Hbase表两种数据备份方法-导入和导出示例
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 seconds3）、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/bak4）、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
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。
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。
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：
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 seconds4)基于快照恢复表(原hbase表emp需要删除)
hbase(main):027:0>gt; 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$Counter
BYTES_COPIED=9985
BYTES_EXPECTED=9985
BYTES_SKIPPED=0
COPY_FAILED=0
FILES_COPIED=2
FILES_SKIPPED=0
MISSING_FILES=0
File Input Format Counters
Bytes Read=0
File Output Format Counters
Bytes Written=0
2018-05-16 10:29:26,556 INFO [main] snapshot.ExportSnapshot: Finalize the Snapshot Export
2018-05-16 10:29:26,563 INFO [main] snapshot.ExportSnapshot: Verify snapshot integrity
2018-05-16 10:29:26,647 INFO [main] snapshot.ExportSnapshot: Export Completed: emp_snapshotlocalhost:hbase_data a6$ 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