【发布时间】:2016-06-13 18:42:34
【问题描述】:
我已经尝试将 SPARK_LOCAL_IP 设置为“127.0.0.1”并检查端口是否被占用。这是完整的错误文本:
Launching java with spark-submit command /usr/hdp/2.4.0.0-
169/spark/bin/spark-submit "sparkr-shell" /tmp/RtmpZo44il/backend_port998540c56917
/usr/hdp/2.4.0.0-169/spark/bin/load-spark-env.sh: line 72: export: `load-spark-env.sh': not a valid identifier
16/06/13 11:28:24 ERROR RBackend: Server shutting down: failed with exception
java.net.BindException: Cannot assign requested address
at sun.nio.ch.Net.bind0(Native Method)
at sun.nio.ch.Net.bind(Net.java:433)
at sun.nio.ch.Net.bind(Net.java:425)
at sun.nio.ch.ServerSocketChannelImpl.bind(ServerSocketChannelImpl.java:223)
at sun.nio.ch.ServerSocketAdaptor.bind(ServerSocketAdaptor.java:74)
at io.netty.channel.socket.nio.NioServerSocketChannel.doBind(NioServerSocketChannel.java:125)
at io.netty.channel.AbstractChannel$AbstractUnsafe.bind(AbstractChannel.java:485)
at io.netty.channel.DefaultChannelPipeline$HeadContext.bind(DefaultChannelPipeline.java:1089)
at io.netty.channel.AbstractChannelHandlerContext.invokeBind(AbstractChannelHandlerContext.java:430)
at io.netty.channel.AbstractChannelHandlerContext.bind(AbstractChannelHandlerContext.java:415)
at io.netty.channel.DefaultChannelPipeline.bind(DefaultChannelPipeline.java:903)
at io.netty.channel.AbstractChannel.bind(AbstractChannel.java:198)
at io.netty.bootstrap.AbstractBootstrap$2.run(AbstractBootstrap.java:348)
at io.netty.util.concurrent.SingleThreadEventExecutor.runAllTasks(SingleThreadEventExecutor.java:357)
at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:357)
at io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:111)
at io.netty.util.concurrent.DefaultThreadFactory$DefaultRunnableDecorator.run(DefaultThreadFactory.java:137)
at java.lang.Thread.run(Thread.java:745)
Error in SparkR::sparkR.init() : JVM is not ready after 10 seconds
上述错误是在启动 ./bin/sparkR 时出现的。 Spark-shell 将再次正常执行。
更多信息。 Spark-shell 启动时会自动搜索端口,直到它解决了一个没有绑定异常的端口。即使我将默认的 SparkR 后端端口设置为未使用的端口,它也会失败。
【问题讨论】:
标签: apache-spark pyspark sparkr