Using EXPLAIN to Analyze and Optimize SQL Queries

By Raman Kumar

Updated on Nov 07, 2024

In this tutorial, we're using EXPLAIN to analyze and optimize SQL queries. 

SQL query optimization is crucial for improving the performance of your database-driven applications. The EXPLAIN statement is one of the most powerful tools for analyzing and optimizing SQL queries. By using it, you can gain insight into how your query is executed, allowing you to identify bottlenecks and improve performance.

We'll explore the EXPLAIN command, what it means, and how you can leverage it to optimize your queries step by step.

EXPLAIN to Analyze and Optimize SQL Queries

Step 1: Introduction to the EXPLAIN Command

The EXPLAIN statement provides information about how MySQL or PostgreSQL executes a SQL query. When you prepend EXPLAIN to a SELECT, DELETE, INSERT, or UPDATE statement, it outputs details about how the query will be executed, such as:

  • How tables are read (order and method)
  • The indexes being used
  • Estimated rows examined
  • The join type

This information is useful to understand and fine-tune the performance of your queries.

Example Syntax:

EXPLAIN SELECT * FROM employees WHERE department = 'IT';

Step 2: Understanding the EXPLAIN Output Columns

The output of EXPLAIN may contain different columns depending on your SQL server (e.g., MySQL, MariaDB, or PostgreSQL). Here are some key columns to pay attention to:

  • id: Indicates the order in which tables are accessed.
  • select_type: The type of SELECT query (e.g., SIMPLE, PRIMARY, SUBQUERY).
  • table: The table referenced in the output row.
  • type: Shows the join type, such as ALL, INDEX, RANGE, REF, EQ_REF, or CONST. Generally, the closer to CONST or REF, the better.
  • possible_keys: Lists all possible indexes that could be used to find rows in this table.
  • key: The index actually chosen by the query optimizer.
  • key_len: Length of the key being used.
  • ref: Columns or constants used with the key.
  • rows: The estimated number of rows examined by the query.
  • Extra: Contains additional information, such as Using where, Using index, etc.

Step 3: Running EXPLAIN on Basic Queries

Let's start with a basic SELECT query:

EXPLAIN SELECT * FROM employees WHERE department = 'IT';

Analyze the output:

  • type: If the type column shows ALL, it indicates a full table scan, which may suggest performance issues for large datasets.
  • possible_keys and key: If possible_keys lists indexes that are relevant but key is empty, it means no index is being used. You may need to create an index.

Step 4: Using Indexes to Improve Performance

Indexes can dramatically speed up queries. Consider this example:

CREATE INDEX idx_department ON employees(department);

After creating the index, run the same query:

EXPLAIN SELECT * FROM employees WHERE department = 'IT';

Check the key column to confirm that the new index is being used. If it is, you should notice a reduced number of rows and potentially a better type.

Step 5: Optimizing Joins

When queries involve multiple tables, EXPLAIN helps analyze how tables are joined. Consider this query:

SELECT e.name, d.name
FROM employees e
JOIN departments d ON e.department_id = d.id
WHERE e.salary > 50000;

Run the EXPLAIN:

EXPLAIN SELECT e.name, d.name
FROM employees e
JOIN departments d ON e.department_id = d.id
WHERE e.salary > 50000;

Focus on:

  • Join Order: Look at the id column and how tables are accessed. The optimizer may change the order to reduce the number of rows examined.
  • Index Usage: Ensure that indexes exist on columns used in ON clauses or WHERE conditions.
  • Join Type: EXPLAIN may show ALL, INDEX, REF, EQ_REF, or others. ALL indicates a full table scan, which can often be optimized.

Step 6: Interpreting the Extra Column

The Extra column contains additional execution details. Common values include:

  • Using where: Indicates filtering conditions are applied.
  • Using index: All necessary columns are retrieved from the index without reading the table (known as a covering index).
  • Using temporary: Shows that a temporary table is used, which may slow down queries.
  • Using filesort: Indicates a sort operation, which can be optimized by indexes.

Step 7: Optimizing Queries Based on EXPLAIN Output

  • Avoid Full Table Scans: Ensure relevant indexes are created and utilized.
  • Reduce the Number of Rows Examined: Use appropriate WHERE conditions and indexes.
  • Improve Join Conditions: Create indexes on columns involved in joins.
  • Use Covering Indexes: Ensure all columns used in SELECT queries are covered by an index.
  • Rewrite Queries: Consider rewriting subqueries as joins or using temporary tables for complex aggregations.

Step 8: Advanced Query Optimizations

For complex queries, you might see more detailed performance issues. Use ANALYZE (e.g., ANALYZE EXPLAIN) to get real-time performance data or tools like MySQL Workbench and pgAdmin for query profiling.

Step 9: Example Optimizations in Action

Before Optimization:

EXPLAIN SELECT * FROM orders WHERE status = 'Pending' AND total > 1000;

If the type is ALL and no index is used, the query can be slow.

After Optimization (Adding Index):

CREATE INDEX idx_status_total ON orders(status, total);
EXPLAIN SELECT * FROM orders WHERE status = 'Pending' AND total > 1000;

You should see improved performance metrics in the EXPLAIN output.

Conclusion

The EXPLAIN statement is an invaluable tool for analyzing and optimizing SQL queries. By understanding and acting on the information it provides, you can drastically improve the performance of your database queries. Mastering EXPLAIN enables you to make data-driven decisions to optimize your database and ensure high-performing applications.

Feel free to adapt and apply these techniques in your queries to see tangible improvements. Happy optimizing!

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