5 Tips to Improve SQL Query Performance

Strong knowledge of databases and SQL is required for all data-related roles. In practice, you will query very large database tables (with several thousand or even millions of rows) in a typical work day. Therefore, SQL query performance becomes an important factor in deciding the overall performance of the application.

Poorly optimized queries can lead to slower response times, increased server load, and poor user experience. Therefore, it is critical to understand and apply SQL query optimization techniques.

This tutorial provides practical tips for optimizing SQL queries. Let's get started.

Before you begin: Get a sample database table

You can use the following tips when writing SQL queries for any database you are working with. But if you want to use a sample database table to run these queries, you can use This Python script.

Connects to a SQLite database: employees.dbcreate a employees table and fills it with 10,000 records. As mentioned, you can always create your own example.

1. Don't use SELECT *; select specific columns instead

It is quite common for beginners to use SELECT * to retrieve all columns in the table. This can be inefficient if you only need a few columns, which is almost always the case.

Wearing SELECT * Therefore, it can lead to excessive data processing, especially if the table has many columns or if you are working with a large data set.

Instead of this:

Do this:

SELECT employee_id, first_name, last_name FROM employees;

Reading only the necessary columns can make queries more readable and maintainable.

2. Avoid using SELECT DISTINCT; use GROUP BY instead

SELECT DIFFERENT This can be expensive because it requires sorting and filtering the results to remove duplicates. It is best to ensure that the data being queried is unique by design (using primary keys or unique constraints).

Instead of this:

SELECT DISTINCT department FROM employees;

The following query with the GROUP BY clause is much more useful:

SELECT department FROM employees GROUP BY department;

GROUP BY can be more efficient, especially with proper indexing (we'll talk about indexes later). So when writing queries, make sure you understand your data (the different fields) at the data model level.

3. Limit query results

You will often query large tables with thousands of rows, but you don't always need (and can't) process all the rows. LIMIT The clause (or its equivalent) helps reduce the number of rows returned, which can speed up query performance.

You can limit the results to 15 records:

SELECT employee_id, first_name, last_name FROM employees LIMIT 15;

Using a LIMIT clause reduces the size of the result set, which decreases the amount of data that must be processed and transferred. This is also useful for paginating results in applications.

4. Use indexes for faster retrieval

Indexes can significantly improve query performance by allowing the database to find rows faster than if it were to scan the entire table. They are particularly useful for columns that are frequently used in WHERE, JOIN, and ORDER BY clauses.

Below is an example of an index created on the 'department' column:

CREATE INDEX idx_employee_department ON employees(department);

You can now run queries that involve filtering on the “department” column and compare the execution times. You should be able to see that the results are much faster with the index. For more information on creating indexes and performance improvements, see How to Speed ​​Up SQL Queries Using Indexes (Python Edition).

As mentioned, indexing improves the efficiency of queries that filter on indexed columns. But creating too many indexes can be too beneficial. Which brings us to the next tip!

5. Use indexes with caution

While indexes improve read performance, they can degrade write performance (INSERT, UPDATE, and DELETE queries) because the index must be updated every time the table is modified. It is important to balance the number and types of indexes based on the type of queries that are frequently run.

As rules to follow:

• Index only those columns that are frequently queried.
• Avoid over-indexing on columns with low cardinality (few unique values)
• Periodically review indexes and update and delete them as necessary.

In short, create indexes to speed up retrieval of columns that are frequently queried but rarely updated. This ensures that the benefits of indexes outweigh their maintenance costs.

Ending

Optimizing SQL queries involves understanding the specific needs of your queries and the structure of your data.

By avoiding SELECT *, being careful when using SELECT DISTINCT, limiting query results, creating appropriate indexes, and being aware of the disadvantages of indexing, you can significantly improve the performance and efficiency of your database operations.

Enjoy your consultations!

twitter.com/balawc27″ rel=”noopener”>girl priya c Bala is a technical developer and writer from India. She enjoys working at the intersection of mathematics, programming, data science, and content creation. Her areas of interest and expertise include DevOps, data science, and natural language processing. She enjoys reading, writing, programming, and drinking coffee! Currently, she is working on learning and sharing her knowledge with the developer community by creating tutorials, how-to guides, opinion pieces, and more. Bala also creates interesting resource overviews and coding tutorials.