Sqoop基本語法簡介

阿新 • • 發佈：2018-07-04

info behavior relax 執行 parameter tab could list over

簡介：
本篇文章主要介紹sqoop的基本語法及簡單使用方法。

1.查看命令幫助

[hadoop@hadoop000 ~]$ sqoop help
usage: sqoop COMMAND [ARGS]

Available commands:
  codegen            Generate code to interact with database records
  create-hive-table  Import a table definition into Hive
  eval               Evaluate a SQL statement and display the results
  export             Export an HDFS directory to a database table
  help               List available commands
  import             Import a table from a database to HDFS
  import-all-tables  Import tables from a database to HDFS
  import-mainframe   Import datasets from a mainframe server to HDFS
  job                Work with saved jobs
  list-databases     List available databases on a server
  list-tables        List available tables in a database
  merge              Merge results of incremental imports
  metastore          Run a standalone Sqoop metastore
  version            Display version information

See ‘sqoop help COMMAND‘ for information on a specific command.

# 這裏提示我們使用sqoop help command（要查詢的命令）進行該命令的詳細查詢

2.list-databases

# 查看list-databases命令幫助
[hadoop@hadoop000 ~]$ sqoop help list-databases
usage: sqoop list-databases [GENERIC-ARGS] [TOOL-ARGS]

Common arguments:
   --connect <jdbc-uri>                         Specify JDBC connect
                                                string
   --connection-manager <class-name>            Specify connection manager
                                                class name
   --connection-param-file <properties-file>    Specify connection
                                                parameters file
   --driver <class-name>                        Manually specify JDBC
                                                driver class to use
   --hadoop-home <hdir>                         Override
                                                $HADOOP_MAPRED_HOME_ARG
   --hadoop-mapred-home <dir>                   Override
                                                $HADOOP_MAPRED_HOME_ARG
   --help                                       Print usage instructions
-P                                              Read password from console
   --password <password>                        Set authentication
                                                password
   --password-alias <password-alias>            Credential provider
                                                password alias
   --password-file <password-file>              Set authentication
                                                password file path
   --relaxed-isolation                          Use read-uncommitted
                                                isolation for imports
   --skip-dist-cache                            Skip copying jars to
                                                distributed cache
   --username <username>                        Set authentication
                                                username
   --verbose                                    Print more information
                                                while working

# 簡單使用
[hadoop@oradb3 ~]$ sqoop list-databases > --connect jdbc:mysql://localhost:3306 > --username root > --password 123456

# 結果
information_schema
mysql
performance_schema
slow_query_log
sys
test

3.list-tables

# 命令幫助
[hadoop@hadoop000 ~]$ sqoop help list-tables
usage: sqoop list-tables [GENERIC-ARGS] [TOOL-ARGS]

Common arguments:
   --connect <jdbc-uri>                         Specify JDBC connect
                                                string
   --connection-manager <class-name>            Specify connection manager
                                                class name
   --connection-param-file <properties-file>    Specify connection
                                                parameters file
   --driver <class-name>                        Manually specify JDBC
                                                driver class to use
   --hadoop-home <hdir>                         Override
                                                $HADOOP_MAPRED_HOME_ARG
   --hadoop-mapred-home <dir>                   Override
                                                $HADOOP_MAPRED_HOME_ARG
   --help                                       Print usage instructions
-P                                              Read password from console
   --password <password>                        Set authentication
                                                password
   --password-alias <password-alias>            Credential provider
                                                password alias
   --password-file <password-file>              Set authentication
                                                password file path
   --relaxed-isolation                          Use read-uncommitted
                                                isolation for imports
   --skip-dist-cache                            Skip copying jars to
                                                distributed cache
   --username <username>                        Set authentication
                                                username
   --verbose                                    Print more information
                                                while working

# 使用方法
[hadoop@hadoop000 ~]$ sqoop list-tables > --connect jdbc:mysql://localhost:3306/test > --username root > --password 123456

# 結果
t_order
test0001
test_1013
test_dyc
test_tb

4.將mysql導入HDFS中(import)

