Apache Sqoop - Part 1: Import data from mysql into HDFS

Apache Sqoop

Apache Sqoop is a tool designed for efficiently transferring bulk data in a distributed manner between Apache Hadoop and structured datastores such as relational databases, enterprise data warehouses, and NoSQL systems.  Sqoop can be used to import data into HBase, HDFS and Hive and out of it into RDBMS, in an automated fashion, leveraging Oozie for scheduling.  It has a connector based architecture that supports plugins that provide connectivity to new external systems.

Behind the scenes, the dataset being transferred is split into partitions and map only jobs are launched for each partition with the mappers managing transferring the dataset assigned to it.  Sqoop uses the database metadata to infer the types, and handles the data in a type safe manner.


The following diagrams are from the Apache documentation...

Import process:

Export process:

Supported databases:

Database	version	--direct support?	connect string matches
HSQLDB	1.8.0+	No	jdbc:hsqldb:*//
MySQL	5.0+	Yes	jdbc:mysql://
Oracle	10.2.0+	No	jdbc:oracle:*//
PostgreSQL	8.3+	Yes (import only)	jdbc:postgresql://

A) What's covered in this post:

This blog is mostly notes for myself from what I have learned - with examples that can be tried out.

I used the documentation at the link below for my self-study:
http://archive.cloudera.com/cdh4/cdh/4/sqoop-1.4.2-cdh4.2.0/SqoopUserGuide.html

Versions covered:
Sqoop (1.4.2) with Mysql (5.1.69 ) 

Topics covered:

B) MySql database setup and sample data
C) Installing Sqoop
D) Download save mySQl driver 
E) sqoop list commands
F) Importing data into HDFS
    F1. Importing a table into HDFS - basic import
    F2. Executing imports with an options file for static information
    F3. Import all rows of a table in mySQL, but specific columns of the table
    F4. Import all columns, filter rows using where clause
    F5. Import with a free form query without where clause
    F6. Import with a free form query with where clause
    F7. Controlling parallelism and what's with the $CONDITIONS in the where clause?
    F8. Direct connector
    F9. Import formats
    F10. Split by
    F11. Boundary query
    F12. Fetch size
    F13. Compression
    F14. Incremental imports
    F15. Output line formatting options
    F16. Import all tables
G.  Other functionality
    G1. Direct and quick queries or inserts and updates with sqoop eval
    G2. Sqoop code-gen
    G.3. Sqoop merge


Subsequent blogs will cover importing into Hive and HBase, and export out of HDFS into mysql.


My blogs on Sqoop:

Blog 1: Import from mysql into HDFS
Blog 2: Import from mysql into Hive
Blog 3: Export from HDFS and Hive into mysql
Blog 4: Sqoop best practices
Blog 5: Scheduling of Sqoop tasks using Oozie
Blog 6: Sqoop2

Your thoughts/updates:
If you want to share your thoughts/updates, email me at airawat.blog@gmail.com.

B) MySql Setup:

Step 1: Installing MySql and logging onto MySql

1. Install MySql server
sudo yum install mysql-server

2. To start MySql..
sudo service mysqld start

3. Set root password
Run the command below and follow steps:
sudo /usr/bin/mysql_secure_installation

4. Install MySql client
sudo yum install mysql

5. Check what got installed
sudo rpm -qa|grep -i mysql

Here is the output from my installation:

----------------------------------------------------

mysql-server-5.1.69-1.el6_4.x86_64
mysql-5.1.69-1.el6_4.x86_64
perl-DBD-MySQL-4.013-3.el6.x86_64
mysql-libs-5.1.69-1.el6_4.x86_64

