Hbase表两种数据备份方法-导入和导出示例[未测试]

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 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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lib/jersey-core-1.9.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/hdfs/lib/jsp-api-2.1.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/hdfs/lib/commons-codec-1.4.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/hdfs/lib/netty-3.6.2.Final.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/hdfs/lib/jetty-6.1.26.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/hdfs/lib/jersey-server-1.9.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/hdfs/lib/asm-3.2.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/hdfs/lib/commons-lang-2.6.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/hdfs/lib/commons-el-1.0.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/hdfs/lib/jackson-mapper-asl-1.9.13.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/hdfs/lib/commons-daemon-1.0.13.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/hdfs/hadoop-hdfs-2.6.5-tests.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/hdfs/hadoop-hdfs-2.6.5.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/hdfs/hadoop-hdfs-nfs-2.6.5.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/lib/jaxb-impl-2.2.3-1.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/lib/jsr305-1.3.9.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/lib/activation-1.1.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/lib/aopalliance-1.0.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/lib/guice-servlet-3.0.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/lib/xz-1.0.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/lib/commons-httpclient-3.1.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/lib/stax-api-1.0-2.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/lib/jline-0.9.94.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/lib/jaxb-api-2.2.2.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/lib/jackson-jaxrs-1.9.13.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/lib/commons-logging-1.1.3.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/lib/jersey-json-1.9.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/lib/log4j-1.2.17.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/lib/commons-cli-1.2.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/lib/servlet-api-2.5.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/lib/protobuf-java-2.5.0.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/lib/jackson-xc-1.9.13.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/lib/jetty-util-6.1.26.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/lib/guava-11.0.2.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/lib/commons-compress-1.4.1.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/lib/commons-io-2.4.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/lib/jackson-core-asl-1.9.13.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/lib/jersey-core-1.9.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/lib/commons-codec-1.4.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/lib/netty-3.6.2.Final.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/lib/jetty-6.1.26.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/lib/jersey-server-1.9.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/lib/guice-3.0.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/lib/jersey-client-1.9.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/lib/jersey-guice-1.9.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/lib/zookeeper-3.4.6.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/lib/commons-collections-3.2.2.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/lib/jettison-1.1.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/lib/asm-3.2.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/lib/commons-lang-2.6.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/lib/leveldbjni-all-1.8.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/lib/jackson-mapper-asl-1.9.13.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/lib/javax.inject-1.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/hadoop-yarn-common-2.6.5.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/hadoop-yarn-server-web-proxy-2.6.5.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/hadoop-yarn-server-nodemanager-2.6.5.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/hadoop-yarn-server-resourcemanager-2.6.5.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/hadoop-yarn-server-common-2.6.5.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/hadoop-yarn-client-2.6.5.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/hadoop-yarn-registry-2.6.5.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/hadoop-yarn-applications-distributedshell-2.6.5.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/hadoop-yarn-server-applicationhistoryservice-2.6.5.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/hadoop-yarn-applications-unmanaged-am-launcher-2.6.5.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/hadoop-yarn-server-tests-2.6.5.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/yarn/hadoop-yarn-api-2.6.5.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/mapreduce/lib/aopalliance-1.0.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/mapreduce/lib/guice-servlet-3.0.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/mapreduce/lib/xz-1.0.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/mapreduce/lib/junit-4.11.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/mapreduce/lib/avro-1.7.4.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/mapreduce/lib/log4j-1.2.17.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/mapreduce/lib/hadoop-annotations-2.6.5.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/mapreduce/lib/protobuf-java-2.5.0.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/mapreduce/lib/commons-compress-1.4.1.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/mapreduce/lib/commons-io-2.4.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/mapreduce/lib/jackson-core-asl-1.9.13.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/mapreduce/lib/jersey-core-1.9.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/mapreduce/lib/netty-3.6.2.Final.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/mapreduce/lib/jersey-server-1.9.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/mapreduce/lib/guice-3.0.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/mapreduce/lib/jersey-guice-1.9.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/mapreduce/lib/paranamer-2.3.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/mapreduce/lib/asm-3.2.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/mapreduce/lib/hamcrest-core-1.3.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/mapreduce/lib/leveldbjni-all-1.8.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/mapreduce/lib/snappy-java-1.0.4.1.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/mapreduce/lib/jackson-mapper-asl-1.9.13.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/mapreduce/lib/javax.inject-1.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/mapreduce/hadoop-mapreduce-client-common-2.6.5.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/mapreduce/hadoop-mapreduce-client-shuffle-2.6.5.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/mapreduce/hadoop-mapreduce-client-jobclient-2.6.5.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/mapreduce/hadoop-mapreduce-client-app-2.6.5.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/mapreduce/hadoop-mapreduce-client-jobclient-2.6.5-tests.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/mapreduce/hadoop-mapreduce-client-hs-plugins-2.6.5.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/mapreduce/hadoop-mapreduce-client-hs-2.6.5.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.6.5.jar:/Users/a6/Applications/hadoop-2.6.5/share/hadoop/mapreduce/hadoop-mapreduce-client-core-2.6.5.jar:/Users/a6/Applications/hadoop-2.6.5/contrib/capacity-scheduler/*.jar 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

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 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

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