今天讲讲怎么用sqoop将hbase或者hive的东西导出到mysql。不过事先要告诉大家
目前sqoop没有办法把数据直接从Hbase导出到mysql。必须要通过Hive建立2个表，一个外部表是基于这个Hbase表的，另一个是单纯的基于hdfs的hive原生表，然后把外部表的数据导入到原生表（临时），然后通过hive将临时表里面的数据导出到mysql
数据准备
mysql建立空表
CREATE TABLE `employee` ( `rowkey` int(11) NOT NULL, `id` int(11) NOT NULL, `name` varchar(20) NOT NULL, PRIMARY KEY (`id`) ) ENGINE=MyISAM DEFAULT CHARSET=utf8;
注意：因为大家习惯性的把hive表用于映射Hbase的rowkey的字段命名为key，所以在建立mysql的table的时候有可能也建立对应的key字段，但是key是mysql的保留字，会导致insert语句无法插入的问题
Hbase建立employee表
建立employee表，并插入数据
hbase(main):005:0> create 'employee','info' 0 row(s) in 0.4740 seconds => Hbase::Table - employee hbase(main):006:0> put 'employee',1,'info:id',1 0 row(s) in 0.2080 seconds hbase(main):008:0> scan 'employee' ROW COLUMN+CELL 1 column=info:id, timestamp=1417591291730, value=1 1 row(s) in 0.0610 seconds hbase(main):009:0> put 'employee',1,'info:name','peter' 0 row(s) in 0.0220 seconds hbase(main):010:0> scan 'employee' ROW COLUMN+CELL 1 column=info:id, timestamp=1417591291730, value=1 1 column=info:name, timestamp=1417591321072, value=peter 1 row(s) in 0.0450 seconds hbase(main):011:0> put 'employee',2,'info:id',2 0 row(s) in 0.0370 seconds hbase(main):012:0> put 'employee',2,'info:name','paul' 0 row(s) in 0.0180 seconds hbase(main):013:0> scan 'employee' ROW COLUMN+CELL 1 column=info:id, timestamp=1417591291730, value=1 1 column=info:name, timestamp=1417591321072, value=peter 2 column=info:id, timestamp=1417591500179, value=2 2 column=info:name, timestamp=1417591512075, value=paul 2 row(s) in 0.0440 seconds
建立Hive外部表
hive 有分为原生表和外部表，原生表是以简单文件方式存储在hdfs里面，外部表依赖别的框架，比如Hbase，我们现在建立一个依赖于我们刚刚建立的employee hbase表的hive 外部表
hive> CREATE EXTERNAL TABLE h_employee(key int, id int, name string)
> STORED BY 'org.apache.hadoop.hive.hbase.HBaseStorageHandler'
> WITH SERDEPROPERTIES ("hbase.columns.mapping" = ":key, info:id,info:name")
> TBLPROPERTIES ("hbase.table.name" = "employee");
OK
Time taken: 0.324 seconds
hive> select * from h_employee;
OK
1 1 peter
2 2 paul
Time taken: 1.129 seconds, Fetched: 2 row(s)建立Hive原生表
这个hive原生表只是用于导出的时候临时使用的，所以取名叫 h_employee_export，字段之间的分隔符用逗号
CREATE TABLE h_employee_export(key INT, id INT, name STRING) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\054';
我们去看下实际存储的文本文件是什么样子的
$ hdfs dfs -cat /user/hive/warehouse/h_employee_export/000000_0 1,1,peter 2,2,paul
源Hive表导入数据到临时表
第一步先将数据从 h_employee(基于Hbase的外部表)导入到 h_employee_export(原生Hive表)
hive> insert overwrite table h_employee_export select * from h_employee;
hive> select * from h_employee_export; OK 1 1 peter 2 2 paul Time taken: 0.359 seconds, Fetched: 2 row(s)
我们去看下实际存储的文本文件长什么样子
$ hdfs dfs -cat /user/hive/warehouse/h_employee_export/000000_0 1,1,peter 2,2,paul
从Hive导出数据到mysql
$ sqoop export --connect jdbc:mysql://localhost:3306/sqoop_test --username root --password root --table employee --m 1 --export-dir /user/hive/warehouse/h_employee_export/ Warning: /usr/lib/sqoop/../hive-hcatalog does not exist! HCatalog jobs will fail. Please set $HCAT_HOME to the root of your HCatalog installation. Warning: /usr/lib/sqoop/../accumulo does not exist! Accumulo imports will fail. Please set $ACCUMULO_HOME to the root of your Accumulo installation. 14/12/05 08:49:35 INFO sqoop.Sqoop: Running Sqoop version: 1.4.4-cdh5.0.1 14/12/05 08:49:35 WARN tool.BaseSqoopTool: Setting your password on the command-line is insecure. Consider using -P instead. 