前言：前段时间将hadoop01的虚拟机弄的崩溃掉了，也没有备份，重新从hadoop02虚拟上克隆过来的，结果hadoop-eclipse插件一样的编译，居然用不起了，找了3天的原因，最后还是没有解决，只能用hadoop shell 命令去测试了，反正影响不大，只不过用着不方便而已。
心累中...........
正文：
解压安装Hadoop
[[email protected] ~]$ cp /home/hadoop/Resources/hadoop-3.2.0.tar.gz ~/ [[email protected] ~]$ tar -zxvf ~/hadoop-3.2.0.tar.gz [[email protected] ~]$ cd hadoop-3.2.0 [[email protected] hadoop-3.2.0]$ ls -l total 184 drwxr-xr-x. 2 hadoop hadoop 203 Jan 8 2019 bin drwxr-xr-x. 3 hadoop hadoop 20 Jan 8 2019 etc drwxr-xr-x. 2 hadoop hadoop 106 Jan 8 2019 include drwxr-xr-x. 3 hadoop hadoop 20 Jan 8 2019 lib drwxr-xr-x. 4 hadoop hadoop 4096 Jan 8 2019 libexec -rw-rw-r--. 1 hadoop hadoop 150569 Oct 19 2018 LICENSE.txt -rw-rw-r--. 1 hadoop hadoop 22125 Oct 19 2018 NOTICE.txt -rw-rw-r--. 1 hadoop hadoop 1361 Oct 19 2018 README.txt drwxr-xr-x. 3 hadoop hadoop 4096 Jan 8 2019 sbin drwxr-xr-x. 4 hadoop hadoop 31 Jan 8 2019 share
配置Hadoop环境变量
[[email protected] hadoop-3.2.0]$ gedit /home/hadoop/hadoop-3.2.0/etc/hadoop/hadoop-env.sh 编辑文件并保存： # The java implementation to use. By default, this environment # variable is REQUIRED on ALL platforms except OS X! # export JAVA_HOME= export JAVA_HOME=/usr/java/jdk1.8.0_11/
配置YARN环境变量
[[email protected] hadoop-3.2.0]$ gedit /home/hadoop/hadoop-3.2.0/etc/hadoop/yarn-env.sh 编辑文件并保存： export JAVA_HOME=/usr/java/jdk1.8.0_11/
配置核心组件文件（core-site.xml）
[[email protected] hadoop-3.2.0]$ gedit /home/hadoop/hadoop-3.2.0/etc/hadoop/core-site.xml 编辑文件并保存： <configuration> <!--hdfs 的默认地址、端口 访问地址--> <property> <name>fs.defaultFS</name> <value>hdfs://hadoop01:9802</value> </property> <!--hdfs临时路径--> <property> <name>hadoop.tmp.dir</name> <value>/home/hadoop/hadoopdata</value> </property> </configuration>
配置文件系统（hdfs-site.xml）
[[email protected] hadoop-3.2.0]$ gedit /home/hadoop/hadoop-3.2.0/etc/hadoop/hdfs-site.xml
编辑文件并保存：
<configuration>
<!--hdfs web的地址 -->
<property>
<name>dfs.namenode.http-address</name>
<value>hadoop01:50070</value>
</property>
<!-- 副本数-->
<property>
<name>dfs.replication</name>
<value>3</value>
</property>
<!-- 是否启用hdfs权限检查 false 关闭 -->
<property>
<name>dfs.permissions.enabled</name>
<value>false</value>
</property>
<!-- 块大小,默认字节, 可使用 k m g t p e-->
<property>
<name>dfs.blocksize</name>
<!--128m-->
<value>134217728</value>
</property>
<!--hadoop的name和data目录路径-->
<property>
<name>dfs.namenode.name.dir</name>
<value>file:/home/hadoop/hdfs/name</value>
</property>
<property>
<name>dfs.datanode.data.dir</name>
<value>file:/home/hadoop/hdfs/data</value>
</property>
</configuration>
配置yarn-site.xml文件
[[email protected] hadoop-3.2.0]$ gedit /home/hadoop/hadoop-3.2.0/etc/hadoop/yarn-site.xml
编辑文件并保存：
<configuration>
<!-- Site specific YARN configuration properties -->
<!--集群master，-->
<property>
<name>yarn.resourcemanager.hostname</name>
<value>hadoop01</value>
</property>
<!-- NodeManager上运行的附属服务-->
<property>
<name>yarn.nodemanager.aux-services</name>
<value>mapreduce_shuffle</value>
</property>
<!--容器可能会覆盖的环境变量，而不是使用NodeManager的默认值-->
<property>
<name>yarn.nodemanager.env-whitelist</name>
<value> JAVA_HOME,HADOOP_COMMON_HOME,HADOOP_HDFS_HOME,HADOOP_CONF_DIR,CLASSPATH_PREPEND_DISTCACHE,HADOOP_YARN_HOME,HADOOP_HOME,PATH,LANG,TZ</value>
</property>
<!-- 关闭内存检测，虚拟机需要，不配会报错-->
<property>
<name>yarn.nodemanager.vmem-check-enabled</name>
<value>false</value>
</property>
</configuration>
配置MapReduce计算框架文件
[[email protected] hadoop-3.2.0]$ gedit /home/hadoop/hadoop-3.2.0/etc/hadoop/mapred-site.xml
编辑文件并保存：
<configuration>
<!--local表示本地运行，classic表示经典mapreduce框架，yarn表示新的框架-->
<property>
<name>mapreduce.framework.name</name>
<value>yarn</value>
</property>
<!--如果map和reduce任务访问本地库（压缩等），则必须保留原始值
当此值为空时，设置执行环境的命令将取决于操作系统：
Linux：LD_LIBRARY_PATH=$HADOOP_COMMON_HOME/lib/native.
