Hbase表两种数据备份方法-导入和导出示例

今天药忘吃喽~ 2022-05-25 13:29 426阅读 0赞

本文将提供两种备份方法 ——

**1) 基于Hbase提供的类对hbase中某张表进行备份**

**2) 基于Hbase snapshot数据快速备份方法**

场合：由于线上和测试环境是分离的，无法在测试环境访问线上库，所以需要将线上的hbase表导出一部分到测试环境中的hbase表，这就是本文的由来。

**一、基于hbase提供的类对hbase中某张表进行备份**

本文使用hbase提供的类把hbase中某张表的数据导出hdfs，之后再导出到测试hbase表中。

首先介绍一下相关参数选项：

**(1)  从hbase表导出(\# 默认不写file://的时候就是导出到hdfs上了  )**

HBase数据导出到HDFS或者本地文件
    hbase org.apache.hadoop.hbase.mapreduce.Export emp file:///Users/a6/Applications/experiment_data/hbase_data/bak
    HBase数据导出到本地文件
    hbase org.apache.hadoop.hbase.mapreduce.Export emp /hbase/emp_bak

**(2) 导入hbase表(\# 默认不写file://的时候就是导出到hdfs上了  )**

将hdfs上的数据导入到备份目标表中
    localhost:bin a6$ hbase org.apache.hadoop.hbase.mapreduce.Driver import emp_bak /hbase/emp_bak/*
    将本地文件上的数据导入到备份目标表中
    hbase org.apache.hadoop.hbase.mapreduce.Driver import emp_bak file:///Users/a6/Applications/experiment_data/hbase_data/bak/*

**(3) 导出时可以限制scanner.batch的大小**  
如果在hbase中的一个row出现大量的数据，那么导出时会报出ScannerTimeoutException的错误。这时候需要设置hbase.export.scaaner.batch 这个参数。这样导出时的错误就可以避免了。

hbase org.apache.hadoop.hbase.mapreduce.Export -Dhbase.export.scanner.batch=2000  emp file:///Users/a6/Applications/experiment_data/hbase_data/bak

**(4)为了节省空间可以使用compress选项**

hbase的数据导出的时候，如果不适用compress的选项，数据量的大小可能相差5倍。因此使用compress的选项，备份数据的时候是可以节省不少空间的。

并且本人测试了compress选项的导出速度，和无此选项时差别不大（几乎无差别）：

hbase org.apache.hadoop.hbase.mapreduce.Export -Dhbase.export.scanner.batch=2000 -D mapred.output.compress=true  emp file:///Users/a6/Applications/experiment_data/hbase_data/bak

通过添加compress选项，最终导出文件的大小由335字节变成了325字节，
    File Output Format Counters                             File Output Format Counters
    Bytes Written=335		                        Bytes Written=323

**(5)导出指定行键范围和列族**

在公司准备要更换数据中心，需要将hbase数据库中的数据进行迁移。虽然进行hbase数据库数据迁移时，使用其自带的工具import和export是很方便的。只不过，在迁移大量数据时，可能需要运行很长的时间，甚至可能出错。这时，是可以通过指定行键范围和列族，来减少单次export工具的运行时间。可以看出，支持的选项有好几个。假如，我们想导出表test的数据，且只要列族Info，行键范围在000到001之间，可以这样写：

这样就可以了，且数据将会保存在hdfs中。

通过指定列族和行键范围，可以只导出部分数据，避免export启动的mapreduce任务运行时间过长。也就是可以分多次导出数据。

./hbase org.apache.hadoop.hbase.mapreduce.Export -D hbase.mapreduce.scan.column.family=Info -D hbase.mapreduce.scan.row.start=000 -D hbase.mapreduce.scan.row.stop=001 test /test_datas

**闲话少叙，例子就来：**

查到了HBase自带的export/import机制可以实现Backup Restore功能。而且可以实现增量备份。  
原理都是用了MapReduce来实现的。  
1、Export是以表为单位导出数据的，若想完成整库的备份需要执行n遍。  
2、Export在shell中的调用方式类似如下格式：  
./hbase org.apache.hadoop.hbase.mapreduce.Export 表名 备份路径 （版本号） （起始时间戳） （结束时间戳）  
括号内为可选项，例如  
Usage: Export \[-D <property=value>\]\* <tablename> <outputdir> \[<versions> \[<starttime> \[<endtime>\]\] \[^\[regex pattern\] or \[Prefix\] to filter\]\]    
hbase org.apache.hadoop.hbase.mapreduce.Export emp /hbase/emp\_bak  1  123456789  
备份 emp 这张表到 /hbase/emp\_bak 目录下（最后一级目录必须由Export自己创建），版本号为1，备份记录从123456789这个时间戳开始到当前时间内所有的执行过put操作的记录。  
注意：为什么是所有put操作记录？因为在备份时是扫描所有表中所有时间戳大于等于123456789这个值的记录并导出。如果是delete操作，则表中这条记录已经删除，扫描时也无法获取这条记录信息  
当不指定时间戳时，备份的就是当前完整表中的数据。

**1）、创建hbase表emp**

localhost:bin a6$ pwd
    /Users/a6/Applications/hbase-1.2.6/bin
    localhost:bin a6$ hbase shell
    create 'emp','personal data','professional data'

**2）、插入数据并查看数据**

将第一行的值插入到emp表如下所示。
    hbase(main):005:0> put 'emp','1','personal data:name','raju'
    0 row(s) in 0.6600 seconds
    hbase(main):006:0> put 'emp','1','personal data:city','hyderabad'
    0 row(s) in 0.0410 seconds
    hbase(main):007:0> put 'emp','1','professional data:designation','manager'
    0 row(s) in 0.0240 seconds
    hbase(main):007:0> put 'emp','1','professional data:salary','50000'
    0 row(s) in 0.0240 seconds
    
