hadoop入门学习

学习资料：wiki,官网

Apache Hadoop版本分为两代，我们将第一代Hadoop称为Hadoop 1.0，第二代Hadoop称为Hadoop2.0。第一代Hadoop包含三个大版本，分别是0.20.x，0.21.x和0.22.x，其 中，0.20.x最后演化成1.0.x，变成了稳定版。第二代Hadoop包含两个版本，分别是0.23.x和2.x，它们完全不同于Hadoop 1.0，是一套全新的架构，均包含HDFS Federation和YARN两个系统，相比于0.23.x，2.x增加了NameNode HA和Wire-compatibility两个重大特性。

hadoop2.7.2伪分布式安装

环境

• 虚拟机 ubuntu64位
• JDK：1.7.0_79 64位
• Hadoop：2.7.2

在Hadoop安装过程中需要关闭防火墙和SElinux，否则会出现异常。

软件安装要求：

1.java

java -version
java version "1.7.0_79"
Java(TM) SE Runtime Environment (build 1.7.0_79-b15)
Java HotSpot(TM) 64-Bit Server VM (build 24.79-b02, mixed mode)

2.ssh---sshd必须运行，使得能够用hadoop脚本管理hadoop节点。

$ sudo apt-get install ssh
$ sudo apt-get install rsync

hadoop2.7.2伪分布式安装

1.edit the file etc/hadoop/hadoop-env.sh

# set to the root of your Java installation export

JAVA_HOME=/usr/java/latest

2.etc/hadoop/core-site.xml:

<configuration>
    <property>
        <name>fs.defaultFS</name>
        <value>hdfs://localhost:9000</value>
    </property>
</configuration>

3.etc/hadoop/hdfs-site.xml:

<configuration>
    <property>
        <name>dfs.replication</name>
        <value>1</value>
    </property>
</configuration>

Setup passphraseless ssh

Now check that you can ssh to the localhost without a passphrase:

  $ ssh localhost

If you cannot ssh to localhost without a passphrase, execute the following commands:

  $ ssh-keygen -t dsa -P '' -f ~/.ssh/id_dsa
  $ cat ~/.ssh/id_dsa.pub >> ~/.ssh/authorized_keys
  $ chmod 0600 ~/.ssh/authorized_keys

执行

Execution

The following instructions are to run a MapReduce job locally. If you want to execute a job on YARN, see YARN on Single Node.

    Format the filesystem:

      $ bin/hdfs namenode -format

    Start NameNode daemon and DataNode daemon:

      $ sbin/start-dfs.sh

    The hadoop daemon log output is written to the $HADOOP_LOG_DIR directory (defaults to $HADOOP_HOME/logs).

    Browse the web interface for the NameNode; by default it is available at:
        NameNode - http://localhost:50070/

    Make the HDFS directories required to execute MapReduce jobs:

      $ bin/hdfs dfs -mkdir /user
      $ bin/hdfs dfs -mkdir /user/<username>

    Copy the input files into the distributed filesystem:

      $ bin/hdfs dfs -put etc/hadoop input

    Run some of the examples provided:

      $ bin/hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.2.jar grep input output 'dfs[a-z.]+'

    Examine the output files: Copy the output files from the distributed filesystem to the local filesystem and examine them:

      $ bin/hdfs dfs -get output output
      $ cat output/*

    or

    View the output files on the distributed filesystem:

      $ bin/hdfs dfs -cat output/*

    When you’re done, stop the daemons with:

      $ sbin/stop-dfs.sh

YARN

YARN on a Single Node

You can run a MapReduce job on YARN in a pseudo-distributed mode by setting a few parameters and running ResourceManager daemon and NodeManager daemon in addition.

The following instructions assume that 1. ~ 4. steps of the above instructions are already executed.

    Configure parameters as follows:etc/hadoop/mapred-site.xml:

    <configuration>
        <property>
            <name>mapreduce.framework.name</name>
            <value>yarn</value>
        </property>
    </configuration>

    etc/hadoop/yarn-site.xml:

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

    Start ResourceManager daemon and NodeManager daemon:

      $ sbin/start-yarn.sh

    Browse the web interface for the ResourceManager; by default it is available at:
        ResourceManager - http://localhost:8088/

    Run a MapReduce job.

    When you’re done, stop the daemons with:

      $ sbin/stop-yarn.sh

伪分布式环境搭建成功！