Tips to fine-tune your SQL queries
Relational databases play an important role in any application development. An application that stores data in a relational database need SQL queries to perform various operations like create, drop, alter, insert, modify, or delete records from it. In this article, our main focus will lie on how to fine-tune your SQL queries to perform such operations effectively and efficiently.
SQL is the most powerful tool to handle such data operations. It is quite important to have efficient and optimized SQL queries so that it will not slow down the database process. Let’s say you run a data warehouse or a separate analytical database for production purposes. While querying such large databases, optimization is necessary, especially in a production environment. An inefficient query will deplete resources that such data warehouses are using and may cause loss of service and performance inefficiency for users.
Read on to get some tips on how to fine-tune your SQL queries and why is it necessary.
Table of contents
- Avoid using Select *
- Use of Wise Temporary Tables
- Don’t Use Count ()
- Don’t try to use Select distinct
- Use inner joins and don’t use Where
- Wildcard Patterns should not be used in front of LIKE but at the end
- Schedule off-peak times
- Sampling Query Results using Limit
- Limiting Data set Size
- Loops in Queries
- Wrapping Up
Avoid using Select*
Developers love to use Select * (Select * is also known as Select all) for some reason. It is a short query that lists all the data available in the table. If a table has thousands of rows and columns, this may give unwanted results and might deplete database resources. Developers must use the Select statement with the required column names as this will only query the data you need to meet the business requirements and avoid fetching unnecessary records
Avoid using Temporary Tables
As the name suggests, temporary tables exist only temporarily on the SQL server. They are particularly useful for storing immediate result sets that need to be accessed multiple times. But remember, the use of temporary tables may increase the complexity of a query exponentially, so it is good to avoid them.
Avoid using Count ()
Just like Select *, developers love to use Count (). Count () is used to check if certain data inside the table exists or not. The disadvantage of using Count () is that it scans the entire table and returns all the records that satisfy certain conditions. One alternative to this is to use Exists (), just because the loop will exit as soon as the desired results are spotted. The query will function in a more efficient way.
Avoid using Select distinct
The main use of the Select distinct keyword is to remove duplicates from a given query. This works by grouping all the rows and columns inside the queries to give unique results. But grouping leads to high processing, which should be avoided.
Use Inner Joins and avoid Where
When you use the where keyword, two or more tables are merged by the rule of cartesian product. In this, all combinations of variables are created and the unwanted records are removed to get the final join. This can be avoided by using INNER JOIN which would only generate desired records. Some databases automatically convert where clause to inner joins. In such databases, there is no difference between where and inner joins.
Avoid Wildcard Patterns before the LIKE keyword
Wildcards are used to search the plain text data and they are possibly one of the largest search processes. So it is better to avoid using them immediately after the Like keyword. Instead, use it at the end.
Schedule off-peak times
Try to run complex queries during off-peak times when there are a smaller number of users. Specifically use this time to run queries like -
- Select *
- All the Wildcard Searches
- Nested Queries
Sampling Query Results using Limit
For any query which you run for the first time, try to use the Limit statement along with it. Through this, only a specified number of records are returned. It thus prevents putting an additional workload on the database.
Indexing helps to retrieve data much faster and provides quality query optimization. Make maximum use of clustered and non-clustered indexes. As the cluster size increases, the space required to store the cluster will also increase. Non-clustered indexes require much more space.
Limiting Data set Size
Try to limit your data set size while testing the database. For example, if you want to test your database, you don’t need to access every field present in your database. In short, don’t query your entire database. Instead, focus on only specific values. This directly translates to a smaller volume of resources being used and avoids draining your production database. Always remember – the execution speed of the query is directly proportional to the volume of data; the smaller the data, the faster will be the execution of the query.
Avoid using loops
Avoid using loops in your SQL queries. It can slow down the process. Use as many inserts and updates as possible.
Now that you have seen all these tips, you can pretty much speed up your querying process in databases. Efficiency and optimization are very essential while querying larger databases as inefficient queries might lead to unwanted loss of resources and produce poor service to important users, which may lead to business loss. Upgrading hardware alone will not resolve this and fine-tuning your query is a must.