SQL (Structured Query Language) is a popular tool used by organizations to manage and analyse a large amount of data. SQL allows users to retrieve and manipulate data stored in relational databases. However, as the amount of data grows, the time it takes to execute SQL queries can also increase, which can lead to slow application performance and user frustration. SQL query optimization is the process of improving the performance of SQL queries by identifying and addressing performance bottlenecks. In this article, we will explore some common tools and techniques used for SQL query optimization, which can help you to achieve better performance and faster data retrieval times.
SQL Query Optimization Techniques:
When working with databases, it's important to ensure that your SQL queries are running efficiently. Slow queries can lead to longer load times and decreased performance, which can ultimately impact the user experience.
To optimize SQL queries, there are a number of techniques you can use to streamline and improve their performance. Below are some of the techniques that you can use to speed up your database queries and improve the overall performance of your applications.
1. Minimize the use of wildcard characters
Avoid the use of wildcard characters such as % and _ in SQL queries as they can slow down query performance. When using wildcard characters, the database has to scan the entire table to find matching records. Instead, it is recommended to use indexes on columns that are frequently used in WHERE clauses and avoid using wildcard characters at the beginning of a phrase.
Here’s an example of how you can optimize a query that uses a wildcard character:
SELECT * FROM customers WHERE last_name_city LIKE 'P%';
This query will work, but it will be slower than a query that uses an index on the last_name_city column. The query can be improved by adding an index to the last_name_city column and rewriting it as follows:
SELECT * FROM customers WHERE last_name_city >= 'P' AND last_name_city < 'Q';
2. Use Indexes
Indexes are used in SQL query optimization to speed up the retrieval of data from a database table. An index is a data structure that improves the speed of data retrieval operations on a database table at the cost of additional writes and storage space to maintain the index data structure.
Here’s an example of how you can use indexes for query optimization:
SELECT * FROM Users2 WHERE 10 < income AND income < 20;
The query contains 2 index predicates. Indexes idx1, idx2, midx2, and midx3 are all applicable. For index idx1, 10 < income is a start predicate and income < 20 is a stop predicate.
3. Use appropriate data types
To optimize SQL queries, it is important to use appropriate data types for columns in a database. This can significantly improve query performance. For example, using an integer data type for a column that contains numeric values can help reduce the amount of memory required to store the data and speed up queries that use that column. Similarly, using a date data type for columns that contain dates can help improve query performance when filtering by date.
Here’s an example of how to use appropriate data types in a SQL query:
SELECT empName, empRole FROM emp WHERE empID = 98422;
In this example, the appropriate data types are used for each column in the emp table. The empID column is likely an integer data type since it is being compared to an integer value. The empName and empRole columns are likely character data types since they are selected as text values.
4. Avoid the use of subqueries
Subqueries can slow down query performance, especially when used in the WHERE or HAVING clauses. It is important to avoid subqueries whenever possible and to use JOINs or other techniques instead. For example, consider a query that finds all customers who have placed an order in the last 30 days:
SELECT customer_id FROM orders WHERE order_date >= DATEADD(day,-30,GETDATE());
This query can be rewritten using a JOIN as follows:
SELECT DISTINCT customer_id FROM orders o JOIN customers c ON o.customer_id = c.customer_id WHERE order_date >= DATEADD(day,-30,GETDATE());
In this example, the subquery has been replaced with a JOIN between the orders and customers tables. This can help improve query performance by reducing the number of times the database needs to access the tables.
5. Use LIMIT or TOP
Using LIMIT or TOP can help improve query performance by reducing the amount of data that needs to be processed. For example, consider a query that returns all rows from a table:
SELECT * FROM orders;
If the table contains millions of rows, this query can take a long time to execute. However, if you only need to see the first 10 rows, you can use the LIMIT or TOP clause to limit the number of rows returned:
SELECT * FROM orders LIMIT 10;
SELECT TOP 10 * FROM orders;
This can help improve query performance by reducing the amount of data that needs to be processed.
6. Use SELECT instead of SELECT *
Using SELECT * to retrieve all columns from a table can be slow and inefficient. This is because the database has to read all of the data from each row, even if you only need a subset of the columns.
For example, consider a table with the following columns:
CREATE TABLE users ( id INT PRIMARY KEY, name VARCHAR(255), email VARCHAR(255), password VARCHAR(255) );
If you only need to retrieve the name and email columns, you should specify those columns explicitly in your query:
SELECT name, email FROM users;
This can help improve query performance by reducing the amount of data that needs to be read from the disk and transferred over the network.
7. Use EXISTS instead of IN
Using EXISTS instead of IN reduces the amount of data that needs to be processed. This is because EXISTS only needs to check if a row exists, while IN needs to retrieve all of the rows that match the subquery.
