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[Sqoop]Sqoop使用

版权声明：本文为博主原创文章，未经博主允许不得转载。 https://blog.csdn.net/SunnyYoona/article/details/53163460

Sqoop的本质还是一个命令行工具，和HDFS，MapReduce相比，并没有什么高深的理论。

我们可以通过sqoop help命令来查看sqoop的命令选项，如下：

  1. 16/11/13 20:10:17 INFO sqoop.Sqoop: Running Sqoop version: 1.4.6

  2. usage: sqoop COMMAND [ARGS]

  4. Available commands:

  5.  codegen            Generate code to interact with database records

  6.  create-hive-table  Import a table definition into Hive

  7.  eval               Evaluate a SQL statement and display the results

  8.  export             Export an HDFS directory to a database table

  9.  help               List available commands

  10.  import             Import a table from a database to HDFS

  11.  import-all-tables  Import tables from a database to HDFS

  12.  import-mainframe   Import datasets from a mainframe server to HDFS

  13.  job                Work with saved jobs

  14.  list-databases     List available databases on a server

  15.  list-tables        List available tables in a database

  16.  merge              Merge results of incremental imports

  17.  metastore          Run a standalone Sqoop metastore

  18.  version            Display version information

  20. See 'sqoop help COMMAND' for information on a specific command.

其中使用频率最高的选项还是import 和 export 选项。

1. codegen

将关系型数据库表的记录映射为一个Java文件，Java class类以及相关的jar包，该命令将数据库表的记录映射为一个Java文件，在该Java文件中对应有表的各个字段。生成的jar和class文件在Metastore功能使用时会用到。该命令选项的参数如下图所示：

举例：

  1. sqoop codegen --connect jdbc:mysql://localhost:3306/test --table order_info -outdir /home/xiaosi/test/ --username root -password root

上面实例以test数据库的order_info表来生成Java代码，其中-outdir指定了Java代码生成的路径

运行结果信息如下：

  1. 16/11/13 21:50:34 INFO sqoop.Sqoop: Running Sqoop version: 1.4.6

  2. Enter password:

  3. 16/11/13 21:50:38 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.

  4. 16/11/13 21:50:38 INFO tool.CodeGenTool: Beginning code generation

  5. 16/11/13 21:50:38 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `order_info` AS t LIMIT 1

  6. 16/11/13 21:50:38 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `order_info` AS t LIMIT 1

  7. 16/11/13 21:50:38 INFO orm.CompilationManager: HADOOP_MAPRED_HOME is /opt/hadoop-2.7.2

  8. 注: /tmp/sqoop-xiaosi/compile/ea41fe40e1f12f6b052ad9fe4a5d9710/order_info.java使用或覆盖了已过时的 API。

  9. 注: 有关详细信息, 请使用 -Xlint:deprecation 重新编译。

  10. 16/11/13 21:50:39 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-xiaosi/compile/ea41fe40e1f12f6b052ad9fe4a5d9710/order_info.jar

我们还可以使用-bindir指定编译成的class文件以及将生成文件打包为jar的jar包文件输出路径：

  1. 16/11/13 21:53:55 INFO sqoop.Sqoop: Running Sqoop version: 1.4.6

  2. Enter password:

  3. 16/11/13 21:53:58 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.

  4. 16/11/13 21:53:58 INFO tool.CodeGenTool: Beginning code generation

  5. 16/11/13 21:53:58 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `order_info` AS t LIMIT 1

  6. 16/11/13 21:53:58 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `order_info` AS t LIMIT 1

  7. 16/11/13 21:53:58 INFO orm.CompilationManager: HADOOP_MAPRED_HOME is /opt/hadoop-2.7.2

  8. 注: /home/xiaosi/data/order_info.java使用或覆盖了已过时的 API。

  9. 注: 有关详细信息, 请使用 -Xlint:deprecation 重新编译。

  10. 16/11/13 21:53:59 INFO orm.CompilationManager: Writing jar file: /home/xiaosi/data/order_info.jar

上面实例指定编译成的class文件（order_info.class）以及将生成文件打包为jar的jar包文件（order_info.jar）输出路径为/home/xiaosi/data路径，java文件（order_info.java）路径为/home/xiaosi/test