----------------------------------------------------

6. Verify if MySql is running
sudo service mysqld status

7. Configure MySql services to be enabled at boot time..
sudo chkconfig --levels 235 mysqld on

8. Login to MySql
To login as root, from Unix command prompt, enter the command below; Enter password;
mysql -u root -p

9. Create a user
create user 'airawat'@'localhost' identified by 'myPassword';

10. Login as user
mysql -u airawat -p

Step 2: Load sample data

a) Load the sample data available at the link below, following instructions detailed at the site.
http://dev.mysql.com/doc/employee/en/index.html

b) Use the following command to load the employee database, while logged in as root..
mysql -u root -p -t < employees.sql

c) Validate the install by running through the following commands in the MySql shell-

show databases; --Should see employees
use employees; --Use the employees database
show tables;--This lists various tables that can be validated against instructions for the sample data setup

d) Next, still logged in as root, grant the user access to the database.
grant ALL on employees.* to airawat@localhost identified by 'myPassword';

e) Exit out from root and login as the user you set up, and get familiar with the tables

mysql -u airawat -p employees

Now we have the data available to start playing with Sqoop.

C) Installing Sqoop

Refer the Apache Sqoop website for installation.

Sqoop is a client-side tool, so it needs to be installed only on the client on which you wish to use Sqoop.

D) Save mySQl driver 

Download the-

            mysql driver called mysql-connector-java-5.1.25-bin.jar, 

            from http://dev.mysql.com/downloads/connector/j/

            and place it at $SQOOP_HOME/lib on node running sqoop

Note: If you installed CDH, copy to 

/opt/cloudera/parcels/CDH-4.2.0-1.cdh4.2.0.p0.10/lib/sqoop/lib/

E) sqoop list commands

Run the commands on the Unix prompt, on the node where you have sqoop installed.

E.1. List databases

Lists databases in your mysql database.

$ sqoop list-databases --connect jdbc:mysql://<<mysql-server>>/employees --username airawat --password myPassword

.

.

.

13/05/31 16:45:58 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.

information_schema

employees

test

E.2. List tables

Lists tables in your mysql database.

$ sqoop list-tables --connect jdbc:mysql://<<mysql-server>>/employees --username airawat --password myPassword

.

.

.

13/05/31 16:45:58 INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.

departments

dept_emp

dept_manager

employees

employees_exp_stg

employees_export

salaries

titles

F) Importing data in MySql into HDFS 

Replace "airawat-mySqlServer-node" with the host name of the node running mySQL server, replace login credentials and target directory.

F1. Importing a table into HDFS - basic import

$ sqoop import \
--connect jdbc:mysql://airawat-mySqlServer-node/employees \
--username myUID \
--password myPWD \
--table employees \
-m 1 \
--target-dir /user/airawat/sqoop-mysql/employees

.

.

.

.9139 KB/sec)

13/05/31 22:32:25 INFO mapreduce.ImportJobBase: Retrieved 300024 records

F2. Executing imports with an options file for static information

Rather than repeat the import command along with connection related input required, each time, you can pass an options file as an argument to sqoop.  Create a text file, as follows, and save it someplace, locally on the node you are running the sqoop client on.  

Note: This blog does not cover it, but you can create and use a password file as well, that you can pass as argument --password-file <<filename>>.

F2.1. Sample Options file:

___________________________________________________________________________

$ vi SqoopImportOptions.txt 

#

#Options file for sqoop import

#

import

--connect

jdbc:mysql://airawat-mySqlServer-node/employees

--username

myUID

--password

myPwd

#

#All other commands should be specified in the command line

___________________________________________________________________________

F2.2. Command to execute import, using an options file:

Note: Provide the proper path for the options file

F2.2.1. The command

$ sqoop --options-file SqoopImportOptions.txt \
--table departments \
-m 1 \
--target-dir /user/airawat/sqoop-mysql/departments

.

.

.

13/05/31 22:48:55 INFO mapreduce.ImportJobBase: Transferred 153 bytes in 26.2453 seconds (5.8296 bytes/sec)

13/05/31 22:48:55 INFO mapreduce.ImportJobBase: Retrieved 9 records.