(默認導入當前用戶目錄下/user/用戶名/表名)
說到這裏擴展一個小知識點：

hadoop fs -ls 顯示的是當前的用戶目錄即/user/hadoop
hadoop fs -ls / 顯示的是HDFS根目錄

# 查看命令幫助
[hadoop@hadoop000 ~]$ sqoop help import
# 執行import
[hadoop@hadoop000 ~]$ sqoop import > --connect jdbc:mysql://localhost:3306/test > --username root > --password 123456 > --table students

這時很可能會出現這個錯誤
Exception in thread "main" java.lang.NoClassDefFoundError: org/json/JSONObject
這裏我們需要導入java-json.jar包下載地址把java-json.jar添加到../sqoop/lib目錄下即可

# 再次執行 import導入
[hadoop@hadoop000 ~]$ sqoop import > --connect jdbc:mysql://localhost:3306/test > --username root > --password 123456 > --table students

18/07/04 13:28:35 INFO sqoop.Sqoop: Running Sqoop version: 1.4.6-cdh5.7.0
18/07/04 13:28:35 WARN tool.BaseSqoopTool: Setting your password on the command-line is insecure. Consider using -P instead.
18/07/04 13:28:35 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.
18/07/04 13:28:35 INFO tool.CodeGenTool: Beginning code generation
18/07/04 13:28:35 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `students` AS t LIMIT 1
18/07/04 13:28:35 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `students` AS t LIMIT 1
18/07/04 13:28:35 INFO orm.CompilationManager: HADOOP_MAPRED_HOME is /home/hadoop/app/hadoop-2.6.0-cdh5.7.0
18/07/04 13:28:37 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-hadoop/compile/3024b8df04f623e8c79ed9b5b30ace75/students.jar
18/07/04 13:28:37 WARN manager.MySQLManager: It looks like you are importing from mysql.
18/07/04 13:28:37 WARN manager.MySQLManager: This transfer can be faster! Use the --direct
18/07/04 13:28:37 WARN manager.MySQLManager: option to exercise a MySQL-specific fast path.
18/07/04 13:28:37 INFO manager.MySQLManager: Setting zero DATETIME behavior to convertToNull (mysql)
18/07/04 13:28:37 INFO mapreduce.ImportJobBase: Beginning import of students
18/07/04 13:28:38 INFO Configuration.deprecation: mapred.jar is deprecated. Instead, use mapreduce.job.jar
18/07/04 13:28:39 INFO Configuration.deprecation: mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps
18/07/04 13:28:39 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
18/07/04 13:28:41 INFO db.DBInputFormat: Using read commited transaction isolation
18/07/04 13:28:41 INFO db.DataDrivenDBInputFormat: BoundingValsQuery: SELECT MIN(`id`), MAX(`id`) FROM `students`
18/07/04 13:28:41 INFO db.IntegerSplitter: Split size: 0; Num splits: 4 from: 1001 to: 1003
18/07/04 13:28:41 INFO mapreduce.JobSubmitter: number of splits:3
18/07/04 13:28:42 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1530598609758_0015
18/07/04 13:28:42 INFO impl.YarnClientImpl: Submitted application application_1530598609758_0015
18/07/04 13:28:42 INFO mapreduce.Job: The url to track the job: http://oradb3:8088/proxy/application_1530598609758_0015/
18/07/04 13:28:42 INFO mapreduce.Job: Running job: job_1530598609758_0015
18/07/04 13:28:52 INFO mapreduce.Job: Job job_1530598609758_0015 running in uber mode : false
18/07/04 13:28:52 INFO mapreduce.Job:  map 0% reduce 0%
18/07/04 13:28:58 INFO mapreduce.Job:  map 33% reduce 0%
18/07/04 13:28:59 INFO mapreduce.Job:  map 67% reduce 0%
18/07/04 13:29:00 INFO mapreduce.Job:  map 100% reduce 0%
18/07/04 13:29:00 INFO mapreduce.Job: Job job_1530598609758_0015 completed successfully
18/07/04 13:29:00 INFO mapreduce.Job: Counters: 30
...
18/07/04 13:29:00 INFO mapreduce.ImportJobBase: Transferred 40 bytes in 21.3156 seconds (1.8766 bytes/sec)
18/07/04 13:29:00 INFO mapreduce.ImportJobBase: Retrieved 3 records.
# 生成的日誌信息大家一定要好好理解
# 查看HDFS上的文件
[hadoop@hadoop000 ~]$ hadoop fs -ls /user/hadoop/students
Found 4 items
-rw-r--r--   1 hadoop supergroup          0 2018-07-04 13:28 /user/hadoop/students/_SUCCESS
-rw-r--r--   1 hadoop supergroup         13 2018-07-04 13:28 /user/hadoop/students/part-m-00000
-rw-r--r--   1 hadoop supergroup         13 2018-07-04 13:28 /user/hadoop/students/part-m-00001
-rw-r--r--   1 hadoop supergroup         14 2018-07-04 13:28 /user/hadoop/students/part-m-00002
[hadoop@hadoop000 ~]$ hadoop fs -cat /user/hadoop/students/"part*"
1001,lodd,23
1002,sdfs,21
1003,sdfsa,24

