Hadoop2.2.0 开发环境搭建测试

程序员文章站 2022-05-18 17:56:10

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Hadoop2.2.0 单机开发搭建。环境：系统 CentOS 6.3 64位 Jdk版本 oracle jdk 1.7 Hadoop版本 2.2.0 使用linux用户 Hadoop 目录配置 /home/hadoop 用户目录 /app/hadoop/hadoop-2.2.0 软件home /app/hadoop/dfs/name 数据和编辑文件 /app/hadoop/dfs/data

Hadoop2.2.0 单机开发搭建。

环境：

系统	CentOS 6.3 64位
Jdk版本	oracle jdk 1.7
Hadoop版本	2.2.0
使用linux用户	Hadoop

目录配置

/home/hadoop	用户目录
/app/hadoop/hadoop-2.2.0	软件home
/app/hadoop/dfs/name	数据和编辑文件
/app/hadoop/dfs/data	数据和编辑文件
/app/hadoop/mapred/local	存放数据
/app/hadoop/mapred/system	存放数据

1. 安装jdk

sudo vim /etc/profile
export JAVA_HOME=/usr/lib/jvm/jdk1.7.0_45
export JRE_HOME=$JAVA_HOME/jre
export CLASSPATH=$JAVA_HOME/lib:$CLASSPATH
export PATH=$JAVA_HOME/bin:$PATH
source /etc/profile

2. ssh无密码登录

Hadoop用户操作：

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

Root用户操作：

chmod go-w ?/home/hadoop/.ssh
chmod 600 /home/hadoop/.ssh/authorized_keys

测试：

Hadoop用户

[hadoop@hadoop01 ~]$ ssh localhost

3. 安装hadoop

可以自己下载源码包编译成适合本地native包

为了简单我直接下载编译好的hadoop包：

地址：http://apache.fayea.com/apache-mirror/hadoop/common/hadoop-2.2.0/

解压到目录;

移动解压软件到软件目录:

/app/hadoop/hadoop-2.2.0

4. 修改hadoop参数文件

Vim ?core-site.xml

        fs.default.name
        hdfs://hadoop-host:8020
        The name of the defaultfile system. Either the literal string "local" a host:port forNDFS.
        true

修改hdfs-site.xml

        dfs.namenode.name.dir
        file:/app/hadoop/dfs/name
        true
        dfs.datanode.data.dir
        file:/app/hadoop/dfs/data
        Determineswhere on the local filesystem an DFS data node should store its blocks. If thisis a comma-delimited list of directories, then data will be stored in all nameddirectories, typically on different devices.Directories that do not exist areignored.
        true
        dfs.replication
        1
        dfs.permissions
        false

修改Mapred-site.xml

        mapreduce.framework.name
        yarn
        mapred.system.dir
        file:/app/hadoop/mapred/system
        true
        mapred.local.dir
        file:/app/hadoop/mapred/local
        true

修改Yarn-site.xml

        yarn.nodemanager.aux-services
        mapreduce_shuffle

如果要配置成集群环境则Yarn-site.xml的配置如下：

       yarn.nodemanager.aux-services
       mapreduce_shuffle
       yarn.nodemanager.aux-services.mapreduce.shuffle.class
       org.apache.hadoop.mapred.ShuffleHandler
       yarn.resourcemanager.address
       Master.Hadoop:8032
       yarn.resourcemanager.scheduler.address
       Master.Hadoop:8030
       yarn.resourcemanager.resource-tracker.address
       Master.Hadoop:8031
       yarn.resourcemanager.admin.address
       Master.Hadoop:8033
       yarn.resourcemanager.webapp.address
       Master.Hadoop:8088

修改 hadoop-env.sh：

增加：

export JAVA_HOME=/usr/java/jdk1.7.0_45

创建本地目录

mkdir –p ?/app/hadoop/dfs/name
mkdir -p ?/app/hadoop/dfs/data
mkdir –p ?/app/hadoop/mapred/local
mkdir -p ?/app/hadoop/mapred/system

启动hadoop

格式化namenode

[hadoop@hadoop01 ~]$ hdfs namenode –format

开启dfs守护进程

启动：start-all.sh

停止：stop-all.sh

开启yarn守护进程

启动：start-yarn.sh

停止：stop-yarn.sh

使用jps查看启动的进程：

[hadoop@ttpod sbin]$ jps

7621 NameNode

11834 Jps

7734 DataNode

7881 SecondaryNameNode

10156 NodeManager

10053 ResourceManager

有以上内容说明已经启动

查看hadoop资源管理页面

http://192.168.6.124:8088/

Hadoop2.2.0 开发环境搭建测试

查看hdfs界面：http://192.168.6.124:50070

Hadoop2.2.0 开发环境搭建测试

测试：

使用pi程序：

hadoop jar ?$HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.2.0.jar pi 10 10

hadoop jar hadoop-mapreduce-examples-2.2.0.jar pi 10 10

Number of Maps? = 10Samples per Map = 1013/12/13 16:27:23 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform… using builtin-java classes where applicableWrote input for Map #0

Wrote input for Map #1

Wrote input for Map #2

Wrote input for Map #3

Wrote input for Map #4

Wrote input for Map #5

Wrote input for Map #6

Wrote input for Map #7

Wrote input for Map #8

Wrote input for Map #9

Starting Job

13/12/13 16:27:24 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032

13/12/13 16:27:25 INFO input.FileInputFormat: Total input paths to process : 10

13/12/13 16:27:25 INFO mapreduce.JobSubmitter: number of splits:10

13/12/13 16:27:25 INFO Configuration.deprecation: user.name is deprecated. Instead, use mapreduce.job.user.name

