/A
     ApplicationMaster RPC port: -1
     queue: default
     start time: 1463581754050
     final status: FAILED
     tracking URL: http://ip-10-24-0-75.ec2.internal:8088/cluster/app/application_1463578502297_0011
     user: hadoop
16/05/18 14:29:21 INFO Client: Deleting staging directory .sparkStaging/application_1463578502297_0011
16/05/18 14:29:21 ERROR SparkContext: Error initializing SparkContext.
org.apache.spark.SparkException: Yarn application has already ended! It might have been killed or unable to launch application master.
    at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.waitForApplication(YarnClientSchedulerBackend.scala:124)
    at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:64)
    at org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:144)
    at org.apache.spark.SparkContext.<init>(SparkContext.scala:530)
    at org.apache.spark.api.java.JavaSparkContext.<init>(JavaSparkContext.scala:59)
    at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
    at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:57)
    at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
    at java.lang.reflect.Constructor.newInstance(Constructor.java:526)
    at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:234)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:381)
    at py4j.Gateway.invoke(Gateway.java:214)
    at py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:79)
    at py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:68)
    at py4j.GatewayConnection.run(GatewayConnection.java:209)
    at java.lang.Thread.run(Thread.java:745)

我也尝试不使用额外的 jar，但与 mariadb.jdbc 连接，我读过的是默认驱动程序：

from pyspark.sql import SQLContext
sqlctx = SQLContext(sc)
df = sqlctx.read.format("jdbc").option("url", "jdbc:mysql://ip:port/db").option("driver", "com.mariadb.jdbc.Driver").option("dbtable", "...").option("user", "....").option("password", "...").load()

但我得到

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/lib/spark/python/pyspark/sql/readwriter.py", line 139, in load
    return self._df(self._jreader.load())
  File "/usr/lib/spark/python/lib/py4j-0.9-src.zip/py4j/java_gateway.py", line 813, in __call__
  File "/usr/lib/spark/python/pyspark/sql/utils.py", line 45, in deco
    return f(*a, **kw)
  File "/usr/lib/spark/python/lib/py4j-0.9-src.zip/py4j/protocol.py", line 308, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o81.load.
: java.lang.ClassNotFoundException: com.mariadb.jdbc.Driver
    at java.net.URLClassLoader$1.run(URLClassLoader.java:366)
    at java.net.URLClassLoader$1.run(URLClassLoader.java:355)
    at java.security.AccessController.doPrivileged(Native Method)
    at java.net.URLClassLoader.findClass(URLClassLoader.java:354)
    at java.lang.ClassLoader.loadClass(ClassLoader.java:425)
    at java.lang.ClassLoader.loadClass(ClassLoader.java:358)
    at org.apache.spark.sql.execution.datasources.jdbc.DriverRegistry$.register(DriverRegistry.scala:38)
    at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$createConnectionFactory$1.apply(JdbcUtils.scala:45)
    at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$createConnectionFactory$1.apply(JdbcUtils.scala:45)
    at scala.Option.foreach(Option.scala:236)
    at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$.createConnectionFactory(JdbcUtils.scala:45)
    at org.apache.spark.sql.execution.datasources.jdbc.JDBCRDD$.resolveTable(JDBCRDD.scala:120)
    at org.apache.spark.sql.execution.datasources.jdbc.JDBCRelation.<init>(JDBCRelation.scala:91)
    at org.apache.spark.sql.execution.datasources.jdbc.DefaultSource.createRelation(DefaultSource.scala:57)
    at org.apache.spark.sql.execution.datasources.ResolvedDataSource$.apply(ResolvedDataSource.scala:158)
    at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:119)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:606)
    at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:381)
    at py4j.Gateway.invoke(Gateway.java:259)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.GatewayConnection.run(GatewayConnection.java:209)
    at java.lang.Thread.run(Thread.java:745)

应该怎么做呢？

谢谢你， 佩德罗·罗萨内斯.

如果您想在 Amazon EMR 3.x 或 EMR 4.x 上运行任何 Spark 作业，您需要执行以下操作：

1）你可以提到火花默认值.conf引导时的属性，即您可以更改的配置驱动程序类路径 and 执行器类路径财产也最大化资源分配（如果需要，请在评论中询问更多信息。）docs http://docs.aws.amazon.com/ElasticMapReduce/latest/ReleaseGuide/emr-spark-configure.html#spark-change-defaults

2）您需要下载所有必需的 jar，即（mysql-connector.jar 和 mariadb-connector.jar），在您的情况下，MariaDB 和 MySQL 连接器 JDBC jar 到所有类路径位置，例如所有节点上的 Spark、Yarn 和 Hadoop是大师、核心或任务（Spark On Yarn 场景覆盖最多）引导脚本文档 https://docs.aws.amazon.com/ElasticMapReduce/latest/ManagementGuide/emr-plan-bootstrap.html#CustombootstrapCLI

3）如果您的 Spark 作业仅从驱动程序节点到数据库进行通信，那么您可能只需要使用它--jars并且不会给你例外并且工作正常。

4）还推荐您尝试Master作为纱线簇代替local or 纱线客户端

就您而言，如果您使用 MariaDB 或 MySQL，请将您的 jar 复制到$SPARK_HOME/lib, $HADOOP_HOME/lib等等，然后在集群的每个节点上尝试一下。

稍后您可以使用引导操作在创建集群时将 jar 复制到所有节点上。

请在下面评论以获取更多信息。