intro
Let's learn everything you need to know about the two most common types of indexes in this clustered vs non-clustered index comparison article!
SQL indexes are essential tools that database developers use to speed up queries, but not all database indexes are created equal. To truly master and understand them, you must recognize the differences between clustered and non-clustered indexes. This article will provide a detailed clustered vs non-clustered index comparison.
In this guide, you will learn about both clustered and non-clustered indexes, digging into everything you need to know about these two distinct types of SQL indexes.
Let’s dive in!
Clustered Index
Start this clustered vs non-clustered index comparison article by exploring clustered indexes in detail.
Definition
A clustered index organizes the data in a table by physically sorting and storing the rows on the database server's disk according to the values of the index key column. This means the order of the rows on the disk matches the order defined by the clustered index.
For example, if a table named Employees has a clustered index on the EmployeeID column, the data rows on the disk will be stored in ascending order of EmployeeID. So, if the table contains EmployeeID values 103, 101, and 102, the rows on the disk will be rearranged to 101, 102, 103. This arrangement improves query performance for searches involving a range of EmployeeID values or sorting operations.
In other words, a clustered index essentially defines the storage structure of a table, organizing the data on the disk to make it faster to perform data retrieval for range queries and sorted operations.
Each table can have only one clustered index, as the rows themselves are stored in the order specified by this index. In most databases, a clustered index is created by default by assigning a primary key column. At the same time, creating a clustered index on a non-primary key column may also be possible, depending on the use case.
Note: When a table has a clustered index, the table is called a “clustered table.” If a table has no clustered index, its data rows are stored in an unordered structure called a “heap.”
At the same time, “heap” is not to be confused with one of the storage engines available within MySQL—HEAP was the former name of the MEMORY storage engine, and tables running the HEAP—nowadays referred to as MEMORY—storage engine would have all of their contents stored entirely in the server’s memory.
Use Cases
Below are the most useful use cases for clustered indexes:
Support
| Database | Supports Clustered Index | Notes |
|---|---|---|
| MySQL | Yes | Each InnoDB table has a dedicated clustered index that stores row data. Typically, the clustered index is associated with the primary key. |
| PostgreSQL | No | Implements similar functionality via table ordering but doesn't explicitly support clustered indexes. |
| SQL Server | Yes | Fully supports clustered indexes; created by default on primary keys. |
| Oracle | No | Does not support clustered indexes but achieves something similar through ITOs (index-organized tables). |
Non-Clustered Index
Continue the clustered vs non-clustered index comparison article by checking out non-clustered indexes.
Definition
A non-clustered index is a data structure that stores a separate list of pointers to the actual data rows in a table. Unlike a clustered index—which physically arranges data rows in the table based on the index key—a non-clustered index creates a separate object with the indexed column values and corresponding row pointers.
That enables you to define multiple non-clustered indexes on a table. Thus, non-clustered indexes are key to speeding up lookups and queries that filter on one or more non-primary key columns, making data retrieval more efficient without reorganizing the table's physical storage.
When a non-clustered index is created on a column, the database creates a sorted copy of the indexed column (or columns) along with pointers to the corresponding rows in the table. When a query is executed, the database engine first searches the index for the required values and then uses the pointers to quickly retrieve the corresponding rows from the data table. You can verify that with an SQL EXPLAIN query.
For example, consider a table Employees with the following data:
| EmployeeID | Name | Department |
|---|---|---|
| 1 | Alice | HR |
| 2 | Bob | IT |
| 3 | Charlie | IT |
| 4 | David | HR |
If you create a non-clustered index on the Department column, the non-clustered index may look like this:
| Department | Pointer to Row |
|---|---|
| HR | Row 1 |
| HR | Row 4 |
| IT | Row 2 |
| IT | Row 3 |
When you query the Employees table for a specific department, like HR, the database will first search the index and retrieve the pointers (Row 1 and Row 4), then fetch the data rows directly. Searching the index is faster than performing a normal query because the index is sorted and optimized for quick lookups, allowing the database to quickly locate the relevant pointers without having to scan the entire table.
There are several types of non-clustered indexes. These are:
Use Cases
These are some of the most common use cases for non-clustered indexes:
Support
| Database | Supports Non-Clustered Index | Notes |
|---|---|---|
| MySQL | Yes | Supported by InnoDB and MyISAM storage engines. |
| PostgreSQL | Yes | Fully supports non-clustered indexes for query optimization. |
| SQL Server | Yes | Fully supports non-clustered indexes alongside clustered indexes. |
| Oracle | Yes | Fully supports non-clustered indexes for performance improvement. |
Clustered vs Non-Clustered Index: Summary Table
Now that you know the ups and downs of both clustered and non-clustered indexes, check out a table that summarizes the clustered vs non-clustered index comparison:
| Aspect | Clustered Index | Non-Clustered Index |
|---|---|---|
| Definition | Sorts and stores data rows based on the index key | Stores a separate structure with pointers to data rows |
| Data Storage | Data is stored in the leaf nodes of the index itself. | Stores pointers to the data, not the actual data in leaf nodes. |
| Speed | Faster, as it defines the order of data and directly stores it | Slower, requires extra steps to find data via pointers |
| Memory Usage | Requires less memory | Requires more memory for operations |
| Number of Indexes | A table can have only one clustered index | A table can have multiple non-clustered indexes |
| Data Order | Defines the physical order of data in the table | Does not affect the physical order of data in the table |
| Database Support | MySQL, SQL Server | MySQL, PostgreSQL, SQL Server, Oracle |
Conclusion
In this blog post, you learned about clustered and non-clustered indexes, how they work, and their availability in MySQL, PostgreSQL, SQL Server, and Oracle. You now understand that some databases only support clustered indexes, as well as the differences between clustered and non-clustered indexes.
As discussed, managing indexes may not be intuitive, but it becomes much easier with a database tool like DbVisualizer. This database client supports visual index creation and management, simplifying the entire process:
DbVisualizer also offers many other features, such as query execution, data exploration, and table discovery. Additionally, it includes advanced features like query optimization, SQL formatting, and ERD-like schema generation. Try DbVisualizer for free today!
FAQ
How to create indexes in SQL?
To create an index in SQL, use the CREATE INDEX statement. For example:
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CREATE INDEX idx_name ON table_name(column_name_1, ..., column_name_n);
Usually, this instruction will create a non-clustered index. For more information, refer to our guide on index creation in SQL.
Alternatively, you can use the ALTER TABLE query to modify a table and add an index when modifying it:
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ALTER TABLE table_name ADD INDEX idx_name(column_name_1, ..., column_name_n);
How to create clustered and non-clustered indexes in SQL Server?
To create a clustered index in SQL Server, launch:
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CREATE CLUSTERED INDEX idx_name ON table_name(column_name_1, ..., column_name_n);
To create a non-clustered index, use:
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CREATE NONCLUSTERED INDEX idx_name ON table_name(column_name_1, ..., column_name_n);
Note that NONCLUSTERED is added by default by SQL Server and can be omitted.
How to deal with duplicate indexes in MySQL?
To deal with duplicate indexes in MySQL, you must first identify indexes with different names but that act on the same columns. You can do that by exploring all indexes on a table with a SHOW INDEXES query:
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SHOW INDEXES FROM table_name;
Then, drop specific duplicate indexes using:
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DROP INDEX duplicate_idx_name ON table_name;
For complete guidance, read our guide on duplicate indexes in MySQL.
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