2. create-hive-table

这个命令上一篇文章[Sqoop导入与导出]中已经使用过了，作用就是生成与关系数据库表的表结构对应的Hive表。该命令选项的参数如下图所示：

  1. sqoop create-hive-table --connect jdbc:mysql://localhost:3306/test --table employee --username root -password root --fields-terminated-by ','

3. eval

eval命令选项可以让Sqoop使用SQL语句对关系性数据库进行操作，在使用import这种工具进行数据导入的时候，可以预先了解相关的SQL语句是否正确，并能将结果显示在控制台。

3.1 选择查询评估计算

使用eval工具，我们可以评估计算任何类型的SQL查询。我们以test数据库的order_info表为例子：

  1. sqoop eval --connect jdbc:mysql://localhost:3306/test --username root --query "select * from order_info limit 3" -P

运行结果信息：

  1. 16/11/13 22:25:19 INFO sqoop.Sqoop: Running Sqoop version: 1.4.6

  2. Enter password:

  3. 16/11/13 22:25:22 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.

  4. ------------------------------------------------------------

  5. | id                   | order_time           | business   |

  6. ------------------------------------------------------------

  7. | 358574046793404      | 2016-04-05           | flight     |

  8. | 358574046794733      | 2016-08-03           | hotel      |

  9. | 358574050631177      | 2016-05-08           | vacation   |

  10. ------------------------------------------------------------

3.2 插入评估计算

Sqoop的eval工具可以适用于两个模拟和定义的SQL语句。这意味着，我们可以使用eval的INSERT语句了。下面的命令用于在test数据库的order_info表中插入新行：

  1. sqoop eval --connect jdbc:mysql://localhost:3306/test --username root --query "insert into order_info (id, order_time, business) values('358574050631166', '2016-11-13', 'hotel')" -P

运行结果信息输出：

  1. 16/11/13 22:29:42 INFO sqoop.Sqoop: Running Sqoop version: 1.4.6

  2. Enter password:

  3. 16/11/13 22:29:44 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.

  4. 16/11/13 22:29:44 INFO tool.EvalSqlTool: 1 row(s) updated.

如果命令成功执行，会在控制台上显示更新的行的状态。或者我们可以在mysql中查询我们刚插入的那条信息：

  1. mysql> select * from order_info where id = "358574050631166";

  2. +-----------------+------------+----------+

  3. | id              | order_time | business |

  4. +-----------------+------------+----------+

  5. | 358574050631166 | 2016-11-13 | hotel    |

  6. +-----------------+------------+----------+

  7. 1 row in set (0.00 sec)
4. export 

从HDFS中将数据导出到关系性数据库中。该命令选项的参数如下图所示：

在HDFS文件中的员工数据的一个例子，数据如下：

  1. hadoop fs -text /user/xiaosi/employee/* | less

  2. yoona,qunar,创新事业部

  3. xiaosi,qunar,创新事业部

  4. jim,ali,淘宝

  5. kom,ali,淘宝

  6. lucy,baidu,搜索

  7. jim,ali,淘宝

在将HDFS中数据导出到关系性数据库时，必须在关系性数据库中新建一张来接受数据的表，如下：

  1. CREATE TABLE `employee` (

  2.  `name` varchar(255) DEFAULT NULL,

  3.  `company` varchar(255) DEFAULT NULL,

  4.  `depart` varchar(255) DEFAULT NULL

  5. );

下面执行导出操作，命令如下：

  1. sqoop export --connect jdbc:mysql://localhost:3306/test --table employee --export-dir /user/xiaosi/employee --username root -m 1 --fields-terminated-by ',' -P

  1. 16/11/13 23:40:49 INFO mapreduce.Job: The url to track the job: http://localhost:8080/

  2. 16/11/13 23:40:49 INFO mapreduce.Job: Running job: job_local611430785_0001

  3. 16/11/13 23:40:49 INFO mapred.LocalJobRunner: OutputCommitter set in config null

  4. 16/11/13 23:40:49 INFO mapred.LocalJobRunner: OutputCommitter is org.apache.sqoop.mapreduce.NullOutputCommitter

