Hadoop Shell 命令与 WordCount

﹏ヽ暗。殇╰゛Y 2022-02-26 19:12 256阅读 0赞

### 前言 ###

在前2章内, 我们分别介绍了Hadoop安装的3种形式(`Standalone mode`/ `Pseudo-Distributed mode`/`Cluster mode`). 本章, 我们介绍如何使用HDFS命令进行一些基本的操作. 官方的操作文档可以查看[Hadoop Shell命令][Hadoop Shell].

--------------------

### 正文 ###

##### 前置条件 #####

已经安装`Hadoop`集群, 并启动. 从页面可以看到, 我们`HDFS`系统的文件目录.  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly95YW54bWwuYmxvZy5jc2RuLm5ldA_size_16_color_FFFFFF_t_70]

##### 基本操作 #####

对于文件系统用的最多的就是, `增删查改`与`权限系统`一直是我们操作文件系统的基本命令.它们的基本操作分别如下所示:

*  查看文件目录 `ls`

# 本地仓库
    localhost:current Sean$ hadoop fs -ls /
    Picked up JAVA_TOOL_OPTIONS: -Dfile.encoding=UTF-8
    19/03/30 16:15:42 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
    Found 3 items
    -rw-r--r--   1 Sean supergroup          2 2019-03-25 11:55 /1.log
    drwx------   - Sean supergroup          0 2019-03-25 12:11 /tmp
    drwxr-xr-x   - Sean supergroup          0 2019-03-25 13:16 /user
    
    # 全路径
    localhost:current Sean$ hadoop fs -ls hdfs://localhost:9000/
    Picked up JAVA_TOOL_OPTIONS: -Dfile.encoding=UTF-8
    19/03/30 16:16:32 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
    Found 3 items
    -rw-r--r--   1 Sean supergroup          2 2019-03-25 11:55 hdfs://localhost:9000/1.log
    drwx------   - Sean supergroup          0 2019-03-25 12:11 hdfs://localhost:9000/tmp
    drwxr-xr-x   - Sean supergroup          0 2019-03-25 13:16 hdfs://localhost:9000/user

*  上传文件 `put`

# 上传文件
    localhost:current Sean$ hadoop fs -put hello2019.sh /
    # 查询上传的文件
    localhost:current Sean$ hadoop fs -ls /
    Picked up JAVA_TOOL_OPTIONS: -Dfile.encoding=UTF-8
    Found 4 items
    -rw-r--r--   1 Sean supergroup          2 2019-03-25 11:55 /1.log
    -rw-r--r--   1 Sean supergroup         10 2019-03-30 16:19 /hello2019.sh
    drwx------   - Sean supergroup          0 2019-03-25 12:11 /tmp
    drwxr-xr-x   - Sean supergroup          0 2019-03-25 13:16 /user
    
    # 手动合并(文件是可以还原的.)
    cat blk_1073741983 >> tmp.file
    cat blk_1073741984 >> tmp.file

默认文件切分大小为128M, 大于的话会切分成2快.

*  查看文件内容 `cat`

# 通过hadoop查看
    localhost:current Sean$ hadoop fs -cat /hello2019.sh
    hello2019
    
    #通过本地linux查看
    localhost:current Sean$ cat finalized/subdir0/subdir0/blk_1073741983
    hello2019
    
    localhost:current Sean$ pwd
    /Users/Sean/Software/hadoop/current/tmp/dfs/data/current/BP-586017156-127.0.0.1-1553485799471/current

*  下载文件 `get`

localhost:current Sean$ hadoop fs -get /hello2019.sh
    localhost:current Sean$ ls
    VERSION		dfsUsed		finalized	hello2019.sh	rbw
    localhost:current Sean$ cat hello2019.sh
    hello2019

*  创建文件目录 `mkdir`

localhost:current Sean$ hadoop fs -mkdir -p /wordcount/input
    localhost:current Sean$ hadoop fs -ls /wordcount
    Found 1 items
    drwxr-xr-x   - Sean supergroup          0 2019-03-30 16:40 /wordcount/input

