HBase备份之ExportSnapshot或CopyTable_hbase org.apache.hadoop.hbase.snapshot.exportsnaps

作者：码创造者 | 2024-08-12 12:50:39

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hbase org.apache.hadoop.hbase.snapshot.exportsnapshot

文章《HBase备份之导入导出》介绍了使用HBase的自带工具Export和Import来实现在主集群和从集群之间拷贝表的目的。本篇介绍一种相比导入导出而言，更快速的一种备份办法。即ExportSnapshot。

1、ExportSnapshot

和Export类似，ExportSnapshot也是使用MapReduce方式来进行表的拷贝。不过和Export不同，ExportSnapshot导出的是表的快照。我们可以使用ExportSnapshot将表的快照数据先导出到从集群，然后再从集群中使用restore_snapshot命令恢复快照，即可实现表在主从集群之间的复制工作。具体的操作步骤如下：

1）在主集群中为表建立快照

$ cd $HBASE_HOME/
$ bin/hbase shell
2014-08-13 15:59:12,495 INFO [main] Configuration.deprecation: hadoop.native.lib is deprecated. Instead, use io.native.lib.available
HBase Shell; enter 'help<RETURN>' for list of supported commands.
Type "exit<RETURN>" to leave the HBase Shell
Version 0.98.2-hadoop2, r1591526, Wed Apr 30 20:17:33 PDT 2014
hbase(main):001:0> snapshot 'test_table', 'test_table_snapshot'
0 row(s) in 0.3370 seconds

2）使用ExportSnapshot命令导出快照数据

$ cd $HBASE_HOME/
$ bin/hbase org.apache.hadoop.hbase.snapshot.ExportSnapshot -snapshot test_table_snapshot -copy-to hdfs://follow_cluster_namenode:8082/hbase

其中，test_table_snapshot为刚建的快照名，hdfs://follow_cluster_namenode:8082/hbase为从集群的hbase的hdfs根目录的全路径。

ExportSnapshot命令也可以限定mapper个数，如下：

$ bin/hbase org.apache.hadoop.hbase.snapshot.ExportSnapshot -snapshot test_table_snapshot -copy-to hdfs://follow_cluster_namenode:8082/hbase -mapers n

还可以限定拷贝的流量，如下：

$ bin/hbase org.apache.hadoop.hbase.snapshot.ExportSnapshot -snapshot test_table_snapshot -copy-to hdfs://follow_cluster_namenode:8082/hbase -mapers n -bandwidth 200

上面的例子将拷贝的流量限定为200M。

执行ExportSnapshot命令之后的输出很长，部分如下：

2014-08-13 16:08:26,318 INFO [main] mapreduce.Job: Running job: job_1407910396081_0027
2014-08-13 16:08:33,494 INFO [main] mapreduce.Job: Job job_1407910396081_0027 running in uber mode : false
2014-08-13 16:08:33,495 INFO [main] mapreduce.Job: map 0% reduce 0%
2014-08-13 16:08:41,567 INFO [main] mapreduce.Job: map 100% reduce 0%
2014-08-13 16:08:42,581 INFO [main] mapreduce.Job: Job job_1407910396081_0027 completed successfully
2014-08-13 16:08:42,677 INFO [main] mapreduce.Job: Counters: 30
File System Counters
FILE: Number of bytes read=0
FILE: Number of bytes written=116030
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=1386
HDFS: Number of bytes written=988
HDFS: Number of read operations=7
HDFS: Number of large read operations=0
HDFS: Number of write operations=3
Job Counters
Launched map tasks=1
Rack-local map tasks=1
Total time spent by all maps in occupied slots (ms)=13518
Total time spent by all reduces in occupied slots (ms)=0
Map-Reduce Framework
Map input records=1
Map output records=0
Input split bytes=174
Spilled Records=0
Failed Shuffles=0
Merged Map outputs=0
GC time elapsed (ms)=23
CPU time spent (ms)=1860
Physical memory (bytes) snapshot=323575808
Virtual memory (bytes) snapshot=1867042816
Total committed heap usage (bytes)=1029177344
org.apache.hadoop.hbase.snapshot.ExportSnapshot$Counter
BYTES_COPIED=988
BYTES_EXPECTED=988
FILES_COPIED=1
File Input Format Counters
Bytes Read=224
File Output Format Counters
Bytes Written=0
2014-08-13 16:08:42,685 INFO [main] snapshot.ExportSnapshot: Finalize the Snapshot Export
2014-08-13 16:08:42,697 INFO [main] snapshot.ExportSnapshot: Verify snapshot validity
2014-08-13 16:08:42,698 INFO [main] Configuration.deprecation: fs.default.name is deprecated. Instead, use fs.defaultFS
2014-08-13 16:08:42,713 INFO [main] snapshot.ExportSnapshot: Export Completed: test_table_snapshot

