10 SQL Performance Optimization Tips You Need to Know

Being a professional database administrator who has been optimizing SQL queries for many years, I have faced many difficulties related to database performance improvement. In this detailed guide, I will give my insights and practical ideas to help you improve your SQL query performance significantly. These optimizations have been proven in my daily life and are a great chance that they will be helpful to you too.

Proper Indexing: The Foundation of Query Performance

When it comes to my experience, indexing is the cornerstone of SQL performance optimization. They are like the table of contents in a book enabling the database engine to locate the data it needs quickly without scanning the entire table.

Key Points:

• Create indexes on columns which are used in the WHERE clauses and JOIN conditions frequently
• Try composite indexes for the queries that filter on more than one column
• Avoid too much indexing since it is slowing down the write operations

Example:

CREATE INDEX idx_last_name ON employees(last_name);
CREATE INDEX idx_dept_emp_id ON employees(department_id, employee_id);

Applying these ways of indexing; I have seen query execution times drop by up to 90% in some cases.

Query Optimization: Crafting Efficient SQL Statements

One thing that bothered me for a long period of time was the fact that writing a good SQL query is more like a form of all I have learned throughout the years. Here are some of the techniques I use often:

Key Points:

• Employ the EXPLAIN PLAN command in checking the query execution paths
• Write SAS/IML to create just the result you want, not all fields
• Select JOIN types INNER, LEFT, or RIGHT relevant to the data you desire from the tables

Example of using EXPLAIN PLAN:

EXPLAIN PLAN FOR
SELECT e.employee_id, e.last_name, d.department_name
FROM employees e
JOIN departments d ON e.department_id = d.department_id
WHERE e.salary > 50000;
SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY);

This technique of detecting issues in complex queries and mending them has led to large-scale improvements of the database performance.

Minimize the Use of Wildcard Characters in LIKE Clauses

My observation is that a lot of wildcards, especially already in the beginning of a search pattern, can really slow down the query.

Key Points:

• Avoid the use of ‘%’ when starting with the LIKE pattern in the condition
• Add more efficient text search by full-text search
• Be cautious when using LIKE with large datasets

For example, OP’s query is to retrieve customers that belong to the last name that ends with -son. This is the SQL version proposed by him:

SELECT * FROM customers WHERE last_name LIKE '%son';

In this improved version, OP’s query is to retrieve customers that belong to the last name that starts with John. This is the SQL version proposed by him:

SELECT * FROM customers WHERE last_name LIKE 'John%';

Giving these changes, the query response times have dramatically improved, especially for the large tables.

Utilize Partitioning for Large Tables

As to my own experience, apart from that, partitioning has been an excellent solution to the issue of huge tables in my projects. This involves splitting big tables into smaller ones for easier management.

Key Points:

• Divide the tables into partitions on the basis of the commonly used filter criteria (e.g., date ranges)
• Ensure queries can leverage partition pruning
• Perform regular maintenance of partitions in order to negate fragmentation

A partitioned table will look as follows:

CREATE TABLE sales (
    sale_id INT,
    sale_date DATE,
    amount DECIMAL(10,2)
)
PARTITION BY RANGE (sale_date) (
    PARTITION sales_2020 VALUES LESS THAN ('2021-01-01'),
    PARTITION sales_2021 VALUES LESS THAN ('2022-01-01'),
    PARTITION sales_2022 VALUES LESS THAN (MAXVALUE)
);

Like this, I have been able to reduce query execution times considerably on tables with billions of rows.

Avoid Using Functions in WHERE Clauses

In most cases, the main thing is that some operators like the use of functions in WHERE clauses, which disables the possibility of using the indexes.

Key Points:

• Move function calls to the right side of the comparison
• Consider creating computed columns for frequently used expressions
• Use proper data types to prevent optional conversions

Wrongly Codded Query: Here, the result is not only slower than it should be, but also the column orders are in random fashion.

SELECT * FROM orders WHERE YEAR(order_date) = 2022;

Rendered query in the proper way: This query does the same as the previous one, but it defines a range of dates, so you will only have the records from the last month. It runs much faster than the other.

SELECT * FROM orders WHERE order_date >= '2022-01-01' AND order_date < '2023-01-01';

Now, the turnover matters are usually fixed up to the order of magnitude because of this single correction.

Leverage Query Caching

My finding is that the use of cache memory in the case of frequently run queries is an effective technique for the enhancement of performance.

