1. 从HDFS导出到RDBMS数据库
1.1 准备工作
写一个文件
sqoop_export.txt
1201,laojiao, manager,50000, TP
1202,fantj,preader,50000,TP
1203,jiao,dev,30000,AC
1204,laowang,dev,30000,AC
1205,laodu,admin,20000,TP
1206,laop,grp des,20000,GR
上传到hdfs：
hadoop fs -put sqoop_export.txt /sqoop/export/
创建mysql数据库并增加授权:
create database sqoopdb;
grant all privileges on sqoopdb.* to 'sqoop'@'%' identified by 'sqoop';
grant all privileges on sqoopdb.* to 'sqoop'@'localhost' identified by 'sqoop';
grant all privileges on sqoopdb.* to 'sqoop'@'s166' identified by 'sqoop';
flush privileges;
创建表：
use sqoopdb;
CREATE TABLE employee (
id INT NOT NULL PRIMARY KEY,
name VARCHAR(20),
deg VARCHAR(20),
salary INT,
dept VARCHAR(10));
1.2 执行导出命令
bin/sqoop export \
--connect jdbc:mysql://s166:3306/sqoopdb \
--username sqoop \
--password sqoop \
--table employee \
--export-dir /sqoop/export/emp/ \
--input-fields-terminated-by ','
我执行的时候发现它总在报这个错：
ERROR tool.ExportTool: Encountered IOException running export job: java.io.FileNotFoundException: File does not exist: hdfs://s166/home/fantj/sqoop/lib/avro-mapred-1.5.3.jar
然后找了很多解决方案：
- 替换mysql-java的jar包，换个高版本的。
- 修改hadoop的
mapred-site.xml文件（先更名mv mapred-site.xml.template mapred-site.xml）
mapreduce.framework.name
yarn
解决后再执行：
Map-Reduce Framework
Map input records=6
Map output records=6
Input split bytes=107
Spilled Records=0
Failed Shuffles=0
Merged Map outputs=0
GC time elapsed (ms)=95
CPU time spent (ms)=1210
Physical memory (bytes) snapshot=97288192
Virtual memory (bytes) snapshot=2075623424
Total committed heap usage (bytes)=17006592
File Input Format Counters
Bytes Read=0
File Output Format Counters
Bytes Written=0
22:34:37 INFO mapreduce.ExportJobBase: Transferred 274 bytes in 47.346 seconds (5.7872 bytes/sec)
22:34:37 INFO mapreduce.ExportJobBase: Exported 6 records.
说明处理成功！
1.3 验证mysql表
mysql> select * from employee;
+------+---------+----------+--------+------+
| id | name | deg | salary | dept |
+------+---------+----------+--------+------+
| 1201 | laojiao | manager | 50000 | TP |
| 1202 | fantj | preader | 50000 | TP |
| 1203 | jiao | dev | 30000 | AC |
| 1204 | laowang | dev | 30000 | AC |
| 1205 | laodu | admin | 20000 | TP |
| 1206 | laop | grp des | 20000 | GR |
+------+---------+----------+--------+------+
6 rows in set (0.07 sec)
2. 导入表表数据到HDFS
bin/sqoop import \
--connect jdbc:mysql://s166:3306/sqoopdb \
--username sqoop \
--password sqoop \
--table employee --m 1
22:44:26 INFO mapreduce.Job: The url to track the job: http://s166:8088/proxy/application_1532679575794_0002/
File System Counters
FILE: Number of bytes read=0
FILE: Number of bytes written=123111
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=87
HDFS: Number of bytes written=161
HDFS: Number of read operations=4
HDFS: Number of large read operations=0
HDFS: Number of write operations=2
Job Counters
Launched map tasks=1
Other local map tasks=1
Total time spent by all maps in occupied slots (ms)=5972
Total time spent by all reduces in occupied slots (ms)=0
Total time spent by all map tasks (ms)=5972
Total vcore-seconds taken by all map tasks=5972
Total megabyte-seconds taken by all map tasks=6115328
Map-Reduce Framework
Map input records=6
Map output records=6
Input split bytes=87
Spilled Records=0
Failed Shuffles=0
Merged Map outputs=0
GC time elapsed (ms)=195
CPU time spent (ms)=970
Physical memory (bytes) snapshot=99921920
Virtual memory (bytes) snapshot=2079825920
Total committed heap usage (bytes)=18358272
File Input Format Counters
Bytes Read=0
File Output Format Counters
Bytes Written=161
22:44:57 INFO mapreduce.ImportJobBase: Transferred 161 bytes in 34.5879 seconds (4.6548 bytes/sec)
22:44:57 INFO mapreduce.ImportJobBase: Retrieved 6 records.
3. 导入关系表到HIVE
sqoop import --connect jdbc:mysql://s166:3306/sqoopdb --username sqoop --password sqoop --table employee --hive-import --m 1
4. 导入到HDFS指定目录
sqoop import \
--connect jdbc:mysql://s166:3306/sqoopdb \
--username sqoop \
--password sqoop \
--target-dir /queryresult \
--table employee --m 1
5. 导入表数据子集
我们可以导入表的使用Sqoop导入工具，"where"子句的一个子集。并将结果存储在HDFS的目标目录。
sqoop import \
--connect jdbc:mysql://s166:3306/sqoopdb \
--username sqoop \
--password sqoop \
--where "salary>10000" \
--target-dir /wherequery \
--table employee --m 1