intro
Let’s learn everything you need to know about the order of execution in SQL to become a master of writing queries that run as intended.
Every time a query is run, your database has to execute it and the way your database executes a query is vital to reach the end goal of your application. The order of execution of an SQL query is vital to the internals of your database—each database management system executes and explains queries in a way that’s slightly different from the other, and without a standardized execution plan, all of your effort may be in vein.
In this guide, you will dig into the SQL execution order, understanding how it is implemented by database engines and why it differs from the order of clauses in SQL syntax.
What Is the Execution Order of an SQL Query?
The execution order of an SQL query refers to the specific sequence in which each query clause is processed by the database engine. This sequence ensures that filtering, joining, grouping, and sorting operations happen in the correct order, which is essential for consistent, replicable results.
Note that the order of execution is a different concept than the order in which clauses are written in an SQL query. Writing clauses in a particular order is a matter of SQL syntax, which defines how a query must be structured for the database engine to interpret it correctly. Once a syntactically correct query is submitted, the database engine processes its clauses according to a standard execution order.
To learn about SQL syntax, check out the SQL cheat sheet.
Order of Execution in SQL: Clause by Clause
Let’s explore the SQL execution order by examining how a sample query with all major clauses is processed by a database engine.
Consider the following query:
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SELECT D.name AS department_name, COUNT(E.id) AS employee_count
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FROM employees E
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JOIN departments D ON E.department_id = D.id
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WHERE E.salary > 50000
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GROUP BY D.id
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HAVING COUNT(E.id) > 5
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ORDER BY employee_count DESC
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LIMIT 10 OFFSET 5;
This query retrieves the names of departments with a count of employees who have a salary above 50,000, only showing departments with more than five such employees. It then sorts these departments in a descending order by the employee count, displaying 10 results but starting from the fifth result.
This is an ideal example for understanding the execution order in SQL because it incorporates:
Time to break down the execution order for each clause in dedicated subchapters, showing how most popular DBMS technologies like MySQL process a query.
1. FROM Clause
The SQL execution order begins with the FROM clause. The database first identifies the tables involved in the query by examining the FROM clause (including any specified JOINs) to determine the total dataset to be queried.
In this case, the tables of interest are employees (aliased as E) and departments (aliased as D). Learn more about the SQL alias mechanism.
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FROM employees E
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JOIN departments D ON E.department_id = D.id
As you can see, these are not the first lines of the query, which means that the order of execution does not follow the syntax order.
2. JOIN Clause
Once the tables are identified, the database engine focuses on the JOIN clauses and applies them.
In this example, JOIN is used to merge data from employees and departments based on the matching department_id in employees and id in departments:
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JOIN departments D ON E.department_id = D.id
The result is a combined dataset with employee and department information.
The way the database engine merges different tables depends on the type of JOIN specified in the query. Discover them all in our guide on SQL JOIN types.
3. WHERE Clause
Next, the WHERE clause is executed to apply filters on the current dataset.
In this case, it keeps only records from the merged dataset where an employee’s salary is greater than 50,000:
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WHERE E.salary > 50000
If the WHERE clause contains multiple conditions, popular database engines usually evaluate them based on operator precedence—rather than strictly from left to right.
Applying WHERE early reduces the data volume that subsequent clauses need to process, improving performance.
4. GROUP BY Clause
After filtering records, the next step in the order of execution in SQL is running the GROUP BY clause.
In this case, GROUP BY clause groups the data based on D.id (the department ID):
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GROUP BY D.id
This means that rows with the same department ID are consolidated into a single group, which is essential for aggregating SQL data aggregation.
5. HAVING Clause
After grouping records, you can filter groups using aggregate functions in the SQL HAVING clause.
Here, HAVING is used to filter for departments—that meet the salary condition—and have more than five employees:
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HAVING COUNT(E.id) > 5
HAVING is like WHERE, but it works on grouped data instead of individual rows.
6. SELECT Clause
Only now is the SELECT clause executed, even though it appears first in the query, to extract specific columns from the dataset.