    插入完成整个表格，会得到下面的输出。
    hbase(main):002:0> scan 'emp'
    ROW                                                                   COLUMN+CELL
     1                                                                    column=personal data:city, timestamp=1526269334560, value=hyderabad
     1                                                                    column=personal data:name, timestamp=1526269326929, value=raju
     1                                                                    column=professional data:designation, timestamp=1526269345044, value=manager
     1                                                                    column=professional data:salary, timestamp=1526269352605, value=50000
    1 row(s) in 0.2230 seconds

**3）、将hbase表emp的数据导出到hdfs的路径/hbase/emp\_bak上面**

localhost:bin a6$ pwd
    /Users/a6/Applications/hbase-1.2.6/bin
    localhost:bin a6$ hbase org.apache.hadoop.hbase.mapreduce.Export emp /hbase/emp_bak
    SLF4J: Class path contains multiple SLF4J bindings.
    SLF4J: Found binding in [jar:file:/Users/a6/Applications/hbase-1.2.6/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class]
    SLF4J: Found binding in [jar:file:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/common/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class]
    SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
    SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]
    2018-05-15 17:31:18,340 WARN  [main] util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
    2018-05-15 17:31:18,412 INFO  [main] mapreduce.Export: versions=1, starttime=0, endtime=9223372036854775807, keepDeletedCells=false
    2018-05-15 17:31:19,224 INFO  [main] client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
    2018-05-15 17:31:23,325 INFO  [main] zookeeper.RecoverableZooKeeper: Process identifier=hconnection-0x5ed731d0 connecting to ZooKeeper ensemble=localhost:2182
    2018-05-15 17:31:23,332 INFO  [main] zookeeper.ZooKeeper: Client environment:zookeeper.version=3.4.6-1569965, built on 02/20/2014 09:09 GMT
    2018-05-15 17:31:23,333 INFO  [main] zookeeper.ZooKeeper: Client environment:host.name=localhost
    2018-05-15 17:31:23,333 INFO  [main] zookeeper.ZooKeeper: Client environment:java.version=1.8.0_131
    2018-05-15 17:31:23,333 INFO  [main] zookeeper.ZooKeeper: Client environment:java.vendor=Oracle Corporation
    2018-05-15 17:31:23,333 INFO  [main] zookeeper.ZooKeeper: Client environment:java.home=/Library/Java/JavaVirtualMachines/jdk1.8.0_131.jdk/Contents/Home/jre
    2018-05-15 17:31:23,333 INFO  [main] zookeeper.ZooKeeper: Client environment:java.class.path=/Users/a6/Applications/hbase-1.2.6/bin/../conf:/Library/Java/JavaVirtualMachines/jdk1.8.0_131.jdk/Contents/Home/lib/tools.jar:/Users/a6/Applications/hbase-1.2.6/bin/..:/Users/a6/Applications/hbase-1.2.6/bin/../lib/activation-1.1.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/aopalliance-1.0.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/apacheds-i18n-2.0.0-M15.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/apacheds-kerberos-codec-2.0.0-M15.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/api-asn1-api-1.0.0-M20.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/api-util-1.0.0-M20.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/asm-3.1.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/avro-1.7.4.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/commons-beanutils-1.7.0.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/commons-beanutils-core-1.8.0.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/commons-cli-1.2.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/commons-codec-1.9.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/commons-collections-3.2.2.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/commons-compress-1.4.1.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/commons-configuration-1.6.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/commons-daemon-1.0.13.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/commons-digester-1.8.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/commons-el-1.0.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/commons-httpclient-3.1.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/commons-io-2.4.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/commons-lang-2.6.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/commons-logging-1.2.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/commons-math-2.2.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/commons-math3-3.1.1.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/commons-net-3.1.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/disruptor-3.3.0.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/findbugs-annotations-1.3.9-1.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/guava-12.0.1.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/guice-3.0.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/guice-servlet-3.0.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/hadoop-annotations-2.5.1.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/hadoop-auth-2.5.1.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/hadoop-client-2.5.1.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/hadoop-common-2.5.1.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/hadoop-hdfs-2.5.1.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/hadoop-mapreduce-client-app-2.5.1.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/hadoop-mapreduce-client-common-2.5.1.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/hadoop-mapreduce-client-core-2.5.1.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/hadoop-mapreduce-client-jobclient-2.5.1.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/hadoop-mapreduce-client-shuffle-2.5.1.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/hadoop-yarn-api-2.5.1.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/hadoop-yarn-client-2.5.1.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/hadoop-yarn-common-2.5.1.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/hadoop-yarn-server-common-2.5.1.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/hbase-annotations-1.2.6-tests.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/hbase-annotations-1.2.6.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/hbase-client-1.2.6.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/hbase-common-1.2.6-tests.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/hbase-common-1.2.6.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/hbase-examples-1.2.6.