简介：
本篇文章主要介绍sqoop的基本语法及简单使用方法。
黄平网站建设公司创新互联建站,黄平网站设计制作，有大型网站制作公司丰富经验。已为黄平上1000家提供企业网站建设服务。企业网站搭建\成都外贸网站建设公司要多少钱，请找那个售后服务好的黄平做网站的公司定做！
1.查看命令帮助
[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.
# 这里提示我们使用sqoop help command（要查询的命令）进行该命令的详细查询
2.list-databases
# 查看list-databases命令帮助
[hadoop@hadoop000 ~]$ sqoop help list-databases
usage: sqoop list-databases [GENERIC-ARGS] [TOOL-ARGS]
Common arguments:
--connect Specify JDBC connect
string
--connection-manager Specify connection manager
class name
--connection-param-file Specify connection
parameters file
--driver Manually specify JDBC
driver class to use
--hadoop-home Override
$HADOOP_MAPRED_HOME_ARG
--hadoop-mapred-home Override
$HADOOP_MAPRED_HOME_ARG
--help Print usage instructions
-P Read password from console
--password Set authentication
password
--password-alias Credential provider
password alias
--password-file Set authentication
password file path
--relaxed-isolation Use read-uncommitted
isolation for imports
--skip-dist-cache Skip copying jars to
distributed cache
--username Set authentication
username
--verbose Print more information
while working
# 简单使用
[hadoop@oradb3 ~]$ sqoop list-databases \
> --connect jdbc:MySQL://localhost:3306 \
> --username root \
> --password 123456
# 结果
information_schema
mysql
performance_schema
slow_query_log
sys
test
3.list-tables
# 命令帮助
[hadoop@hadoop000 ~]$ sqoop help list-tables
usage: sqoop list-tables [GENERIC-ARGS] [TOOL-ARGS]
Common arguments:
--connect Specify JDBC connect
string
--connection-manager Specify connection manager
class name
--connection-param-file Specify connection
parameters file
--driver Manually specify JDBC
driver class to use
--hadoop-home Override
$HADOOP_MAPRED_HOME_ARG
--hadoop-mapred-home Override
$HADOOP_MAPRED_HOME_ARG
--help Print usage instructions
-P Read password from console
--password Set authentication
password
--password-alias Credential provider
password alias
--password-file Set authentication
password file path
--relaxed-isolation Use read-uncommitted
isolation for imports
--skip-dist-cache Skip copying jars to
distributed cache
--username Set authentication
username
--verbose Print more information
while working
# 使用方法
[hadoop@hadoop000 ~]$ sqoop list-tables \
> --connect jdbc:mysql://localhost:3306/test \
> --username root \
> --password 123456
# 结果
t_order
test0001
test_1013
test_dyc
test_tb
4.将mysql导入HDFS中(import)
(默认导入当前用户目录下/user/用户名/表名)
说到这里扩展一个小知识点：
- hadoop fs -ls 显示的是当前的用户目录 即/user/hadoop
hadoop fs -ls / 显示的是HDFS根目录
# 查看命令帮助
[hadoop@hadoop000 ~]$ sqoop help import
# 执行import
[hadoop@hadoop000 ~]$ sqoop import \
> --connect jdbc:mysql://localhost:3306/test \
> --username root \
> --password 123456 \
> --table students
这时很可能会出现这个错误
Exception in thread "main" java.lang.NoClassDefFoundError: org/json/JSONObject
这里我们需要导入java-json.jar包 下载地址 把java-json.jar添加到../sqoop/lib目录下即可
# 再次执行 import导入
[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-cdh6.7.0
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-cdh6.7.0
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.
