spark :安装与配置

      参见 HDP2.4安装(五)：集群及组件安装 ，安装配置的spark版本为1.6, 在已安装hbase、hadoop集群的基础上通过 ambari 自动安装Spark集群，基于hadoop yarn 的运行模式。

目录：

• Spark集群安装
• 参数配置
• 测试验证

Spark集群安装：

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• 在ambari -service 界面选择 “add Service”,如图：
• 
• 在弹出界面选中spark服务，如图：

• “下一步”，分配host节点，因为前期我们已经安装了hadoop 和hbase集群，按向导分配 spark history Server即可
• 分配client,如下图：
  • 
• 发布安装，如下正确状态
  • 

参数配置：

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• 安装完成后，重启hdfs 和 yarn
• 查看 spark服务，spark thrift server 未正常启动，日志如下：

  16/08/30 14:13:25 INFO Client: Verifying our application has not requested more than the maximum memory capability of the cluster (512 MB per container)
  16/08/30 14:13:25 ERROR SparkContext: Error initializing SparkContext.
  Java.lang.IllegalArgumentException: Required executor memory (1024+384 MB) is above the max threshold (512 MB) of this cluster! Please check the values of 'yarn.scheduler.maximum-allocation-mb' and/or 'yarn.nodemanager.resource.memory-mb'.
      at org.apache.spark.deploy.yarn.Client.verifyClusterResources(Client.scala:284)
      at org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:140)
      at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:56)
      at org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:144)
      at org.apache.spark.SparkContext.<init>(SparkContext.scala:530)
      at org.apache.spark.sql.hive.thriftserver.SparkSqlEnv$.init(SparkSQLEnv.scala:56)
      at org.apache.spark.sql.hive.thriftserver.HiveThriftServer2$.main(HiveThriftServer2.scala:76)
      at org.apache.spark.sql.hive.thriftserver.HiveThriftServer2.main(HiveThriftServer2.scala)
      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 org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:731)
      at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:181)
      at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:206)
      at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:121)
      at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)

  标签： · spark