From Cluster to Specialized: Types of SQL Indexes and When to Use Them

Given the huge amount of data flowing through digital pipelines, knowledge of SQL indexes should be a daily bread and butter. Here we cut through a thick shell of complexity to reveal clustered and non-cluster indices and their cousins.

Why is it worth going through it? Because we live in an age of instant graffiti. The speed at which data can be downloaded can be the difference between being a leader and staying behind. Therefore, we will go through the ins and outs of cluster and non-cluster indices and their specialized relatives. Along the way, it should be remembered that each type has its role. For each of them there is a certain scenario in which its purpose becomes the most obvious.

The idea here is to know which one to use to get the desired performance at the right time.

Clustered and non-clustered SQL indexes

Cluster Indexes

The cluster index grabs the data “by the hair” and arranges it neatly on the disk. Like books stacked on a shelf. A cluster index keeps an eye on the order by arranging each row of data exactly where it should be according to its index. This meticulous layout is a boon for those scope queries that are hungry for big chunks of data, because everything is exactly where it's expected.

You can only create a cluster index per table once. Why? Because a stack of papers can only be sorted one way at a time. In most cases, the master key takes over the task, automatically becoming a cluster index, since it is extremely suitable for maintaining order.

When to use:

• Master key queries: They are ideal when you have a primary key, which is often used in queries. For example, if you retrieve records in a sequence or perform range queries.
• High cardinality columns: Columns that have unique or near-unique values are good candidates for unclustered indexing because the index can quickly direct a query to the exact location of the data.
• Tables requiring intensive reading: If the table is primarily used to read data, a cluster index can increase performance by minimizing the number of I/O operations required on the disk.

Non-Cluster Indexes

Non-clustered SQL indexes are discrete organizers of the database world. They keep a separate ledger from the data in the table itself, keeping a register of key values and indicators that connect directly to the corresponding rows. This allows the table to host multiple unclustered indexes, each of which is tailored to improve the search for specific datasets. Their separation from the physical data of the table means that they can quickly direct queries to the correct location without having to scan the entire table.

Unlike cluster indexes, non-cluster indexes do not dictate the order of the physical data in a table; they exist as separate units that reference the table data. This architecture allows for faster operations on data, such as inserting and updating, since these actions do not require changing the order of the actual rows of the table. However, retrieving the data requires an additional step because the database must first reference the non-clustered index to locate the position of the data in the table.

When to use:

• Frequently used columns: Non-clustered SQL indexes are best used on columns frequently used in queries that do not change the physical order. For example, if users frequently search for both “names” and “email address,” non-cluster indexes on these columns can speed up those queries.
• Tables with a large number of records: Because non-cluster indexes do not change the physical order of the data in the table, they are less of a performance burden when frequent insertions, updates, or deletions are performed.
• Query Coverage: If the query can only be answered using the data contained in the index, then a non-cluster index can drastically improve the performance of the query without referring to the table data.

Unique indexes

Unique SQL indexes are those that ensure that all values in a column or set of columns remain distinct. They enforce data uniqueness, which is critical for key identifiers such as transaction IDs or user emails. In this way, they ensure that no two rows have the same value in the indexed columns. This is especially important for maintaining the integrity of the data, which must be uniquely identifiable throughout the system.

Creating a unique SQL index on a column changes the way a database handles inserting and updating data. Any attempt to insert or update data that would result in duplicate entries in indexed columns is automatically rejected by the database system. This check occurs at the time of the change attempt, which means that the integrity of the data is maintained continuously and automatically.

When to use unique SQL indexes:

• Master key columns: Automatically applied to most databases, the unique index on the primary key columns ensures that each record can be uniquely identified.
• Business Critical Uniqueness: For fields that require uniqueness for business reasons, such as email addresses or social security numbers, a unique index prevents duplication of data.
• Ensuring Data Integrity: In applications where data integrity is paramount and duplicates could lead to errors or misunderstandings, unique indexes act as a safeguard.

Composite indexes

Composite indexes are multi-lane highways built “by data”. They are designed to handle a larger traffic of complex queries involving multiple conditions or sorting operations. When you set up such an index, it organizes the data by setting specific columns in a specific order. Such an arrangement allows the database system to navigate through the data in a purposeful manner, using structured complex key paths to quickly reach the data points it needs.

