First, let’s talk about what kind of data you can easily create with MySQL function calls:

You can generate a decimal number between zero and another number using the MySQL RAND() function like the following query (here between 0 and 10000):

SELECT RAND() * 10000;

Similarly, you can generate a random integer by adding the FLOOR() function:

SELECT FLOOR(RAND() * 10000)

You can generate a random string of 32 characters using MD5():

SELECT MD5(RAND() * 10000)

You can return a random integer between 500 and 1000 with the following:

SELECT FLOOR( 500 + RAND() * (1000 - 500))

You can return a random string from a list of strings by using a table to hold the list. A subselect can select a random name from the list of names.

create table names(id int auto_increment primary key, name varchar(20));
insert into names (name) values ('Justin','Jerry','James','Josh','Julien');
select (select name from names where id = 1 + rand() * 4);

Now we can generate a “fact” table with many rows using fairly simple SQL statements.

First create a table to generate data into:

CREATE TABLE fact (
  dim1 int,
  dim2 int,
  name varchar(20),
  hash varchar(32),
  measure1 double
);

Seed the table with one initial row:

INSERT INTO fact
VALUES (1,1,'Justin',md5(''), .1);

Now grow the table by selecting from the table but providing new random values for the inserted rows:

INSERT INTO fact
SELECT FLOOR(1+ rand()*9999),
       FLOOR(1 + rand()*499),
       (select name from names where id = 1 + rand() * 4),
       MD5(1+rand()*9999),
       rand()
 FROM fact;

As you repeat the INSERT … SELECT, the table will grow exponentially. You may want to add a LIMIT clause to the INSERT … SELECT to reduce the amount of data generated as the table grows.

You will create a table with an even data distribution for each column. You can then add some queries to add skew, either using INSERT … SELECT or UPDATE, for example:

INSERT INTO fact
SELECT 1,1,'Justin',md5(''), .1
  FROM fact
 LIMIT 10000;

That will skew the values by creating many rows with the same data as our initial row.

Using these simple tools, you can generate a data set that is great for testing purposes. For example, dim1 might be a customer_id and dim2 a product_id, and you would populate those tables with 10000 and 500 rows, respectively.