  5. 16/11/13 23:40:49 INFO mapred.LocalJobRunner: Waiting for map tasks

  6. 16/11/13 23:40:49 INFO mapred.LocalJobRunner: Starting task: attempt_local611430785_0001_m_000000_0

  7. 16/11/13 23:40:49 INFO mapred.Task:  Using ResourceCalculatorProcessTree : [ ]

  8. 16/11/13 23:40:49 INFO mapred.MapTask: Processing split: Paths:/user/xiaosi/employee/part-m-00000:0+120

  9. 16/11/13 23:40:49 INFO Configuration.deprecation: map.input.file is deprecated. Instead, use mapreduce.map.input.file

  10. 16/11/13 23:40:49 INFO Configuration.deprecation: map.input.start is deprecated. Instead, use mapreduce.map.input.start

  11. 16/11/13 23:40:49 INFO Configuration.deprecation: map.input.length is deprecated. Instead, use mapreduce.map.input.length

  12. 16/11/13 23:40:49 INFO mapreduce.AutoProgressMapper: Auto-progress thread is finished. keepGoing=false

  13. 16/11/13 23:40:49 INFO mapred.LocalJobRunner:

  14. 16/11/13 23:40:49 INFO mapred.Task: Task:attempt_local611430785_0001_m_000000_0 is done. And is in the process of committing

  15. 16/11/13 23:40:49 INFO mapred.LocalJobRunner: map

  16. 16/11/13 23:40:49 INFO mapred.Task: Task 'attempt_local611430785_0001_m_000000_0' done.

  17. 16/11/13 23:40:49 INFO mapred.LocalJobRunner: Finishing task: attempt_local611430785_0001_m_000000_0

  18. 16/11/13 23:40:49 INFO mapred.LocalJobRunner: map task executor complete.

  19. 16/11/13 23:40:50 INFO mapreduce.Job: Job job_local611430785_0001 running in uber mode : false

  20. 16/11/13 23:40:50 INFO mapreduce.Job:  map 100% reduce 0%

  21. 16/11/13 23:40:50 INFO mapreduce.Job: Job job_local611430785_0001 completed successfully

  22. 16/11/13 23:40:50 INFO mapreduce.Job: Counters: 20

  23. File System Counters

  24. FILE: Number of bytes read=22247825

  25. FILE: Number of bytes written=22732498

  26. FILE: Number of read operations=0

  27. FILE: Number of large read operations=0

  28. FILE: Number of write operations=0

  29. HDFS: Number of bytes read=126

  30. HDFS: Number of bytes written=0

  31. HDFS: Number of read operations=12

  32. HDFS: Number of large read operations=0

  33. HDFS: Number of write operations=0

  34. Map-Reduce Framework

  35. Map input records=6

  36. Map output records=6

  37. Input split bytes=136

  38. Spilled Records=0

  39. Failed Shuffles=0

  40. Merged Map outputs=0

  41. GC time elapsed (ms)=0

  42. Total committed heap usage (bytes)=245366784

  43. File Input Format Counters

  44. Bytes Read=0

  45. File Output Format Counters

  46. Bytes Written=0

  47. 16/11/13 23:40:50 INFO mapreduce.ExportJobBase: Transferred 126 bytes in 2.3492 seconds (53.6344 bytes/sec)

  48. 16/11/13 23:40:50 INFO mapreduce.ExportJobBase: Exported 6 records.

导出完毕之后，我们可以在mysql中通过employee表进行查询：

  1. mysql> select name, company from employee;

  2. +--------+---------+

  3. | name   | company |

  4. +--------+---------+

  5. | yoona  | qunar   |

  6. | xiaosi | qunar   |

  7. | jim    | ali     |

  8. | kom    | ali     |

  9. | lucy   | baidu   |

  10. | jim    | ali     |

  11. +--------+---------+

  12. 6 rows in set (0.00 sec)
5. import 

将数据表中的数据导入HDFS或者Hive中，该命令选项的参数如下图所示：

  1. sqoop import --connect jdbc:mysql://localhost:3306/test --target-dir /user/xiaosi/data/order_info --query 'select * from order_info where $CONDITIONS' -m 1 --username root -P

