SQL Server Hosting – How to Make Your SQL Queries Faster?

How to Make Your SQL Queries Faster?

SQL is used to communicate with a database. According to ANSI (American National Standards Institute), it is the standard language for relational database management systems. SQL statements are used to perform tasks such as update data on a database, or retrieve data from a database.

With the availability of ever more powerful programming tools and environments such as Visual Basic and Visual Studio.NET, as well as the availability of powerful database engines such as the free SQL Server 2005 Express Edition, more and more people find themselves having to learn the basics of SQL queries and statements. Sometimes they are professional developers who are experienced in other types of programming, and sometimes they are individuals whose expertise lies in other areas, but they suddenly find themselves programming database applications for fun and/or profit. If you fall into one of these categories, or are just curious about database programming, then this article is for you.

A database query can be either a select query or an action query. A select query is simply a data retrieval query. An action query can ask for additional operations on the data, such as insertion, updating, or deletion.

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.

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 much more 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.

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.

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.

Do delete and update in batches

Here’s another easy technique that gets overlooked a lot. Deleting or updating large amounts of data from huge tables can be a nightmare if you don’t do it right. The problem is that both of these statements run as a single transaction, and if you need to kill them or if something happens to the system while they’re working, the system has to roll back the entire transaction. This can take a very long time. These operations can also block other transactions for their duration, essentially bottlenecking the system.

Do pre-stage data

This is one of my favorite topics because it’s an old technique that’s often overlooked. If you have a report or a procedure (or better yet, a set of them) that will do similar joins to large tables, it can be a benefit for you to pre-stage the data by joining the tables ahead of time and persisting them into a table. Now the reports can run against that pre-staged table and avoid the large join.


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