In this example, it operates on the grouped and filtered dataset to retrieve D.name as department_name and counts the employees in each department, storing that as employee_count:
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SELECT D.name AS department_name, COUNT(E.id) AS employee_count
Keep in mind that the SELECT clause only processes the data after all previous operations are complete. Thus, you cannot reference aliases defined in the SELECT clause in earlier clauses mentioned in this list.
7. ORDER BY Clause
Once the data filtered horizontally by columns is selected, the SQL ORDER BY clause sorts the final results.
In the current sample query, the dataset is ordered by employee_count in a descending order, displaying the rows with highest number of employees first:
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ORDER BY employee_count DESC
Note that in the ORDER BY clause, you can refer to aliased fields because the SELECT clause has already been executed.
Sorting is generally applied to prepare the dataset for display.
8. LIMIT and OFFSET Clauses
The order of execution of an SQL query ends with the [SQL LIMIT and OFFSET clauses, which control the number of results returned.
Note: Do not forget that LIMIT is not part of the SQL standard, but it was included in this example because it is supported by MySQL, PostgreSQL, and other popular database engines. Many database users are more familiar with it than with the equivalent standard OFFSET...FETCH syntax introduced in the SQL standard in 2008.
Here, LIMIT 10 restricts the output to 10 rows, while OFFSET 5 skips the first 5 rows:
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LIMIT 10 OFFSET 5
This pagination mechanism is often used to manage large result sets.
Why You Need to Know the SQL Order of Execution
In summary, the SQL order of execution consists of the following steps:
Understanding the order of execution in SQL is crucial for writing correct queries, as you know how the database will handle each clause. For example, consider the following query:
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SELECT D.name AS department_name, COUNT(E.id) AS employee_count
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FROM employees E
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JOIN departments D ON E.department_id = D.id
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GROUP BY D.id
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HAVING COUNT(employee_count) > 5;
Here, the HAVING clause incorrectly refers to an aliased column in the SELECT statement. This will lead to an error since the HAVING clause is executed before the SELECT clause.
If you attempt to execute the above query in an advanced database client with syntax analysis and error handling like DbVisualizer, you will receive an error message indicating that the alias cannot be referenced in HAVING:
Similarly, you cannot filter by aliased columns:
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SELECT id, name AS department_name
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FROM departments
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WHERE department_name = "Finance";
Executing this query will also result in an error, as the alias department_name is not available in the WHERE clause:
Wonderful! You are now an execution order of SQL query master.
Conclusion
In this guide, you learned about the SQL order of execution and how it differs from the order of clauses in SQL syntax. You now understand how database engines execute clauses in a query to produce results.
As shown here, knowing the order of execution in an SQL query is key to writing queries that the database can effectively execute. For feedback on errors and insights on how to fix your queries, consider using a powerful and visual database client like DbVisualizer!
DbVisualizer is a popular database client that supports multiple DBMS technologies and is equipped with advanced features such as query optimization, SQL formatting, and ERD-like schema generation. Try DbVisualizer for free today!
FAQ
Does the SQL order of execution change by DBMS?
No, the SQL order of execution is standardized across most DBMSs, including MySQL, PostgreSQL, and SQL Server. While specific implementations may vary in syntax or features, the fundamental execution order of clauses remains consistent across different database management systems.
Does the execution order of an SQL query change based on the way the query is written?
No, the execution order of an SQL query remains the same regardless of how the query is written. The database engine processes clauses in a predefined order, ensuring consistent results even if the syntax order differs from the execution order.
How can you obtain useful information about a query's execution plan as devised by the database engine?
You can use the SQL EXPLAIN statement before your SQL query to get insights into how the database engine executes the query, including join methods, index usage, and estimated row counts. This also helps optimize performance. For simplified and advanced analysis, DbVisualizer comes with a dedicated Explain Plan feature.
Does the order of execution in SQL match the syntax order?
No, the order of execution in SQL does not match the syntax order. Although the SELECT clause appears first in a query, it is executed after FROM, WHERE, GROUP BY, and HAVING, following a specific execution sequence defined by SQL standards.
Why should I use an SQL client?
Using an SQL client, such as DbVisualizer, enables you to visualize your data, making your development tasks easier. A great SQL client offers a variety of tools that simplify data management and analysis, regardless of the database management system you are working with. These features enhance productivity, streamline workflows, and help you gain insights from your data more effectively.
