我正在和rapidminer一起从一个大数据集中提取规则。radoop是hadoop生态系统的扩展，sparkrm操作符允许进行fp增长，从从从hive检索数据到探索分析。我正在工作：-windows 8.1-hadoop 6.2-spark 1.5-hive 2.1我已将spark默认配置如下：

# spark.master                     yarn

# spark.eventLog.enabled           true

# spark.eventLog.dir               hdfs://namenode:8021/directory

# spark.serializer                 org.apache.spark.serializer.KryoSerializer

# spark.driver.memory              2G

# spark.driver.cores                    1

# spark.yarn.driver.memoryOverhead  384MB

# spark.yarn.am.memory             1G

# spark.yarn.am.cores               1

# spark.yarn.am.memoryOverhead      384MB

# spark.executor.memory            1G

# spark.executor.instances          1

# spark.executor.cores              1

# spark.yarn.executor.memoryOverhead    384MB

# spark.executor.extraJavaOptions  -XX:+PrintGCDetails -Dkey=value -Dnumbers="one two three"

我拥有的站点xml文件：

<property>
    <name>yarn.resourcemanager.schedular.address</name>
    <value>localhost:8030</value>
</property>

<property>
    <name>yarn.resourcemanager.admin.address</name>
    <value>localhost:8033</value>
</property>

<property>
    <name>yarn.resourcemanager.resource-tracker.address</name>
    <value>localhost:8031</value>
</property>

<property>
    <name>yarn.resourcemanager.resource.cpu-vcores</name>
    <value>2</value>
</property>

<property>
    <name>yarn.resourcemanager.resource.memory-mb</name>
    <value>2048</value>
</property>

<property>
    <name>yarn.resourcemanager.hostname</name>
    <value>localhost</value>
</property>

<property>
    <name>yarn.resourcemanager.address</name>
    <value>localhost:8032</value>
</property>

<property>
    <name>yarn.resourcemanager.webapp.address</name>
    <value>localhost:8088</value>
</property>

<property>
    <name>yarn.nodemanager.aux-services</name>
    <value>mapreduce_shuffle</value>
</property>

<property>
    <name>yarn.nodemanager.aux-services.mapreduce.shuffle.class</name>
    <value>org.apache.hadoop.mapred.ShuffleHandler</value>
</property>

<property>
    <name>yarn.nodemanager.log-dirs</name>
    <value>/E:/tweets/hadoopConf/userlog</value>
    <final>true</final>
</property>

<property>
    <name>yarn.nodemanager.local-dirs</name>
    <value>/E:/tweets/hadoopConf/temp/nm-localdir</value>
</property>

<property>
    <name>yarn.nodemanager.delete.debug-delay-sec</name>
    <value>600</value>
</property>

<property>
    <name>yarn.nodemanager.resource.memory-mb</name>
    <value>2048</value>
</property>

<property>
    <name>yarn.scheduler.minimum-allocation-mb</name>
    <value>512</value>
</property>

<property>
    <name>yarn.scheduler.maximum-allocation-mb</name>
    <value>2048</value>
</property>

<property>
    <name>yarn.nodemanager.resource.cpu-vcores</name>
    <value>1</value>
</property>     

<property>
    <name>yarn.scheduler.minimum-allocation-vcores</name>
    <value>1</value>
</property>

<property>
    <name>yarn.scheduler.maximum-allocation-vcores</name>
    <value>3</value>
</property>

<property>
<name>yarn.application.classpath</name>
<value>
/tweets/hadoop/,
/tweets/hadoop/share/hadoop/common/*,
/tweets/hadoop/share/hadoop/common/lib/*,
/tweets/hadoop/share/hadoop/hdfs/*,
/tweets/hadoop/share/hadoop/hdfs/lib/*,
/tweets/hadoop/share/hadoop/mapreduce/*,
/tweets/hadoop/share/hadoop/mapreduce/lib/*,
/tweets/hadoop/share/hadoop/yarn/*,
/tweets/hadoop/share/hadoop/yarn/lib/*
/C:/spark/lib/spark-assembly-1.5.0-hadoop2.6.0.jar
</value>
</property>
</configuration>

hadoop的快速连接测试成功完成。当我运行rapidminer进程时，它由一个错误完成：

Process failed before getting into running state. this indicates that an error occurred during submitting or starting the spark job or writing the process output or the exception to the disc. Please check the logs of the spark job on the YARN Resource Manager interface for more information about the error.

在localhost:8088 i 在此处输入诊断信息的图像描述
这是作业的调度程序，请在此处输入图像描述
我是hadoop和spark的新手，无法有效地配置内存。