如上代码从查询结果中导入数据到HDFS中，存储路径由--target-dir参数指定。这里，使用了--query选项，不能同时与--table选项使用。同时，变量$CONDITIONS必须在WHERE语句之后，供Sqoop进程运行命令过程中使用。

  1. 16/11/14 12:08:50 INFO mapreduce.Job: The url to track the job: http://localhost:8080/

  2. 16/11/14 12:08:50 INFO mapreduce.Job: Running job: job_local127577466_0001

  3. 16/11/14 12:08:50 INFO mapred.LocalJobRunner: OutputCommitter set in config null

  4. 16/11/14 12:08:50 INFO output.FileOutputCommitter: File Output Committer Algorithm version is 1

  5. 16/11/14 12:08:50 INFO mapred.LocalJobRunner: OutputCommitter is org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter

  6. 16/11/14 12:08:50 INFO mapred.LocalJobRunner: Waiting for map tasks

  7. 16/11/14 12:08:50 INFO mapred.LocalJobRunner: Starting task: attempt_local127577466_0001_m_000000_0

  8. 16/11/14 12:08:50 INFO output.FileOutputCommitter: File Output Committer Algorithm version is 1

  9. 16/11/14 12:08:50 INFO mapred.Task:  Using ResourceCalculatorProcessTree : [ ]

  10. 16/11/14 12:08:50 INFO db.DBInputFormat: Using read commited transaction isolation

  11. 16/11/14 12:08:50 INFO mapred.MapTask: Processing split: 1=1 AND 1=1

  12. 16/11/14 12:08:50 INFO db.DBRecordReader: Working on split: 1=1 AND 1=1

  13. 16/11/14 12:08:50 INFO db.DBRecordReader: Executing query: select * from order_info where ( 1=1 ) AND ( 1=1 )

  14. 16/11/14 12:08:50 INFO mapreduce.AutoProgressMapper: Auto-progress thread is finished. keepGoing=false

  15. 16/11/14 12:08:50 INFO mapred.LocalJobRunner:

  16. 16/11/14 12:08:51 INFO mapred.Task: Task:attempt_local127577466_0001_m_000000_0 is done. And is in the process of committing

  17. 16/11/14 12:08:51 INFO mapred.LocalJobRunner:

  18. 16/11/14 12:08:51 INFO mapred.Task: Task attempt_local127577466_0001_m_000000_0 is allowed to commit now

  19. 16/11/14 12:08:51 INFO output.FileOutputCommitter: Saved output of task 'attempt_local127577466_0001_m_000000_0' to hdfs://localhost:9000/user/xiaosi/data/order_info/_temporary/0/task_local127577466_0001_m_000000

  20. 16/11/14 12:08:51 INFO mapred.LocalJobRunner: map

  21. 16/11/14 12:08:51 INFO mapred.Task: Task 'attempt_local127577466_0001_m_000000_0' done.

  22. 16/11/14 12:08:51 INFO mapred.LocalJobRunner: Finishing task: attempt_local127577466_0001_m_000000_0

  23. 16/11/14 12:08:51 INFO mapred.LocalJobRunner: map task executor complete.

  24. 16/11/14 12:08:51 INFO mapreduce.Job: Job job_local127577466_0001 running in uber mode : false

  25. 16/11/14 12:08:51 INFO mapreduce.Job:  map 100% reduce 0%

  26. 16/11/14 12:08:51 INFO mapreduce.Job: Job job_local127577466_0001 completed successfully