我們還可以加一些其他參數使導入過程更加可控

-m 指定啟動map進程個數，默認是4個
--delete-target-dir 刪除目標目錄
--mapreduce-job-name 指定mapreduce的job的名字
--target-dir 導入到指定目錄
--fields-terminated-by 指定字段之間的分隔符
--null-string 含義是 string類型的字段，當Value是NULL，替換成指定的字符
--null-non-string 含義是非string類型的字段，當Value是NULL，替換成指定字符
--columns 導入表中的部分字段
--where 按條件導入數據
--query 按照sql語句進行導入使用--query關鍵字，就不能使用--table和--columns
--options-file 在文件中執行

# 執行導入
[hadoop@hadoop000 ~]$ sqoop import > --connect jdbc:mysql://localhost:3306/test > --username root --password 123456 > --mapreduce-job-name FromMySQL2HDFS > --delete-target-dir > --table students > -m 1

# HDFS中查看
[hadoop@hadoop000 ~]$ hadoop fs -ls /user/hadoop/students              
Found 2 items
-rw-r--r--   1 hadoop supergroup          0 2018-07-04 13:53 /user/hadoop/students/_SUCCESS
-rw-r--r--   1 hadoop supergroup         40 2018-07-04 13:53 /user/hadoop/students/part-m-00000
[hadoop@oradb3 ~]$ hadoop fs -cat /user/hadoop/students/"part*"
1001,lodd,23
1002,sdfs,21
1003,sdfsa,24

# 使用where 參數
[hadoop@hadoop000 ~]$ sqoop import > --connect jdbc:mysql://localhost:3306/test > --username root --password 123456 > --table students > --mapreduce-job-name FromMySQL2HDFS2 > --delete-target-dir > --fields-terminated-by ‘\t‘ > -m 1 > --null-string 0 > --columns "name" > --target-dir STU_COLUMN_WHERE > --where ‘id<1002‘

# HDFS 結果
[hadoop@hadoop000 ~]$ hadoop fs -cat STU_COLUMN_WHERE/"part*"
lodd

# 使用query 參數
[hadoop@hadoop000 ~]$ sqoop import > --connect jdbc:mysql://localhost:3306/test > --username root --password 123456 > --mapreduce-job-name FromMySQL2HDFS3 > --delete-target-dir > --fields-terminated-by ‘\t‘ > -m 1 > --null-string 0 > --target-dir STU_COLUMN_QUERY > --query "select * from students where id>1001 and \$CONDITIONS"

# HDFS查看
[hadoop@hadoop000 ~]$ hadoop fs -cat STU_COLUMN_QUERY/"part*"
1002    sdfs    21
1003    sdfsa   24

# 使用options-file參數
[hadoop@hadoop000 ~]$ vi sqoop-import-hdfs.txt
import
--connect
jdbc:mysql://localhost:3306/test
--username
root
--password
123456
--table
students
--target-dir
STU_option_file
# 執行導入
[hadoop@hadoop000 ~]$ sqoop --options-file /home/hadoop/sqoop-import-hdfs.txt
# HDFS查看
[hadoop@hadoop000 ~]$ hadoop fs -cat STU_option_file/"part*"
1001,lodd,23
1002,sdfs,21
1003,sdfsa,24

