Sqoop基本语法简介
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简介：
本篇文章主要介绍sqoop的基本语法及简单使用方法。
1.查看命令帮助
[[email protected] ~]$ 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
[[email protected] ~]$ 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
$HADOOP_MAPRED_HOME_ARG
--help Print usage instructions
-P Read password from console
--password <password> Set authentication
password
--password-alias <password-alias> Credential provider
password alias
--password-file <password-file> Set authentication
password file path
--relaxed-isolation Use read-uncommitted
isolation for imports
--skip-dist-cache Skip copying jars to
distributed cache
--username <username> Set authentication
username
--verbose Print more information
while working
# 简单使用
[[email protected] ~]$ sqoop list-databases > --connect jdbc:mysql://localhost:3306 > --username root > --password 123456
# 结果
information_schema
mysql
performance_schema
slow_query_log
sys
test3.list-tables
# 命令帮助
[[email protected] ~]$ 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
$HADOOP_MAPRED_HOME_ARG
--help Print usage instructions
-P Read password from console
--password <password> Set authentication
password
--password-alias <password-alias> Credential provider
password alias
--password-file <password-file> Set authentication
password file path
--relaxed-isolation Use read-uncommitted
isolation for imports
--skip-dist-cache Skip copying jars to
distributed cache
--username <username> Set authentication
username
--verbose Print more information
while working
# 使用方法
[[email protected] ~]$ sqoop list-tables > --connect jdbc:mysql://localhost:3306/test > --username root > --password 123456
# 结果
t_order
test0001
test_1013
test_dyc
test_tb4.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
[[email protected] ~]$ sqoop help import
# 执行import
[[email protected] ~]$ 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
[[email protected] ~]$ 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.
# 生成的日志信息大家一定要好好理解
# 查看HDFS上的文件
[[email protected] ~]$ 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
[[email protected]doop000 ~]$ 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
[[email protected] ~]$ 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
[[email protected] ~]$ 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
[[email protected] ~]$ hadoop fs -cat /user/hadoop/students/"part*"
1001,lodd,23
1002,sdfs,21
1003,sdfsa,24# 使用where 参数
[[email protected] ~]$ sqoop import > --connect jdbc:mysql://localhost:3306/test > --username root --password 123456 > --table students > --mapreduce-job-name FromMySQL2HDFS2 > --delete-target-dir > --fields-terminated-by ‘ ‘ > -m 1 > --null-string 0 > --columns "name" > --target-dir STU_COLUMN_WHERE > --where ‘id<1002‘
# HDFS 结果
[[email protected] ~]$ hadoop fs -cat STU_COLUMN_WHERE/"part*"
lodd# 使用query 参数
[[email protected] ~]$ sqoop import > --connect jdbc:mysql://localhost:3306/test > --username root --password 123456 > --mapreduce-job-name FromMySQL2HDFS3 > --delete-target-dir > --fields-terminated-by ‘ ‘ > -m 1 > --null-string 0 > --target-dir STU_COLUMN_QUERY > --query "select * from students where id>1001 and $CONDITIONS"
# HDFS查看
[[email protected] ~]$ hadoop fs -cat STU_COLUMN_QUERY/"part*"
1002 sdfs 21
1003 sdfsa 24# 使用options-file参数
[[email protected] ~]$ 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
[[email protected] ~]$ sqoop --options-file /home/hadoop/sqoop-import-hdfs.txt
# HDFS查看
[[email protected] ~]$ hadoop fs -cat STU_option_file/"part*"
1001,lodd,23
1002,sdfs,21
1003,sdfsa,245.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
[[email protected] ~]$ 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
$HADOOP_MAPRED_HOME_ARG
--help Print usage instructions
-P Read password from console
--password <password> Set authentication
password
--password-alias <password-alias> Credential provider
password alias
--password-file <password-file> Set authentication
password file path
--relaxed-isolation Use read-uncommitted
isolation for imports
--skip-dist-cache Skip copying jars to
distributed cache
--username <username> Set authentication
username
--verbose Print more information
while working
SQL evaluation arguments:
-e,--query <statement> Execute ‘statement‘ in SQL and exit
# 执行
[[email protected] ~]$ 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
[[email protected] ~]$ hadoop fs -cat /user/hadoop/students/part-m-00000
1001,lodd,23
1002,sdfs,21
1003,sdfsa,24
# export导出到mysql
[[email protected] ~]$ 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)如果再导入一次会追加在表中
# 增加columns参数
[[email protected] ~]$ 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.<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
[[email protected] lib]$ pwd
/home/hadoop/app/hive-1.1.0-cdh5.7.0/lib
[[email protected] lib]$ cp hive-common-1.1.0-cdh5.7.0.jar /home/hadoop/app/sqoop-1.4.6-cdh5.7.0/lib/
[[email protected] lib]$ cp hive-shims* /home/hadoop/app/sqoop-1.4.6-cdh5.7.0/lib/# 报错解决后执行导入
[[email protected] ~]$ 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 ‘ ‘ > -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
[[email protected] ~]$ 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 ‘ ‘ > -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<job-id> 创建一个新的job.--delete<job-id> 删除job--exec<job-id> 执行job--show<job-id> 显示job的参数--list列出所有的job
# 创建job
[[email protected] ~]$ 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
[[email protected] ~]$ sqoop job --list
Available jobs:
person_job1
# 执行job 会提示输入mysql root用户密码
[[email protected] ~]$ sqoop job --exec person_job1
# HDFS查看
[[email protected] 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
[[email protected] conf]$ pwd
/home/hadoop/app/sqoop-1.4.6-cdh5.7.0/conf
[[email protected] 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>参考文章：https://blog.csdn.net/yu0_zhang0/article/details/79069251
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