-m argument is to specify number of mappers.  The department table has a handful of records, so I am setting it to 1.

F2.2.2. Files created in HDFS:

$ hadoop fs -ls -R sqoop-mysql/

drwxr-xr-x   - airawat airawat          0 2013-05-31 22:48 sqoop-mysql/departments

-rw-r--r--   3 airawat airawat          0 2013-05-31 22:48 sqoop-mysql/departments/_SUCCESS

drwxr-xr-x   - airawat airawat          0 2013-05-31 22:48 sqoop-mysql/departments/_logs

drwxr-xr-x   - airawat airawat          0 2013-05-31 22:48 sqoop-mysql/departments/_logs/history

-rw-r--r--   3 airawat airawat      79467 2013-05-31 22:48 sqoop-mysql/departments/_logs/history/cdh-jt01_1369839495962_job_201305290958_0062_conf.xml

-rw-r--r--   3 airawat airawat      12441 2013-05-31 22:48 sqoop-mysql/departments/_logs/history/job_201305290958_0062_1370058514473_ airawat_departments.jar

-rw-r--r--   3 airawat airawat        153 2013-05-31 22:48 sqoop-mysql/departments/part-m-00000

F2.2.3. Data file contents:

$ hadoop fs -cat sqoop-mysql/departments/part-m-00000 | more

d009,Customer Service
d005,Development
d002,Finance
d003,Human Resources
d001,Marketing
d004,Production
d006,Quality Management
d008,Research
d007,Sales

F3. Import all rows of a table in mySQL, but specific columns of the table

Note: This did not work for me..I am merely providing the statement...will try back to see if this  is a defect in my syntax or a sqoop defect..might be related to case of the column names defined in the database versus the sqoop import statement

$ sqoop --options-file SqoopImportOptions.txt \
--table  dept_emp \
--columns “EMP_NO,DEPT_NO,FROM_DATE,TO_DATE” \
--as-textfile \
-m 1 \
--target-dir /user/airawat/sqoop-mysql/DeptEmp

Error: 
13/05/31 23:01:53 ERROR util.SqlTypeMap: It seems like you are looking up a column that does not
13/05/31 23:01:53 ERROR util.SqlTypeMap: exist in the table. Please ensure that you've specified
13/05/31 23:01:53 ERROR util.SqlTypeMap: correct column names in Sqoop options.
13/05/31 23:01:53 ERROR tool.ImportTool: Imported Failed: column not found: ‘EMP_NO'

F4. Import all columns, filter rows using where clause

 $ sqoop --options-file SqoopImportOptions.txt \
--table employees  \
--where "emp_no > 499948" \
--as-textfile \
-m 1 \
--target-dir /user/airawat/sqoop-mysql/employeeGtTest

F5. Import with a free form query without where clause

 $ sqoop --options-file SqoopImportOptions.txt \
--query 'select EMP_NO,FIRST_NAME,LAST_NAME from employees where  $CONDITIONS' \
-m 1 \
--target-dir /user/airawat/sqoop-mysql/employeeFrfrmQrySmpl2

(Case of the column needs to match that used to create table, or else the import fails)

F6. Import with a free form query with where clause

$ sqoop --options-file SqoopImportOptions.txt \
--query 'select EMP_NO,FIRST_NAME,LAST_NAME from employees where EMP_NO < 20000 AND $CONDITIONS' \
-m 1 \
--target-dir /user/airawat/sqoop-mysql/employeeFrfrmQry1

F7. Controlling parallelism and what's with the $CONDITIONS in the where clause?

This section is straight from the Apache User Guide.

$Conditions

If you want to import the results of a query in parallel, then each map task will need to execute a copy of the query, with results partitioned by bounding conditions inferred by Sqoop. Your query must include the token $CONDITIONS which each Sqoop process will replace with a unique condition expression. You must also select a splitting column with --split-by.