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/hbase-external-blockcache-1.2.6.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/hbase-hadoop-compat-1.2.6.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/hbase-hadoop2-compat-1.2.6.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/hbase-it-1.2.6-tests.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/hbase-it-1.2.6.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/hbase-prefix-tree-1.2.6.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/hbase-procedure-1.2.6.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/hbase-protocol-1.2.6.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/hbase-resource-bundle-1.2.6.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/hbase-rest-1.2.6.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/hbase-server-1.2.6-tests.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/hbase-server-1.2.6.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/hbase-shell-1.2.6.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/hbase-thrift-1.2.6.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/htrace-core-3.1.0-incubating.jar:/Users/a6/Applications/hbase-1.2.6/bin/../lib/httpclient-4.2.5.jar:/Users/a6/Applications/hbase-1.2.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    2018-05-15 17:31:23,336 INFO  [main] zookeeper.ZooKeeper: Client environment:java.library.path=/Users/a6/Applications/hadoop-2.6.5/lib/native
    2018-05-15 17:31:23,336 INFO  [main] zookeeper.ZooKeeper: Client environment:java.io.tmpdir=/var/folders/bm/dccwv2v97y75hdshqnh1bbpr0000gn/T/
    2018-05-15 17:31:23,336 INFO  [main] zookeeper.ZooKeeper: Client environment:java.compiler=<NA>
    2018-05-15 17:31:23,336 INFO  [main] zookeeper.ZooKeeper: Client environment:os.name=Mac OS X
    2018-05-15 17:31:23,336 INFO  [main] zookeeper.ZooKeeper: Client environment:os.arch=x86_64
    2018-05-15 17:31:23,336 INFO  [main] zookeeper.ZooKeeper: Client environment:os.version=10.13.2
    2018-05-15 17:31:23,337 INFO  [main] zookeeper.ZooKeeper: Client environment:user.name=a6
    2018-05-15 17:31:23,337 INFO  [main] zookeeper.ZooKeeper: Client environment:user.home=/Users/a6
    2018-05-15 17:31:23,337 INFO  [main] zookeeper.ZooKeeper: Client environment:user.dir=/Users/a6/Applications/hbase-1.2.6/bin
    2018-05-15 17:31:23,338 INFO  [main] zookeeper.ZooKeeper: Initiating client connection, connectString=localhost:2182 sessionTimeout=90000 watcher=hconnection-0x5ed731d00x0, quorum=localhost:2182, baseZNode=/hbase
    2018-05-15 17:31:23,360 INFO  [main-SendThread(localhost:2182)] zookeeper.ClientCnxn: Opening socket connection to server localhost/127.0.0.1:2182. Will not attempt to authenticate using SASL (unknown error)
    2018-05-15 17:31:23,361 INFO  [main-SendThread(localhost:2182)] zookeeper.ClientCnxn: Socket connection established to localhost/127.0.0.1:2182, initiating session
    2018-05-15 17:31:23,371 INFO  [main-SendThread(localhost:2182)] zookeeper.ClientCnxn: Session establishment complete on server localhost/127.0.0.1:2182, sessionid = 0x163615ea3e6000c, negotiated timeout = 40000
    2018-05-15 17:31:23,455 INFO  [main] util.RegionSizeCalculator: Calculating region sizes for table "emp".
    2018-05-15 17:31:23,834 INFO  [main] client.ConnectionManager$HConnectionImplementation: Closing master protocol: MasterService
    2018-05-15 17:31:23,834 INFO  [main] client.ConnectionManager$HConnectionImplementation: Closing zookeeper sessionid=0x163615ea3e6000c
    2018-05-15 17:31:23,837 INFO  [main] zookeeper.ZooKeeper: Session: 0x163615ea3e6000c closed
    2018-05-15 17:31:23,837 INFO  [main-EventThread] zookeeper.ClientCnxn: EventThread shut down
    2018-05-15 17:31:23,933 INFO  [main] mapreduce.JobSubmitter: number of splits:1
    2018-05-15 17:31:23,955 INFO  [main] Configuration.deprecation: io.bytes.per.checksum is deprecated. Instead, use dfs.bytes-per-checksum
    2018-05-15 17:31:24,115 INFO  [main] mapreduce.JobSubmitter: Submitting tokens for job: job_1526346976211_0003
    2018-05-15 17:31:24,513 INFO  [main] impl.YarnClientImpl: Submitted application application_1526346976211_0003
    2018-05-15 17:31:24,561 INFO  [main] mapreduce.Job: The url to track the job: http://localhost:8088/proxy/application_1526346976211_0003/
    2018-05-15 17:31:24,562 INFO  [main] mapreduce.Job: Running job: job_1526346976211_0003
    2018-05-15 17:31:36,842 INFO  [main] mapreduce.Job: Job job_1526346976211_0003 running in uber mode : false
    2018-05-15 17:31:36,844 INFO  [main] mapreduce.Job:  map 0% reduce 0%
    2018-05-15 17:31:43,965 INFO  [main] mapreduce.Job:  map 100% reduce 0%
    2018-05-15 17:31:44,980 INFO  [main] mapreduce.Job: Job job_1526346976211_0003 completed successfully
    2018-05-15 17:31:45,120 INFO  [main] mapreduce.Job: Counters: 43
    	File System Counters
    		FILE: Number of bytes read=0
    		FILE: Number of bytes written=139577
    		FILE: Number of read operations=0
    		FILE: Number of large read operations=0
    		FILE: Number of write operations=0
    		HDFS: Number of bytes read=64
    		HDFS: Number of bytes written=323
    		HDFS: Number of read operations=4
    		HDFS: Number of large read operations=0
    		HDFS: Number of write operations=2
    	Job Counters
    		Launched map tasks=1
    		Data-local map tasks=1
    		Total time spent by all maps in occupied slots (ms)=4842
    		Total time spent by all reduces in occupied slots (ms)=0
    		Total time spent by all map tasks (ms)=4842
    		Total vcore-seconds taken by all map tasks=4842
    		Total megabyte-seconds taken by all map tasks=4958208
    	Map-Reduce Framework
    		Map input records=1
    		Map output records=1
    		Input split bytes=64
    		Spilled Records=0
    		Failed Shuffles=0
    		Merged Map outputs=0
    		GC time elapsed (ms)=73
    		CPU time spent (ms)=0
    		Physical memory (bytes) snapshot=0
    		Virtual memory (bytes) snapshot=0
    		Total committed heap usage (bytes)=111149056
    	HBase Counters
    		BYTES_IN_REMOTE_RESULTS=0
    		BYTES_IN_RESULTS=210
    		MILLIS_BETWEEN_NEXTS=517
    		NOT_SERVING_REGION_EXCEPTION=0
    		NUM_SCANNER_RESTARTS=0
    		NUM_SCAN_RESULTS_STALE=0
    		REGIONS_SCANNED=1
    		REMOTE_RPC_CALLS=0
    		REMOTE_RPC_RETRIES=0
    		ROWS_FILTERED=0
    		ROWS_SCANNED=1
    		RPC_CALLS=3
    		RPC_RETRIES=0
    	File Input Format Counters
    		Bytes Read=0
    	File Output Format Counters
    		Bytes Written=323

查看生成的目录并查看导出到hdfs上的二进制数据

localhost:bin a6$ hadoop dfs -ls /hbase/emp_bak
    DEPRECATED: Use of this script to execute hdfs command is deprecated.
    Instead use the hdfs command for it.
    
