Databases are designed based on assumptions about the data types in the database and how often different variables will be in the same query. Databases are also designed based on assumptions about key variables in the database. For example, a SQL database design might assume that queries include the customer ID while the customer’s phone number is secondary. However, if a company learns customers contacting their call center do not know their customer number, SQL server database tuning might be needed to access customer records using the phone number rather than the customer number.
The example above may indicate a clear need for database tuning, but other situations may be less obvious. Should the database be tuned, or should users change their queries to better use the current database design?
In some respects, tuning a database is like repairing a car’s engine.
Both can run roughly or slower than desired and diagnosing the source of the problem requires detective work. One method of tuning databases is to isolate queries that consume many resources. Running these queries and alternate versions of the queries against partial datasets allows you to determine which queries are most efficient while not consuming heavy resources while performing the tests. These results can help inform whether to change the queries or tune the database.
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