SQL Server Hosting

SQL Server is a relational database management system (RDBMS) from Microsoft that’s designed for the enterprise environment. SQL Server runs on T-SQL (Transact -SQL), a set of programming extensions from Sybase and Microsoft that add several features to standard SQL, including transaction control, exception and error handling, row processing, and declared variables.

Tips for Faster SQL Queries

There are some good principles you can follow that should yield results in one combination or another. I’ve encapsulated them in a list of SQL dos and don’ts that often get overlooked or are hard to spot. These techniques should give you a little more insight into the minds of your DBAs, as well as the ability to start thinking of processes in a production-oriented way.

1. Don’t use UPDATE instead of CASE

This issue is very common, and though it’s not hard to spot, many developers often overlook it because using UPDATE has a natural flow that seems logical.

Take this scenario, for instance: You’re inserting data into a temp table and need it to display a certain value if another value exists. Maybe you’re pulling from the Customer table and you want anyone with more than $100,000 in orders to be labeled as “Preferred.” Thus, you insert the data into the table and run an UPDATE statement to set the CustomerRank column to “Preferred” for anyone who has more than $100,000 in orders. The problem is that the UPDATE statement is logged, which means it has to write twice for every single write to the table. The way around this, of course, is to use an inline CASE statement in the SQL query itself. This tests every row for the order amount condition and sets the “Preferred” label before it’s written to the table. The performance increase can be staggering.

2. Don’t blindly reuse code

This issue is also very common. It’s very easy to copy someone else’s code because you know it pulls the data you need. The problem is that quite often it pulls muchmore data than you need, and developers rarely bother trimming it down, so they end up with a huge superset of data. This usually comes in the form of an extra outer join or an extra condition in the WHERE clause. You can get huge performance gains if you trim reused code to your exact needs.

3. Do pull only the number of columns you need

This issue is similar to issue No. 2, but it’s specific to columns. It’s all too easy to code all your queries with SELECT * instead of listing the columns individually. The problem again is that it pulls more data than you need. I’ve seen this error dozens and dozens of times. A developer does a SELECT * query against a table with 120 columns and millions of rows, but winds up using only three to five of them. At that point, you’re processing so much more data than you need it’s a wonder the query returns at all. You’re not only processing more data than you need, but you’re also taking resources away from other processes.

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