    18/05/15 17:34:29 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
    Found 2 items
    -rw-r--r--   1 a6 supergroup          0 2018-05-15 17:31 /hbase/emp_bak/_SUCCESS
    -rw-r--r--   1 a6 supergroup        323 2018-05-15 17:31 /hbase/emp_bak/part-m-00000
    localhost:bin a6$ hadoop dfs -cat /hbase/emp_bak/*
    DEPRECATED: Use of this script to execute hdfs command is deprecated.
    Instead use the hdfs command for it.
    
    18/05/15 17:34:37 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
    SEQ1org.apache.hadoop.hbase.io.ImmutableBytesWritable%org.apache.hadoop.hbase.client.ResultI~
    �F�;�H��[$���1�
    ,
    personal datacity ����,(2	hyderabad
    '
    personal dataname ����,(2raju
    5
    1professional data
                      designation ����,(2manager
    .
    1professional datasalary ����,(250000 )

将hbase数据备份到本地文件

localhost:bin a6$ hbase org.apache.hadoop.hbase.mapreduce.Export emp file:///Users/a6/Applications/experiment_data/hbase_data/bak

**4）、创建备份到的目标hbase表**

create 'emp_bak','personal data','professional data'

**5)、将hdfs上的数据导入到备份目标表中**

localhost:bin a6$ hbase org.apache.hadoop.hbase.mapreduce.Driver import emp_bak /hbase/emp_bak/*
    SLF4J: Class path contains multiple SLF4J bindings.
    SLF4J: Found binding in [jar:file:/Users/a6/Applications/hbase-1.2.6/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class]
    SLF4J: Found binding in [jar:file:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/common/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class]
    SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
    SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]
    2018-05-15 17:37:07,154 WARN  [main] util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
    2018-05-15 17:37:08,045 INFO  [main] client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
    2018-05-15 17:37:09,852 INFO  [main] input.FileInputFormat: Total input paths to process : 1
    2018-05-15 17:37:09,907 INFO  [main] mapreduce.JobSubmitter: number of splits:1
    2018-05-15 17:37:10,026 INFO  [main] mapreduce.JobSubmitter: Submitting tokens for job: job_1526346976211_0005
    2018-05-15 17:37:10,384 INFO  [main] impl.YarnClientImpl: Submitted application application_1526346976211_0005
    2018-05-15 17:37:10,413 INFO  [main] mapreduce.Job: The url to track the job: http://localhost:8088/proxy/application_1526346976211_0005/
    2018-05-15 17:37:10,413 INFO  [main] mapreduce.Job: Running job: job_1526346976211_0005
    2018-05-15 17:37:18,621 INFO  [main] mapreduce.Job: Job job_1526346976211_0005 running in uber mode : false
    2018-05-15 17:37:18,622 INFO  [main] mapreduce.Job:  map 0% reduce 0%
    2018-05-15 17:37:25,705 INFO  [main] mapreduce.Job:  map 100% reduce 0%
    2018-05-15 17:37:25,716 INFO  [main] mapreduce.Job: Job job_1526346976211_0005 completed successfully
    2018-05-15 17:37:25,832 INFO  [main] mapreduce.Job: Counters: 30
    	File System Counters
    		FILE: Number of bytes read=0
    		FILE: Number of bytes written=139121
    		FILE: Number of read operations=0
    		FILE: Number of large read operations=0
    		FILE: Number of write operations=0
    		HDFS: Number of bytes read=436
    		HDFS: Number of bytes written=0
    		HDFS: Number of read operations=3
    		HDFS: Number of large read operations=0
    		HDFS: Number of write operations=0
    	Job Counters
    		Launched map tasks=1
    		Data-local map tasks=1
    		Total time spent by all maps in occupied slots (ms)=4804
    		Total time spent by all reduces in occupied slots (ms)=0
    		Total time spent by all map tasks (ms)=4804
    		Total vcore-seconds taken by all map tasks=4804
    		Total megabyte-seconds taken by all map tasks=4919296
    	Map-Reduce Framework
    		Map input records=1
    		Map output records=1
    		Input split bytes=113
    		Spilled Records=0
    		Failed Shuffles=0
    		Merged Map outputs=0
    		GC time elapsed (ms)=86
    		CPU time spent (ms)=0
    		Physical memory (bytes) snapshot=0
    		Virtual memory (bytes) snapshot=0
    		Total committed heap usage (bytes)=112197632
    	File Input Format Counters
    		Bytes Read=323
    	File Output Format Counters
    		Bytes Written=0
    2018-05-15 17:37:25,842 INFO  [main] mapreduce.Job: Running job: job_1526346976211_0005
    2018-05-15 17:37:25,848 INFO  [main] mapreduce.Job: Job job_1526346976211_0005 running in uber mode : false
    2018-05-15 17:37:25,849 INFO  [main] mapreduce.Job:  map 100% reduce 0%
    2018-05-15 17:37:25,855 INFO  [main] mapreduce.Job: Job job_1526346976211_0005 completed successfully
    2018-05-15 17:37:25,862 INFO  [main] mapreduce.Job: Counters: 30
    	File System Counters
    		FILE: Number of bytes read=0
    		FILE: Number of bytes written=139121
    		FILE: Number of read operations=0
    		FILE: Number of large read operations=0
    		FILE: Number of write operations=0
    		HDFS: Number of bytes read=436
    		HDFS: Number of bytes written=0
    		HDFS: Number of read operations=3
    		HDFS: Number of large read operations=0
    		HDFS: Number of write operations=0
    	Job Counters
    		Launched map tasks=1
    		Data-local map tasks=1
    		Total time spent by all maps in occupied slots (ms)=4804
    		Total time spent by all reduces in occupied slots (ms)=0
    		Total time spent by all map tasks (ms)=4804
    		Total vcore-seconds taken by all map tasks=4804
    		Total megabyte-seconds taken by all map tasks=4919296
    	Map-Reduce Framework
    		Map input records=1
    		Map output records=1
    		Input split bytes=113
    		Spilled Records=0
    		Failed Shuffles=0
    		Merged Map outputs=0
    		GC time elapsed (ms)=86
    		CPU time spent (ms)=0
    		Physical memory (bytes) snapshot=0
    		Virtual memory (bytes) snapshot=0
    		Total committed heap usage (bytes)=112197632
    	File Input Format Counters
    		Bytes Read=323
    	File Output Format Counters
    		Bytes Written=0

