Development

We use a queueing metaphor for dataloads for the majority of our imports, and it’s been a challenge to figure out how to generate dynamic ranges to add to the queue. In other words, we want to tell our system to go and pcik data from a source that has an id value >x and <=y, and then do it again in a few minutes with a new x and y value. I should say that it’s been a challenge because I’m still not used to SQL 2005 features; I came across a bit of code using a CTE and recursion to generate a numbers table (thanks to Stefan Keir Gordon). Here’s a slightly modified version of his code:

/*thanks to Stefan Keir Gordon for the inspiration
 http://www.keirgordon.com*/

SET STATISTICS IO ON

;WITH Numbers(n) AS
   (SELECT 1 AS n
	UNION ALL
	SELECT (n + 1) AS n
	FROM Numbers
	WHERE n <= 32767)
SELECT n
FROM Numbers
WHERE n<=32767
OPTION(MAXRECURSION 32767)

 ;WITH Ranges(MinValue) AS
  (SELECT 1 AS MinValue
   UNION ALL
   SELECT (Minvalue + 1) AS MinValue
   FROM Ranges WHERE MinValue <= 32767)
SELECT MinValue, MinValue + 1 as MaxValue
FROM Ranges
WHERE MinValue <= 32767
OPTION(MAXRECURSION 32767)

I end up with a two-column table with a MaxValue > MinValue, and the ranges increase by 1. But I had some issues to address:

• I needed to be able to pass in Start and End values to this code.
• I needed chunks bigger than 1 value in size; I also had a minimum size I wanted to use.
• In some cases 32767 ranges would be appropriate; in some cases, I needed less.

Here’s the final code; setting the Start and End valeus for the range of ranges is easy to understand, but the other two values may be a bit confusing. The first is @MaxRanges, which simply says “I want no more than this number of ranges to get from the Start to the End”. The maximum value for this number is 32767. @MinChunk refers to the minumum chunk size, or the space between MinValue and MaxValue for a given row.

It may be easier to just play with the values and see; you should never have more rows than @MaxValue, and the difference between MinValue and MaxValue for a given row should always be greater than or equal to @MinChunk:

/*A dynamically generated table of ranges.
2009-01-25 Stuart R Ainsworth
	http://codegumbo.com/

Thanks to Stefan Keir Gordon for the inspiration
 http://www.keirgordon.com */

SET STATISTICS IO ON

DECLARE @MaxRanges int, @MinChunk int, @Start int, @End int
SELECT @Start=10, @End=200000

/*@MaxRanges must be <= 32767;
  if the range between Start and End is large,
  and @MaxRanges > 32767, the End point won't be reached.*/
SELECT @MaxRanges=1000, @MinChunk=205

IF (SELECT CEILING((@End-@Start)/(@MaxRanges*1.00)))>@MinChunk
	SET @MinChunk = CEILING((@End-@Start)/(@MaxRanges*1.00))

;WITH Ranges(MinValue) AS
  (SELECT @Start AS MinValue
   UNION ALL
   SELECT (Minvalue + @MinChunk) AS MinValue
   FROM Ranges WHERE MinValue < @End)
SELECT MinValue, MaxValue = CASE WHEN MinValue + @MinChunk < @End
			     THEN MinValue + @MinChunk
			     ELSE @End End
FROM Ranges
WHERE MinValue < @End
OPTION(MAXRECURSION 32767)

Good luck; happy coding!

I found this article by Armando Prato from facility9’ssite; for the most part, the article explains the differences between using a uniqueidentifier and integer-based identity values to auto-generate (surrogate) primary key values. However, the conclusion to the article has a logical flaw; can you spot it?:

Here’s the hint: page-splitting has less to do with data-types than it does with relationship of the clustered index to the natural order of insertion for the data. the author assumes that the PRIMARY KEY is located on the clustered index, and that’s one of my pet peeves. A PRIMARY KEY constraint is a physical implementation of a logical constraint; it’s used to enforce data validation rules, and has nothing to do with data storage or performance concerns. A clustered index, on the other hand, is one of the basic tools for enhancing database performance. Although SQL Server automatically creates a unique clustered index when building a PRIMARY KEY constraint (if no clustered index exists), a good database design never assumes that the clustered index should automatically sit on the same columns as the PRIMARY KEY constraint.

