Hadoop-hdfs和mapreduce学习笔记二

不念不忘少年蓝@ 2022-08-20 04:18 136阅读 0赞

Hadoop-hdfs和mapreduce

## 一、初始hdfs ##

1、HDFS管理界面。

http://192.168.0.205:50070/

![Image 1][]

上传文件

hadoop fs -put jdk-7u65-linux-i586.tar.gzhdfs://node1:9000/

再次网页上刷新查看。

![Image 1][]

点击可以下载

![Image 1][]

命令行下载

hadoop fs -gethdfs://node1:9000/jdk-7u65-linux-i586.tar.gz

![Image 1][]

## 二、初始mapreduce ##

在安装目录下有个share目录下面的hadoop里面有默认的一些jar包。

计算圆周率运行jar

hadoop jar hadoop-mapreduce-examples-2.4.1.jar pi 5 5

Number of Maps  = 5

Samples per Map = 5

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

Starting Job

16/01/25 13:18:15 INFO client.RMProxy:Connecting to ResourceManager at node1/192.168.0.205:8032

16/01/25 13:18:16 INFOinput.FileInputFormat: Total input paths to process : 5

16/01/25 13:18:16 INFOmapreduce.JobSubmitter: number of splits:5

16/01/25 13:18:17 INFOmapreduce.JobSubmitter: Submitting tokens for job: job\_1453697466705\_0001

16/01/25 13:18:18 INFO impl.YarnClientImpl:Submitted application application\_1453697466705\_0001

16/01/25 13:18:18 INFO mapreduce.Job: Theurl to track the job: http://node1:8088/proxy/application\_1453697466705\_0001/

16/01/25 13:18:18 INFO mapreduce.Job:Running job: job\_1453697466705\_0001

16/01/25 13:18:26 INFO mapreduce.Job: Jobjob\_1453697466705\_0001 running in uber mode : false

16/01/25 13:18:26 INFO mapreduce.Job:  map 0% reduce 0%

16/01/25 13:18:54 INFO mapreduce.Job:  map 100% reduce 0%

16/01/25 13:19:15 INFO mapreduce.Job:  map 100% reduce 100%

16/01/25 13:19:16 INFO mapreduce.Job: Jobjob\_1453697466705\_0001 completed successfully

16/01/25 13:19:17 INFO mapreduce.Job:Counters: 49

File System Counters

FILE: Number of bytes read=116

FILE: Number of byteswritten=559761

FILE: Number of readoperations=0

FILE: Number of large readoperations=0

FILE: Number of writeoperations=0

HDFS: Number of bytes read=1305

HDFS: Number of byteswritten=215

HDFS: Number of readoperations=23

HDFS: Number of large readoperations=0

HDFS: Number of writeoperations=3

Job Counters

Launched map tasks=5

Launched reduce tasks=1

Data-local map tasks=5

Total time spent by all maps inoccupied slots (ms)=151444

Total time spent by all reducesin occupied slots (ms)=12933

Total time spent by all maptasks (ms)=151444

Total time spent by all reducetasks (ms)=12933

Total vcore-seconds taken byall map tasks=151444

Total vcore-seconds taken byall reduce tasks=12933

Total megabyte-seconds taken byall map tasks=155078656

Total megabyte-seconds taken byall reduce tasks=13243392

Map-Reduce Framework

Map input records=5

Map output records=10

Map output bytes=90

Map output materializedbytes=140

Input split bytes=715

Combine input records=0

Combine output records=0

Reduce input groups=2

Reduce shuffle bytes=140

Reduce input records=10

Reduce output records=0

Spilled Records=20

Shuffled Maps =5

Failed Shuffles=0

Merged Map outputs=5

GC time elapsed (ms)=321

CPU time spent (ms)=7610

Physical memory (bytes)snapshot=1077932032

Virtual memory (bytes)snapshot=3148177408

Total committed heap usage(bytes)=850395136

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=590

File Output Format Counters

Bytes Written=97

Job Finished in 62.334 seconds

Estimated value of Pi is3.68000000000000000000

## 三、基本命令 ##

创建目录命令（中间红色部分可以省略）

hadoop fs -mkdir hdfs://node1:9000/wordcout

hadoop fs -mkdir /wordcout/input

新建一个test.txt文件

vi test.txt

hello world

hello tom

hello jim

hello kitty

hello angelabay

hello wolf

hello laolan

上传test.txt

hadoop fs -put test.txt /wordcout/input

计算

hadoop jarhadoop-mapreduce-examples-2.4.1.jar wordcount /wordcout/input /wordcunt/output

![Image 1][]

跑完了。

使用命令查看目录

hadoop fs -ls /wordcout/input

hadoop fs -ls /wordcunt/output

![Image 1][]

查看hdfs上面的文件（上面的计算结果）

hadoop fs -cat/wordcunt/output/part-r-00000

命令详解

hadoop fs

![Image 1][]

如：

hadoop fs -ls /

![Image 1][]

1表示副本数量。

修改权限及组

hadoop fs -chown angelababy:mygirls/jdk-7u65-linux-i586.tar.gz

![Image 1][]

查看结果

Hadoop权限检查比较弱

修改权限

fs -chmod 777 /jdk-7u65-linux-i586.tar.gz

![Image 1][]

Copy文件（和put一样）

hadoop fs -copyFromLocal./hadoop-mapreduce-client-app-2.4.1.jar /

![Image 1][]

在hdfs上面cp

hadoop fs -cp/hadoop-mapreduce-client-app-2.4.1.jar /wordcout

![Image 1][]

查看空间大小

hadoop fs -df -h /

![Image 1][]

查看文件大小

hadoop fs -du -s -h /

![Image 1][]

hadoop fs -du -s -h hdfs://node1:9000/\*

![Image 1][]

创建目录

hadoop fs -mkdir -p /aa/bb

![Image 1][]

删除

hadoop fs -rm -r /aa/bb

![Image 1][]

可以看到删除的时候是到回收站，有提示。

![Image 1][]

hadoop jar

hdfs不支持修改，可以追加。

![Image 1][]

## 四、hdfs读写过程讲解及思想 ##

1、读取过程（多个副本）

![Image 1][]

2、基本思想

![Image 1][]

[Image 1]:

Hadoop-hdfs和mapreduce学习笔记二

发表评论取消回复

还没有评论，来说两句吧...

相关阅读

相关 MapReduce剖析笔记之二：Job提交的过程

相关 Hadoop-hdfs和mapreduce学习笔记二

相关 IgniteRDD学习笔记(二)IgniteContext和IgniteRDD

相关 hadoop学习笔记2：eclipse开发MapReduce

相关 odps mapreduce学习笔记

相关浅析MapReduce(二）

相关 MapReduce学习之二

相关 Python学习笔记（二）

相关 MapReduce的学习和使用

相关【jQuery】学习笔记（二）