这样基本就完成了hbase表中的数据我们可以转化为mapreduce任务进程开始导出导入。当然也可以这么备份的。

**6)、最后我们仔细看一下hbase导出和导入的关键命令参数**

localhost:bin a6$ hbase org.apache.hadoop.hbase.mapreduce.Export
    ERROR: Wrong number of arguments: 0
    Usage: Export [-D <property=value>]* <tablename> <outputdir> [<versions> [<starttime> [<endtime>]] [^[regex pattern] or [Prefix] to filter]]
    
      Note: -D properties will be applied to the conf used.
      For example:
       -D mapreduce.output.fileoutputformat.compress=true
       -D mapreduce.output.fileoutputformat.compress.codec=org.apache.hadoop.io.compress.GzipCodec
       -D mapreduce.output.fileoutputformat.compress.type=BLOCK
      Additionally, the following SCAN properties can be specified
      to control/limit what is exported..
       -D hbase.mapreduce.scan.column.family=<familyName>
       -D hbase.mapreduce.include.deleted.rows=true
       -D hbase.mapreduce.scan.row.start=<ROWSTART>
       -D hbase.mapreduce.scan.row.stop=<ROWSTOP>
    For performance consider the following properties:
       -Dhbase.client.scanner.caching=100
       -Dmapreduce.map.speculative=false
       -Dmapreduce.reduce.speculative=false
    For tables with very wide rows consider setting the batch size as below:
       -Dhbase.export.scanner.batch=10
    localhost:bin a6$ hbase org.apache.hadoop.hbase.mapreduce.Driver import
    ERROR: Wrong number of arguments: 0
    Usage: Import [options] <tablename> <inputdir>
    By default Import will load data directly into HBase. To instead generate
    HFiles of data to prepare for a bulk data load, pass the option:
      -Dimport.bulk.output=/path/for/output
     To apply a generic org.apache.hadoop.hbase.filter.Filter to the input, use
      -Dimport.filter.class=<name of filter class>
      -Dimport.filter.args=<comma separated list of args for filter
     NOTE: The filter will be applied BEFORE doing key renames via the HBASE_IMPORTER_RENAME_CFS property. Futher, filters will only use the Filter#filterRowKey(byte[] buffer, int offset, int length) method to identify  whether the current row needs to be ignored completely for processing and  Filter#filterKeyValue(KeyValue) method to determine if the KeyValue should be added; Filter.ReturnCode#INCLUDE and #INCLUDE_AND_NEXT_COL will be considered as including the KeyValue.
    To import data exported from HBase 0.94, use
      -Dhbase.import.version=0.94
    For performance consider the following options:
      -Dmapreduce.map.speculative=false
      -Dmapreduce.reduce.speculative=false
      -Dimport.wal.durability=<Used while writing data to hbase. Allowed values are the supported durability values like SKIP_WAL/ASYNC_WAL/SYNC_WAL/...>

**二、基于Hbase snapshot数据快速备份方法**

**1.Snapshot备份的优点是什么？**

**HBase以往数据的备份基于distcp或者copyTable等工具，这些备份机制或多或少对当前的online数据读写存在一定的影响，Snapshot提供了一种快速的数据备份方式，无需进行数据copy。  
参见下图  
![022335hjqjm2xmgdxjbjgx.jpg][]  
2.HBase数据的备份的方式有几种？Snapshot包括在线和离线的，他们之间有什么区别？**

**Snapshot包括在线和离线的   
(1)离线方式是disabletable，由HBase Master遍历HDFS中的table metadata和hfiles，建立对他们的引用。  
(2)在线方式是enabletable，由Master指示region server进行snapshot操作，在此过程中，master和regionserver之间类似两阶段commit的snapshot操作。  
![022336c9gtwnon2fazeaon.jpg][]**

**HFile是不可变的，只能append和delete， region的split和compact，都不会对snapshot引用的文件做删除(除非删除snapshot文件)，这些文件会归档到archive目录下，进而需要重新调整snapshot文件中相关hfile的引用位置关系。**

**![022336dtxdevx0ojx0f8ef.jpg][]   
基于snapshot文件，可以做clone一个新表，restore，export到另外一个集群中操作；其中clone生成的新表只是增加元数据，相关的数据文件还是复用snapshot指定的数据文件  
参见clone新表操作示意图：  
  
![022337emnjk8fmxjxszns8.jpg][]**

**3.snashot的shell的命令都由哪些？如何删除、查看快照？如何导出到另外一个集群？**

snashot相关的操作命令如下：

1)创建快照(查看快照->查看快照snapshot命令相关参数->创建快照—>查看快照)

hbase(main):002:0> list_snapshots
    SNAPSHOT                                                              TABLE + CREATION TIME
    0 row(s) in 0.0290 seconds
    
    => []
    hbase(main):003:0> snapshot
    
    ERROR: wrong number of arguments (0 for 2)
    
    Here is some help for this command:
    Take a snapshot of specified table. Examples:
    
      hbase> snapshot 'sourceTable', 'snapshotName'
      hbase> snapshot 'namespace:sourceTable', 'snapshotName', {SKIP_FLUSH => true}
    
    
    hbase(main):004:0> snapshot 'emp','emp_snapshot'
    0 row(s) in 0.3730 seconds
    
    hbase(main):005:0> list_snapshots
    SNAPSHOT                                                              TABLE + CREATION TIME
     emp_snapshot                                                         emp (Wed May 16 09:44:53 +0800 2018)
    1 row(s) in 0.0190 seconds
    
    => ["emp_snapshot"]