True usability appears in scenarios involving several fields. Thanks to the composite index, the database has a direct route drawn. As a result, it is able to effectively locate and retrieve relevant data without unnecessary detours. This approach simplifies the search process, while significantly speeding it up.

When to use compound indexes:

• Complex query terms: For queries that consistently contain conditions for multiple columns, complex indexes can drastically reduce query time by providing easy access to data in the required order.
• Sorting and filtering: They are especially useful for optimizing queries that require sorting or filtering data in multiple columns. By adapting the index structure to the query structure, they minimize the need for additional sorting and filtering when executing the query.
• Efficient data access: Complex indexes reduce the load on the database engine. Especially in scenarios where data access patterns are unambiguous and involve consistent queries.

Covering Indexes

Overlapping SQL indexes are designed to optimize query performance by ensuring that all the columns needed for the query are in the index itself. Basically, they contain everything a query might need - filter columns, sort columns, and even those listed in the SELECT statement. With this setup, the database can address queries directly from the index, reducing the need for disk I/O operations and significantly speeding up response time.

This type of SQL index turns the database into a self-sufficient unit when it comes to read operations, especially beneficial for applications where data retrieval speed is paramount. Because the index contains all the required data, the database skips the potentially slow step of reading from the table. This streamlined process not only speeds up data retrieval, but also reduces the consumption of system resources, making overlay indexes a critical tool in optimizing database performance.

When to use overlay indexes:

• High Performance Readings: Ideal for scenarios where query performance is critical and the overhead of accessing table data can lead to unacceptable delays. Covering indexes are especially useful in reporting and data analysis applications where queries are complex and span multiple columns.
• Minimize disk I/O operations: They are beneficial in environments where the priority is to limit I/O operations on the disk. Since all the necessary data is available in the index, the number of reads from the disk is minimal.
• Simplifying execution plans: Overlay indexes can simplify the execution plans generated by the query optimizer. By providing all the necessary data in the index, the database engine does not have to perform additional links or searches that can complicate execution plans and reduce performance.

“Specialty” indexes

Specialized indexes, such as partial, filtered and functional indexes, offer targeted database optimization solutions. They accomplish this by addressing specific query patterns and subsets of data. These indexes are wherever conventional indexing may be insufficient, providing efficient data retrieval for specific query scenarios.

Partial

We create partial indexes to index only a subset of table rows that meet certain criteria. This selective indexing strategy is beneficial for large tables where we often only evaluate part of the data. By indexing a subset, partial indexes reduce the size of the SQL index, which can lead to lower storage requirements and faster maintenance tasks compared to indexing the entire table

Advantages of partial indices:

• Performance in large tables: They are particularly effective in increasing the performance of very large tables. There, only a small part of the data is regularly queried.
• Less resource consumption: Partial indexes consume less disk space and memory, making them an economical choice for optimizing database performance.
• Tailored to your specific queries: By focusing on the lines that are most likely to be searched, partial indexes provide faster responses to queries. What can avoiding unnecessary data scanning do, right?

Filtered

Filtered indexes are similar to partial indexes, except for their specific optimization for queries that use deterministic filtering criteria. These indexes include only rows that match a predefined filter and are extremely useful for queries that often access rows with common attributes.

Advantages of filtered indexes:

• Query Performance: They greatly increase query performance by reducing the size of the index scan, making them faster than indexes of full tables.
• Storage saving: Filtered indexes require less storage because they index only the relevant rows, reducing the overall memory footprint.
• Customizable: These indexes can be tailored to the specific needs of your application, focusing on the most relevant subsets of data.

Functional

Functional indexes are based on expressions or functions applied to data. Instead of directly indexing a column, a functional index can index the result of a function. Or, alternatively, an expression that includes one or more columns. This type of index is especially useful when queries often include calculated columns.

Advantages of functional indexes:

• Expanded Query Capabilities: Functional indexes allow you to efficiently query the results of calculations. This can be critical for applications that include data transformations in queries.
• Performance improvement: They improve performance by pre-calculating expressions and storing results. The result speeds up the processing of queries that include these expressions.
• Versatility: This indexing strategy supports a variety of data transformations. Thus, it allows you to optimize queries that involve complex conditions and calculations.