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Strong database and SQL skills are essential for any data-related role. In practice, you will often query very large database tables (with thousands or even millions of rows) during a typical workday. Therefore, SQL query performance becomes a crucial factor in determining the overall performance of the application.
Poorly optimized queries can lead to slower response times, increased server load, and a suboptimal user experience. Hence, understanding and applying SQL query optimization techniques is vital.
This tutorial reviews 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 work with. However, if you want to use a sample database table to run these queries, you can use this Python script.
It connects to an SQLite database: employees.db, creates an employees table, and populates it with 10,000 records. As mentioned, you can always create your own sample.
1. Avoid SELECT *; Select Specific Columns Instead
Beginners often use SELECT * to retrieve all columns from the table. This can be inefficient if you only need a few columns, which is almost always the case.
Using SELECT * can lead to excessive data processing, especially if the table has many columns or you are working with a large dataset.
Instead:
Do this:
SELECT employee_id, first_name, last_name FROM employees;Reading only the necessary columns can make queries more readable and easier to manage.
2. Avoid Using SELECT DISTINCT; Use GROUP BY Instead
SELECT DISTINCT can be costly as it requires sorting and filtering the results to remove duplicates. It is better to ensure that the queried data is unique by design, using primary keys or unique constraints.
Instead:
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 will talk about indexes later). Thus, when writing queries, make sure to understand your data (the different fields) at the data model level.
3. Limit Query Results
Often, you will query large tables containing thousands of rows, but you do not always need (and cannot) process all rows. Using the LIMIT (or its equivalent) reduces 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, thus decreasing the amount of data to process and transfer. 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 by scanning the entire table. They are particularly useful for columns frequently used in WHERE, JOIN, and ORDER BY clauses.
Here is an example of an index created on the ‘department’ column:
CREATE INDEX idx_employee_department ON employees(department);You can now run queries involving filtering on the ‘department’ column and compare execution times. You should see that the results are much faster with the index. To learn more about creating indexes and improving performance, use 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 become a good thing gone too far. Which brings us to the next tip!
5. Use Indexes Sparingly
While indexes improve read performance, they can degrade write performance (INSERT, UPDATE, and DELETE queries) because the index must be updated each time the table is modified. It is important to balance the number and types of indexes based on the types of queries you frequently run.
As general rules:
- Only index columns that are frequently queried.
- Avoid excessive indexing on low-cardinality columns (few unique values).
- Regularly review indexes, update, and remove them if necessary.
In summary, create indexes to speed up the retrieval of frequently queried but rarely updated columns. This ensures that the benefits of indexes outweigh their maintenance costs.
Wrapping Up
Optimizing SQL queries involves understanding the specific needs of your queries and the structure of your data.
By avoiding SELECT *, being cautious with the use of SELECT DISTINCT, limiting query results, creating appropriate indexes, and being aware of the trade-offs of indexing, you can significantly improve the performance and efficiency of your database operations.
Happy querying!
Bala Priya C is an Indian developer and technical writer. 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 loves reading, writing, coding, and drinking coffee! Currently, she is focused on learning and sharing her knowledge with the developer community by creating tutorials, how-to guides, opinion pieces, and more. Bala also creates engaging resource roundups and coding tutorials.