13/12/13 16:27:25 INFO Configuration.deprecation: mapred.jar is deprecated. Instead, use mapreduce.job.jar

13/12/13 16:27:25 INFO Configuration.deprecation: mapred.map.tasks.speculative.execution is deprecated. Instead, use mapreduce.map.speculative

13/12/13 16:27:25 INFO Configuration.deprecation: mapred.reduce.tasks is deprecated. Instead, use mapreduce.job.reduces

13/12/13 16:27:25 INFO Configuration.deprecation: mapred.output.value.class is deprecated. Instead, use mapreduce.job.output.value.class

13/12/13 16:27:25 INFO Configuration.deprecation: mapred.reduce.tasks.speculative.execution is deprecated. Instead, use mapreduce.reduce.speculative

13/12/13 16:27:25 INFO Configuration.deprecation: mapreduce.map.class is deprecated. Instead, use mapreduce.job.map.class

13/12/13 16:27:25 INFO Configuration.deprecation: mapred.job.name is deprecated. Instead, use mapreduce.job.name

13/12/13 16:27:25 INFO Configuration.deprecation: mapreduce.reduce.class is deprecated. Instead, use mapreduce.job.reduce.class

13/12/13 16:27:25 INFO Configuration.deprecation: mapreduce.inputformat.class is deprecated. Instead, use mapreduce.job.inputformat.class

13/12/13 16:27:25 INFO Configuration.deprecation: mapred.input.dir is deprecated. Instead, use mapreduce.input.fileinputformat.inputdir

13/12/13 16:27:25 INFO Configuration.deprecation: mapred.output.dir is deprecated. Instead, use mapreduce.output.fileoutputformat.outputdir

13/12/13 16:27:25 INFO Configuration.deprecation: mapreduce.outputformat.class is deprecated. Instead, use mapreduce.job.outputformat.class

13/12/13 16:27:25 INFO Configuration.deprecation: mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps

13/12/13 16:27:25 INFO Configuration.deprecation: mapred.output.key.class is deprecated. Instead, use mapreduce.job.output.key.class

13/12/13 16:27:25 INFO Configuration.deprecation: mapred.working.dir is deprecated. Instead, use mapreduce.job.working.dir

13/12/13 16:27:25 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1386923206015_0001

13/12/13 16:27:26 INFO impl.YarnClientImpl: Submitted application application_1386923206015_0001 to ResourceManager at /0.0.0.0:8032

13/12/13 16:27:26 INFO mapreduce.Job: The url to track the job: http://ttpod:8088/proxy/application_1386923206015_0001/

13/12/13 16:27:26 INFO mapreduce.Job: Running job: job_1386923206015_0001

13/12/13 16:27:34 INFO mapreduce.Job: Job job_1386923206015_0001 running in uber mode : false

13/12/13 16:27:34 INFO mapreduce.Job:? map 0% reduce 0%

13/12/13 16:27:56 INFO mapreduce.Job:? map 60% reduce 0%

13/12/13 16:28:13 INFO mapreduce.Job:? map 100% reduce 0%

13/12/13 16:28:14 INFO mapreduce.Job:? map 100% reduce 100%

13/12/13 16:28:15 INFO mapreduce.Job: Job job_1386923206015_0001 completed successfully

13/12/13 16:28:16 INFO mapreduce.Job: Counters: 43

File System Counters

FILE: Number of bytes read=226

FILE: Number of bytes written=871752

FILE: Number of read operations=0

FILE: Number of large read operations=0

FILE: Number of write operations=0

HDFS: Number of bytes read=2610

HDFS: Number of bytes written=215

HDFS: Number of read operations=43

HDFS: Number of large read operations=0

HDFS: Number of write operations=3

Job Counters

Launched map tasks=10

Launched reduce tasks=1

Data-local map tasks=10

Total time spent by all maps in occupied slots (ms)=185391

Total time spent by all reduces in occupied slots (ms)=15412

Map-Reduce Framework

Map input records=10

Map output records=20

Map output bytes=180

Map output materialized bytes=280

Input split bytes=1430

Combine input records=0

Combine output records=0

Reduce input groups=2

Reduce shuffle bytes=280

Reduce input records=20

Reduce output records=0

Spilled Records=40

Shuffled Maps =10

Failed Shuffles=0

Merged Map outputs=10

GC time elapsed (ms)=1841

CPU time spent (ms)=7600

Physical memory (bytes) snapshot=2507419648

Virtual memory (bytes) snapshot=9588948992

Total committed heap usage (bytes)=1944584192

Shuffle Errors

BAD_ID=0

CONNECTION=0

IO_ERROR=0

WRONG_LENGTH=0

WRONG_MAP=0

WRONG_REDUCE=0

File Input Format Counters

Bytes Read=1180

File Output Format Counters

Bytes Written=97

Job Finished in 51.367 seconds

Estimated value of Pi is 3.20000000000000000000

正常.

如出现什么异常请自行去查看运行日志。

原文地址：Hadoop2.2.0 开发环境搭建测试, 感谢原作者分享。

相关标签： Hadoop2.2.0 开发环境搭建测试 Hadoop

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