Sqoop import examples Lesson1

________________________________________$ mysql -u root

mysql>

mysql> show databases;
+——————–+
| Database           |
+——————–+
| information_schema |
| datahub            |
| datamart           |
| halitics           |
| hivemetastore      |
| movielens          |
| mysql              |
| product            |
| siri               |
| training           |
+——————–+
10 rows in set (0.11 sec)

mysql>

mysql> create database practice;
Query OK, 1 row affected (0.02 sec)

mysql> use practice;
Database changed

mysql> show tables;
Empty set (0.00 sec)

mysql> create table samp(id int primary key,
->     name char(10), sal int,
->   sex char(1), dno int);
Query OK, 0 rows affected (0.00 sec)

mysql> describe samp;
+——-+———-+——+—–+———+——-+
| Field | Type     | Null | Key | Default | Extra |
+——-+———-+——+—–+———+——-+
| id    | int(11)  | NO   | PRI | NULL    |       |
| name  | char(10) | YES  |     | NULL    |       |
| sal   | int(11)  | YES  |     | NULL    |       |
| sex   | char(1)  | YES  |     | NULL    |       |
| dno   | int(11)  | YES  |     | NULL    |       |
+——-+———-+——+—–+———+——-+
5 rows in set (0.00 sec)

mysql>

mysql>
mysql> insert into samp values(101,’aaaa’,10000,’m’,12)
-> ;
Query OK, 1 row affected (0.01 sec)

mysql> insert into samp values(102,’addaaa’,20000,’f’,12);
Query OK, 1 row affected (0.00 sec)

mysql> insert into samp values(103,’ada’,20000,’f’,13);
Query OK, 1 row affected (0.00 sec)

mysql> insert into samp values(104,’ada’,50000,’f’,11);
Query OK, 1 row affected (0.00 sec)

mysql> insert into samp values(105,’addda’,70000,’m’,12);
Query OK, 1 row affected (0.00 sec)

mysql> insert into samp values(106,’adddda’,80000,’m’,11);
Query OK, 1 row affected (0.00 sec)

mysql> insert into samp values(107,’xadddda’,70000,’f’,12);
Query OK, 1 row affected (0.00 sec)

mysql>

mysql> select * from samp;
+—–+———+——-+——+——+
| id  | name    | sal   | sex  | dno  |
+—–+———+——-+——+——+
| 101 | aaaa    | 10000 | m    |   12 |
| 102 | addaaa  | 20000 | f    |   12 |
| 103 | ada     | 20000 | f    |   13 |
| 104 | ada     | 50000 | f    |   11 |
| 105 | addda   | 70000 | m    |   12 |
| 106 | adddda  | 80000 | m    |   11 |
| 107 | xadddda | 70000 | f    |   12 |
+—–+———+——-+——+——+
7 rows in set (0.00 sec)

______________________

[training@localhost ~]$ sqoop import    –connect  jdbc:mysql://localhost/practice   –username root   –table  samp   –target-dir   sqimp1

[training@localhost ~]$ hadoop fs -ls sqimp1
Found 6 items
-rw-r–r–   1 training supergroup          0 2016-06-01 19:36 /user/training/sqimp1/_SUCCESS
drwxr-xr-x   – training supergroup          0 2016-06-01 19:36 /user/training/sqimp1/_logs
-rw-r–r–   1 training supergroup         42 2016-06-01 19:36 /user/training/sqimp1/part-m-00000
-rw-r–r–   1 training supergroup         38 2016-06-01 19:36 /user/training/sqimp1/part-m-00001
-rw-r–r–   1 training supergroup         21 2016-06-01 19:36 /user/training/sqimp1/part-m-00002
-rw-r–r–   1 training supergroup         45 2016-06-01 19:36 /user/training/sqimp1/part-m-00003
[training@localhost ~]$

[training@localhost ~]$ hadoop fs -cat sqimp1/part-m-00000
101,aaaa,10000,m,12
102,addaaa,20000,f,12
[training@localhost ~]$ hadoop fs -cat sqimp1/part-m-00001
103,ada,20000,f,13
104,ada,50000,f,11
[training@localhost ~]$ hadoop fs -cat sqimp1/part-m-00002
105,addda,70000,m,12
[training@localhost ~]$ hadoop fs -cat sqimp1/part-m-00003
106,adddda,80000,m,11
107,xadddda,70000,f,12
[training@localhost ~]$

by default sqoop initiates 4 mappers.

these mappers will parallely importing data .

that means, table is splitted into 4 parts,
each part is taken by seperate mapper.

how to controll number of mappers.