14/12/05 08:49:35 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset. 14/12/05 08:49:35 INFO tool.CodeGenTool: Beginning code generation 14/12/05 08:49:36 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `employee` AS t LIMIT 1 14/12/05 08:49:36 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `employee` AS t LIMIT 1 14/12/05 08:49:36 INFO orm.CompilationManager: HADOOP_MAPRED_HOME is /usr/lib/hadoop-mapreduce Note: /tmp/sqoop-wlsuser/compile/d16eb4166baf6a1e885d7df0e2638685/employee.java uses or overrides a deprecated API. Note: Recompile with -Xlint:deprecation for details. 14/12/05 08:49:39 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-wlsuser/compile/d16eb4166baf6a1e885d7df0e2638685/employee.jar 14/12/05 08:49:39 INFO mapreduce.ExportJobBase: Beginning export of employee 14/12/05 08:49:41 INFO Configuration.deprecation: mapred.jar is deprecated. Instead, use mapreduce.job.jar 14/12/05 08:49:43 INFO Configuration.deprecation: mapred.reduce.tasks.speculative.execution is deprecated. Instead, use mapreduce.reduce.speculative 14/12/05 08:49:43 INFO Configuration.deprecation: mapred.map.tasks.speculative.execution is deprecated. Instead, use mapreduce.map.speculative 14/12/05 08:49:43 INFO Configuration.deprecation: mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps 14/12/05 08:49:43 INFO client.RMProxy: Connecting to ResourceManager at hadoop01/192.111.78.111:8032 14/12/05 08:49:45 INFO input.FileInputFormat: Total input paths to process : 1 14/12/05 08:49:45 INFO input.FileInputFormat: Total input paths to process : 1 14/12/05 08:49:45 INFO mapreduce.JobSubmitter: number of splits:1 14/12/05 08:49:46 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1406097234796_0037 14/12/05 08:49:46 INFO impl.YarnClientImpl: Submitted application application_1406097234796_0037 14/12/05 08:49:46 INFO mapreduce.Job: The url to track the job: http://hadoop01:8088/proxy/application_1406097234796_0037/ 14/12/05 08:49:46 INFO mapreduce.Job: Running job: job_1406097234796_0037 14/12/05 08:49:59 INFO mapreduce.Job: Job job_1406097234796_0037 running in uber mode : false 14/12/05 08:49:59 INFO mapreduce.Job: map 0% reduce 0% 14/12/05 08:50:10 INFO mapreduce.Job: map 100% reduce 0% 14/12/05 08:50:10 INFO mapreduce.Job: Job job_1406097234796_0037 completed successfully 14/12/05 08:50:10 INFO mapreduce.Job: Counters: 30 File System Counters FILE: Number of bytes read=0 FILE: Number of bytes written=99761 FILE: Number of read operations=0 FILE: Number of large read operations=0 FILE: Number of write operations=0 HDFS: Number of bytes read=166 HDFS: Number of bytes written=0 HDFS: Number of read operations=4 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)=8805 Total time spent by all reduces in occupied slots (ms)=0 Total time spent by all map tasks (ms)=8805 Total vcore-seconds taken by all map tasks=8805 Total megabyte-seconds taken by all map tasks=9016320 Map-Reduce Framework Map input records=2 Map output records=2 Input split bytes=144 Spilled Records=0 Failed Shuffles=0 Merged Map outputs=0 GC time elapsed (ms)=97 CPU time spent (ms)=1360 Physical memory (bytes) snapshot=167555072 Virtual memory (bytes) snapshot=684212224 Total committed heap usage (bytes)=148897792 File Input Format Counters Bytes Read=0 File Output Format Counters Bytes Written=0 14/12/05 08:50:10 INFO mapreduce.ExportJobBase: Transferred 166 bytes in 27.0676 seconds (6.1328 bytes/sec) 14/12/05 08:50:10 INFO mapreduce.ExportJobBase: Exported 2 records.
注意
在这段日志中有这样一句话
14/12/05 08:49:46 INFO mapreduce.Job: The url to track the job: http://hadoop01:8088/proxy/application_1406097234796_0037/
意思是你可以用浏览器访问这个地址去看下任务的执行情况，如果你的任务长时间卡主没结束就是出错了，可以去这个地址查看详细的错误日志
查看结果
mysql> select * from employee; +--------+----+-------+ | rowkey | id | name | +--------+----+-------+ | 1 | 1 | peter | | 2 | 2 | paul | +--------+----+-------+ 2 rows in set (0.00 sec) mysql>
导入成功
