Using WITH (NOLOCK)

The WITH (nolock) hint is an explicit command directed at a specific table or view used to set the transaction isolation level against the table or tables within a view for a query. Once issued, locks will not be used against the data within the table. The advantage to this is there is no chance a deadlock will occur against any other queries running against the table. The other indirect advantage is that less memory will be used in order to hold locks against that data. Example: [cc lang=”sql”] SELECT first_name, last_name, FROM dbo.person p WITH (NOLOCK) JOIN dbo.employee e WITH (NOLOCK) ON e.person_id = p.person_id WHERE p.person_id = 1; [/cc] The nolock setting above is explicit for the table it is being set against. To set the value globally for the scope of the connection, see SET TRANSACTION ISOLATION LEVEL Advantages: Deadlocks will not occur against other queries running against the same data Less memory is utilized due to the lack of row, page, or range level locking Typically allows for much higher concurrency due to lower footprint Disadvantages: Uncommitted data can be read leading to dirty reads Explicit hints against a table are generally bad practice Usage In most places I have worked, with (nolock) has been a generally accepted practice in the specific areas of the system that are not sensitive to data being slightly out of sync. It is important to know where things could go wrong though. The biggest red flag I can think of […]

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Detecting Row Level Changes Using HASHBYTES

A common situation in a data warehouse is the requirement to detect changes in data in order to track what rows need to be imported. The traditional method of comparing the values of each field is performance intensive. Luckily there are other methods to quickly track the changes that involve creating hashes (or a fingerprint) of a particular data row. In using this method, if we want to synchronize two separate tables, we can simply join on the primary key and only compare this fingerprint column in order to determine what has changed. There are two major methods I’ve used to create a row valued hash key. The first is by using the CHECKSUM function. The other is to use the HASHBYTES function. Both of these function return back a single value representing a hash, however their parameters differ. With CHECKSUM you can pass in a list of columns to evaluate and it returns an integer value. Whereas HASHBYTES requires a single parameter to be passed in and returns back a 16-bit binary value. The trick to forcing HASHBYTES into accepting multiple column values is to use the FOR XML function which will generate a single value to pass in. The obvious difference between the two functions is the size and datatype of the hash being returned. To make a long story short, there are rare occasions (that I have witnessed more than once) where passing in different column values into CHECKSUM will return back the exact same value. Granted […]

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How to Order Numeric Values in a Varchar Field

If you have ever tried to order numerical values in a varchar field you’ll know that the sort doesn’t occur numerically as it would if the datatype were numeric. In other words, the values of 1 and 10 will be sorted together because they both start with a leading 1. To overcome this, we have to cast the values as numeric. But this raises another issue. Since it’s a varchar field we cannot ensure that the values are all numeric which means we additionally have to take into account characters. Let’s start by creating a sample table and populating some values: [cc lang=”sql”] CREATE TABLE #varchar_field ( ID INT IDENTITY(1,1), mixed_field varchar(100), ) INSERT INTO #varchar_field (mixed_field) SELECT ‘1’ union all SELECT ‘4.9’ union all SELECT ’10’ union all SELECT ’50’ union all SELECT ‘6’ union all SELECT ‘a’ union all SELECT ‘z’ union all SELECT ‘A’ [/cc] Now let’s run a simple sort so we can see the default behavior. [cc lang=”sql”] SELECT * FROM #varchar_field ORDER BY mixed_field [/cc] Here we see everything is only sorted by it’s leading character, not taking into consideration the value of the numbers. Now let’s execute a revised version of this order by. [cc lang=”sql”] SELECT * FROM #varchar_field ORDER BY CASE WHEN ISNUMERIC(mixed_field) = 1 THEN CAST(mixed_field AS FLOAT) WHEN ISNUMERIC(LEFT(mixed_field,1)) = 0 THEN ASCII(LEFT(LOWER(mixed_field),1)) ELSE 2147483647 END [/cc] Here we check to see if the field is numeric first. If it is, we cast it to float to deal with […]

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Creating Hot Swap Tables to Hide Complex Logic

Highly concurrent systems that feed off normalized data stores typically require a middle layer of logic to serve the front-end needs. More often than not, this middle layer of logic is stored in the same stored procedures that the web layer accesses. While sometimes this may be the right place for simple logic, for more complex calculations and joins it is simply not efficient. The answer in these cases is to create a new meta layer of data that pre-joins data and rolls up necessary aggregations. To paint a better picture – in an ideal database each procedure that feeds the front-end would house a simple select statement from a single table. We know in real life this is not always possible, however we should think in these terms with every web proc we write. The reason is simple – complex logic is both IO and CPU intensive. We have no control over the web traffic, but we do have control over what logic we use to serve the web. Often times it is better to run jobs in the background that perform complicated logic on behalf of the web procs and dump the results into static tables. This methodology basically creates a denormalized meta layer of data on top of the normalized data. The argument against this is that the data will not truly be real-time. However you need to ask yourself what’s more important, “real-time” data that is 5-10 times slower or preaggregated data that is potentially seconds […]

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Substring Between Two Characters

Though SQL may not be the most elegant language for string handling, it does perform most functions and in a set based manner. The SQL substring function basically has the same syntax as found in other languages. In this example we will take the common scenario of extracting a string from between two fixed characters. In our example we’ll start with a domain name that contains the subdomain. We will extract only the top level domain from the results. Keep in mind for the purposes of this example all the URLs listed in the table need to have a subdomain. Setup the Data Let’s begin by creating a new table named URLs and insert some data. [cc lang=”sql”] IF EXISTS (SELECT * FROM dbo.sysobjects WHERE id = OBJECT_ID(N’dbo.URLs’) AND OBJECTPROPERTY(id, N’IsUserTable’) = 1) –DROP TABLE dbo.URLs CREATE TABLE dbo.URLs( url nvarchar(1024) NULL ) INSERT dbo.URLs (url) VALUES (N’www.litwareinc.com/’) GO INSERT dbo.URLs (url) VALUES (N’www.treyresearch.net/’) GO INSERT dbo.URLs (url) VALUES (N’www.adatum.com/’) GO INSERT dbo.URLs (url) VALUES (N’www.northwindtraders.com/’) GO INSERT dbo.URLs (url) VALUES (N’www.wideworldimporters.com/’) GO INSERT dbo.URLs (url) VALUES (N’www.proseware.com/’) [/cc] Now that we have the data setup let’s start with the extraction. For the substring function, the first parameter is the string we want to parse from, the next parameter is the starting position to be extracted and the last parameter is the number of characters to extract. [cc lang=”sql”] DECLARE @first_char nvarchar(10) DECLARE @second_char nvarchar(10) SET @first_char = ‘.’; SET @second_char = ‘/’; SELECT SUBSTRING ( — column url — […]

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