Unpivoting Data
Unpivoting data is a very useful technique for normalizing denormalized tables. It refers to the process of transposing multiple columns across into rows in a single column. In other words, taking the data below:
sales_year first_quarter second_quarter third_quarter fourth_quarter
---------- ------------- -------------- ------------- --------------
2006 211203.50 381594.95 439187.00 503155.80
2007 231205.10 451101.25 601209.40 531907.30
and converting to look like this:
sales_year sales_quarter sales_amount
----------- ------------- ------------
2006 1 211203.50
2006 2 381594.95
2006 3 439187.00
2006 4 503155.80
2007 1 231205.10
2007 2 451101.25
2007 3 601209.40
2007 4 531907.30
Here are different methods to achieve that:
-- Create sample table
CREATE TABLE QuarterlySales (
sales_year INT PRIMARY KEY,
first_quarter DECIMAL(10, 2),
second_quarter DECIMAL(10, 2),
third_quarter DECIMAL(10, 2),
fourth_quarter DECIMAL(10, 2));
-- Insert data
INSERT INTO QuarterlySales VALUES(2006, 211203.50, 381594.95, 439187.00, 503155.80);
INSERT INTO QuarterlySales VALUES(2007, 231205.10, 451101.25, 601209.40, 531907.30);
-- Method 1) Using UNION
SELECT sales_year,
1 AS sales_quarter,
first_quarter AS sales_amount
FROM QuarterlySales
UNION ALL
SELECT sales_year, 2, second_quarter
FROM QuarterlySales
UNION ALL
SELECT sales_year, 3, third_quarter
FROM QuarterlySales
UNION ALL
SELECT sales_year, 4, fourth_quarter
FROM QuarterlySales
ORDER BY sales_year, sales_quarter;
-- Method 2) Using cross join with table with numbers
SELECT sales_year,
qtr AS sales_quarter,
CASE qtr
WHEN 1 THEN first_quarter
WHEN 2 THEN second_quarter
WHEN 3 THEN third_quarter
WHEN 4 THEN fourth_quarter
END AS sales_amount
FROM QuarterlySales
CROSS JOIN
(SELECT 1 UNION ALL
SELECT 2 UNION ALL
SELECT 3 UNION ALL
SELECT 4) AS Quarters(qtr)
ORDER BY sales_year, sales_quarter;
-- Method 3) Using the UNPIVOT operator in SQL Server 2005
SELECT sales_year,
CASE sales_quarter
WHEN 'first_quarter' THEN 1
WHEN 'second_quarter' THEN 2
WHEN 'third_quarter' THEN 3
WHEN 'fourth_quarter' THEN 4
END AS sales_quarter,
sales_amount
FROM QuarterlySales
UNPIVOT
(sales_amount FOR
sales_quarter IN
(first_quarter, second_quarter,
third_quarter, fourth_quarter)) AS U
ORDER BY sales_year, sales_quarter;
It is good no note that unpivoting multiple columns (like amount1, date1, amount2, date2 to amount and date columns) is not supported by the SQL Server 2005 UNPIVOT operator. This can be achieved only by using the first two methods.
Additional Resources:
Using PIVOT and UNPIVOT
http://msdn2.microsoft.com/en-us/library/ms177410.aspx
Hi Plamen,
I solved the problem without tally table (number table).
I used GROUPING SET for copy some instance of data, then using ROW_NUMBER for sequence numbers then solving the unpivoting problem.
SELECT sales_year, rec_id AS sales_quarter,
CASE rec_id
WHEN 1 THEN first_quarter
WHEN 2 THEN second_quarter
WHEN 3 THEN third_quarter
WHEN 4 THEN fourth_quarter
END AS sales_amount
FROM
(
SELECT *, ROW_NUMBER() OVER(PARTITION BY sales_year ORDER BY (SELECT NULL)) AS rec_id
FROM QuarterlySales
GROUP BY GROUPING SETS
(
(sales_year, first_quarter, second_quarter, third_quarter, fourth_quarter),
(sales_year, first_quarter, second_quarter, third_quarter, fourth_quarter),
(sales_year, first_quarter, second_quarter, third_quarter, fourth_quarter),
(sales_year, first_quarter, second_quarter, third_quarter, fourth_quarter)
)
) D
This is interesting approach Mohammad. To my opinion if you are already using SQL Server 2005/2008 features it is much shorter syntax and clear to understand by using the UNPIVOT operator.
Hi Plamen,
I am back with a much clear version!
What are you thinking about this, Do you think this version is so clear and simple?
SELECT sales_year,
RIGHT(COALESCE(CAST(first_quarter AS VARCHAR)+ '1',
CAST(second_quarter AS VARCHAR) + '2',
CAST(third_quarter AS VARCHAR) + '3',
CAST(fourth_quarter AS VARCHAR) + '4'), 1) AS sales_quarter,
COALESCE(first_quarter, second_quarter, third_quarter, fourth_quarter) AS sales_amount
FROM QuarterlySales
GROUP BY GROUPING SETS
(
(sales_year, first_quarter),
(sales_year, second_quarter),
(sales_year, third_quarter),
(sales_year, fourth_quarter)
)
ORDER BY sales_year, sales_quarter;
The result sets
/
sales_year sales_quarter sales_amount
———– ————- ————-
2006 —— 1 ———– 211203.50
2006 —— 2 ———– 381594.95
2006 —— 3 ———– 439187.00
2006 —— 4 ———– 503155.80
2007 —— 1 ———– 231205.10
2007 —— 2 ———– 451101.25
2007 —— 3 ———– 601209.40
2007 —— 4 ———– 531907.30
/
Yes, this version is simplified, great use of grouping sets!
Thank you!
Hi Plamen,
If you use 2005/2008 version it seems the best is to avoiding UNPIVOT table operator, and using this method:
SELECT sales_year,
sales_quarter,
sales_amount
FROM QuarterlySales
CROSS APPLY
(SELECT 1, first_quarter
UNION ALL
SELECT 2, second_quarter
UNION ALL
SELECT 3, third_quarter
UNION ALL
SELECT 4, fourth_quarter) D(sales_quarter, sales_amount);
Wouldn't running the query with many CAST operations over a large set affect its performance quite a bit? RE:
SELECT sales_year,
RIGHT(COALESCE(CAST(first_quarter AS VARCHAR)+ '1',
CAST(second_quarter AS VARCHAR) + '2',
CAST(third_quarter AS VARCHAR) + '3',
CAST(fourth_quarter AS VARCHAR) + '4'), 1) AS sales_quarter,
COALESCE(first_quarter, second_quarter, third_quarter, fourth_quarter) AS sales_amount
FROM QuarterlySales
GROUP BY GROUPING SETS
(
(sales_year, first_quarter),
(sales_year, second_quarter),
(sales_year, third_quarter),
(sales_year, fourth_quarter)
)
ORDER BY sales_year, sales_quarter;
Hi Vladimir,
Yes, the CAST function will have impact on performance on a very large set. Difficult to say how much without testing with real data.
Great article Plamen! I love the pivot feature as I find it’s easier to translate data this way in the database than to get some custom code to do it or do it in Excel.
I’ve used it a few times in SQL Server. Oracle has a similar keyword for doing this, which I’ve written about here: https://www.databasestar.com/oracle-sql-pivot/.
I also haven’t considered using the CROSS JOIN method before. I’ll have to keep that in mind.
Thanks!
Ben