Introduction
Need to rough up some bulk test data in a hurry? A carefully thought-out
Cross Join could be the answer.
Take any SQL query that joins two or more tables, delete the joining clause,
and what do you get? In SQL terms you get a Cross Join, in relational database
theory you get a Cartesian Product. Whatever you call it, you usually end up
with far more rows than you wanted, and most of them make no sense. Although
Cross Join queries are not normally much use, with a bit of thought we can use
them to quickly create large amounts of useful test data.
A simple example
Take the following query:
select * from
(
select “Fred” as fName union
select “Wilma” union
select “Barney” union
select “Betty”
) as flintstones_1 CROSS JOIN
(
select “Flintstone” as lName union
select “Rubble”
) as flintstones_2
This will produce 8 rows – the result of multiplying the four rows in the
first derived table (flintstones_1) against the two rows in the second derived
table (flintstones_2):
fName lName
—— ———-
Betty Rubble
Betty Flintstone
Barney Rubble
Barney Flintstone
Wilma Rubble
Wilma Flintstone
Fred Rubble
Fred Flintstone
(8 row(s) affected)
Needless to say, not all the above are real Flintstones, but that is not the
point. The point is that we have a cheap and cheerful way of generating multiple
unique names. For a small extra investment we can generate eighteen, not eight,
unique names:
select * from
(
select “Fred” as fName union
select “Wilma” union
select “Barney” union
select “Betty” union
select “Al” union
select “Peggy”
) as characters_1 CROSS JOIN
(
select “Flintstone” as lName union
select “Rubble” union
select “Bundy”
) as characters_2
As many tables as you need can be Cross Joined to generate
exponentially-large amounts of test data. This simple query generates 27
mostly-fake politicians with middle names:
select * from
(
select “Harry” as fName union
select “Winston” union
select “Vladimir”
) as polit1 CROSS JOIN
(
select “S ” as mName union
select “Spencer” union
select “Ilich”
) as polit2 CROSS JOIN
(
select "Trueman" as lName union
select “Churchill” union
select “Lenin”
) as polit3
A more practical example
In the following query I have raided a few more US Sitcoms to make a simple
query that will generate no less than 150 unique authors in the PUBS database.
Note that I have serialised the two parts of the data that will make up the
author ID (and the phone number) to keep them unique, but I have chosen -55- to
be the center portion of all my generated IDs (010-55-0010 for example) There
were none in the initial authors table that matched this pattern so this gives
me an at-a-glance way of identifying my auto-generated authors.
insert authors
select au_id1 + ‘-‘ + au_id2 as au_ud,
fName,
lName,
au_id1 + ‘ 5’ + au_id2 as phone,
‘Test address for ‘ + fName + ‘ ‘ + lName,
‘London’,
‘UK’,
‘12345’,
1
from
(
select ‘009’ as au_id1, ‘Fred’ as fName union
select ‘010’, ‘Wilma’ union
select ‘012’, ‘Barney’ union
select ‘013’, ‘Betty’ union
select ‘014’, ‘Al’ union
select ‘015’, ‘Peggy’ union
select ‘016’, ‘Frasier’ union
select ‘017’, ‘Niles’ union
select ‘018’, ‘Homer’ union
select ‘019’, ‘Marge’ union
select ‘020’, ‘Hawkeye’ union
select ‘021’, ‘Trapper’ union
select ‘024’, ‘Sam’ union
select ‘025’, ‘Diane’ union
select ‘026’, ‘Rebecca’
) as test_authors_part_1 CROSS JOIN
(
select ’55-0010′ as au_id2, ‘Flintstone’ as lName union
select ’55-0021′, ‘Rubble’ union
select ’55-0022′, ‘Bundy’ union
select ’55-0023′, ‘Crane’ union
select ’55-0024′, ‘Simpson’ union
select ’55-0025′, ‘Pierce’ union
select ’55-0026′, ‘John’ union
select ’55-0028′, ‘Malone’ union
select ’55-0029′, ‘Chambers’ union
select ’55-0030′, ‘Howe’
) as test_authors_part_2
Summary
The principle will work for any test data provided you construct your query
carefully – you can generate multiple orders for multiple books across multiple
stores for multiple dates. The data will exhibit a regular pattern, rather than
real-world randomness, but in most cases that will not be a problem.