--------------------

### Others ###

hdfs的命令操作, 可以通过`hadoop fs`直接显示所以命令.

localhost:mapreduce Sean$ hadoop fs
    Usage: hadoop fs [generic options]
    	[-appendToFile <localsrc> ... <dst>]
    	[-cat [-ignoreCrc] <src> ...]
    	[-checksum <src> ...]
    	[-chgrp [-R] GROUP PATH...]
    	[-chmod [-R] <MODE[,MODE]... | OCTALMODE> PATH...]
    	[-chown [-R] [OWNER][:[GROUP]] PATH...]
    	[-copyFromLocal [-f] [-p] [-l] <localsrc> ... <dst>]
    	[-copyToLocal [-p] [-ignoreCrc] [-crc] <src> ... <localdst>]
    	[-count [-q] [-h] <path> ...]
    	[-cp [-f] [-p | -p[topax]] <src> ... <dst>]
    	[-createSnapshot <snapshotDir> [<snapshotName>]]
    	[-deleteSnapshot <snapshotDir> <snapshotName>]
    	[-df [-h] [<path> ...]]
    	[-du [-s] [-h] <path> ...]
    	[-expunge]
    	[-find <path> ... <expression> ...]
    	[-get [-p] [-ignoreCrc] [-crc] <src> ... <localdst>]
    	[-getfacl [-R] <path>]
    	[-getfattr [-R] {-n name | -d} [-e en] <path>]
    	[-getmerge [-nl] <src> <localdst>]
    	[-help [cmd ...]]
    	[-ls [-d] [-h] [-R] [<path> ...]]
    	[-mkdir [-p] <path> ...]
    	[-moveFromLocal <localsrc> ... <dst>]
    	[-moveToLocal <src> <localdst>]
    	[-mv <src> ... <dst>]
    	[-put [-f] [-p] [-l] <localsrc> ... <dst>]
    	[-renameSnapshot <snapshotDir> <oldName> <newName>]
    	[-rm [-f] [-r|-R] [-skipTrash] <src> ...]
    	[-rmdir [--ignore-fail-on-non-empty] <dir> ...]
    	[-setfacl [-R] [{-b|-k} {-m|-x <acl_spec>} <path>]|[--set <acl_spec> <path>]]
    	[-setfattr {-n name [-v value] | -x name} <path>]
    	[-setrep [-R] [-w] <rep> <path> ...]
    	[-stat [format] <path> ...]
    	[-tail [-f] <file>]
    	[-test -[defsz] <path>]
    	[-text [-ignoreCrc] <src> ...]
    	[-touchz <path> ...]
    	[-truncate [-w] <length> <path> ...]
    	[-usage [cmd ...]]
    
    Generic options supported are
    -conf <configuration file>     specify an application configuration file
    -D <property=value>            use value for given property
    -fs <local|namenode:port>      specify a namenode
    -jt <local|resourcemanager:port>    specify a ResourceManager
    -files <comma separated list of files>    specify comma separated files to be copied to the map reduce cluster
    -libjars <comma separated list of jars>    specify comma separated jar files to include in the classpath.
    -archives <comma separated list of archives>    specify comma separated archives to be unarchived on the compute machines.
    
    The general command line syntax is
    bin/hadoop command [genericOptions] [commandOptions]

*  `help`  
    输出命令参数手册.
 *  `mkdir`  
    创建目录. `hadoop fs -mkdir -p /abc/acc`
 *  `moveFromLocal / moveToLocal`  
    从本地移动HDFS(本地原文件删除). `hadoop fs -moveFromLocal abc.txt /`  
    从HDFS移动本地(HDFS原文件删除). `hadoop fs -moveFromLocal abc.txt /`
 *  `appendToFile`  
    追加到文件上面. `hadoop fs -appendToFile abc.txt /hello2019.txt`

localhost:mapreduce Sean$ echo xxoo >> hello.txt
    localhost:mapreduce Sean$ hadoop fs -appendToFile hello.txt /hello2019.sh
    localhost:mapreduce Sean$ hadoop fs -cat /hello2019.sh
    Picked up JAVA_TOOL_OPTIONS: -Dfile.encoding=UTF-8
    hello2019
    xxoo