Controlling parallelism

Sqoop imports data in parallel from most database sources. You can specify the number of map tasks (parallel processes) to use to perform the import by using the -m or --num-mappers argument. Each of these arguments takes an integer value which corresponds to the degree of parallelism to employ. By default, four tasks are used. Some databases may see improved performance by increasing this value to 8 or 16. Do not increase the degree of parallelism greater than that available within your MapReduce cluster; tasks will run serially and will likely increase the amount of time required to perform the import. Likewise, do not increase the degree of parallism higher than that which your database can reasonably support. Connecting 100 concurrent clients to your database may increase the load on the database server to a point where performance suffers as a result.

When performing parallel imports, Sqoop needs a criterion by which it can split the workload. Sqoop uses a splitting column to split the workload. By default, Sqoop will identify the primary key column (if present) in a table and use it as the splitting column. The low and high values for the splitting column are retrieved from the database, and the map tasks operate on evenly-sized components of the total range. For example, if you had a table with a primary key column of id whose minimum value was 0 and maximum value was 1000, and Sqoop was directed to use 4 tasks, Sqoop would run four processes which each execute SQL statements of the form SELECT * FROM sometable WHERE id >= lo AND id < hi, with (lo, hi) set to (0, 250), (250, 500), (500, 750), and (750, 1001) in the different tasks.

If the actual values for the primary key are not uniformly distributed across its range, then this can result in unbalanced tasks. You should explicitly choose a different column with the --split-by argument. For example, --split-by employee_id. 

Note: Sqoop cannot currently split on multi-column indices. If your table has no index column, or has a multi-column key, then you must also manually choose a splitting column.

F8. Direct connector

By default, the import process will use JDBC.  Performance can be improved by using database specific and native data movement tools. Like for example, MySQL provides the mysqldump tool which can export data from MySQL to other systems very quickly. When we provide the argument, "--direct" we are specifying that Sqoop should attempt the direct import channel. 

Note: Currently, direct mode does not support imports of large object columns.

$ sqoop --options-file SqoopImportOptions.txt \

--query 'select EMP_NO,FIRST_NAME,LAST_NAME from employees where EMP_NO < 20000 AND $CONDITIONS' \

-m 1 \

--direct \

--target-dir /user/airawat/sqoop-mysql/employeeUsingDirect

F9. Import formats

With mysql, text file is the only format supported;  Avro and Sequence file formatted imports are feasible through other RDBMS - refer Apache Sqoop documentation for more information.

Text file is the default format.

F10. Split by

Refer section on controlling parallelism..

$ sqoop --options-file SqoopImportOptions.txt \
--query 'select EMP_NO,FIRST_NAME,LAST_NAME from employees where $CONDITIONS' \

--split-by EMP_NO \

--direct \

--target-dir /user/airawat/sqoop-mysql/SplitByExampleImport

F11. Boundary query

Again related to controlling parallelism..

By default sqoop will use query select min(<split-by>), max(<split-by>) from <table name> to find out boundaries for creating splits. In some cases this query is not the most optimal so you can specify any arbitrary query returning two numeric columns using --boundary-query argument.

(Note: This did not work for me..got an error;  Need to try again, to see if it is an error on my end, or sqoop related)

$ sqoop --options-file SqoopImportOptions.txt \

--query 'select EMP_NO,FIRST_NAME,LAST_NAME from employees where $CONDITIONS' \

--boundary-query “SELECT MIN(EMP_NO), MAX(EMP_NO) from employees” \

--split-by EMP_NO \

--direct \

--target-dir /user/airawat/sqoop-mysql/BoundaryQuerySample

F12. Fetch size

This argument specifies to sqoop the number of entries to read from database at once.

$ sqoop --options-file SqoopImportOptions.txt \

--query 'select EMP_NO,FIRST_NAME,LAST_NAME from employees where $CONDITIONS' \

--fetch-size=50000 \

--split-by EMP_NO \

--direct \

--target-dir /user/airawat/sqoop-mysql/FetchSize

F13. Compression

Use the --compress argument to enable compression; If you dont specify a compression codec (--compression-codec), the default gzip will be used.