# 生成的日志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
我们还可以加一些其他参数 使导入过程更加可控
-m 指定启动map进程个数，默认是4个
--delete-target-dir 删除目标目录
--mapreduce-job-name 指定mapreduce的job的名字
--target-dir 导入到指定目录
--fields-terminated-by 指定字段之间的分隔符
--null-string 含义是 string类型的字段，当Value是NULL，替换成指定的字符
--null-non-string 含义是非string类型的字段，当Value是NULL，替换成指定字符
--columns 导入表中的部分字段
--where 按条件导入数据
--query 按照sql语句进行导入 使用--query关键字，就不能使用--table和--columns
--options-file 在文件中执行
# 执行导入
[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 参数
[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 结果
[hadoop@hadoop000 ~]$ hadoop fs -cat STU_COLUMN_WHERE/"part*"
lodd
# 使用query 参数
[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参数
[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
# 执行导入
[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
查看帮助命令对与该命令的解释为： Evaluate a SQL statement and display the results，也就是说执行一个SQL语句并查询出结果。
# 查看命令帮助
[hadoop@hadoop000 ~]$ sqoop help eval
usage: sqoop eval [GENERIC-ARGS] [TOOL-ARGS]
Common arguments:
--connect Specify JDBC connect
string
--connection-manager Specify connection manager
class name
--connection-param-file Specify connection
parameters file
--driver Manually specify JDBC
driver class to use
--hadoop-home Override
$HADOOP_MAPRED_HOME_ARG
--hadoop-mapred-home Override
$HADOOP_MAPRED_HOME_ARG
--help Print usage instructions
-P Read password from console
--password Set authentication
password
--password-alias Credential provider
password alias
--password-file Set authentication
password file path
--relaxed-isolation Use read-uncommitted
isolation for imports
--skip-dist-cache Skip copying jars to
distributed cache
--username Set authentication
username
--verbose Print more information
while working
SQL evaluation arguments:
-e,--query Execute 'statement' in SQL and exit
# 执行
[hadoop@hadoop000 ~]$ 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-cdh6.7.0
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数据导出到MySQL或Hive中的数据导入到MySQL)
常用参数：
--table 指定导出表的名称
--input-fields-terminated-by 指定hdfs上文件的分隔符，默认是逗号
--export-dir 导出数据的目录
--columns 指定导出的字段
在执行导出语句前mysql要先创建表（不创建表会报错）：
# 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导出到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-cdh6.7.0
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-cdh6.7.0
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)
如果再导入一次会追加在表中
# 增加columns参数
[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结果
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的中的数据导入到Hive中
常用参数：
--create-hive-table 创建目标表，如果有会报错
--hive-database 指定hive数据库
--hive-import 指定导入hive（没有这个条件导入到hdfs中）
--hive-overwrite 覆盖
--hive-table 指定hive中表的名字，如果不指定使用导入的表的表名
--hive-partition-key 指定Hive分区表字段
--hive-partition-value 指定导入的分区值
首次导入可能会报错如下：
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.
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
解决方法：到hive目录的lib下拷贝几个jar包，问题就解决了
# 报错解决方法
[hadoop@hadoop000 lib]$ pwd
/home/hadoop/app/hive-1.1.0-cdh6.7.0/lib
[hadoop@hadoop000 lib]$ cp hive-common-1.1.0-cdh6.7.0.jar /home/hadoop/app/sqoop-1.4.6-cdh6.7.0/lib/
[hadoop@hadoop000 lib]$ cp hive-shims* /home/hadoop/app/sqoop-1.4.6-cdh6.7.0/lib/
# 报错解决后执行导入
[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)
建议：导入Hive不建议大家使用–create-hive-table参数,建议事先创建好hive表；因为自动创建的表字段类型可能并不是我们想要的。
# 增加partition参数
[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可以将执行的语句变成一个job，并不是在创建语句的时候执行，你可以查看该job，可以任何时候执行该job，也可以删除job，这样就方便我们进行任务的调度。
--create 创建一个新的job.
--delete 删除job
--exec 执行job
--show 显示job的参数
--list 列出所有的job
# 创建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
# 执行job 会提示输入mysql root用户密码
[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
我们发现执行person_job的时候，需要输入数据库的密码，怎么样能不输入密码呢
配置sqoop-site.xml即可解决
# 将sqoop.metastore.client.record.password参数的注释去掉 或者再添加一下
[hadoop@hadoop000 conf]$ pwd
/home/hadoop/app/sqoop-1.4.6-cdh6.7.0/conf
[hadoop@hadoop000 conf]$ vi sqoop-site.xml
sqoop.metastore.client.record.password
true
If true, allow saved passwords in the metastore.
参考文章：https://blog.csdn.net/yu0_zhang0/article/details/79069251
网页题目：Sqoop基本语法简介
网站URL：
http://cdkjz.cn/article/jighjh.html