2)删除并查看快照

hbase(main):006:0> delete_snapshot 'emp_snapshot'
    0 row(s) in 0.0390 seconds
    
    hbase(main):007:0> list_snapshots
    SNAPSHOT                                                              TABLE + CREATION TIME
    0 row(s) in 0.0040 seconds
    
    => []

3)基于快照，clone一个新表

hbase(main):011:0> clone_snapshot 'emp_snapshot','new_emp'
    0 row(s) in 0.5290 seconds
    
    hbase(main):013:0> scan 'new_emp'
    ROW                                                                   COLUMN+CELL
     1                                                                    column=personal data:city, timestamp=1526269334560, value=hyderabad
     1                                                                    column=personal data:name, timestamp=1526269326929, value=raju
     1                                                                    column=professional data:designation, timestamp=1526269345044, value=manager
     1                                                                    column=professional data:salary, timestamp=1526269352605, value=50000
    1 row(s) in 0.1050 seconds
    
    hbase(main):014:0> desc 'new_emp'
    Table new_emp is ENABLED
    new_emp
    COLUMN FAMILIES DESCRIPTION
    {NAME => 'personal data', BLOOMFILTER => 'ROW', VERSIONS => '1', IN_MEMORY => 'false', KEEP_DELETED_CELLS => 'FALSE', DATA_BLOCK_ENCODING => 'NONE', TTL => 'FOREVER', COMPRESSION => 'NONE', MIN_VERSIONS => '0', BLOCKCACHE => 'true', BLOCKSIZE => '65536', REPLICATION_SCOPE
     => '0'}
    {NAME => 'professional data', BLOOMFILTER => 'ROW', VERSIONS => '1', IN_MEMORY => 'false', KEEP_DELETED_CELLS => 'FALSE', DATA_BLOCK_ENCODING => 'NONE', TTL => 'FOREVER', COMPRESSION => 'NONE', MIN_VERSIONS => '0', BLOCKCACHE => 'true', BLOCKSIZE => '65536', REPLICATION_S
    COPE => '0'}
    2 row(s) in 0.0370 seconds

4)基于快照恢复表(原hbase表emp需要删除)

hbase(main):027:0> list
    TABLE
    new_emp
    t1
    test
    3 row(s) in 0.0130 seconds
    
    => ["new_emp", "t1", "test"]
    hbase(main):028:0> list_snapshots
    SNAPSHOT                                                              TABLE + CREATION TIME
     emp_snapshot                                                         emp (Wed May 16 09:45:25 +0800 2018)
    1 row(s) in 0.0130 seconds
    
    => ["emp_snapshot"]
    hbase(main):029:0> restore_snapshot 'emp_snapshot'
    0 row(s) in 0.3700 seconds
    
    hbase(main):030:0> list
    TABLE
    emp
    new_emp
    t1
    test
    4 row(s) in 0.0240 seconds
    
    => ["emp", "new_emp", "t1", "test"]

5)基于快照将数据导出到另外一个集群中的本地文件中

利用mapreduce job将emp\_snapshot这个snapshot 导出到本地目录/Users/a6/Applications/experiment\_data/hbase\_data中的bak\_emp\_snapshot(不存在)

localhost:bin a6$ hbase org.apache.hadoop.hbase.snapshot.ExportSnapshot -snapshot 'emp_snapshot' -copy-to file:///Users/a6/Applications/experiment_data/hbase_data/bak_emp_snapshot -mappers 16
    SLF4J: Class path contains multiple SLF4J bindings.
    SLF4J: Found binding in [jar:file:/Users/a6/Applications/hbase-1.2.6/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class]
    SLF4J: Found binding in [jar:file:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/common/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class]
    SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
    SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]
    2018-05-16 10:21:47,310 WARN  [main] util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
    2018-05-16 10:21:47,633 INFO  [main] snapshot.ExportSnapshot: Copy Snapshot Manifest
    2018-05-16 10:21:47,922 INFO  [main] client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
    2018-05-16 10:21:50,233 INFO  [main] snapshot.ExportSnapshot: Loading Snapshot 'emp_snapshot' hfile list
    2018-05-16 10:21:50,547 INFO  [main] mapreduce.JobSubmitter: number of splits:2
    2018-05-16 10:21:50,732 INFO  [main] mapreduce.JobSubmitter: Submitting tokens for job: job_1526434993990_0001
    2018-05-16 10:21:51,182 INFO  [main] impl.YarnClientImpl: Submitted application application_1526434993990_0001
    2018-05-16 10:21:51,268 INFO  [main] mapreduce.Job: The url to track the job: http://localhost:8088/proxy/application_1526434993990_0001/
    2018-05-16 10:21:51,269 INFO  [main] mapreduce.Job: Running job: job_1526434993990_0001
    2018-05-16 10:22:02,425 INFO  [main] mapreduce.Job: Job job_1526434993990_0001 running in uber mode : false
    2018-05-16 10:22:02,427 INFO  [main] mapreduce.Job:  map 0% reduce 0%
    2018-05-16 10:22:09,722 INFO  [main] mapreduce.Job:  map 50% reduce 0%
    2018-05-16 10:22:10,731 INFO  [main] mapreduce.Job:  map 100% reduce 0%
    2018-05-16 10:22:10,740 INFO  [main] mapreduce.Job: Job job_1526434993990_0001 completed successfully
    2018-05-16 10:22:10,848 INFO  [main] mapreduce.Job: Counters: 37
    	File System Counters
    		FILE: Number of bytes read=9985
    		FILE: Number of bytes written=291407
    		FILE: Number of read operations=0
    		FILE: Number of large read operations=0
    		FILE: Number of write operations=0
    		HDFS: Number of bytes read=408
    		HDFS: Number of bytes written=0
    		HDFS: Number of read operations=2
    		HDFS: Number of large read operations=0
    		HDFS: Number of write operations=0
    	Job Counters
    		Launched map tasks=2
    		Other local map tasks=2
    		Total time spent by all maps in occupied slots (ms)=9683
    		Total time spent by all reduces in occupied slots (ms)=0
    		Total time spent by all map tasks (ms)=9683
    		Total vcore-seconds taken by all map tasks=9683
    		Total megabyte-seconds taken by all map tasks=9915392
    	Map-Reduce Framework
    		Map input records=2
    		Map output records=0
    		Input split bytes=408
    		Spilled Records=0
    		Failed Shuffles=0
    		Merged Map outputs=0
    		GC time elapsed (ms)=155
    		CPU time spent (ms)=0
    		Physical memory (bytes) snapshot=0
    		Virtual memory (bytes) snapshot=0
    		Total committed heap usage (bytes)=212860928
    	org.apache.hadoop.hbase.snapshot.ExportSnapshot$Counter
    		BYTES_COPIED=9985
    		BYTES_EXPECTED=9985
    		BYTES_SKIPPED=0
    		COPY_FAILED=0
    		FILES_COPIED=2
    		FILES_SKIPPED=0
    		MISSING_FILES=0
    	File Input Format Counters
    		Bytes Read=0
    	File Output Format Counters
    		Bytes Written=0
    2018-05-16 10:22:10,851 INFO  [main] snapshot.ExportSnapshot: Finalize the Snapshot Export
    2018-05-16 10:22:10,852 INFO  [main] snapshot.ExportSnapshot: Verify snapshot integrity
    2018-05-16 10:22:10,875 INFO  [main] snapshot.ExportSnapshot: Export Completed: emp_snapshot