use the option -m <number>

ex:   -m 2

[training@localhost ~]$ sqoop import \
>  –connect  jdbc:mysql://localhost/practice \
>  –username root  \
>  –table samp -m 2 –target-dir sqimp2

[training@localhost ~]$ hadoop fs -ls sqimp2
Found 4 items
-rw-r–r–   1 training supergroup          0 2016-06-01 19:48 /user/training/sqimp2/_SUCCESS
drwxr-xr-x   – training supergroup          0 2016-06-01 19:48 /user/training/sqimp2/_logs
-rw-r–r–   1 training supergroup         61 2016-06-01 19:48 /user/training/sqimp2/part-m-00000
-rw-r–r–   1 training supergroup         85 2016-06-01 19:48 /user/training/sqimp2/part-m-00001
[training@localhost ~]$ hadoop fs -cat sqimp2/part-m-00000
101,aaaa,10000,m,12
102,addaaa,20000,f,12
103,ada,20000,f,13
[training@localhost ~]$

____________________

mysql> create table damp(id int,
->   name char(10), sal int, sex char(1),
->   dno int);
Query OK, 0 rows affected (0.00 sec)

mysql> insert into damp select * from samp;
Query OK, 7 rows affected (0.01 sec)
Records: 7  Duplicates: 0  Warnings: 0

mysql> select * from damp;
+——+———+——-+——+——+
| id   | name    | sal   | sex  | dno  |
+——+———+——-+——+——+
|  101 | aaaa    | 10000 | m    |   12 |
|  102 | addaaa  | 20000 | f    |   12 |
|  103 | ada     | 20000 | f    |   13 |
|  104 | ada     | 50000 | f    |   11 |
|  105 | addda   | 70000 | m    |   12 |
|  106 | adddda  | 80000 | m    |   11 |
|  107 | xadddda | 70000 | f    |   12 |
+——+———+——-+——+——+
7 rows in set (0.00 sec)

mysql>

if table is not having primary key,

number of mappers should be 1.

why?

when  multiple mappers mappers initiated,
first mapper automatically points to begining  record of the first split. remaining mappers can not point to begining of their splits randomly. bcoz there is no primary key.

so only sequential reading is allowed from beginning of table to end of the table.

to make begining to ending as one split,
only one mapper should be intiated.

-m 1.

______________________

[training@localhost ~]$ sqoop import  –connect  jdbc:mysql://localhost/practice  –username root   –table damp -m 1  –target-dir sqimp4

[training@localhost ~]$ hadoop fs -ls sqimp4
Found 3 items
-rw-r–r–   1 training supergroup          0 2016-06-01 19:58 /user/training/sqimp4/_SUCCESS
drwxr-xr-x   – training supergroup          0 2016-06-01 19:58 /user/training/sqimp4/_logs
-rw-r–r–   1 training supergroup        146 2016-06-01 19:58 /user/training/sqimp4/part-m-00000
[training@localhost ~]$ hadoop fs -cat sqimp4/part-m-00000
101,aaaa,10000,m,12
102,addaaa,20000,f,12
103,ada,20000,f,13
104,ada,50000,f,11
105,addda,70000,m,12
106,adddda,80000,m,11
107,xadddda,70000,f,12
[training@localhost ~]$

_______________________________

to filter rows at the time importing..

[training@localhost ~]$ sqoop import  –connect  jdbc:mysql://localhost/practice  –username root   –table damp -m 1 –where ‘sal<50000’ –target-dir sqimp5

[training@localhost ~]$ hadoop fs -cat sqimp5/part-m-00000
101,aaaa,10000,m,12
102,addaaa,20000,f,12
103,ada,20000,f,13
[training@localhost ~]$

______________________

[training@localhost ~]$ sqoop import  –connect  jdbc:mysql://localhost/practice  –username root   –table damp -m 1 –where ‘sex=”m”‘ –target-dir sqimp6

[training@localhost ~]$ hadoop fs -cat sqimp6/part-m-00000
101,aaaa,10000,m,12
105,addda,70000,m,12
106,adddda,80000,m,11
[training@localhost ~]$

_______________________

importing selective columns….

[training@localhost ~]$ sqoop import  –connect  jdbc:mysql://localhost/practice  –username root   –table damp -m 1 –columns name,sal,dno –target-dir sqimp7

[training@localhost ~]$ hadoop fs -cat sqimp7/part-m-00000
aaaa,10000,12
addaaa,20000,12
ada,20000,13
ada,50000,11
addda,70000,12
adddda,80000,11
xadddda,70000,12
[training@localhost ~]$

———————-END———————-
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