Hbase表兩種數據備份方法-導入和導出示例[未測試]

Hbase表兩種數據備份方法-導入和導出示例

本文將提供兩種備份方法 ——

1) 基於Hbase提供的類對hbase中某張表進行備份

2) 基於Hbase snapshot數據快速備份方法

場合：因爲線上和測試環境是分離的，沒法在測試環境訪問線上庫，因此須要將線上的hbase表導出一部分到測試環境中的hbase表，這就是本文的由來。

1、基於hbase提供的類對hbase中某張表進行備份

本文使用hbase提供的類把hbase中某張表的數據導出hdfs，以後再導出到測試hbase表中。

首先介紹一下相關參數選項：

(1)  從hbase表導出(# 默認不寫file://的時候就是導出到hdfs上了  )

HBase數據導出到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數據導出到本地文件
hbase org.apache.hadoop.hbase.mapreduce.Export emp /hbase/emp_bak
(2) 導入hbase表(# 默認不寫file://的時候就是導出到hdfs上了  )

將hdfs上的數據導入到備份目標表中
localhost:bin a6$ hbase org.apache.hadoop.hbase.mapreduce.Driver import emp_bak /hbase/emp_bak/*
將本地文件上的數據導入到備份目標表中
hbase org.apache.hadoop.hbase.mapreduce.Driver import emp_bak file:///Users/a6/Applications/experiment_data/hbase_data/bak/*
(3) 導出時能夠限制scanner.batch的大小
若是在hbase中的一個row出現大量的數據，那麼導出時會報出ScannerTimeoutException的錯誤。這時候須要設置hbase.export.scaaner.batch 這個參數。這樣導出時的錯誤就能夠避免了。

hbase org.apache.hadoop.hbase.mapreduce.Export -Dhbase.export.scanner.batch=2000  emp file:///Users/a6/Applications/experiment_data/hbase_data/bak
(4)爲了節省空間可使用compress選項

hbase的數據導出的時候，若是不適用compress的選項，數據量的大小可能相差5倍。所以使用compress的選項，備份數據的時候是能夠節省很多空間的。
而且本人測試了compress選項的導出速度，和無此選項時差異不大（幾乎無差異）：

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       
經過添加compress選項，最終導出文件的大小由335字節變成了325字節，
File Output Format Counters                             File Output Format Counters
Bytes Written=335                                Bytes Written=323     
(5)導出指定行鍵範圍和列族

在公司準備要更換數據中心，須要將hbase數據庫中的數據進行遷移。雖然進行hbase數據庫數據遷移時，使用其自帶的工具import和export是很方便的。只不過，在遷移大量數據時，可能須要運行很長的時間，甚至可能出錯。這時，是能夠經過指定行鍵範圍和列族，來減小單次export工具的運行時間。能夠看出，支持的選項有好幾個。假如，咱們想導出表test的數據，且只要列族Info，行鍵範圍在000到001之間，能夠這樣寫：

這樣就能夠了，且數據將會保存在hdfs中。
經過指定列族和行鍵範圍，能夠只導出部分數據，避免export啓動的mapreduce任務運行時間過長。也就是能夠分屢次導出數據。
./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
閒話少敘，例子就來：

查到了HBase自帶的export/import機制能夠實現Backup Restore功能。並且能夠實現增量備份。
原理都是用了MapReduce來實現的。
一、Export是以表爲單位導出數據的，若想完成整庫的備份須要執行n遍。
二、Export在shell中的調用方式相似以下格式：
./hbase org.apache.hadoop.hbase.mapreduce.Export 表名 備份路徑 （版本號） （起始時間戳） （結束時間戳）
括號內爲可選項，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
備份 emp 這張表到 /hbase/emp_bak 目錄下（最後一級目錄必須由Export本身建立），版本號爲1，備份記錄從123456789這個時間戳開始到當前時間內全部的執行過put操做的記錄。
注意：爲何是全部put操做記錄？由於在備份時是掃描全部表中全部時間戳大於等於123456789這個值的記錄並導出。若是是delete操做，則表中這條記錄已經刪除，掃描時也沒法獲取這條記錄信息
當不指定時間戳時，備份的就是當前完整表中的數據。