5.eval

查看幫助命令對與該命令的解釋為： Evaluate a SQL statement and display the results，也就是說執行一個SQL語句並查詢出結果。

# 查看命令幫助
[hadoop@hadoop000 ~]$ sqoop help eval
usage: sqoop eval [GENERIC-ARGS] [TOOL-ARGS]

Common arguments:
   --connect <jdbc-uri>                         Specify JDBC connect
                                                string
   --connection-manager <class-name>            Specify connection manager
                                                class name
   --connection-param-file <properties-file>    Specify connection
                                                parameters file
   --driver <class-name>                        Manually specify JDBC
                                                driver class to use
   --hadoop-home <hdir>                         Override
                                                $HADOOP_MAPRED_HOME_ARG
   --hadoop-mapred-home <dir>                   Override
                                                $HADOOP_MAPRED_HOME_ARG
   --help                                       Print usage instructions
-P                                              Read password from console
   --password <password>                        Set authentication
                                                password
   --password-alias <password-alias>            Credential provider
                                                password alias
   --password-file <password-file>              Set authentication
                                                password file path
   --relaxed-isolation                          Use read-uncommitted
                                                isolation for imports
   --skip-dist-cache                            Skip copying jars to
                                                distributed cache
   --username <username>                        Set authentication
                                                username
   --verbose                                    Print more information
                                                while working

SQL evaluation arguments:
-e,--query <statement>    Execute ‘statement‘ in SQL and exit
# 執行
[hadoop@hadoop000 ~]$ sqoop eval > --connect jdbc:mysql://localhost:3306/test > --username root --password 123456 > --query "select * from students"

18/07/04 14:28:44 INFO sqoop.Sqoop: Running Sqoop version: 1.4.6-cdh5.7.0
18/07/04 14:28:44 WARN tool.BaseSqoopTool: Setting your password on the command-line is insecure. Consider using -P instead.
18/07/04 14:28:44 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.
----------------------------------------------------
| id          | name                 | age         | 
----------------------------------------------------
| 1001        | lodd                 | 23          | 
| 1002        | sdfs                 | 21          | 
| 1003        | sdfsa                | 24          | 
----------------------------------------------------

6.export (HDFS數據導出到MySQL或Hive中的數據導入到MySQL)

常用參數：

--table 指定導出表的名稱
--input-fields-terminated-by 指定hdfs上文件的分隔符，默認是逗號
--export-dir 導出數據的目錄
--columns 指定導出的字段

在執行導出語句前mysql要先創建表（不創建表會報錯）：

# HDFS原文件
[hadoop@hadoop000 ~]$ hadoop fs -cat /user/hadoop/students/part-m-00000
1001,lodd,23
1002,sdfs,21
1003,sdfsa,24
# export導出到mysql
[hadoop@hadoop000 ~]$ sqoop export > --connect jdbc:mysql://localhost:3306/test > --username root > --password 123456 > --table students_demo > --export-dir /user/hadoop/students/

18/07/04 14:46:20 INFO sqoop.Sqoop: Running Sqoop version: 1.4.6-cdh5.7.0
18/07/04 14:46:20 WARN tool.BaseSqoopTool: Setting your password on the command-line is insecure. Consider using -P instead.
18/07/04 14:46:20 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.
18/07/04 14:46:20 INFO tool.CodeGenTool: Beginning code generation
18/07/04 14:46:21 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `students_demo` AS t LIMIT 1
18/07/04 14:46:21 INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `students_demo` AS t LIMIT 1
18/07/04 14:46:21 INFO orm.CompilationManager: HADOOP_MAPRED_HOME is /home/hadoop/app/hadoop-2.6.0-cdh5.7.0
18/07/04 14:46:24 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-hadoop/compile/fc7b53dd6eef701c0731c7a7c4a4b340/students_demo.jar
18/07/04 14:46:24 INFO mapreduce.ExportJobBase: Beginning export of students_demo
18/07/04 14:46:25 INFO Configuration.deprecation: mapred.jar is deprecated. Instead, use mapreduce.job.jar
18/07/04 14:46:25 INFO Configuration.deprecation: mapred.map.max.attempts is deprecated. Instead, use mapreduce.map.maxattempts
18/07/04 14:46:26 INFO Configuration.deprecation: mapred.reduce.tasks.speculative.execution is deprecated. Instead, use mapreduce.reduce.speculative
18/07/04 14:46:26 INFO Configuration.deprecation: mapred.map.tasks.speculative.execution is deprecated. Instead, use mapreduce.map.speculative
18/07/04 14:46:26 INFO Configuration.deprecation: mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps
...
18/07/04 14:46:55 INFO mapreduce.ExportJobBase: Transferred 672 bytes in 29.3122 seconds (22.9256 bytes/sec)
18/07/04 14:46:55 INFO mapreduce.ExportJobBase: Exported 3 records.