*  `cat`  
    显示文件. `hadoop fs -cat /hello2019.sh`. 文件过多`hadoop fs -cat /hello2019.sh | more`或者`hadoop fs -tail /hello2019.sh`
 *  `tail`  
    显示文件末尾. `hadoop fs -tail /hello2019.sh`
 *  `text`  
    已字符形式打印一个文件的内容.`hadoop fs -text /hello2019.sh`.
 *  `chgrp / chmod / chown`  
    `chgrp`更改文件组; `chmod`更改权限; `chown`更改用户和组.

hadoop fs -chmod 666 /hello2019.txt
    hadoop fs -chown someuser:somegrp /hello2019.txt
    
    localhost:mapreduce Sean$ hadoop fs -chmod 777 /hello2019.sh
    localhost:mapreduce Sean$ hadoop fs -ls /
    Picked up JAVA_TOOL_OPTIONS: -Dfile.encoding=UTF-8
    19/03/30 17:09:52 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
    Found 5 items
    -rw-r--r--   1 Sean supergroup          2 2019-03-25 11:55 /1.log
    -rwxrwxrwx   1 Sean supergroup         15 2019-03-30 16:55 /hello2019.sh
    drwx------   - Sean supergroup          0 2019-03-25 12:11 /tmp
    drwxr-xr-x   - Sean supergroup          0 2019-03-25 13:16 /user
    drwxr-xr-x   - Sean supergroup          0 2019-03-30 16:43 /wordcount
    
    # hadoop内没有用户的设计.(所以没创建该用户也可以这样改造.)
    localhost:mapreduce Sean$ hadoop fs -chown hellokitty:hello  /hello2019.sh
    localhost:mapreduce Sean$ hadoop fs -ls /
    Picked up JAVA_TOOL_OPTIONS: -Dfile.encoding=UTF-8
    19/03/30 17:10:40 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
    Found 5 items
    -rw-r--r--   1 Sean       supergroup          2 2019-03-25 11:55 /1.log
    -rwxrwxrwx   1 hellokitty hello              15 2019-03-30 16:55 /hello2019.sh
    drwx------   - Sean       supergroup          0 2019-03-25 12:11 /tmp
    drwxr-xr-x   - Sean       supergroup          0 2019-03-25 13:16 /user
    drwxr-xr-x   - Sean       supergroup          0 2019-03-30 16:43 /wordcount

*  `copyFromlocal / copyToLocal`  
    从本地拷贝; 拷贝到本地
 *  `cp`  
    hdfs内部进行拷贝. `hadoop fs -cp /hello2019.sh /a/hello2019.sh`
 *  `mv`  
    hdfs内部进行移动. `hadoop fs -mv /hello2019.sh /a/`
 *  `get`  
    获取到本地. 类似`copyToLocal`. `hadoop fs -get /hello.sh`
 *  `getmerge`  
    合并下载多个文件. `hadoop fs -getmerge /wordcount/output/* hellomerge.sh`

localhost:mapreduce Sean$ hadoop fs -getmerge /wordcount/output/* hellomerge.sh
    localhost:mapreduce Sean$ cat hellomerge.sh
    2019	1
    able	1
    cat	2
    hello	1
    kitty	1
    pitty	2

*  `put`  
    下载到本地. 类似`copyFromLocal`. `hadoop fs -put hello2019.sh /`
 *  `rm`  
    删除. `hadoop fs -rm -r /hello2019.sh`