windows：PATH =%PATH%;%HADOOP_COMMON_HOME%\\bin.
-->
<property>
<name>mapreduce.admin.user.env</name>
<value>HADOOP_MAPRED_HOME=$HADOOP_HOME</value>
</property>
<!--
可以设置AM【AppMaster】端的环境变量
如果上面缺少配置，可能会造成mapreduce失败
-->
<property>
<name>yarn.app.mapreduce.am.env</name>
<value>HADOOP_MAPRED_HOME=$HADOOP_HOME</value>
</property>
</configuration>
【选】配置slaves文件（hadoop2.x修改slaves）
[[email protected] hadoop-3.2.0]$ gedit /home/hadoop/hadoop-3.2.0/etc/hadoop/slaves
编辑文件并保存：
hadoop02 hadoop03
【选】配置workers文件（hadoop3.x修改workers）
[[email protected] hadoop-3.2.0]$ gedit /home/hadoop/hadoop-3.2.0/etc/hadoop/workers
编辑文件并保存：
hadoop01 hadoop02 hadoop03
复制hadoop01上的Hadoop到hadoop02和hadoop03节点上
scp -r /home/hadoop/hadoop-3.2.0 [email protected]:~/
scp -r /home/hadoop/hadoop-3.2.0 [email protected]:~/
配置操作系统环境变量（需要在所有节点上进行，且使用一般用户权限）
gedit ~/.bash_profile
source ~/.bash_profile
编辑文件并保存：
#以下是新添加入代码 export JAVA_HOME=/usr/java/jdk1.8.0_11/ export PATH=$JAVA_HOME/bin:$PATH #hadoop export HADOOP_HOME=/home/hadoop/hadoop-3.2.0 export PATH=$HADOOP_HOME/bin:$HADOOP_HOME/sbin:$PATH
创建Hadoop数据目录（所有节点操作）
mkdir /home/hadoop/hadoopdata
格式化文件系统（主端进行）
hdfs namenode -format
启动和关闭Hadoop
cd ~/hadoop-3.2.0
sbin/start-all.sh
stop-all.sh
启动成功结果：
[[email protected] hadoop-3.2.0]$ jps 20848 DataNode 21808 Jps 21076 SecondaryNameNode 21322 ResourceManager 20668 NameNode 21468 NodeManager [[email protected] hadoop-3.2.0]$
【最后测试】在Hadoop集群中运行程序
将计算圆周率pi的Java程序包投入运行
[[email protected] hadoop-3.2.0]$ cd ~/hadoop-3.2.0/share/hadoop/mapreduce
[[email protected] mapreduce]$ ls
hadoop-mapreduce-client-app-3.2.0.jar hadoop-mapreduce-client-hs-plugins-3.2.0.jar hadoop-mapreduce-client-shuffle-3.2.0.jar lib
hadoop-mapreduce-client-common-3.2.0.jar hadoop-mapreduce-client-jobclient-3.2.0.jar hadoop-mapreduce-client-uploader-3.2.0.jar lib-examples
hadoop-mapreduce-client-core-3.2.0.jar hadoop-mapreduce-client-jobclient-3.2.0-tests.jar hadoop-mapreduce-examples-3.2.0.jar sources
hadoop-mapreduce-client-hs-3.2.0.jar hadoop-mapreduce-client-nativetask-3.2.0.jar jdiff
[[email protected] mapreduce]$ hadoop jar hadoop-mapreduce-examples-3.2.0.jar
An example program must be given as the first argument.
Valid program names are:
aggregatewordcount: An Aggregate based map/reduce program that counts the words in the input files.
aggregatewordhist: An Aggregate based map/reduce program that computes the histogram of the words in the input files.
bbp: A map/reduce program that uses Bailey-Borwein-Plouffe to compute exact digits of Pi.
dbcount: An example job that count the pageview counts from a database.
distbbp: A map/reduce program that uses a BBP-type formula to compute exact bits of Pi.
grep: A map/reduce program that counts the matches of a regex in the input.
join: A job that effects a join over sorted, equally partitioned datasets
multifilewc: A job that counts words from several files.
pentomino: A map/reduce tile laying program to find solutions to pentomino problems.
pi: A map/reduce program that estimates Pi using a quasi-Monte Carlo method.
randomtextwriter: A map/reduce program that writes 10GB of random textual data per node.
randomwriter: A map/reduce program that writes 10GB of random data per node.
secondarysort: An example defining a secondary sort to the reduce.
sort: A map/reduce program that sorts the data written by the random writer.
sudoku: A sudoku solver.
teragen: Generate data for the terasort
terasort: Run the terasort
teravalidate: Checking results of terasort
wordcount: A map/reduce program that counts the words in the input files.
wordmean: A map/reduce program that counts the average length of the words in the input files.
wordmedian: A map/reduce program that counts the median length of the words in the input files.
wordstandarddeviation: A map/reduce program that counts the standard deviation of the length of the words in the input files.
