Tips and Techniques for SQL Query Optimization

SQL Query Optimization is an iterative technique that improves the performance of every query in terms of execution time, disk accesses, and a variety of other cost-measuring variables. All applications require data to work; to increase performance and user experience, data access should be as quick as feasible. It is a critical task. Because even minor changes to SQL queries can significantly enhance performance.

Although there are no hard and fast rules, we should always follow general recommendations when constructing queries utilized by operators. After that, examine the execution plans to determine which part of the question takes the most time, so we may rewrite it more efficiently. Unleash your potential by enrolling in our SQL Training Institute in Chennai with IBM Certification in Softlogic Systems.

What is SQL Query Optimization?

Query optimization refers to the process of determining the most efficient way to execute a SQL statement. Because SQL is non-procedural, the optimizer can merge, reorganize, and process data in any order. The database optimizes each SQL statement based on statistics acquired about the accessed data.

To determine the optimum plan for a SQL query, the optimizer examines 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 probable transformations.

SQL Query Optimization Techniques

Let’s look at some of the most effective SQL query optimization techniques:

Instead of UPDATE, use CASE

Although utilizing UPDATE is a natural and reasonable form, many developers overlook it, and it is easy to identify.

For example, when entering data into a temp table and displaying a value that already exists. For example, if any of the customers with more ratings have to be “preferred,” the program enters data into the table, performs an UPDATE, and then sets the column to prefer. The disadvantage is that the UPDATE statement has to be executed twice for every single write to the table.

The use of inline CASE in SQL query overcomes this by evaluating every entry for the rating and setting the state to ‘Preferred’.

As a result, performance is unaffected.

Outer Joins Removal

This is determined by a person’s ability or influence to change the table content.

The possibility exists of removing OUTER JOINS by leaving placeholder rows in both tables. As an example, consider the table below, which has an OUTER JOIN defined to ensure that all information is received.

The solution is to insert a placeholder row into the customer table and UPDATE all NULL values in the sales table’s placeholder key.

It not only eliminated the desire for OUTER JOIN, but it also established it as a norm for salesmen with no customers.

This avoids the requirement for developers to create the ISNULL(customer id, “Customers is zero”) line.

Eliminating functions on the RHS of the operator

Example

Taking into account that AccountModifiedOn has an Index, consider changing the query so that the index is not utilized again.

Output

As a result, the query above significantly improves performance.

LIMIT Command

This command can be used to control the number of rows presented from the result set. The result set will only display the required rows. LIMIT must be used within the production dataset to provide an on-demand computation of rows for production purposes.

The query above will only return the first three rows.

SELECT DISTINCT should be avoided

The SELECT DISTINCT command in SQL is used to retrieve distinct results and remove duplicate rows. To accomplish this objective, it merely combines comparable rows and then deletes them. The GROUP BY operation is expensive. As a result, more properties can be added to the SELECT procedure to return alternative results and eliminate duplicate data.

To improve performance and reduce time spent retrieving unique entries, it is preferable to include more attributes in the SELECT query.

Tips for Optimizing SQL Queries

Here are a few instances of the most successful SQL query recommendations.Indexing correctly: An index is a form of data structure that speeds up data retrieval from a database table.

The optimizer generates an execution plan when you perform a query in SQL Server. If it discovers a missing index that may be created to improve performance, it will generate a recommendation that will be published in the warning section. This advice will inform you which columns in the current SQL should be indexed and how performance will improve as a result.

Using the SELECT command

Using SELECT instead of SELECT*

SELECT is used to obtain data from the database. It is not recommended to extract all data from large databases because doing so would necessitate more resources to query such a large volume of data.

The following query will obtain all data from the Employees table, forcing the machine to consume a significant amount of memory and computing power.

SELECT * FROM workers – a less efficient method.

Alternatively, as demonstrated below, it is recommended to indicate the particular columns necessary from data:

SELECT first name, last name, city, and state FROM workers – more efficient method

Avoid Repeating Questions 

Running queries in a loop affects the overall efficiency of the sequence. It is recommended that you utilize bulk insert and then update as needed.

Avoiding Correlated Subqueries

Correlated subqueries run row by row and impair overall process performance.

Always Collect Less Information and Strive for Exact Results

When the amount of data retrieved is reduced, the query will run significantly faster. Rather than utilizing too many client-side filters, aim to filter as much data as possible on the server.

Instead of using HAVING, use WHERE

The HAVING clause’s principal purpose is to filter rows only after all rows have been picked. Always try not to use it for anything else. WHERE statements in SQL are calculated before HAVING statements, making WHERE queries faster.

Avoid using too many JOINS: If you join too many tables to a query, it may get overloaded. Furthermore, a large number of tables from which the data must be fetched may result in an inefficient execution approach. When developing a plan, the SQL query optimizer must identify the order in which tables are connected, when and how filters are used, and when to apply aggregation.

Conclusion

Many useful techniques and methods for enhancing SQL query efficiency were presented in this article. It is recommended that you keep these in mind when writing queries because they will increase application performance and give a fantastic user experience. Learn them practically through our SQL Training in Chennai with Placement Assistance and IBM Certification at Softlogic Systems.