# mysql中查看
mysql> select * from students_demo;
+------+-------+------+
| id   | name  | age  |
+------+-------+------+
| 1001 | lodd  |   23 |
| 1002 | sdfs  |   21 |
| 1003 | sdfsa |   24 |
+------+-------+------+
3 rows in set (0.00 sec)

如果再導入一次會追加在表中

# 增加columns參數
[hadoop@hadoop000 ~]$ sqoop export > --connect jdbc:mysql://localhost:3306/test > --username root > --password 123456 > --table students_demo2 > --export-dir /user/hadoop/students/ > --columns id,name

# mysql結果
mysql> select * from students_demo2;
+------+-------+------+
| id   | name  | age  |
+------+-------+------+
| 1001 | lodd  | NULL |
| 1002 | sdfs  | NULL |
| 1003 | sdfsa | NULL |
+------+-------+------+
3 rows in set (0.00 sec)

7.MySQL的中的數據導入到Hive中

常用參數：

--create-hive-table 創建目標表，如果有會報錯
--hive-database 指定hive數據庫
--hive-import 指定導入hive（沒有這個條件導入到hdfs中）
--hive-overwrite 覆蓋
--hive-table 指定hive中表的名字，如果不指定使用導入的表的表名
--hive-partition-key 指定Hive分區表字段
--hive-partition-value 指定導入的分區值

首次導入可能會報錯如下：
18/07/04 15:06:26 ERROR hive.HiveConfig: Could not load org.apache.hadoop.hive.conf.HiveConf. Make sure HIVE_CONF_DIR is set correctly.<br/>18/07/04 15:06:26 ERROR tool.ImportTool: Encountered IOException running import job: java.io.IOException: java.lang.ClassNotFoundException: org.apache.hadoop.hive.conf.HiveConf
解決方法：到hive目錄的lib下拷貝幾個jar包，問題就解決了

# 報錯解決方法
[hadoop@hadoop000 lib]$ pwd
/home/hadoop/app/hive-1.1.0-cdh5.7.0/lib
[hadoop@hadoop000 lib]$ cp hive-common-1.1.0-cdh5.7.0.jar /home/hadoop/app/sqoop-1.4.6-cdh5.7.0/lib/
[hadoop@hadoop000 lib]$ cp hive-shims* /home/hadoop/app/sqoop-1.4.6-cdh5.7.0/lib/

# 報錯解決後執行導入
[hadoop@hadoop000 ~]$ sqoop import > --connect jdbc:mysql://localhost:3306/test > --username root --password 123456 > --table students > --create-hive-table > --hive-database hive > --hive-import > --hive-overwrite > --hive-table stu_import > --mapreduce-job-name FromMySQL2HIVE > --delete-target-dir > --fields-terminated-by ‘\t‘ > -m 1 > --null-non-string 0

# Hive中查看
hive> show tables;
OK
stu_import
Time taken: 0.051 seconds, Fetched: 1 row(s)
hive> select * from stu_import;
OK
1001    lodd    23
1002    sdfs    21
1003    sdfsa   24
Time taken: 0.969 seconds, Fetched: 3 row(s)

