Updated on 29th Jul, 22 8862 Views

It is a very crucial task. Because even a tiny change in SQL queries will improve the performance by a big margin. There is no hard and fast rule for the same, we should always follow general guidelines while writing queries used by operators. After that, look at execution plans to figure out which portion of the query requires the most time, and we can rewrite it again in a more efficient way.

Table of Content:

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What is Query Optimization in SQL?

The process of selecting the most effective way to carry out a SQL statement is known as query optimization. Due to SQL’s non-procedural nature, the optimizer is permitted to merge, rearrange, and process data in any sequence.

Based on statistics gathered regarding the accessed data, the database optimizes each SQL statement. The optimizer evaluates various access techniques, such as full table scans or index scans, various join techniques, such as nested loop joins and hash joins, various join orders, and potential transformations to identify the best plan for a SQL query.

SQL Query Optimization Techniques

Let’s talk about some of the best SQL Optimization techniques:

Use CASE instead of UPDATE

  • Although using UPDATE is a natural form that seems logical, many developers overlook it and also it is easy to spot.
  • For example, when inserting data into a temp table and if you want to display a value where it already exists. For example, if any of the customers with more ratings needs to be “preferred”, then when it runs, it inserts data into the table and runs an UPDATE and then the column is set to prefer. The drawback is that each time the UPDATE statement is done, it has to run two times, for every single write to the table.
  • To solve this usage of inline CASE in SQL query resolves by testing every row for the rating and state is set to ‘Preferred’.
  • Hence, performance is not hindered.

Removing Outer Joins

  • This depends on the capacity or influence a person has for changing the table content.
  • The possible solution is to remove OUTER JOINS by keeping placeholder rows in both tables. Example, the below table with OUTER JOIN defined to guarantee all info is getting.
Removing Outer Joins
  • Resolving this is to add a placeholder row into the table of customers and UPDATE all NULL values to the placeholder key in the sales table
UPDATE all NULL
  • Not only it removed the urge of OUTER JOIN but also made it a standard for salespeople with no customers.
  • This eliminates the need for developers to write the following statement ISNULL(customer_id, “Customers is zero”).

Read our blog on SQL for Data Science to help gain an understanding of databases and data management.

Avoiding functions on the RHS of the operator

Consider the example:

SELECT *FROM Employee
WHERE YEAR(AccountModifiedOn) == 2016
AND MONTH(AccountModifiedOn) =7
  • By taking into account that AccountModifiedOn has an Index, the example where the query is changed in such a way that the index is not used again.

So after rewriting, it becomes

SELECT *FROM Employee
WHERE AccountModifiedOn between ‘7/1/2016’ AND ‘7/30/2016’
  • Thus the above query increases the performance extremely.

LIMIT Command

This command can be used while controlling no. of rows to be displayed from the result set. Only the required rows will be displayed by the result set. In order to give an on-demand computation of rows for the production purpose, one must use LIMIT within the production dataset.

SELECT * FROM Users
LIMIT 8;

The above query will Fetch the first 3 Rows only.

Avoid Using SELECT DISTINCT

In SQL, the SELECT DISTINCT command is used to retrieve distinct results and eliminate duplicate rows. It basically joins together comparable rows in order to accomplish this task, then deletes them. The GROUP BY procedure is costly. Therefore, one may add extra properties to the SELECT process to retrieve different results and eliminate duplicate data.

It is preferred to include additional attributes in the SELECT query to boost performance and reduce time while fetching unique rows.

Enrich your knowledge of What is PL SQL, various features, advantages, subprograms, and PL SQL tutorials.

SQL Query Optimization Tips

Here are some examples of the most effective tips for SQL queries.

Proper Indexing:

An index is a type of data structure that accelerates data retrieving from the database table.

When you run a query in SQL Server, the optimizer creates an execution plan. If it finds out that there is a missing index that might be created to improve performance, it will create a suggestion that will be displayed in the warning section. This suggestion will tell you which columns should be indexed in current SQL and how performance will be enhanced after that.

Using SELECT

Using SELECT in place of SELECT*:

Data is retrieved from the database using SELECT. It is not advisable to extract all data from large databases because doing so would require more resources to query such a massive amount of data.

The following query will retrieve all data from the Employees table causing the computer to use lots of memory and processing.

less efficient method - SELECT * FROM employees

Alternatively, it is advised to specify the exact columns required from data as shown below:

more efficient -SELECT first_name, last_name, city, state FROM employees

Try to avoid repeating queries:

When you run queries in a loop it reduces the efficiency of the entire sequence. It is suggested that you use bulk insert and then update suiting the situation.

Avoiding correlated subqueries:

Correlated subquery runs row by row and affects the overall performance of the process.

Always gather less information and aim for precise outcomes:

The query will run much faster when the retrieved data is less. Always try to filter as much data as possible on the server instead of using too many filters on the client-side.

Avoid using HAVING, use WHERE instead:

The main purpose of the HAVING clause is that it filters rows only after all rows are selected. Always try to avoid using it for any other purpose. In SQL WHERE statements are calculated before HAVING statements, making WHERE query quicker.

Avoid too many JOINS:

A query could become overloaded if you join many tables to it. Additionally, a vast number of tables from which the data is to be retrieved may lead to a not-so-efficient execution method. The sequence in which tables are joined, how and when filters are applied, and when to use aggregation must be identified by the SQL query optimizer when creating a plan.

Learn about various Data Types in SQL such as numeric, character, and date and time with this SQL tutorial.

Conclusion

We covered many important tips and techniques to improve SQL Query performance. You are suggested to keep them in mind while writing queries as they will improve the performance and provide a great user experience of applications.

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