3）到从集群中恢复快照

$ cd $HBASE_HOME/
$ bin/hbase shell
2014-08-13 16:16:13,817 INFO [main] Configuration.deprecation: hadoop.native.lib is deprecated. Instead, use io.native.lib.available
HBase Shell; enter 'help<RETURN>' for list of supported commands.
Type "exit<RETURN>" to leave the HBase Shell
Version 0.98.2-hadoop2, r1591526, Wed Apr 30 20:17:33 PDT 2014
hbase(main):001:0> restore_snapshot 'test_table_snapshot'
0 row(s) in 16.4940 seconds

4）查看表是否恢复成功

hbase(main):002:0> list
TABLE test_table
1 row(s) in 1.0460 seconds
=> ["test_table"]

另外，还可以通过scan或count命令进行检验。

快照恢复操作一般会很快，相比较Export和Import需要导出和导入两次MapReduce任务才能完成表的复制来讲，使用ExportSnapshot会快很多。

2、CopyTable

首先，看一下CopyTable命令的使用方法

$ bin/hbase org.apache.hadoop.hbase.mapreduce.CopyTable
Usage: CopyTable [general options] [--starttime=X] [--endtime=Y] [--new.name=NEW] [--peer.adr=ADR] <tablename>
Options:
rs.class hbase.regionserver.class of the peer cluster
specify if different from current cluster
rs.impl hbase.regionserver.impl of the peer cluster
startrow the start row
stoprow the stop row
starttime beginning of the time range (unixtime in millis)
without endtime means from starttime to forever
endtime end of the time range. Ignored if no starttime specified.
versions number of cell versions to copy
new.name new table's name
peer.adr Address of the peer cluster given in the format
hbase.zookeeer.quorum:hbase.zookeeper.client.port:zookeeper.znode.parent
families comma-separated list of families to copy
To copy from cf1 to cf2, give sourceCfName:destCfName.
To keep the same name, just give "cfName"
all.cells also copy delete markers and deleted cells
Args:
tablename Name of the table to copy
Examples:
To copy 'TestTable' to a cluster that uses replication for a 1 hour window:
$ bin/hbase org.apache.hadoop.hbase.mapreduce.CopyTable --starttime=1265875194289 --endtime=1265878794289 --peer.adr=server1,server2,server3:2181:/hbase --families=myOldCf:myNewCf,cf2,cf3 TestTable
For performance consider the following general options:
-Dhbase.client.scanner.caching=100
-Dmapred.map.tasks.speculative.execution=false

可以看到，它支持设定需要复制的表的时间范围，cell的版本，也可以指定列簇，设定从集群的地址等。

对于上面的test_table表，我们可以使用如下命令进行拷贝：

$ bin/hbase org.apache.hadoop.hbase.mapreduce.CopyTable --peer.adr=slave1,slave2,slave3:2181:/hbase  test_table

注意：在使用上述语句之前，需要在从集群建立一个模式和主集群表test_table相同的表。

使用上述语句的部分执行结果如下：

2014-08-13 16:18:21,812 INFO [main] mapreduce.Job: Running job: job_1407910396081_0062
2014-08-13 16:18:29,955 INFO [main] mapreduce.Job: Job job_1407910396081_0062 running in uber mode : false
2014-08-13 16:18:29,957 INFO [main] mapreduce.Job: map 0% reduce 0%
2014-08-13 16:18:36,005 INFO [main] mapreduce.Job: map 100% reduce 0%
2014-08-13 16:18:37,029 INFO [main] mapreduce.Job: Job job_1407910396081_0062 completed successfully
2014-08-13 16:18:37,137 INFO [main] mapreduce.Job: Counters: 37
File System Counters
FILE: Number of bytes read=0
FILE: Number of bytes written=117527
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=88
HDFS: Number of bytes written=0
HDFS: Number of read operations=1
HDFS: Number of large read operations=0
HDFS: Number of write operations=0
Job Counters
Launched map tasks=1
Rack-local map tasks=1
Total time spent by all maps in occupied slots (ms)=9740
Total time spent by all reduces in occupied slots (ms)=0
Map-Reduce Framework
Map input records=1
Map output records=1
Input split bytes=88
Spilled Records=0
Failed Shuffles=0
Merged Map outputs=0
GC time elapsed (ms)=254
CPU time spent (ms)=1810
Physical memory (bytes) snapshot=345137152
Virtual memory (bytes) snapshot=1841782784
Total committed heap usage (bytes)=1029177344
HBase Counters
BYTES_IN_REMOTE_RESULTS=34
BYTES_IN_RESULTS=34
MILLIS_BETWEEN_NEXTS=254
NOT_SERVING_REGION_EXCEPTION=0
NUM_SCANNER_RESTARTS=0
REGIONS_SCANNED=1
REMOTE_RPC_CALLS=3
REMOTE_RPC_RETRIES=0
RPC_CALLS=3
RPC_RETRIES=0
File Input Format Counters
Bytes Read=0
File Output Format Counters
Bytes Written=0

然后，就可以对比主集群中的表和从集群中对应的表数据是否一致。

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