# -r recursive 递归的意思
    localhost:mapreduce Sean$ hadoop fs -rm -r /1.log
    Deleted /1.log
    localhost:mapreduce Sean$ hadoop fs -ls /
    Found 4 items
    -rwxrwxrwx   1 hellokitty hello              15 2019-03-30 16:55 /hello2019.sh
    drwx------   - Sean       supergroup          0 2019-03-25 12:11 /tmp
    drwxr-xr-x   - Sean       supergroup          0 2019-03-25 13:16 /user
    drwxr-xr-x   - Sean       supergroup          0 2019-03-30 16:43 /wordcount

*  `rmdir`  
    删除空目录. `hadoop fs - rmdir /abbc`
 *  `df`  
    统计文件系统的可用信息. `hadoop fs -df -h /`

localhost:mapreduce Sean$ hadoop fs -df -h /
    Picked up JAVA_TOOL_OPTIONS: -Dfile.encoding=UTF-8
    Filesystem                Size   Used  Available  Use%
    hdfs://localhost:9000  465.7 G  5.9 M    169.1 G    0%

*  `du`  
    统计文件夹目录的大小. `hadoop fs -du -s -h /abc/d`

# -s 汇总 -h 带单位
    localhost:mapreduce Sean$ hadoop fs -du -s -h /wordcount/
    86  /wordcount
    
    Most commands print help when invoked w/o parameters.
    localhost:mapreduce Sean$ hadoop fs -du -s -h hdfs://localhost:9000/*
    15  hdfs://localhost:9000/hello2019.sh
    4.7 M  hdfs://localhost:9000/tmp
    266.0 K  hdfs://localhost:9000/user
    86  hdfs://localhost:9000/wordcount

*  `count`  
    统计一个目录下的文件数目. `hadoop fs -count /aaa/`
 *  `setrep`  
    设置文件的副本数目. `replication`.

localhost:mapreduce Sean$ hadoop fs -setrep 3 /wordcount/input/hello2019.sh
    Replication 3 set: /wordcount/input/hello2019.sh

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly95YW54bWwuYmxvZy5jc2RuLm5ldA_size_16_color_FFFFFF_t_70 1]![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly95YW54bWwuYmxvZy5jc2RuLm5ldA_size_16_color_FFFFFF_t_70 2]  
如果结点为3个, 但是设置为10个的时候.并不会设置10个, 这个是`namenode`中的原数据的副本数目,但是不一定是真实的副本数目(`视datanode的数目而定`).

--------------------

### WordCount ###

localhost:mapreduce Sean$ hadoop jar hadoop-mapreduce-examples-2.7.5.jar wordcount /wordcount/input/ /wordcount/output
    