**查看快照备份到本地的备份文件结构:**

localhost:hbase_data a6$ ls  -R
    bak_emp_snapshot
    
    ./bak_emp_snapshot:
    archive
    
    ./bak_emp_snapshot/archive:
    data
    
    ./bak_emp_snapshot/archive/data:
    default
    
    ./bak_emp_snapshot/archive/data/default:
    emp
    
    ./bak_emp_snapshot/archive/data/default/emp:
    f8d3b4ead1603d0e9350dc426fce7fd7
    
    ./bak_emp_snapshot/archive/data/default/emp/f8d3b4ead1603d0e9350dc426fce7fd7:
    personal data		professional data
    
    ./bak_emp_snapshot/archive/data/default/emp/f8d3b4ead1603d0e9350dc426fce7fd7/personal data:
    9111be6b05e746ddb8507e8daf5a4eb0
    
    ./bak_emp_snapshot/archive/data/default/emp/f8d3b4ead1603d0e9350dc426fce7fd7/professional data:
    c264d32ef37b4b6f9953b388f007d059
    localhost:hbase_data a6$

6)基于快照将数据导出到另外一个集群中的hdfs上

localhost:bin a6$ hbase org.apache.hadoop.hbase.snapshot.ExportSnapshot -snapshot 'emp_snapshot' -copy-to hdfs:///hbase/bak_emp_snapshot -mappers 16
    SLF4J: Class path contains multiple SLF4J bindings.
    SLF4J: Found binding in [jar:file:/Users/a6/Applications/hbase-1.2.6/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class]
    SLF4J: Found binding in [jar:file:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/common/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class]
    SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
    SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]
    2018-05-16 10:29:02,343 WARN  [main] util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
    2018-05-16 10:29:03,034 INFO  [main] snapshot.ExportSnapshot: Copy Snapshot Manifest
    2018-05-16 10:29:03,423 INFO  [main] client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
    2018-05-16 10:29:04,368 INFO  [main] snapshot.ExportSnapshot: Loading Snapshot 'emp_snapshot' hfile list
    2018-05-16 10:29:04,730 INFO  [main] mapreduce.JobSubmitter: number of splits:2
    2018-05-16 10:29:04,863 INFO  [main] mapreduce.JobSubmitter: Submitting tokens for job: job_1526434993990_0002
    2018-05-16 10:29:05,129 INFO  [main] impl.YarnClientImpl: Submitted application application_1526434993990_0002
    2018-05-16 10:29:05,160 INFO  [main] mapreduce.Job: The url to track the job: http://localhost:8088/proxy/application_1526434993990_0002/
    2018-05-16 10:29:05,160 INFO  [main] mapreduce.Job: Running job: job_1526434993990_0002
    2018-05-16 10:29:13,260 INFO  [main] mapreduce.Job: Job job_1526434993990_0002 running in uber mode : false
    2018-05-16 10:29:13,262 INFO  [main] mapreduce.Job:  map 0% reduce 0%
    2018-05-16 10:29:18,354 INFO  [main] mapreduce.Job: Task Id : attempt_1526434993990_0002_m_000000_0, Status : FAILED
    Error: Java heap space
    2018-05-16 10:29:19,377 INFO  [main] mapreduce.Job: Task Id : attempt_1526434993990_0002_m_000001_0, Status : FAILED
    Error: Java heap space
    2018-05-16 10:29:25,432 INFO  [main] mapreduce.Job:  map 50% reduce 0%
    2018-05-16 10:29:26,438 INFO  [main] mapreduce.Job:  map 100% reduce 0%
    2018-05-16 10:29:26,450 INFO  [main] mapreduce.Job: Job job_1526434993990_0002 completed successfully
    2018-05-16 10:29:26,554 INFO  [main] mapreduce.Job: Counters: 38
    	File System Counters
    		FILE: Number of bytes read=9985
    		FILE: Number of bytes written=281240
    		FILE: Number of read operations=0
    		FILE: Number of large read operations=0
    		FILE: Number of write operations=0
    		HDFS: Number of bytes read=408
    		HDFS: Number of bytes written=9985
    		HDFS: Number of read operations=8
    		HDFS: Number of large read operations=0
    		HDFS: Number of write operations=8
    	Job Counters
    		Failed map tasks=2
    		Launched map tasks=4
    		Other local map tasks=4
    		Total time spent by all maps in occupied slots (ms)=17871
    		Total time spent by all reduces in occupied slots (ms)=0
    		Total time spent by all map tasks (ms)=17871
    		Total vcore-seconds taken by all map tasks=17871
    		Total megabyte-seconds taken by all map tasks=18299904
    	Map-Reduce Framework
    		Map input records=2
    		Map output records=0
    		Input split bytes=408
    		Spilled Records=0
    		Failed Shuffles=0
    		Merged Map outputs=0
    		GC time elapsed (ms)=235
    		CPU time spent (ms)=0
    		Physical memory (bytes) snapshot=0
    		Virtual memory (bytes) snapshot=0
    		Total committed heap usage (bytes)=257949696
    	org.apache.hadoop.hbase.snapshot.ExportSnapshot$Counter
    		BYTES_COPIED=9985
    		BYTES_EXPECTED=9985
    		BYTES_SKIPPED=0
    		COPY_FAILED=0
    		FILES_COPIED=2
    		FILES_SKIPPED=0
    		MISSING_FILES=0
    	File Input Format Counters
    		Bytes Read=0
    	File Output Format Counters
    		Bytes Written=0
    2018-05-16 10:29:26,556 INFO  [main] snapshot.ExportSnapshot: Finalize the Snapshot Export
    2018-05-16 10:29:26,563 INFO  [main] snapshot.ExportSnapshot: Verify snapshot integrity
    2018-05-16 10:29:26,647 INFO  [main] snapshot.ExportSnapshot: Export Completed: emp_snapshot

