How to Create Indexes in SQL Server:
A Complete Guide

When SQL Server performance takes a hit, the first instinct for most developers is to tweak the query. But in many cases, the problem is not the query; it's the missing index silently breaking everything.

Missing indexes commonly cause performance problems in OLTP systems. Without them, SQL Server scans entire tables, bloats execution plans, and burns through resources. The only way to prevent this is by understanding how indexing works and applying it with precision.

This is where tools like dbForge Studio for SQL Server prove valuable. By providing deep visibility into index usage, fragmentation, and query performance, they help teams turn indexing strategy into system-wide results.

This guide delivers the foundation you need to understand how to create an index in SQL Server. You'll get practical guidance on index syntax, index types, when to use included columns, and how to avoid over-indexing pitfalls.

Let's dive in!

What is an index in SQL Server?

An index in SQL Server is a performance-tuning structure that accelerates the retrieval of rows from a table or view. Much like an index in a book helps you quickly locate information without reading every page, a SQL index allows the database engine to find data without scanning every row. It reduces the I/O load and improves the speed of queries, especially in large datasets.

Indexes are built on one or more columns and store a sorted reference to the underlying data, typically organized in a balanced tree (B-tree) structure. This structure allows SQL Server to traverse the index efficiently, reducing the number of reads needed to locate specific rows.

When a query targets those columns, such as in WHERE, JOIN, or ORDER BY clauses, SQL Server can navigate the index instead of performing a full table scan. This becomes especially valuable in transactional systems and analytical workloads where response time is critical.

Types of indexes in SQL Server

SQL Server supports several types of indexes, each designed for specific use cases and query patterns:

• Clustered indexes: This is the backbone of your table’s data structure. A clustered index sorts and stores the data rows in order, meaning the table is the index. It’s the go-to choice for queries that scan ranges—think reports filtered by DateCreated or product listings sorted by price.
• Non-clustered indexes: Unlike clustered indexes, these act like a detailed table of contents, storing pointers to where the data lives. You can create many on a single table to accelerate lookups, joins, and filters—perfect for speeding up searches by CustomerID or Email.
• Unique indexes: These ensure that the indexed columns contain unique values. They can be created explicitly using the CREATE UNIQUE INDEX statement or implicitly by defining UNIQUE constraints on columns. Unique indexes are crucial for maintaining data integrity.
• Full-text indexes: When your users need to search inside paragraphs rather than just match exact values, full-text indexes take over. They break down large text fields into searchable tokens, enabling fast, intelligent queries through CONTAINS or FREETEXT. They are ideal for powering document search, helpdesk queries, or e-commerce filters.
• Spatial indexes: Built for the real world—literally. Spatial indexes accelerate queries on geographical or geometric data, whether you're mapping delivery zones or finding the nearest store. They support complex spatial operations like distance calculations and area containment with precision and speed.

The T-SQL CREATE INDEX statement gives developers granular control over how data is indexed, sorted, and stored, making it an essential part of any performance optimization strategy.

Below, we explain how to create an index in SQL Server, focusing on the two most common types: non-clustered and clustered indexes. Each serves a different purpose and is foundational to high-performing database systems.

Non-clustered indexes are designed for targeted lookups. They store a sorted list of values for one or more columns and pointers to the data rows. This structure makes them ideal for speeding up queries that filter, join, or order by specific columns, without altering the physical order of the table.

Syntax

  CREATE NONCLUSTERED INDEX index_name
  ON table_name (column1 [ASC|DESC], column2 [ASC|DESC])
  INCLUDE (column3, column4)
  WITH (FILLFACTOR = 90);

Example

  CREATE NONCLUSTERED INDEX IX_Customers_Email
  ON Customers (Email ASC)
  INCLUDE (FirstName, LastName)
  WITH (FILLFACTOR = 90);

What this does:

• Creates an index on the Customers table.
• Sorts by Email in ascending order to speed up email-based lookups.
• Includes FirstName and LastName to make the index “covering.”
• Uses FILLFACTOR = 90 to leave 10% space per page—ideal for write-heavy systems.

Non-clustered indexes sort key columns for fast lookups without changing the table’s physical order. Use INCLUDE to cover more queries, and adjust FILLFACTOR to balance read speed with insert/update efficiency. Multiple non-clustered indexes can exist per table.

Use it when you need fast access to specific columns or to optimize complex joins and filters.

Clustered indexes define the actual physical order of the table’s data. Since the data rows are stored in the same order as the index, SQL Server allows only one clustered index per table. Choosing the right column for a clustered index is a strategic decision—it directly influences query speed, data storage, and insert/update performance.

Syntax

  CREATE CLUSTERED INDEX index_name
  ON table_name (column1 [ASC|DESC])
  WITH (FILLFACTOR = 90);

Example

  CREATE CLUSTERED INDEX IX_Orders_OrderDate 
  ON Orders (OrderDate DESC)
  WITH (FILLFACTOR = 90);

What this does:

• Builds a clustered index on the Orders table, using OrderDate in descending order.
• Physically stores the data rows in OrderDate order—ideal for recent-order queries.
• FILLFACTOR = 90 reduces page splits by reserving 10% of each page for future inserts.

Clustered indexes accelerate range and sort-based queries by physically ordering the table. Choose carefully, as frequent changes to key columns can lead to fragmentation and performance overhead.