    localhost:mapreduce Sean$ hadoop jar hadoop-mapreduce-examples-2.7.5.jar wordcount /wordcount/input/ /wordcount/output
    Picked up JAVA_TOOL_OPTIONS: -Dfile.encoding=UTF-8
    19/03/30 16:43:30 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
    19/03/30 16:43:31 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
    19/03/30 16:43:32 INFO input.FileInputFormat: Total input paths to process : 1
    19/03/30 16:43:32 INFO mapreduce.JobSubmitter: number of splits:1
    19/03/30 16:43:32 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1553933297569_0001
    19/03/30 16:43:33 INFO impl.YarnClientImpl: Submitted application application_1553933297569_0001
    19/03/30 16:43:33 INFO mapreduce.Job: The url to track the job: http://localhost:8088/proxy/application_1553933297569_0001/
    19/03/30 16:43:33 INFO mapreduce.Job: Running job: job_1553933297569_0001
    19/03/30 16:43:43 INFO mapreduce.Job: Job job_1553933297569_0001 running in uber mode : false
    19/03/30 16:43:43 INFO mapreduce.Job:  map 0% reduce 0%
    19/03/30 16:43:48 INFO mapreduce.Job:  map 100% reduce 0%
    19/03/30 16:43:54 INFO mapreduce.Job:  map 100% reduce 100%
    19/03/30 16:43:54 INFO mapreduce.Job: Job job_1553933297569_0001 completed successfully
    19/03/30 16:43:54 INFO mapreduce.Job: Counters: 49
    	File System Counters
    		FILE: Number of bytes read=74
    		FILE: Number of bytes written=243693
    		FILE: Number of read operations=0
    		FILE: Number of large read operations=0
    		FILE: Number of write operations=0
    		HDFS: Number of bytes read=157
    		HDFS: Number of bytes written=44
    		HDFS: Number of read operations=6
    		HDFS: Number of large read operations=0
    		HDFS: Number of write operations=2
    	Job Counters
    		Launched map tasks=1
    		Launched reduce tasks=1
    		Data-local map tasks=1
    		Total time spent by all maps in occupied slots (ms)=3271
    		Total time spent by all reduces in occupied slots (ms)=3441
    		Total time spent by all map tasks (ms)=3271
    		Total time spent by all reduce tasks (ms)=3441
    		Total vcore-milliseconds taken by all map tasks=3271
    		Total vcore-milliseconds taken by all reduce tasks=3441
    		Total megabyte-milliseconds taken by all map tasks=3349504
    		Total megabyte-milliseconds taken by all reduce tasks=3523584
    	Map-Reduce Framework
    		Map input records=7
    		Map output records=8
    		Map output bytes=74
    		Map output materialized bytes=74
    		Input split bytes=115
    		Combine input records=8
    		Combine output records=6
    		Reduce input groups=6
    		Reduce shuffle bytes=74
    		Reduce input records=6
    		Reduce output records=6
    		Spilled Records=12
    		Shuffled Maps =1
    		Failed Shuffles=0
    		Merged Map outputs=1
    		GC time elapsed (ms)=123
    		CPU time spent (ms)=0
    		Physical memory (bytes) snapshot=0
    		Virtual memory (bytes) snapshot=0
    		Total committed heap usage (bytes)=306184192
    	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=42
    	File Output Format Counters
    		Bytes Written=44

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly95YW54bWwuYmxvZy5jc2RuLm5ldA_size_16_color_FFFFFF_t_70 3]  
输出目录不能有其他内容, 否则会被进行覆盖.本次测试后的输出目录下出现2个文件:`success / part-r-0000`. 第一个文件为输出标准, 第二个文件是真实的文件.

localhost:mapreduce Sean$ hadoop fs -cat /wordcount/output/part-r-00000
    2019	1
    able	1
    cat	2
    hello	1
    kitty	1
    pitty	2

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly95YW54bWwuYmxvZy5jc2RuLm5ldA_size_16_color_FFFFFF_t_70 4]

localhost:mapreduce Sean$ ls
    hadoop-mapreduce-client-app-2.7.5.jar			hadoop-mapreduce-client-hs-plugins-2.7.5.jar		hadoop-mapreduce-examples-2.7.5.jar
    hadoop-mapreduce-client-common-2.7.5.jar		hadoop-mapreduce-client-jobclient-2.7.5-tests.jar	lib
    hadoop-mapreduce-client-core-2.7.5.jar			hadoop-mapreduce-client-jobclient-2.7.5.jar		lib-examples
    hadoop-mapreduce-client-hs-2.7.5.jar			hadoop-mapreduce-client-shuffle-2.7.5.jar		sources

这个文件夹下方有非常多的测试例子. 可以自己研究.

--------------------

### 基本原理(大体) ###

存储在HDFS内的文件其实还是存储在本地的, 只是它是一个分布式的文件系统而已. 我们看下我们之前存储的.

