hadoop2

I am trying to learn MapReduce but I am a little lost right now. http://hadoop.apache.org/docs/r2.6.0/hadoop-mapreduce-client/hadoop-mapreduce-client-core/MapReduceTutorial.html#Usage Particularly this set of instructions: Compile WordCount.java and create a jar: $ bin/hadoop com.sun.tools.javac.Main WordCount.java When I type in hadoop in my terminal I am able to see the "Help" made which provides arguments so I believe I have hadoop installed. When I type in the command: Compile WordCount.java and create a jar: hadoop com.sun.tools.javac.Main WordCount.java I get the error: Error: Could not

前言 hadoop是分布式系统，运行在linux之上，配置起来相对复杂。对于hadoop1，很多同学就因为不能搭建正确的运行环境，导致学习兴趣锐减。不过，我有免费的学习视频下载，请点击 这里 。 hadoop2出来后，解决了hadoop1的几个固有缺陷，比如单点故障、资源利用率低、支持作业类型少等问题，结构发生了很大变化，是hadoop未来使用的一个趋势。当然，配置也更加复杂，网上也没有一篇详细的教程来知道大家可以轻轻松松搭建起这个环境的。我应该算是第一个吧。 hadoop2体系结构 要想理解本节内容，首先需要了解hadoop1的体系结构。在本博客中和我的视频中都有相关内容，这里不再重复，只讲hadoop2的内容。 hadoop1的核心组成是两部分，即HDFS和MapReduce。在hadoop2中变为HDFS和Yarn。 新的HDFS中的NameNode不再是只有一个了，可以有多个（目前只支持2个）。每一个都有相同的职能。 这两个NameNode的地位如何哪？ 答：一个是active状态的，一个是standby状态的。当集群运行时，只有active状态的NameNode是正常工作的，standby状态的NameNode是处于待命状态的，时刻同步active状态NameNode的数据。一旦active状态的NameNode不能工作，通过手工或者自动切换

I am using Hadoop-2.4.0 and my system configs are 24 cores, 96 GB RAM. I am using following configs mapreduce.map.cpu.vcores=1 yarn.nodemanager.resource.cpu-vcores=10 yarn.scheduler.minimum-allocation-vcores=1 yarn.scheduler.maximum-allocation-vcores=4 yarn.app.mapreduce.am.resource.cpu-vcores=1 yarn.nodemanager.resource.memory-mb=88064 mapreduce.map.memory.mb=3072 mapreduce.map.java.opts=-Xmx2048m Capacity Scheduler configs queue.default.capacity=50 queue.default.maximum_capacity=100 yarn.scheduler.capacity.root.default.user-limit-factor=2 With above configs, I expect yarn won't launch more

I am trying to install Hadoop 2.2.0 in pseudo-distributed mode. While I am trying to start the datanode services it is showing the following error, can anyone please tell how to resolve this? **2**014-03-11 08:48:15,916 INFO org.apache.hadoop.hdfs.server.datanode.DataNode: Block pool <registering> (storage id unknown) service to localhost/127.0.0.1:9000 starting to offer service 2014-03-11 08:48:15,922 INFO org.apache.hadoop.ipc.Server: IPC Server Responder: starting 2014-03-11 08:48:15,922 INFO org.apache.hadoop.ipc.Server: IPC Server listener on 50020: starting 2014-03-11 08:48:16,406 INFO

I have a small query regarding hadoop data writes From Apache documentation For the common case, when the replication factor is three, HDFS’s placement policy is to put one replica on one node in the local rack, another on a node in a different (remote) rack, and the last on a different node in the same remote rack. This policy cuts the inter-rack write traffic which generally improves write performance. The chance of rack failure is far less than that of node failure; In below image, when the write acknowledge is treated as successful? 1) Writing data to first data node? 2) Writing data to

I am submitting a job to YARN (on spark 2.1.1 + kafka 0.10.2.1) which connects to a secured hbase cluster. This job, performs just fine when i am running in "local" mode (spark.master=local[*]). However, as soon as I submit the job with master as YARN (and deploy mode as client), I see the following error message - Caused by: javax.security.auth.login.LoginException: Unable to obtain password from user I am following hortonworks recommendations for providing information to yarn cluster regarding the HBase and keytab etc. Followed this kb article - https://community.hortonworks.com/content

I have set up a multi node Hadoop Cluster. The NameNode and Secondary namenode runs on the same machine and the cluster has only one Datanode. All the nodes are configured on Amazon EC2 machines. Following are the configuration files on the master node: masters 54.68.218.192 (public IP of the master node) slaves 54.68.169.62 (public IP of the slave node) core-site.xml <configuration> <property> <name>fs.default.name</name> <value>hdfs://localhost:9000</value> </property> </configuration> mapred-site.xml <configuration> <property> <name>mapreduce.framework.name</name> <value>yarn</value> <