建議：導入Hive不建議大家使用–create-hive-table參數,建議事先創建好hive表；因為自動創建的表字段類型可能並不是我們想要的。

# 增加partition參數
[hadoop@hadoop000 ~]$ sqoop import > --connect jdbc:mysql://localhost:3306/test > --username root --password 123456 > --table students > --create-hive-table > --hive-database hive > --hive-import > --hive-overwrite > --hive-table stu_import2 > --mapreduce-job-name FromMySQL2HIVE2 > --delete-target-dir > --fields-terminated-by ‘\t‘ > -m 1 > --null-non-string 0 > --hive-partition-key dt > --hive-partition-value "2018-08-08"
# Hive中查看
hive> select * from stu_import2;
OK
1001    lodd    23      2018-08-08
1002    sdfs    21      2018-08-08
1003    sdfsa   24      2018-08-08
Time taken: 0.192 seconds, Fetched: 3 row(s)

8.sqoop job的使用

sqoop job可以將執行的語句變成一個job，並不是在創建語句的時候執行，你可以查看該job，可以任何時候執行該job，也可以刪除job，這樣就方便我們進行任務的調度。

--create <job-id> 創建一個新的job.
--delete <job-id> 刪除job
--exec <job-id> 執行job
--show <job-id> 顯示job的參數
--list 列出所有的job

# 創建job
[hadoop@hadoop000 ~]$ sqoop job --create person_job1 -- import --connect jdbc:mysql://localhost:3306/test > --username root > --password 123456 > --table students_demo > -m 1 > --delete-target-dir
# 查看job
[hadoop@hadoop000 ~]$ sqoop job --list
Available jobs:
  person_job1
# 執行job 會提示輸入mysql root用戶密碼
[hadoop@hadoop000 ~]$ sqoop job --exec person_job1
# HDFS查看
[hadoop@hadoop000 lib]$ hadoop fs -ls /user/hadoop/students_demo
Found 2 items
-rw-r--r--   1 hadoop supergroup          0 2018-07-04 15:34 /user/hadoop/students_demo/_SUCCESS
-rw-r--r--   1 hadoop supergroup         40 2018-07-04 15:34 /user/hadoop/students_demo/part-m-00000

我們發現執行person_job的時候，需要輸入數據庫的密碼，怎麽樣能不輸入密碼呢
配置sqoop-site.xml即可解決

# 將sqoop.metastore.client.record.password參數的註釋去掉 或者再添加一下
[hadoop@hadoop000 conf]$ pwd
/home/hadoop/app/sqoop-1.4.6-cdh5.7.0/conf
[hadoop@hadoop000 conf]$ vi sqoop-site.xml
  <property>
    <name>sqoop.metastore.client.record.password</name>
    <value>true</value>
    <description>If true, allow saved passwords in the metastore.
    </description>
  </property>

參考文章：https://blog.csdn.net/yu0_zhang0/article/details/79069251

Sqoop基本語法簡介

info behavior relax 執行 parameter tab could list over 簡介：本篇文章主要介紹sqoop的基本語法及簡單使用方法。 1.查看命令幫助 [hadoop@hadoop000 ~]$ sqoop help usage: sqoop

Sqoop基本語法簡介

1.查看命令幫助

2.list-databases

3.list-tables

4.將mysql導入HDFS中(import)

5.eval

6.export (HDFS數據導出到MySQL或Hive中的數據導入到MySQL)

7.MySQL的中的數據導入到Hive中

8.sqoop job的使用

Sqoop基本語法簡介

PL/SQL簡介與基本語法

Python簡介、安裝、更新、基本語法及資料型別

JS簡介和基本語法

【1】Groovy語言學習：groovy語言簡介及基本語法

[C#] CSharp 基本語法

Swift基礎學習(一)基本語法

C# 基本語法

SQL語句基本語法

Xml一（基本語法和約束）、

Markdown 基本語法

Day1[下] - Python基礎基本語法、流程控制

python 基本語法速覽，快速入門

R的基本語法總結

1.基本語法

Kotlin VS Java：基本語法差異（轉載）

1go基本語法

shell--grep命令+正則表達式+基本語法

Shell第三篇：基本語法

js基本語法及註意事項

Sqoop基本語法簡介

1.查看命令幫助

2.list-databases

3.list-tables

4.將mysql導入HDFS中(import)

5.eval

6.export (HDFS數據導出到MySQL或Hive中的數據導入到MySQL)

7.MySQL的中的數據導入到Hive中

8.sqoop job的使用

相關推薦