注意,由于我本地的是`DataNode`与`NameNode`安装在一起的, 所以文件目录下结点如下所示:

localhost:tmp Sean$ tree
    .
    ├── dfs
    │   ├── data
    │   │   ├── current
    │   │   │   ├── BP-586017156-127.0.0.1-1553485799471
    │   │   │   │   ├── current
    │   │   │   │   │   ├── VERSION
    │   │   │   │   │   ├── dfsUsed
    │   │   │   │   │   ├── finalized
    │   │   │   │   │   │   └── subdir0
    │   │   │   │   │   │       └── subdir0
    │   │   │   │   │   │           ├── blk_1073741825
    │   │   │   │   │   │           ├── blk_1073741825_1001.meta
    │   │   │   │   │   └── rbw
    │   │   │   │   ├── scanner.cursor
    │   │   │   │   └── tmp
    │   │   │   └── VERSION
    │   │   └── in_use.lock
    │   ├── name
    │   │   ├── current
    │   │   │   ├── VERSION
    │   │   │   ├── edits_0000000000000000001-0000000000000000118
    │   │   │   ├── edits_inprogress_0000000000000001233
    │   │   │   ├── fsimage_0000000000000001230
    │   │   │   ├── fsimage_0000000000000001230.md5
    │   │   │   ├── fsimage_0000000000000001232
    │   │   │   ├── fsimage_0000000000000001232.md5
    │   │   │   └── seen_txid
    │   │   └── in_use.lock
    │   └── namesecondary
    │       ├── current
    │       │   ├── VERSION
    │       │   ├── edits_0000000000000000001-0000000000000000118
    │       │   ├── edits_0000000000000000119-0000000000000000943
    │       │   ├── edits_0000000000000001231-0000000000000001232
    │       │   ├── fsimage_0000000000000001230
    │       │   ├── fsimage_0000000000000001230.md5
    │       │   ├── fsimage_0000000000000001232
    │       │   └── fsimage_0000000000000001232.md5
    │       └── in_use.lock
    └── nm-local-dir
        ├── filecache
        ├── nmPrivate
        └── usercache

*  NameNode: 文件的管理
 *  DataNode: 文件的切分与管理(Socket / Netty)

--------------------

### HDFS命令 ###

*  `hdfs dfsadmin -report` 查看集群状态

localhost:Desktop Sean$ hdfs dfsadmin -report
    Picked up JAVA_TOOL_OPTIONS: -Dfile.encoding=UTF-8
    19/04/03 15:51:20 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
    Configured Capacity: 500068036608 (465.72 GB)
    Present Capacity: 182055092460 (169.55 GB)
    DFS Remaining: 182048903168 (169.55 GB)
    DFS Used: 6189292 (5.90 MB)
    DFS Used%: 0.00%
    Under replicated blocks: 27
    Blocks with corrupt replicas: 0
    Missing blocks: 0
    Missing blocks (with replication factor 1): 0
    
    -------------------------------------------------
    Live datanodes (1):
    
    Name: 127.0.0.1:50010 (localhost)
    Hostname: localhost
    Decommission Status : Normal
    Configured Capacity: 500068036608 (465.72 GB)
    DFS Used: 6189292 (5.90 MB)
    Non DFS Used: 313001643796 (291.51 GB)
    DFS Remaining: 182048903168 (169.55 GB)
    DFS Used%: 0.00%
    DFS Remaining%: 36.40%
    Configured Cache Capacity: 0 (0 B)
    Cache Used: 0 (0 B)
    Cache Remaining: 0 (0 B)
    Cache Used%: 100.00%
    Cache Remaining%: 0.00%
    Xceivers: 1
    Last contact: Wed Apr 03 15:51:21 CST 2019

--------------------

### Reference ###

\[1\].[Hadoop Shell命令][Hadoop Shell]  
\[2\]. [介绍hadoop中的hadoop和hdfs命令][hadoop_hadoop_hdfs]  
\[3\]. [Hadoop学习笔记4之HDFS常用命令][Hadoop_4_HDFS]

[Hadoop Shell]: http://hadoop.apache.org/docs/r1.0.4/cn/hdfs_shell.html
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly95YW54bWwuYmxvZy5jc2RuLm5ldA_size_16_color_FFFFFF_t_70]: /images/20220226/ff24b8c73a6a460e96fb47ee9496b6a4.png
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[hadoop_hadoop_hdfs]: https://www.cnblogs.com/lzfhope/p/6952869.html
[Hadoop_4_HDFS]: https://blog.csdn.net/cskywit/article/details/80502397