Key Points:

• Enable setup and configure the cache memory at the database level
• Be clear about various cache dirty scenarios
• Watch out the cache hit ratios to verify their effectiveness

Example of enabling query cache in MySQL:

SET GLOBAL query_cache_type = 1;
SET GLOBAL query_cache_size = 67108864; -- 64MB

It is quite noticeable that the query times currently go in seconds, to the several basis of milliseconds, and this is along with the application of the query caching operating principle.

Optimize Subqueries and Derived Tables

Basically, subqueries and derived tables can be used as a source of strong effects but if they are used incorrectly, they can be the source of the problem too.

Key Points:

• Whenever it is possible, use JOIN connectives and save the correlated subqueries for other occasions
• Materialize the derived tables that you need and then work on them
• IN may take a long time to do the job because of the time it takes to scan large datasets, whereas EXISTS is the better option

In the indicative example question: substitute the subquery with a JOIN:

-- Subquery version
SELECT * FROM orders o
WHERE o.customer_id IN (SELECT customer_id FROM customers WHERE country = 'USA');
-- JOIN version
SELECT DISTINCT o.* FROM orders o
JOIN customers c ON o.customer_id = c.customer_id
WHERE c.country = 'USA';

This is one of the techniques which reduced query execution time up to 50% in certain cases.

Implement Regular Database Maintenance

Regularly, database maintenance plays the most crucial role as it is demanded to maintain a database’s optimal performance. In reality, I have the impression that lack of this association may result in an insidious decline of the query’s execution with time.

Key Points:

• Regularly update query optimizer’s statistics
• Rebuild or reorganize indexes to reduce fragmentation
• Purge old or obsolete data

Example of updating statistics in SQL Server:

UPDATE STATISTICS dbo.customers;

By promoting the regular maintenance practice, I have been able to ensure the sustainability of the satisfactory query performance even at the time the databases grow in data volume and complexity.

Use Appropriate Data Types

The correct selection of data types is a fundamental part of the database design that may largely influence the efficiency of running queries.

Key Points:

• Try the smallest possible data type that accommodates your needs
• Don’t type VARCHAR letter for fixed-length data
• Preferably use DECIMAL instead of FLOAT for accurate calculations

Example of optimizing data types:

-- Suboptimal
CREATE TABLE employees (
    id INT,
    first_name VARCHAR(255),
    last_name VARCHAR(255),
    birth_date VARCHAR(10),
    salary FLOAT
);
-- Better
CREATE TABLE employees (
    id INT,
    first_name VARCHAR(50),
    last_name VARCHAR(50),
    birth_date DATE,
    salary DECIMAL(10,2)
);

During the selection of more appropriate data types, not only has a boost been provided for performance, but also a cutback on storage space has been effected besides, the data integrity has been also improved.

Monitor and Analyze Query Performance

Constant tracking and checking of the performance of the query point are important tasks in order to maintain a database that is optimally designed. I’ve experienced in my practice that numerous of the problems that could occur at the very start can be avoided if you follow the preventive monitoring approach.

Key Points:

• Get information through the monitoring tools provided by the database or any third-party options
• Configure notifications for queries that are taking too long
• Regularly check, fine-tune, and reorganize the most resource-intensive queries

Example of using SQL Server Dynamic Management Views for monitoring:

SELECT TOP 10 
    qs.execution_count,
    qs.total_elapsed_time / qs.execution_count AS avg_elapsed_time,
    SUBSTRING(qt.text, qs.statement_start_offset/2 + 1,
        (CASE WHEN qs.statement_end_offset = -1
            THEN LEN(CONVERT(NVARCHAR(MAX), qt.text)) * 2
            ELSE qs.statement_end_offset
        END - qs.statement_start_offset)/2) AS query_text
FROM sys.dm_exec_query_stats qs
CROSS APPLY sys.dm_exec_sql_text(qs.sql_handle) qt
ORDER BY avg_elapsed_time DESC;

My proactive adoption in the process of performance monitoring across the whole system has allowed me to detect and solve potential problems even before they afflict end-users.

Final Words

Optimizing SQL performance is a never-ending process, and it calls for thorough understanding of database architecture, query execution, and performance optimization. By applying these ten tips, users can certainly reduce the time used in SQL queries and also achieve a good database performance.

Finally, the central premise behind the optimization is its continual monitoring and enhancement. Thus not only your data will grow and develop but also so will your optimization strategies. It is necessary to be informed about new features and practices in your DBMS, and don’t be afraid of experimenting with different optimization techniques to find the best one for your usage.

Getting used to these practices you are on your way to not only achieving but also maintaining the best performance SQL can give to your software.