  27. 16/11/14 12:08:51 INFO mapreduce.Job: Counters: 20

  28. File System Counters

  29. FILE: Number of bytes read=22247784

  30. FILE: Number of bytes written=22732836

  31. FILE: Number of read operations=0

  32. FILE: Number of large read operations=0

  33. FILE: Number of write operations=0

  34. HDFS: Number of bytes read=0

  35. HDFS: Number of bytes written=3710

  36. HDFS: Number of read operations=4

  37. HDFS: Number of large read operations=0

  38. HDFS: Number of write operations=3

  39. Map-Reduce Framework

  40. Map input records=111

  41. Map output records=111

  42. Input split bytes=87

  43. Spilled Records=0

  44. Failed Shuffles=0

  45. Merged Map outputs=0

  46. GC time elapsed (ms)=0

  47. Total committed heap usage (bytes)=245366784

  48. File Input Format Counters

  49. Bytes Read=0

  50. File Output Format Counters

  51. Bytes Written=3710

  52. 16/11/14 12:08:51 INFO mapreduce.ImportJobBase: Transferred 3.623 KB in 2.5726 seconds (1.4083 KB/sec)

  53. 16/11/14 12:08:51 INFO mapreduce.ImportJobBase: Retrieved 111 records.

我们可以查看HDFS由参数--target-dir指定的路径查看导入的数据：

  1. hadoop fs -text /user/xiaosi/data/order_info/* | less

  2. 358574046793404,2016-04-05,flight

  3. 358574046794733,2016-08-03,hotel

  4. 358574050631177,2016-05-08,vacation

  5. 358574050634213,2015-04-28,train

  6. 358574050634692,2016-04-05,tuan

  7. 358574050650524,2015-07-26,hotel

  8. 358574050654773,2015-01-23,flight

  9. 358574050668658,2015-01-23,hotel

  10. 358574050730771,2016-11-06,train

  11. 358574050731241,2016-05-08,car

  12. 358574050743865,2015-01-23,vacation

  13. 358574050767666,2015-04-28,train

  14. 358574050767971,2015-07-26,flight

  15. 358574050808288,2016-05-08,hotel

  16. 358574050816828,2015-01-23,hotel

  17. 358574050818220,2015-04-28,car

  18. 358574050821877,2013-08-03,flight

再看一个例子：

  1. sqoop import --connect jdbc:mysql://localhost:3306/test --table order_info --columns "business,id,order_time"  -m 1 --username root -P

HDFS上会在/user/xiaosi/目录下新增一个目录order_info，与关系性数据库的表名一致，内容如下：

  1. flight,358574046793404,2016-04-05

  2. hotel,358574046794733,2016-08-03

  3. vacation,358574050631177,2016-05-08

  4. train,358574050634213,2015-04-28

  5. tuan,358574050634692,2016-04-05
6. import-all-tables

将数据库里的所有表导入HDFS中，每个表在HDFS中对应一个独立的目录。该命令选项的参数如下图所示：

7. list-databases

该命令选项可以列出关系性数据库的所有数据库名，命令如下：

  1. sqoop list-databases --connect jdbc:mysql://localhost:3306 --username root -P

  1. 16/11/14 14:30:11 INFO sqoop.Sqoop: Running Sqoop version: 1.4.6

  2. Enter password:

  3. 16/11/14 14:30:14 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.

  4. information_schema

  5. hive_db

  6. mysql

  7. performance_schema

  8. phpmyadmin

  9. test
8. list-tables

该命令选项可以列出关系性数据库的某一个数据库的所有表名，命令如下：

  1. sqoop list-tables --connect jdbc:mysql://localhost:3306/test --username root -P

  1. 16/11/14 14:32:08 INFO sqoop.Sqoop: Running Sqoop version: 1.4.6

  2. Enter password:

  3. 16/11/14 14:32:10 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.

  4. PageView

  5. book

  6. bookID

  7. cc

  8. city_click

  9. country

  10. country2

  11. cup

  12. employee

  13. flightOrder

  14. hotel_book_info

  15. hotel_info

  16. order_info

  17. stu

  18. stu2

  19. stu3

  20. stuInfo

  21. student
9. merge

该命令选项的作用是将HDFS上的两份数据进行合并，在合并的同时进行数据去重。该命令选项的参数如下图所示：

例如，在HDFS的路径/user/xiaosi/old下由一份导入数据，如下：

  1. id name

  2. 1 a

  3. 2 b

  4. 3 c

在HDFS的路径/user/xiaosi/new下也有一份数据，但是在导入时间在第一份之后，如下：

  1. id name

  2. 1 a2

  3. 2 b

  4. 3 c

那么合并的结果为：

  1. id name

  2. 1 a2

  3. 2 b

  4. 3 c

运行如下命令：

  1. sqoop merge -new-data /user/xiaosi/new/part-m-00000 -onto /user/xiaosi/old/part-m-00000 -target-dir /user/xiaosi/final -jar-file /home/xiaosi/test/testmerge.jar -class-name testmerge -merge-key id