**检验并查看hdfs文件:**

localhost:hbase_data a6$ hadoop dfs -ls /hbase/
    DEPRECATED: Use of this script to execute hdfs command is deprecated.
    Instead use the hdfs command for it.
    
    18/05/16 10:29:34 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
    Found 2 items
    drwxr-xr-x   - a6 supergroup          0 2018-05-16 10:29 /hbase/bak_emp_snapshot
    drwxr-xr-x   - a6 supergroup          0 2018-05-15 17:31 /hbase/emp_bak
    localhost:hbase_data a6$ hadoop dfs -ls /hbase/bak_emp_snapshot
    DEPRECATED: Use of this script to execute hdfs command is deprecated.
    Instead use the hdfs command for it.
    
    18/05/16 10:29:45 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
    Found 2 items
    drwxr-xr-x   - a6 supergroup          0 2018-05-16 10:29 /hbase/bak_emp_snapshot/.hbase-snapshot
    drwxr-xr-x   - a6 supergroup          0 2018-05-16 10:29 /hbase/bak_emp_snapshot/archive
    localhost:hbase_data a6$

**查看生成快照文件的目录结构及其文件大小**

localhost:hbase_data a6$ hadoop dfs -ls -R  /hbase/bak_emp_snapshot
    DEPRECATED: Use of this script to execute hdfs command is deprecated.
    Instead use the hdfs command for it.
    
    18/05/16 10:34:15 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
    drwxr-xr-x   - a6 supergroup          0 2018-05-16 10:29 /hbase/bak_emp_snapshot/.hbase-snapshot
    drwxr-xr-x   - a6 supergroup          0 2018-05-16 10:29 /hbase/bak_emp_snapshot/.hbase-snapshot/.tmp
    drwxr-xr-x   - a6 supergroup          0 2018-05-16 10:29 /hbase/bak_emp_snapshot/.hbase-snapshot/emp_snapshot
    -rw-r--r--   1 a6 supergroup          0 2018-05-16 10:29 /hbase/bak_emp_snapshot/.hbase-snapshot/emp_snapshot/.inprogress
    -rw-r--r--   1 a6 supergroup         30 2018-05-16 10:29 /hbase/bak_emp_snapshot/.hbase-snapshot/emp_snapshot/.snapshotinfo
    -rw-r--r--   1 a6 supergroup        703 2018-05-16 10:29 /hbase/bak_emp_snapshot/.hbase-snapshot/emp_snapshot/data.manifest
    drwxr-xr-x   - a6 supergroup          0 2018-05-16 10:29 /hbase/bak_emp_snapshot/archive
    drwxr-xr-x   - a6 supergroup          0 2018-05-16 10:29 /hbase/bak_emp_snapshot/archive/data
    drwxr-xr-x   - a6 supergroup          0 2018-05-16 10:29 /hbase/bak_emp_snapshot/archive/data/default
    drwxr-xr-x   - a6 supergroup          0 2018-05-16 10:29 /hbase/bak_emp_snapshot/archive/data/default/emp
    drwxr-xr-x   - a6 supergroup          0 2018-05-16 10:29 /hbase/bak_emp_snapshot/archive/data/default/emp/f8d3b4ead1603d0e9350dc426fce7fd7
    drwxr-xr-x   - a6 supergroup          0 2018-05-16 10:29 /hbase/bak_emp_snapshot/archive/data/default/emp/f8d3b4ead1603d0e9350dc426fce7fd7/personal data
    -rw-rw-rw-   1 a6 staff            4976 2018-05-16 10:29 /hbase/bak_emp_snapshot/archive/data/default/emp/f8d3b4ead1603d0e9350dc426fce7fd7/personal data/9111be6b05e746ddb8507e8daf5a4eb0
    drwxr-xr-x   - a6 supergroup          0 2018-05-16 10:29 /hbase/bak_emp_snapshot/archive/data/default/emp/f8d3b4ead1603d0e9350dc426fce7fd7/professional data
    -rw-rw-rw-   1 a6 staff            5009 2018-05-16 10:29 /hbase/bak_emp_snapshot/archive/data/default/emp/f8d3b4ead1603d0e9350dc426fce7fd7/professional data/c264d32ef37b4b6f9953b388f007d059
    localhost:hbase_data a6$

参考网址: https://blog.csdn.net/yangbutao/article/details/12911487

其他备份方法:https://www.cnblogs.com/ios123/p/6399699.html

[022335hjqjm2xmgdxjbjgx.jpg]: /images/20220525/c0c9ca3275854a23b2ebf2d38a2c247b.png
[022336c9gtwnon2fazeaon.jpg]: /images/20220525/8369121be81f4fdfa16b47bf61958e2b.png
[022336dtxdevx0ojx0f8ef.jpg]: /images/20220525/7fba7d6297a947f9b8359aebf2493033.png
[022337emnjk8fmxjxszns8.jpg]: /images/20220525/933fa14c28be44cd97dd0ffef4ce5b65.png