The command:
$ sqoop --options-file SqoopImportOptions.txt \

--query 'select EMP_NO,FIRST_NAME,LAST_NAME from employees where $CONDITIONS' \

-z \

--split-by EMP_NO \

--direct \

--target-dir /user/airawat/sqoop-mysql/CompressedSample

The output:

$ hadoop fs -ls -R sqoop-mysql/CompressedSample | grep part*

-rw-r--r--   3 airawat airawat     896377 2013-05-31 23:49 sqoop-mysql/CompressedSample/part-m-00000.gz

-rw-r--r--   3 airawat airawat     499564 2013-05-31 23:49 sqoop-mysql/CompressedSample/part-m-00001.gz

-rw-r--r--   3 airawat airawat     409199 2013-05-31 23:49 sqoop-mysql/CompressedSample/part-m-00002.gz

-rw-r--r--   3 airawat airawat     907330 2013-05-31 23:49 sqoop-mysql/CompressedSample/part-m-00003.gz

F14. Incremental imports

F14.1. Prep

Import part of the employee table ahead of time..

The command:

$ sqoop --options-file SqoopImportOptions.txt \

--query 'select EMP_NO,FIRST_NAME,LAST_NAME from employees where EMP_NO < 15000 AND $CONDITIONS' \

--split-by EMP_NO \

--direct \

--target-dir /user/airawat/sqoop-mysql/IncrementalImports

The number of records imported:

$ hadoop fs -ls -R sqoop-mysql/IncrementalImports |grep part* | awk '{print $8}' |xargs hadoop fs -cat | wc -l

4999

F14.2. Run the incremental import

The three arguments highlighted in yellow need to be specified.

Arguments        

--check-column (col): Specifies the column to be examined when determining which rows to import.

--incremental (mode): Specifies how Sqoop determines which rows are new. Legal values for mode include append and lastmodified.

--last-value (value): Specifies the maximum value of the check column from the previous import.

Command

$ sqoop --options-file SqoopImportOptions.txt \

--query 'select EMP_NO,FIRST_NAME,LAST_NAME from employees where $CONDITIONS' \

--check-column EMP_NO \

--incremental append \

--last-value 14999 \

--split-by EMP_NO \

--direct \

--target-dir /user/airawat/sqoop-mysql/IncrementalImports

Record count in mysql:

mysql> select count(*) from employees;

+----------+
| count(*) |
+----------+
|   300024 |
+----------+
1 row in set (0.12 sec)

Record count in HDFS:

$ hadoop fs -ls -R sqoop-mysql/IncrementalImports |grep part* | awk '{print $8}' |xargs hadoop fs -cat | wc -l

300024

F15. Output line formatting options

Refer Apache sqoop documentation for more on this topic..

$ sqoop --options-file SqoopImportOptions.txt \

--query 'select EMP_NO,FIRST_NAME,LAST_NAME from employees where $CONDITIONS' \

--fields-terminated-by , \

--escaped-by \\ \

--enclosed-by '\"' \

--split-by EMP_NO \

--direct \

--target-dir /user/airawat/sqoop-mysql/LineFormattingOptions

There is an argument called "--mysql-delimiters".  It uses MySQL’s default delimiter set: fields: , lines: \n escaped-by: \ optionally-enclosed-by: '

F16. Import all tables

Command:
$ sqoop --options-file SqoopImportAllTablesOptions.txt \
--direct \
--warehouse-dir sqoop-mysql/EmployeeDatabase

Options file content:
$ more SqoopImportAllTablesOptions.txt
______________________________________________

#
#Options file for sqoop import
#

import-all-tables
--connect
jdbc:mysql://airawat-mySqlServer-node/employees
--username
myUID
--password
myPWD