备注：

在一份数据集中，多行不应具有相同的主键，否则会发生数据丢失。

10. metastore

记录Sqoop作业的元数据信息，如果不启动Metastore实例，则默认的元数据存储目录为~/.sqoop。如果要更改存储目录，可以在配置文件sqoop-site.xml中进行更改。

启动Metastore实例：

  1. sqoop metastore

  1. 16/11/14 14:44:40 INFO sqoop.Sqoop: Running Sqoop version: 1.4.6

  2. 16/11/14 14:44:40 WARN hsqldb.HsqldbMetaStore: The location for metastore data has not been explicitly set. Placing shared metastore files in /home/xiaosi/.sqoop/shared-metastore.db

  3. [Server@52308be6]: [Thread[main,5,main]]: checkRunning(false) entered

  4. [Server@52308be6]: [Thread[main,5,main]]: checkRunning(false) exited

  5. [Server@52308be6]: [Thread[main,5,main]]: setDatabasePath(0,file:/home/xiaosi/.sqoop/shared-metastore.db)

  6. [Server@52308be6]: [Thread[main,5,main]]: checkRunning(false) entered

  7. [Server@52308be6]: [Thread[main,5,main]]: checkRunning(false) exited

  8. [Server@52308be6]: [Thread[main,5,main]]: setDatabaseName(0,sqoop)

  9. [Server@52308be6]: [Thread[main,5,main]]: putPropertiesFromString(): [hsqldb.write_delay=false]

  10. [Server@52308be6]: [Thread[main,5,main]]: checkRunning(false) entered

  11. [Server@52308be6]: [Thread[main,5,main]]: checkRunning(false) exited

  12. [Server@52308be6]: Initiating startup sequence...

  13. [Server@52308be6]: Server socket opened successfully in 3 ms.

  14. [Server@52308be6]: Database [index=0, id=0, db=file:/home/xiaosi/.sqoop/shared-metastore.db, alias=sqoop] opened sucessfully in 153 ms.

  15. [Server@52308be6]: Startup sequence completed in 157 ms.

  16. [Server@52308be6]: 2016-11-14 14:44:40.414 HSQLDB server 1.8.0 is online

  17. [Server@52308be6]: To close normally, connect and execute SHUTDOWN SQL

  18. [Server@52308be6]: From command line, use [Ctrl]+[C] to abort abruptly

  19. 16/11/14 14:44:40 INFO hsqldb.HsqldbMetaStore: Server started on port 16000 with protocol HSQL
11. job

该命令选项可以生产一个Sqoop的作业，但是不会立即执行，需要手动执行，该命令选项目的在于尽可能的服用Sqoop命令。该命令选项的参数如下图所示：

  1. sqoop job -create listTablesJob -- list-tables --connect jdbc:mysql://localhost:3306/test --username root -P

上面代码实现一个job，显示关系性数据库test数据库中所有的表。

  1. sqoop job -exec listTablesJob

上面代码执行我们已经定义好的Job，输出结果信息如下：

  1. 16/11/14 19:51:44 INFO sqoop.Sqoop: Running Sqoop version: 1.4.6

  2. Enter password:

  3. 16/11/14 19:51:47 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.

  4. PageView

  5. book

  6. bookID

  7. cc

  8. city_click

  9. country

  10. country2

  11. cup

  12. employee

  13. flightOrder

  14. hotel_book_info

  15. hotel_info

  16. order_info

  17. stu

  18. stu2

  19. stu3

  20. stuInfo

  21. student

-- 和 list-tables（Job 所要执行的Sqoop命令） 不能挨着。



来自于《Hadoop海量数据处理  技术详解与项目实战》

sql 关系型数据库 java mysql 数据库 使用sqoop 集群使用sqoop 调用sqoop sqoop指定 版本sqoop
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