SQL Tips: Understand Your Queries with EXPLAIN ANALYZE

When working with SQL databases, knowing how your queries are executed and how you can make them faster and more efficient is important.

This is where the EXPLAIN ANALYZE command comes in handy. It's a powerful tool that gives you insight into what happens under the hood of your database when you run a query.


EXPLAIN ANALYZE is a command used in SQL databases to display the execution plan of a query. An execution plan shows how the database's query optimizer intends to access and process the data.

By using EXPLAIN ANALYZE, you can see details like the cost of each operation, the time it takes, and the order in which the operations are performed. This information is crucial for understanding and optimizing the performance of your queries.

This is very useful because how can you improve your queries if you don't know what's happening?

How Does It Work?

If you prefix your SQL query with EXPLAIN ANALYZE, the database executes the query and provides a detailed report. This report includes:

  1. Execution Plan: The database's steps to execute the query.
  2. Cost Estimates: The estimated computational cost of each step.
  3. Actual Time: The actual time to perform each step.
  4. Rows: The number of rows processed at each step.
  5. Width: The average size of the rows.

Show me!

As always, a real example is usually best to understand how EXPLAIN ANALYZE can be used in practice.

Suppose you have a database with a table called employees and want to find all employees in the "Sales" department.

Your initial query might look like this:

SELECT * FROM employees WHERE department = 'Sales';

To analyze this query, you would use:

EXPLAIN ANALYZE SELECT * FROM employees WHERE department = 'Sales';


Seq Scan on employees  (cost=0.00..12.60 rows=1 width=240) (actual time=0.013..0.020 rows=3 loops=1)
  Filter: (department = 'Sales')
  Rows Removed by Filter: 297
Planning Time: 0.027 ms
Execution Time: 0.045 ms

The output of this command will look something like this (note: actual output varies based on the database system and its version).

Analyzing the Output:

Depending on your query and database, this might look drastically different, but I think it's useful to start building some intuition that we look at our example and unpack it:

  1. Seq Scan on employees: The database performed a sequential scan on the employees table, which looked at each row to find matches.
  2. Cost and Actual Time: The estimated cost and the actual time taken for the scan are shown.
  3. Rows and Width: It shows the number of rows processed and the average size of these rows.
  4. Rows Removed by Filter: Indicates how many rows were filtered out because they didn't meet the 'Sales' department criteria.

Optimizing the Query:

The analysis shows that the query scans the entire table, which is inefficient, especially for large tables. You could consider adding an index to the department column to optimize this.

After creating the index, if you run EXPLAIN ANALYZE again, you should see that the database performs an "index scan" instead of a "sequential scan", which is much faster.


Using EXPLAIN ANALYZE in SQL databases is essential for diagnosing performance issues and optimizing queries. By understanding the execution plan and the cost associated with each step of your query, you can make informed decisions on structuring and optimizing your SQL statements for better performance. Remember, a well-optimized query can significantly reduce execution time and resource usage, leading to a more efficient and responsive database system.

Avatar for Niall Maher

Written by Niall Maher

Founder of Codú - The web developer community! I've worked in nearly every corner of technology businesses; Lead Developer, Software Architect, Product Manager, CTO and now happily a Founder.


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