[[email protected] mapreduce]$ hadoop jar hadoop-mapreduce-examples-3.2.0.jar pi 10 10
Number of Maps = 10
Samples per Map = 10
Wrote 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
2019-08-27 13:47:11,866 INFO client.RMProxy: Connecting to ResourceManager at hadoop01/192.168.1.100:8032
2019-08-27 13:47:12,179 INFO mapreduce.JobResourceUploader: Disabling Erasure Coding for path: /tmp/hadoop-yarn/staging/hadoop/.staging/job_1566884685380_0001
2019-08-27 13:47:12,285 INFO input.FileInputFormat: Total input files to process : 10
2019-08-27 13:47:12,341 INFO mapreduce.JobSubmitter: number of splits:10
2019-08-27 13:47:12,372 INFO Configuration.deprecation: yarn.resourcemanager.system-metrics-publisher.enabled is deprecated. Instead, use yarn.system-metrics-publisher.enabled
2019-08-27 13:47:12,479 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1566884685380_0001
2019-08-27 13:47:12,480 INFO mapreduce.JobSubmitter: Executing with tokens: []
2019-08-27 13:47:12,645 INFO conf.Configuration: resource-types.xml not found
2019-08-27 13:47:12,645 INFO resource.ResourceUtils: Unable to find ‘resource-types.xml‘.
2019-08-27 13:47:13,018 INFO impl.YarnClientImpl: Submitted application application_1566884685380_0001
2019-08-27 13:47:13,099 INFO mapreduce.Job: The url to track the job: http://hadoop01:8088/proxy/application_1566884685380_0001/
2019-08-27 13:47:13,099 INFO mapreduce.Job: Running job: job_1566884685380_0001
2019-08-27 13:47:20,205 INFO mapreduce.Job: Job job_1566884685380_0001 running in uber mode : false
2019-08-27 13:47:20,209 INFO mapreduce.Job: map 0% reduce 0%
2019-08-27 13:47:27,371 INFO mapreduce.Job: map 20% reduce 0%
2019-08-27 13:47:46,535 INFO mapreduce.Job: map 20% reduce 7%
2019-08-27 13:47:50,559 INFO mapreduce.Job: map 40% reduce 7%
2019-08-27 13:47:51,570 INFO mapreduce.Job: map 50% reduce 7%
2019-08-27 13:47:53,586 INFO mapreduce.Job: map 60% reduce 7%
2019-08-27 13:47:58,631 INFO mapreduce.Job: map 60% reduce 20%
2019-08-27 13:47:59,641 INFO mapreduce.Job: map 80% reduce 20%
2019-08-27 13:48:00,665 INFO mapreduce.Job: map 100% reduce 20%
2019-08-27 13:48:01,682 INFO mapreduce.Job: map 100% reduce 100%
2019-08-27 13:48:01,708 INFO mapreduce.Job: Job job_1566884685380_0001 completed successfully
2019-08-27 13:48:01,780 INFO mapreduce.Job: Counters: 54
File System Counters
FILE: Number of bytes read=226
FILE: Number of bytes written=2443397
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=2640
HDFS: Number of bytes written=215
HDFS: Number of read operations=45
HDFS: Number of large read operations=0
HDFS: Number of write operations=3
HDFS: Number of bytes read erasure-coded=0
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)=270199
Total time spent by all reduces in occupied slots (ms)=31653
Total time spent by all map tasks (ms)=270199
Total time spent by all reduce tasks (ms)=31653
Total vcore-milliseconds taken by all map tasks=270199
Total vcore-milliseconds taken by all reduce tasks=31653
Total megabyte-milliseconds taken by all map tasks=276683776
Total megabyte-milliseconds taken by all reduce tasks=32412672
Map-Reduce Framework
Map input records=10
Map output records=20
Map output bytes=180
Map output materialized bytes=280
Input split bytes=1460
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)=67681
CPU time spent (ms)=63700
Physical memory (bytes) snapshot=2417147904
Virtual memory (bytes) snapshot=30882955264
Total committed heap usage (bytes)=2966421504
Peak Map Physical memory (bytes)=382750720
Peak Map Virtual memory (bytes)=2810384384
Peak Reduce Physical memory (bytes)=181923840
Peak Reduce Virtual memory (bytes)=2815541248
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 49.977 seconds
Estimated value of Pi is 3.20000000000000000000
原文地址：https://www.cnblogs.com/CQ-LQJ/p/11602927.html
时间： 2024-10-10 09:37:41