For example, consider a table with the following columns:
CREATE TABLE orders ( id INT PRIMARY KEY, customer_id INT, amount DECIMAL(10, 2) );
If you want to find all orders for customers in a specific list, you might be tempted to use the IN operator:
SELECT * FROM orders WHERE customer_id IN (1, 2, 3);
However, this can be slow if the list of customers is large. Instead, you can use the EXISTS operator:
SELECT * FROM orders o WHERE EXISTS ( SELECT * FROM customers c WHERE c.id = o.customer_id AND c.id IN (1, 2, 3) );
8. Use GROUP BY
The GROUP BY clause is used in SQL queries to group rows that have the same values. It is used with aggregate functions like COUNT, SUM, AVG, etc. to group the result-set by one or more columns. This can help in optimizing SQL queries by reducing the number of rows that need to be processed.
For example, consider a query to find the total number of orders placed by each customer:
SELECT customer_id, COUNT (*) as order_count FROM orders GROUP BY customer_id;
Here, the GROUP BY clause groups the result-set by customer_id and the COUNT function returns the number of orders placed by each customer.
9. Add EXPLAIN to the beginning of Queries
The EXPLAIN statement is used in SQL queries to analyze how the query is executed and to identify performance issues. It provides information about how MySQL executes a SELECT statement, including information about how tables are joined and in what order.
For example, consider the following query:
EXPLAIN SELECT * FROM orders WHERE customer_id = 1;
Here, the EXPLAIN statement provides information about how MySQL executes the SELECT statement. It shows which indexes are used, how tables are joined, and in what order.
10. Use Stored Procedure
Stored procedures are precompiled SQL statements that are stored in the database. They can be used to optimize SQL queries by reducing network traffic and improving performance. Here is an example of how you can use stored procedures:
CREATE PROCEDURE GetCustomerOrders @CustomerID int AS SELECT * FROM Orders WHERE CustomerID = @CustomerID
This stored procedure retrieves all orders for a given customer ID. When you execute this stored procedure, SQL Server does not have to parse and compile the query each time it is run. Instead, it can reuse the execution plan that was generated when the stored procedure was created.
11. Avoid Queries inside a Loop
Running queries inside the loop is not an efficient way of executing SQL queries. Instead of running queries inside the loop, you should try to insert and update data in bulk which is a far better approach as compared to queries inside a loop.
Here is an example of how you can avoid queries inside a loop:
SELECT ii.ID AS ITEM_ID, ii.Description AS item_desc, gi.Description AS group_desc FROM Items_Inv ii INNER JOIN Groups_Inv gi ON ii.grpID=gi.ID
This query will give you a table with all items with group descriptions. The group descriptions will be duplicated next to the items.
12. Simplify Joins
Simplifying joins is defined as reducing the number of joins in a query by using subqueries or temporary tables. This can help improve query performance by reducing the amount of data that needs to be processed.
For example, consider the following query:
SELECT *FROM table1 INNER JOIN table2 ON table1.id = table2.id INNER JOIN table3 ON table2.id = table3.id
This query can be simplified by using a subquery or temporary table:
SELECT *FROM table1 INNER JOIN ( SELECT *FROM table2 INNER JOIN table3 ON table2.id = table3.id ) AS t2 ON table1.id = t2.id
This simplifies the query by reducing the number of joins from three to two. By doing so, it reduces the amount of data that needs to be processed and can help improve query performance.
SQL Query Optimization Tools:
Fortunately, there are many tools available that can help you to identify and fix performance issues. Here we will take a look at the top 4 SQL query optimization tools that can help you to improve the performance of the SQL queries and your databases.
1. EverSQL Query Optimizer
EverSQL Query Optimizer is a tool that helps you improve the performance of your SQL queries and your database server by automatically rewriting and indexing your queries using AI-based algorithms. It also provides ongoing performance insights and cost-reduction recommendations for your database.
EverSQL Query Optimizer works by analyzing your query and comparing it with various optimization techniques and best practices. It then generates a rewritten query that is more efficient and faster than the original one. It also suggests the optimal indexes create or drop for your query to run faster. You can see the code comparison and the change notes after the query rewriting to understand how the optimization works.
EverSQL Query Optimizer supports MySQL and PostgreSQL databases and can integrate with various platforms such as Datadog, AWS, Google Cloud, etc. You can use it online or install it as a performance sensor on your server.
Can speed up your queries by up to 5X on average.
Non-intrusive and does not access any of your sensitive data.
Easy to use and integrates with various platforms such as Datadog, AWS, Google Cloud, etc.
Only supports MySQL and PostgreSQL databases, not other types of databases.
