在Fedora18上配置个人的Hadoop开发环境
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在Fedora18上配置个人的Hadoop开发环境
1. 背景
文章中讲述了类似于“personalcondor”的一种“personal hadoop” 配置法。基本的目的是配置文件和日志文件有一个单一的源,
能够用软连接到开发生成的二进制库。这样就能够在所生成二进制库更新的时候维护其它的数据和配置项。
2. 用户案例
1. 比較不用改变现有系统中安装软件的情况下,在本地的沙盒环境中做測试
2. 单一源的配置文件盒日志文件
3. 參考
网页:
http://wiki.apache.org/hadoop/HowToSetupYourDevelopmentEnvironment
http://vichargrave.com/create-a-hadoop-build-and-development-environment-for-hadoop/
http://www.michael-noll.com/tutorials/running-hadoop-on-ubuntu-linux-single-node-cluster/
http://wiki.apache.org/hadoop/
http://docs.hortonworks.com/CURRENT/index.htm#Appendix/Configuring_Ports/HDFS_Ports.htm
书籍:
Hadoop “TheDefinitive Guide”
4. 免责声明
1. 当前是在使用存在maven依赖的非本地开发步骤,具体信息在本地的包中,请查看:https://fedoraproject.org/wiki/Features/Hadoop
2 . 单节点环境搭建步骤在下边列出
5. 先决条件
1. 配置没有password的ssh
yum install openssh openssh-clients openssh-server
# generate a public/private key, if you don‘t already have one
ssh-keygen -t dsa -P ‘‘ -f ~/.ssh/id_dsa
cat ~/.ssh/id_dsa.pub >> ~/.ssh/authorized_keys
chmod 600 ~/.ssh/*
# testing ssh:
ps -ef | grep sshd # verify sshd is running
ssh localhost # accept the certification when prompted
sudo passwd root # Make sure the root has a password
2. 安装其他依赖包
yum install cmake git subversion dh-make ant autoconf automake sharutils libtool asciidoc xmlto curl protobuf-compiler gcc-c++
3. 安装java和开发环境
yum install java-1.7.0-openjdk java-1.7.0-openjdk-devel java-1.7.0-openjdk-javadoc *maven*
改动.bashrc文件信息
export JVM_ARGS="-Xmx1024m -XX:MaxPermSize=512m"
export MAVEN_OPTS="-Xmx1024m -XX:MaxPermSize=512m"
注意:以上的配置用在F18的OpenJDK7上。能够通过下面命令来測试当前环境配置是否成功。
mvn install -Dmaven.test.failure.ignore=true
6. 搭建“personal-hadoop“
1. 下载编译hadoop
git clone git://git.apache.org/hadoop-common.git
cd hadoop-common
git checkout -b branch-2.0.4-alpha origin/branch-2.0.4-alpha
mvn clean package -Pdist -DskipTests
2. 创建沙盒环境
在这个配置中我们默认到/home/tstclair
cd ~
mkdir personal-hadoop
cd personal-hadoop
mkdir -p conf data name logs/yarn
ln -sf <your-git-loc>/hadoop-dist/target/hadoop-2.0.4-alpha home
3. 重写你的环境变量
附加下面信息到家文件夹的.bashrc文件里
# Hadoop env override:
export HADOOP_BASE_DIR=${HOME}/personal-hadoop
export HADOOP_LOG_DIR=${HOME}/personal-hadoop/logs
export HADOOP_PID_DIR=${HADOOP_BASE_DIR}
export HADOOP_CONF_DIR=${HOME}/personal-hadoop/conf
export HADOOP_COMMON_HOME=${HOME}/personal-hadoop/home
export HADOOP_HDFS_HOME=${HADOOP_COMMON_HOME}
export HADOOP_MAPRED_HOME=${HADOOP_COMMON_HOME}
# Yarn env override:
export HADOOP_YARN_HOME=${HADOOP_COMMON_HOME}
export YARN_LOG_DIR=${HADOOP_LOG_DIR}/yarn
#classpath override to search hadoop loc
export CLASSPATH=/usr/share/java/:${HADOOP_COMMON_HOME}/share
#Finally update your PATH
export PATH=${HADOOP_COMMON_HOME}/bin:${HADOOP_COMMON_HOME}/sbin:${HADOOP_COMMON_HOME}/libexec:${PATH}
4. 验证以上步骤
source ~/.bashrc
which hadoop # verify it should be ${HOME}/personal-hadoop/home/bin
hadoop -help # verify classpath is correct.