Granted, if you use integer-based identity values for your surrogate keys, this argument has less power than if you are using uniqueidentifiers; a good rule of thumb to use when choosing columns for clustered indexes is that they should be relatively unique, and they should increase monotonically (i.e, the order of values is such that 1<=1<=2, etc). Identity’s are always unique, and the value of the next identity is always greater than the last.

But there are times when you cannot use an identity column (partitioned views. for example), and the choice of using a uniqueidentifier should not be dismissed for fear of page splits; rather, you should choose a different column for your clustered index. I modified the code from the original article to prove my point:

SET NOCOUNT ON
GO
USE MASTER
GO
CREATE DATABASE CodeGumbo
GO

USE CodeGumbo
GO
-- Start at 1 and increment by 1
CREATE TABLE IDENTITY_TEST1
(
ID INT IDENTITY(1,1) PRIMARY KEY,
TESTCOLUMN CHAR(2000) DEFAULT REPLICATE('X',2000)
)
GO

--uniqueidentifier, clustered by default
CREATE TABLE NEWID_TEST
(
ID UNIQUEIDENTIFIER DEFAULT NEWID() PRIMARY KEY,
TESTCOLUMN CHAR(2000) DEFAULT REPLICATE('X',2000)
)
GO

--uniqueidentifier, clustered on a different column
CREATE TABLE NEWID_TEST2
(
ID UNIQUEIDENTIFIER DEFAULT NEWID() PRIMARY KEY NONCLUSTERED,
TESTCOLUMN CHAR(2000) DEFAULT REPLICATE('X',2000),
UTCDATESTORED datetime DEFAULT GETUTCDATE()
)
GO

CREATE CLUSTERED INDEX clidx_NewID_Test_UtcDateStored ON NEWID_TEST2(UTCDateStored)
GO

-- INSERT 1000 ROWS INTO EACH TEST TABLE
DECLARE @COUNTER INT
SET @COUNTER = 1

WHILE (@COUNTER <= 1000)
BEGIN
INSERT INTO IDENTITY_TEST1 DEFAULT VALUES
INSERT INTO NEWID_TEST DEFAULT VALUES
INSERT INTO NEWID_TEST2 DEFAULT VALUES
SET @COUNTER = @COUNTER + 1
END
GO

SELECT OBJECT_NAME([OBJECT_ID]) as tablename, avg_fragmentation_in_percent, fragment_count, page_count
FROM sys.dm_db_index_physical_stats (DB_ID(), null, null, null, null)
WHERE index_id = 1
ORDER BY tablename
GO

USE MASTER;
CROP DATABASE CodeGumbo;
GO

As you can see from the results below, the clustered index (and thus the table) has minimal fragmentation when the clustered index is on a different column than the uniqueidentifier (NewID_Test2).

To be fair, the index on the primary key is pretty shredded; however, it’s been my experience that having a fragmented nonclustered index is less damaging than a fragmented clustered index. Both will require maintenance over time, but the longer you can keep the clustered index healthy, the better off your database will be. The trick is determining where the clustered index should live, and it’s not always the same column(s) as the primary key.

Because I really want to keep up the rhythm of posting at least one code slice a week, here’s a snippet I wrote a long time ago. In our business, we often want to compare ranges of IP addresses, and so we need to convert them from an quad-based notation (x.x.x.x) to an integer basis. This particular method uses the system function in SQL Server called PARSENAME(), which simply splits a period-delimited varchar into it’s seperate components.

Below is how you flip from quad to integer and back; enjoy!

DECLARE @SrcIP varchar(15),
@SrcIPNbr bigint

SELECT CONVERT(varchar(3),FLOOR(@SrcIPNbr/POWER(256,3))%256) + ‘.’ +
CONVERT(varchar(3),FLOOR(@SrcIPNbr/POWER(256,2))%256) + ‘.’ +
CONVERT(varchar(3),FLOOR(@SrcIPNbr/POWER(256,1))%256) + ‘.’ +
CONVERT(varchar(3),FLOOR(@SrcIPNbr/POWER(256,0))%256)