Not able to optimize complex queries that involve subqueries, joins, etc.
Not account for all the factors that affect query performance, such as data distribution, concurrency, etc.
Require manual verification and testing of the optimized queries before applying them to production.
Price: Price depends on the number of queries you want to optimize. It offers two plans: Free Plan allows you to optimize 50 queries with basic features. Paid Plan ($29 - $999 per month) allows you to optimize unlimited queries, features and support options.
2. ApexSQL Plan
ApexSQL Plan is a tool that helps you view and analyze SQL execution plans and optimize SQL queries for SQL Server databases. It can be used as a standalone program or as an add-in for SQL Server Management Studio (SSMS).
It works by loading a SQL query into the ApexSQL Plan window and clicking on the Execute button to generate an execution plan diagram. The diagram shows you how the query is processed by the server and provides data flow information in real time. You can resize, modify, and observe the query execution and manage property details for each operation in an execution plan. You can also compare different execution plans and use performance recommendations from database advisors.
ApexSQL Plan allows you to:
Generate and compare estimated and actual execution plans.
Generate live actual execution plans and observe the data flow during query execution.
Identify and troubleshoot performance issues such as missing indexes, parameter sniffing, implicit conversions, etc.
Analyze query waits and statistics.
Export and import execution plans in various formats such as XML, HTML, PDF, etc.
Deploy both as a standalone program and an SSMS add-in.
Create execution plan diagrams and show data flow information in real time.
Manage property details for each operation in an execution plan and configure sub-elements.
Does not support all SQL Server versions.
May have some bugs or errors that need to be fixed.
Not have all the features which other SQL query optimizer tools offer.
Price: Free tool
3. SQL Azure Query Performance Insights
SQL Azure Query Performance Insights is a feature that provides intelligent query analysis for single and pooled databases in Azure SQL Database. It helps you identify the top resource-consuming and long-running queries in your workload and find the queries to optimize to improve overall workload performance and efficiently use the resource that you are paying for.
It works by using the Query Store data that is automatically collected and stored in your database. You can access Query Performance Insights from the Azure portal by opening Intelligent Performance > Query Performance Insights from the left-side menu of your database. You can then review the list of top resource-consuming queries by CPU, duration, and execution count, and select an individual query to view its details, such as query text and history of resource utilization. You can also see performance recommendations from database advisors and use sliders or zoom icons to change the observed interval.
It provides deeper insight into your database's resource consumption and details on top database queries by CPU, duration, and execution count.
It helps you find the queries to optimize to improve overall workload performance and efficiently use the resource that you are paying for.
It allows you to drill down into details of a query, and to view the query text and history of resource utilization.
It shows performance recommendations from database advisors and allows you to configure automatic tuning options.
Requires Query store to be active on your database and properly configured.
May not detect all types of performance issues or provide all possible solutions.
If the Query store has not captured enough data or if the database has no activity, it will not render the proper information.
Price: Free features included in Azure SQL Database.
4. Toad SQL Optimizer for Oracle
Toad SQL Optimizer for Oracle is a tool that validates and optimizes SQL code for Oracle databases. It helps you develop and deploy high-quality, high-performing Oracle database code by using a patented optimization engine that finds alternative versions of the original SQL statement that will run faster in the database.
It works by loading a problematic SQL statement into the Toad Editor window and clicking on the Auto-Optimize icon on the Editor’s toolbar. Toad will prompt you to provide some information about this SQL statement, such as the database type, the optimization scope, and the execution plan mode. Toad will then run your SQL with a variety of options that include various hints and SQL rewrites to find the optimal performing SQL automatically. You can compare the results of different SQL statements and choose the best one for your needs.
Validates and optimizes SQL code for Oracle databases by using a patented optimization engine that finds alternative versions of the original SQL statement that will run faster in the database.
Automates the validation of SQL and PL/SQL to ensure the best possible performance and generates index options based on continuous database SQL execution workload.
Provides dynamic code violation notifications and performance recommendations from database advisors.
Integrates with Toad for Oracle Xpert Edition, which provides you with all of Toad’s development, editing, debugging, and project management features.
Does not support all versions of Oracle or Windows.
Have some bugs or errors that need to be fixed.
Does not have all the features or options that other SQL optimizer tools have.
Price: Toad SQL Optimizer for Oracle is included in Toad for Oracle Xpert Edition, which has a subscription price of $1995 per user per year.
Optimizing SQL queries is a crucial task for improving the performance and efficiency of database operations. With the right tools and techniques, you can identify and fix performance issues, resulting in faster query execution and improved database performance. By incorporating the tools and techniques into your database, you can ensure that your database is running at its optimal level and delivering the best possible user experience.