5. 创建初始化单一源的配置文件
拷贝默认的配置文件
cp ${HADOOP_COMMON_HOME}/etc/hadoop/* ${HADOOP_BASE_DIR}/conf
更新你的hdfs-site.xml文件:
<?
xml version="1.0" encoding="UTF-8"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<!--
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. See accompanying LICENSE file.
-->
<!-- Override tstclair with your home directory -->
<configuration>
<property>
<name>fs.default.name</name>
<value>hdfs://localhost/</value>
</property>
<property>
<name>dfs.name.dir</name>
<value>file:///home/tstclair/personal-hadoop/name</value>
</property>
<property>
<name>dfs.http.address</name>
<value>0.0.0.0:50070</value>
</property>
<property>
<name>dfs.data.dir</name>
<value>file:///home/tstclair/personal-hadoop/data</value>
</property>
<property>
<name>dfs.datanode.address</name>
<value>0.0.0.0:50010</value>
</property>
<property>
<name>dfs.datanode.http.address</name>
<value>0.0.0.0:50075</value>
</property>
<property>
<name>dfs.datanode.ipc.address</name>
<value>0.0.0.0:50020</value>
</property>
</configuratio
更新mapred-site.xml文件
<?xml version="1.0" encoding="UTF-8"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<!--
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. See accompanying LICENSE file.
-->
<!-- Update or append these vars -->
<configuration>
<property>
<name>mapreduce.cluster.temp.dir</name>
<value>
</value>
<description>No description</description>
<final>true</final>
</property>
<property>
<name>mapreduce.cluster.local.dir</name>
<value>
</value>
<description>No description</description>
<final>true</final>
</property>
</configuration>
最后更新yarn-site.xml文件
<?xml version="1.0"?
>
<!--
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. See accompanying LICENSE file.
-->
<configuration>
<!-- Site specific YARN configuration properties -->
<property>
<name>yarn.resourcemanager.resource-tracker.address</name>
<value>localhost:8031</value>
<description>host is the hostname of the resource manager and
port is the port on which the NodeManagers contact the Resource Manager.
</description>
</property>
<property>
<name>yarn.resourcemanager.scheduler.address</name>
<value>localhost:8030</value>
<description>host is the hostname of the resourcemanager and port is the port
on which the Applications in the cluster talk to the Resource Manager.
</description>
</property>
<property>
<name>yarn.resourcemanager.scheduler.class</name>
<value>org.apache.hadoop.yarn.server.resourcemanager.scheduler.capacity.CapacityScheduler</value>
<description>In case you do not want to use the default scheduler</description>
</property>
<property>
<name>yarn.resourcemanager.address</name>
<value>localhost:8032</value>
<description>the host is the hostname of the ResourceManager and the port is the port on
which the clients can talk to the Resource Manager. </description>
</property>
<property>
<name>yarn.nodemanager.local-dirs</name>
<value>
</value>
<description>the local directories used by the nodemanager</description>
</property>
<property>
<name>yarn.nodemanager.address</name>
<value>localhost:8034</value>
<description>the nodemanagers bind to this port</description>
</property>
<property>
<name>yarn.nodemanager.resource.memory-mb</name>
<value>10240</value>
<description>the amount of memory on the NodeManager in GB</description>
</property>
<property>
<name>yarn.nodemanager.aux-services</name>
<value>mapreduce.shuffle</value>
<description>shuffle service that needs to be set for Map Reduce to run </description>
</property>
</configuration>
7. 开启单节点的Hadoop集群
格式化namenode
hadoop namenode -format
#verify output is correct.
开启hdfs:
start-dfs.sh
打开浏览器http://localhost:50070。查看是否有一个节点已经被启动
接下来开启yarn
start-yarn.sh
通过查看日志文件来验证是否正常启动
最后通过执行MapReduce任务来检查Hadoop是否正常执行
cd ${HADOOP_COMMON_HOME}/share/hadoop/mapreduce
hadoop jar hadoop-mapreduce-example-2.0.4-alpha.jar randomwriter out
文章出处:http://timothysc.github.io/blog/2013/04/22/personalhadoop/
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