#
#All other commands should be specified in the command line

______________________________________________

Files generated:
$ hadoop fs -ls -R sqoop-mysql/EmployeeDatabase/*/part* | awk '{print $8}'

sqoop-mysql/EmployeeDatabase/departments/part-m-00000

sqoop-mysql/EmployeeDatabase/departments/part-m-00001

sqoop-mysql/EmployeeDatabase/departments/part-m-00002

sqoop-mysql/EmployeeDatabase/departments/part-m-00003

sqoop-mysql/EmployeeDatabase/dept_emp/part-m-00000

sqoop-mysql/EmployeeDatabase/dept_emp/part-m-00001

sqoop-mysql/EmployeeDatabase/dept_emp/part-m-00002

sqoop-mysql/EmployeeDatabase/dept_emp/part-m-00003

sqoop-mysql/EmployeeDatabase/dept_manager/part-m-00000

sqoop-mysql/EmployeeDa...........

G1. Direct and quick queries or inserts and updates with sqoop eval

From the Apache Sqoop user guide - "The eval tool allows users to quickly run simple SQL queries against a database; results are printed to the console. This allows users to preview their import queries to ensure they import the data they expect.

G1.1. Query:

$ sqoop eval --connect jdbc:mysql://airawat-mySqlServer-node/employees \

--username myUID \

--password myPWD \

--query "select * from employees limit 2"

---------------------------------------------------------------------------------

| emp_no      | birth_date | first_name     | last_name        | gender | hire_date  |

---------------------------------------------------------------------------------

| 10001       | 1953-09-02 | Georgi         | Facello          | M | 1986-06-26 |

| 10002       | 1964-06-02 | Bezalel        | Simmel           | F | 1985-11-21 | 

G1.2. Insert:

sqoop eval --connect jdbc:mysql://airawat-mySqlServer-node/employees \

--username myUID \

--password myPWD \

-e "insert into employees_export values(550000,'1977-08-08','Mouse','Mickey','M','1999-04-12')"

G2. Sqoop code-gen

The codegen tool generates Java classes which encapsulate and interpret imported records. The Java definition of a record is instantiated as part of the import process, but can also be performed separately. For example, if Java source is lost, it can be recreated. New versions of a class can be created which use different delimiters between fields, and so on.

$ hadoop fs -mkdir sqoop-mysql/jars

$ sqoop codegen --connect jdbc:mysql://airawat-mySqlServer-node/employees \

--username myUID \

--password myPWD \

--table employees \

--outdir /user/airawat/sqoop-mysql/jars

--Note: Sqoop could not create directory; I created it and tried again, it failed yet again.  Need to look into this.

13/05/31 16:19:24 ERROR orm.CompilationManager: Could not make directory: /user/airawat/sqoop-mysql/jars

13/05/31 16:19:24 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-airawat/compile/c88afaebb5744b4b2e45b6119540834a/employees.jar

G3. Sqoop merge

The merge tool allows you to combine two datasets where entries in one dataset should overwrite entries of an older dataset. For example, an incremental import run in last-modified mode will generate multiple datasets in HDFS where successively newer data appears in each dataset. The merge tool will "flatten" two datasets into one, taking the newest available records for each primary key.

G3.1. Create test data in Mysql

G3.1.1 - Initial dataset

mysql> create table initial_emp as

select emp_no,birth_date, first_name,last_name,gender,hire_date  from employees where emp_no <= 100000

union

select emp_no,birth_date, 'null' as first_name,last_name,gender,hire_date  from employees where emp_no > 100000 and emp_no <= 300000;

--I created a table with fewer records than the employee table, and with some nulls for first name; My next import will include the records from the employee table with emp_no > 100000 and <= 300000, and also records with emp_no > 300000; With the Merge, we should see the final dataset with the full employee table;

G3.1.2 - Incremental dataset

mysql> create table final_emp as

select emp_no,birth_date,first_name,last_name,gender,hire_date  from employees where emp_no > 100000 and emp_no <= 300000

