Relational databases are integral to any application development. Any application that you use to store data in a relational database needs SQL queries. And these queries help you perform various operations like create, drop, alter, insert, modify, or delete records from the database. In this post, we will share with you some easy-to-do SQL query performance tuning tips. You can also make use of SQL query optimizer tools for SQL server to do this job.
SQL is a powerful tool that enables you to handle data operations with ease. But efficient and optimized queries are a must so that you do not slow down the database operations while querying. While working with large databases working with an approach that enables effective SQL query performance tuning saves both time and resources.
Steps that can fine-tune your queries
Avoid the use of SELECT*:
Developers have a habit to use Select *, an abbreviation for Select all. This short query lists all the data in the table. But if a table has thousands of rows and columns then this short query comes up with unwanted results and costs the database its valuable resources. Developers must use Select statements with the mention of required column names as it will only query for the data they need and save on system resources and time.
Avoid the use of Temporary Tables:
Temporary tables exist only temporarily on the SQL server. They come in handy when you want to store immediate result sets that need to be accessed multiple times. But at the same time, the use of temporary tables may increase the complexity of a query exponentially, so better avoid their use.
Avoid the use of COUNT ():
Similar to Select *, developers have an unexplained habit to use Count (). Count () comes in handy when you are looking for certain data inside the table. But this query scans the entire table and comes up with the data that meets specific requirements while wasting valuable database resources. To avoid this situation use Exists (). When you use Exists () query it will exit the table immediately after detecting the desired results. It is an efficient approach to achieving your purpose.
Avoid the use of SELECT DISTINCT:
Select Distinct keyword is valuable when you want to get rid of duplicates with the use of a given query. It operates by grouping all the rows and columns inside the queries and giving unique results. But this grouping procedure consumes more CPU time which should be avoided.
Use Inner Joins-Avoid Where:
When you use the where keyword, more than 2 tables are merged by the rule of cartesian product. During this process, all variable combinations are created and unwanted records are removed to get the final join. You can avoid this resource-wasting exercise by using INNER JOIN which would only deliver desired records. Some databases do this resource-saving exercise automatically by converting clauses to inner joins. Such databases don’t differentiate between where and inner joins.
Avoid the use of wildcard patterns before Like keyword:
Wildcards are used to look out for plain text data and they are one of the largest search processes. So, you should avoid using them immediately after the Like keyword.
Sample query results using Limit:
Whenever you are running a query for the first time, try to use the Limit statement along with it. This approach will return only a specified number of records without putting any unwanted workload on the database.
Indexing is very helpful when you want to access data faster and alongside that, it also does SQL query performance tuning. Use clustered and non-clustered indexes to the maximum extent possible. As with the increase in cluster size, the space required to store it also increases and non-clustered indexes consume more space.
Now when you are well aware of all ways that are used to fine-tune a query. You can effectively speed up your querying process in databases. Efficiency and optimization are essential to query large databases with avoidable delays. As inefficient queries challenge system resources and provide poor user service, resulting in business loss. Investing in hardware up-gradation is not on its own an answer to poor database performance, fine-tuning queries is also important.