1）、建立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）、插入數據並查看數據

將第一行的值插入到emp表以下所示。
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

插入完成整個表格，會獲得下面的輸出。
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）、將hbase表emp的數據導出到hdfs的路徑/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
查看生成的目錄並查看導出到hdfs上的二進制數據
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��[$���1�
,
personal datacity ����,(2    hyderabad
'
personal dataname ����,(2raju
5
1professional data
                  designation ����,(2manager
.
1professional datasalary ����,(250000 )
將hbase數據備份到本地文件
localhost:bin a6$ hbase org.apache.hadoop.hbase.mapreduce.Export emp file:///Users/a6/Applications/experiment_data/hbase_data/bak
4）、建立備份到的目標hbase表
create 'emp_bak','personal data','professional data'
5)、將hdfs上的數據導入到備份目標表中

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
這樣基本就完成了hbase表中的數據咱們能夠轉化爲mapreduce任務進程開始導出導入。固然也能夠這麼備份的。

6)、最後咱們仔細看一下hbase導出和導入的關鍵命令參數

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/...>
2、基於Hbase snapshot數據快速備份方法

1.Snapshot備份的優勢是什麼？

HBase以往數據的備份基於distcp或者copyTable等工具，這些備份機制或多或少對當前的online數據讀寫存在必定的影響，Snapshot提供了一種快速的數據備份方式，無需進行數據copy。
參見下圖

2.HBase數據的備份的方式有幾種？Snapshot包括在線和離線的，他們之間有什麼區別？

Snapshot包括在線和離線的 
(1)離線方式是disabletable，由HBase Master遍歷HDFS中的table metadata和hfiles，創建對他們的引用。
(2)在線方式是enabletable，由Master指示region server進行snapshot操做，在此過程當中，master和regionserver之間相似兩階段commit的snapshot操做。

HFile是不可變的，只能append和delete， region的split和compact，都不會對snapshot引用的文件作刪除(除非刪除snapshot文件)，這些文件會歸檔到archive目錄下，進而須要從新調整snapshot文件中相關hfile的引用位置關係。

 
基於snapshot文件，能夠作clone一個新表，restore，export到另一個集羣中操做；其中clone生成的新表只是增長元數據，相關的數據文件仍是複用snapshot指定的數據文件
參見clone新表操做示意圖：

3.snashot的shell的命令都由哪些？如何刪除、查看快照？如何導出到另一個集羣？

snashot相關的操做命令以下：

1)建立快照(查看快照->查看快照snapshot命令相關參數->建立快照—>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)刪除並查看快照

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)基於快照，clone一個新表

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)基於快照恢復表(原hbase表emp須要刪除)

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)基於快照將數據導出到另一個集羣中的本地文件中

利用mapreduce job將emp_snapshot這個snapshot 導出到本地目錄/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
查看快照備份到本地的備份文件結構:

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)基於快照將數據導出到另一個集羣中的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$Counter
        BYTES_COPIED=9985
        BYTES_EXPECTED=9985
        BYTES_SKIPPED=0
        COPY_FAILED=0
        FILES_COPIED=2
        FILES_SKIPPED=0
        MISSING_FILES=0
    File Input Format Counters
        Bytes Read=0
    File Output Format Counters
        Bytes Written=0
2018-05-16 10:29:26,556 INFO  [main] snapshot.ExportSnapshot: Finalize the Snapshot Export
2018-05-16 10:29:26,563 INFO  [main] snapshot.ExportSnapshot: Verify snapshot integrity
2018-05-16 10:29:26,647 INFO  [main] snapshot.ExportSnapshot: Export Completed: emp_snapshot
檢驗並查看hdfs文件:
localhost:hbase_data a6$ 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錄結構及其文件大小
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$ 參考網址: https://blog.csdn.net/yangbutao/article/details/12911487 其餘備份方法:https://www.cnblogs.com/ios123/p/6399699.html