Read this before your next SQL query!

Boost Your SQL Performance With These Expert Tips

After over five years as a data engineer and almost ten years working with Structured Query Language (SQL), I decided it was time to share with you, my audience, some valuable tips that I learned from my own experience. Anyone who works with data, whether in software development, analytics, data science and engineering, or even artificial intelligence, needs SQL! Writing basic queries is only one aspect of mastering SQL. This involves learning how to manage and optimize data effectively, and I have to admit that it has often improved my understanding of working with data in general.

That being said, the better your SQL skills are, the more you can reduce your query execution time, improve readability, understand the data better, and make data retrieval faster and more reliable.

Hello, my name is CyCoderX and in this article I will cover some of the best tips to help you write better and faster SQL queries and keep your data workflows running smoothly.

Let’s go!

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SQL Database Sagas by CyCoderX

When I first started coding SQL, I would frequently extract all columns from a table using SELECT *. At first this seemed simpler, but I soon discovered that it could seriously hurt performance, especially when using huge tables. Every column is retrieved when SELECT * is used, increasing the time it takes to retrieve data and putting strain on the database.

What I do differently now:

• Select only the columns I need in each query.

Indexing has been one of the biggest efficiency improvements I’ve discovered in SQL. The database can find data more quickly thanks to indexes, which act as shortcuts. I found that query times decreased significantly when I started using indexes on commonly used columns in WHERE, JOIN, or ORDER BY clauses.

What I do now is different:

• In particular, I make sure to index primary keys and any foreign keys used in joins, as well as any columns I use frequently in queries.
• However, because too many indexes can prevent data from being entered or updated, I try to avoid overindexing. The secret is to find a balance between read and write performance.

At first, I had a hard time understanding JOINs, especially when it came to choosing which one to use and why in each case. Combining data from multiple tables requires JOINs, and each type (INNER, LEFT, RIGHT, and FULL) has a specific use. Knowing how each JOIN works made it easier for me to get the correct data and avoid unnecessary lines in my results.

What I do differently now:

• I use INNER JOIN when I only need matching records from both tables, which keeps my results clean and efficient.
• I use LEFT JOIN when I want all records from the left table, even if there is no match in the right table. This is useful for finding unmatched records or handling missing data.
• By choosing the right JOIN type, I can keep my queries efficient and avoid bloating my results with unnecessary data.

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Long table names and complicated column names were common in my queries when I first started programming SQL, making them difficult to read. My searches became much clearer and simpler when I used aliases, which are short, temporary names for tables or columns. Aliases make complex queries easier to track and maintain, especially when working with multiple tables.

What I do differently now:

• I assign short aliases to each table, usually a single letter or abbreviation, so that I don’t have to repeat long names.
• For calculated or derived columns, I give descriptive aliases to clarify their purpose. For example, instead of a vague “total”, I would use something like “total_sales” to specify what is being calculated.

Example:

SELECT c.customer_id, o.order_id, o.amount AS total_sales
FROM customers AS c
JOIN orders AS o ON c.customer_id = o.customer_id;

With aliases I find it much easier to track columns, especially in more complex queries.

Although aggregation functions (like SUM, AVG, COUNT, MAXAnd MIN) are very useful for data analysis, I have found that their careful use significantly affects query performance. Initially, I frequently used aggregates without thinking about their necessity, which could slow down the execution of my queries and produce erroneous results when combined with specific filter strategies.

For example, I now simply count rows in a table that meet specific requirements rather than counting each row.

What I do differently now:

• I don’t calculate every possible value, but I focus on important data points and use aggregates selectively.
• I only use GROUP BY to group similar data together when appropriate, and I always filter data before calculating aggregates on unnecessary rows.

Example:

-- Inefficient: Using HAVING to filter raw data
SELECT department, SUM(salary) AS total_salary
FROM employees
GROUP BY department
HAVING status = 'Active';
-- Efficient: Using WHERE to filter before aggregating
SELECT department, SUM(salary) AS total_salary
FROM employees
WHERE status = 'Active'
GROUP BY department;

In the beginning, I frequently used subqueries to break down complex tasks because I was unaware that subqueries can sometimes slow down performance. Subqueries are great in many circumstances, but when I switched to using JOINs as an alternative, I noticed a significant improvement in the performance and speed of my queries! JOINs tend to be more efficient, especially for retrieving across multiple tables.

What I do differently now:

• I use JOINs whenever possible to reduce nested operations and streamline my queries.
• In cases where subqueries are necessary, I make sure they are optimized by filtering and limiting the data within the subquery itself.

Example:

-- Using a subquery (can be slower)
SELECT customer_id, (SELECT COUNT(*) FROM orders WHERE orders.customer_id = customers.customer_id) AS order_count
FROM customers;
-- Using a JOIN (often more efficient)
SELECT c.customer_id, COUNT(o.order_id) AS order_count
FROM customers AS c
LEFT JOIN orders AS o ON c.customer_id = o.customer_id
GROUP BY c.customer_id;

By reducing the use of subqueries and switching to JOINs, I was able to improve query performance and readability.

As my experience grew, I realized the value of frequently evaluating the performance of my queries, especially as the databases and tables I work on continued to grow and the queries became more complicated.

Performance tools like EXPLAIN (or EXPLAIN ANALYZE in some systems) have become indispensable for me. They allow me to visualize the query execution plan, identify bottlenecks, and determine which sections of the query may need optimization.

What I do differently now:

• I run EXPLAIN on complex queries to see where the database is spending the most time and spot potential performance issues.
• I regularly review indexing, filtering, and join strategies to ensure my queries run efficiently as data grows or changes.

Example:

-- Using EXPLAIN to analyze query performance
EXPLAIN SELECT department, SUM(salary) AS total_salary
FROM employees
WHERE status = 'Active'
GROUP BY department;

This query plan insight helped me make smarter optimization decisions and detect inefficiencies before they impact users or systems.

Every developer and engineer should understand that mastering SQL takes time and is a continuous process of learning and progress.

From the fundamentals of selecting only the necessary columns and understanding JOINs to more sophisticated strategies like monitoring performance and optimizing subqueries, each of these suggestions has helped me build queries Better and more efficient SQL.

Whether you’re new to SQL or want to improve your skills, focusing on these essential techniques can help you avoid common pitfalls and get the most out of interacting with your databases. With these practices, creating clean, efficient, and understandable SQL queries becomes second nature.

Good luck and good request!

Thank you for taking the time to read my article. Article first published on Medium by CyCoderX.

Hi, I am CyCoderX! Engineer passionate about sharing knowledge, I write articles on Python, SQL, Data Science, Data Engineering and much more!

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