Union

select emp_no,birth_date,first_name,last_name,gender,hire_date  from employees where emp_no > 300000;

G3.2. Import the first dataset into HDFS

$ sqoop --options-file SqoopImportOptions.txt \

--query 'select emp_no,birth_date, first_name,last_name,gender,hire_date  from initial_emp where $CONDITIONS' \

--split-by EMP_NO \

--direct \

--target-dir /user/airawat/sqoop-mysql/InitialDataSet

hadoop fs -ls -R sqoop-mysql/InitialDataSet |grep part* | awk '{print $8}' |xargs hadoop fs -cat | wc -l

200024

$ hadoop fs -ls -R sqoop-mysql/InitialDataSet |grep part* | awk '{print $8}' | xargs hadoop fs -cat | grep 'null' | wc -l

110024

G3.3. Import the last dataset into HDFS

$ sqoop --options-file SqoopImportOptions.txt \

--query 'select emp_no,birth_date,first_name,last_name,gender, hire_date  from final_emp where $CONDITIONS' \

--split-by EMP_NO \

--direct \

--target-dir /user/airawat/sqoop-mysql/FinalDataSet

$ hadoop fs -ls -R sqoop-mysql/FinalDataSet |grep part* | awk '{print $8}' |xargs hadoop fs -cat | wc -l

210024

G3.4. Generate jar and class file for employee table

$ sqoop codegen --connect jdbc:mysql://cdh-dev01/employees \

--username myUID \

--password myPWD \

--table employees \

--outdir /user/airawat/sqoop-mysql/jars

.

.

.

13/06/03 10:29:18 ERROR orm.CompilationManager: Could not make directory: /user/airawat/sqoop-mysql/jars

13/06/03 10:29:18 INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-airawat/compile/879394521045bc924ad9321fe46374bc/employees.jar

Files created:

$ ls /tmp/sqoop-airawat/compile/879394521045bc924ad9321fe46374bc/

employees.class  employees.jar  employees.java

Copy files to your home directory:

cp /tmp/sqoop-airawat/compile/879394521045bc924ad9321fe46374bc/* .

G3.4. Execute the merge

$ sqoop merge --new-data sqoop-mysql/FinalDataSet --onto sqoop-mysql/InitialDataSet --target-dir sqoop-mysql/MergedDataSet \

    --jar-file employees.jar --class-name employees --merge-key emp_no

Note: If I tried running this command with “EMP_NO” as merge-key instead of “emp-no”, I got errors.

$ hadoop fs -ls -R sqoop-mysql/MergedDataSet |grep part* | awk '{print $8}' |xargs hadoop fs -cat | wc -l

300024

We have the expected number of records - 300024; Now lets check if any have 'null' in them...they should not.

$ hadoop fs -ls -R sqoop-mysql/MergedDataSet |grep part* | awk '{print $8}' | xargs hadoop fs -cat | grep 'null' | wc -l

0

A quick look at the data...

$ hadoop fs -cat sqoop-mysql/MergedDataSet/part-r-00000 | more

100000,1956-01-11,Hiroyasu,Emden,M,1991-07-02
100002,1957-03-04,Claudi,Kolinko,F,1988-02-20
100004,1960-04-16,Avishai,Nitsch,M,1986-01-03
100006,1956-07-13,Janalee,Himler,F,1986-01-15
100008,1953-05-14,Otmar,Selvestrel,M,1987-05-05
100011,1956-07-20,Shmuel,Birge,M,1989-11-23

Looks good.

H. Other database specific sqoop arguments/functionality

Available at:
http://archive.cloudera.com/cdh4/cdh/4/sqoop-1.4.2-cdh4.2.0/SqoopUserGuide.html#_compatibility_notes


That's it for this post.  In my next post, I will cover Hive.  Subsequent posts will cover exports, integration with Oozie, and finally integration with HBase.  Hope this blog has been helpful.