Alex的Hadoop菜鸟教程:第9课Sqoop1从Hbase或者Hive导出mysql_MySQL

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Alex的Hadoop菜鸟教程:第9课Sqoop1从Hbase或者Hive导出mysql_MySQL

Alex的Hadoop菜鸟教程:第9课Sqoop1从Hbase或者Hive导出mysql_MySQL: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

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导读Alex的Hadoop菜鸟教程:第9课Sqoop1从Hbase或者Hive导出mysql_MySQL: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

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

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