Use it when your queries rely heavily on date or numeric ranges or require frequent sorting on a specific column.

When to create an index in SQL Server

Indexing is a strategic decision that requires understanding your data's query workload and structure. While indexes can drastically improve performance, creating them blindly can lead to wasted resources, degraded write performance, and bloated storage.

Below are clear scenarios where indexes pay off, and cases where they may cause more harm than good.

When to create an index

Creating indexes pays off most when they align with real-world query behavior. Here are the situations where indexing can lead to meaningful performance gains:

• Frequently queried columns: Indexes shine when applied to columns used regularly in WHERE, JOIN, ORDER BY, and GROUP BY clauses. If a query runs often and always filters by a specific field, that field is a candidate for indexing.
• Large tables: Full scans on large tables are expensive. Indexes dramatically cut search time by limiting the rows SQL Server needs to read. For high-volume tables, well-chosen indexes can distinguish between a query that takes milliseconds and one that times out.
• Foreign keys and lookup tables: Indexes on foreign key columns improve join performance and help enforce referential integrity efficiently.
• Covering queries: If you can build a non-clustered index that includes all the columns a query needs, SQL Server can return results directly from the index without accessing the base table. This avoids a key lookup (a bookmark lookup), which would otherwise fetch missing columns from the clustered index or heap, saving time and I/O.

When not to create an index

Indexing is not always beneficial. In the following scenarios, adding indexes may offer little or even harm performance:

• Small tables: A full table scan is often faster than an index if a table only holds a few hundred rows. The overhead of maintaining the index outweighs any read-time benefit.
• Columns with low selectivity: Indexing columns with very few distinct values (e.g., IsActive, Gender, Status) rarely improves performance, because the database engine still has to scan a large portion of the table.
• High-write workloads: Tables that are frequently updated, inserted into, or deleted, such as transaction logs or audit trails, may suffer from excessive index maintenance. Every write operation has to update the relevant indexes, which can significantly slow performance.

Performance trade-offs

Creating an index improves read performance but increases overhead on write operations. Every INSERT, UPDATE, or DELETE must also update all related indexes, which can impact throughput in write-heavy systems. Additional indexes also consume more storage and can increase index fragmentation if not maintained regularly.

Best practice: Start with minimal indexing based on actual query patterns. Monitor performance and use tools like execution plans and sys.dm_db_index_usage_stats to justify adding or dropping indexes over time.

How to add an index to an existing table in SQL Server

In SQL Server, adding an index to an existing table follows the same core principle as creating one from scratch—you use the SQL CREATE INDEX statement. Unlike MySQL, SQL Server does not support ALTER TABLE ... ADD INDEX. This is a common point of confusion, but the correct approach is direct and explicit.

Adding indexes to existing tables is one of the most effective ways to resolve performance bottlenecks, but only when guided by real query patterns and execution data.

Step-by-step: Adding an index to a column

Follow these steps to add an index to a column in an existing SQL Server table, based on actual query performance data:

1. Identify the query bottleneck using execution plans or DMVs (e.g., sys.dm_exec_query_stats and sys.dm_db_index_usage_stats).
2. Choose the right column(s) that are heavily used in WHERE, JOIN, or sorting operations.
3. Decide on the index type—clustered or non-clustered, possibly with INCLUDE columns.
4. Execute the SQL CREATE INDEX statement directly on the existing table.

Example: Adding a Non-clustered Index

  CREATE NONCLUSTERED INDEX IX_Employees_LastName
  ON Employees (LastName)  
  INCLUDE (FirstName, DepartmentID);

This index improves queries filtering or sorting by LastName while covering FirstName and DepartmentID to avoid additional lookups. Here’s a closer look at its impact on performance:

• Read performance improves because the query engine can use the index to avoid scanning the entire table.
• Write performance may degrade slightly, as SQL Server must maintain the index during INSERT, UPDATE, and DELETE operations.
• Storage usage increases—each index consumes disk space, so plan capacity accordingly.

For large tables, consider creating indexes during off-peak hours and monitoring their usage post-deployment. SQL Server also supports online index creation in Enterprise and higher editions, allowing queries to run concurrently while the index is being built.

Troubleshooting index creation issues in SQL Server

Creating indexes in SQL Server is typically straightforward, but the process can become a performance drain on large, active, or poorly maintained tables. Long execution times, blocking sessions, and resource exhaustion are common signs that index creation needs to be approached more strategically.

Below are the most frequent problems encountered and how to address them effectively.

Even though CREATE INDEX is a standard operation, its impact can vary drastically based on the table size, system load, and resource availability. The following are common challenges that can occur during index creation, and why they matter:

• Excessive creation time: On large tables, creating an index can take hours if the server lacks sufficient memory or CPU resources, or if the table is actively in use.
• Blocking and locking: CREATE INDEX acquires a schema modification lock (SCH-M), which blocks all reads and writes until the operation completes. This can freeze workflows in production environments.
• Disk and TempDB pressure: Index creation uses memory, disk I/O, and tempdb for sorting and intermediate operations. Insufficient space or contention in tempdb can cause failures or severe slowdowns.
• Fragmented or stale data: Existing fragmentation or outdated statistics can lead to suboptimal index structures or inefficient query plans.

Solutions and workarounds

Adjust your index creation strategy based on your environment’s scale and workload to avoid slowdowns or production impacts. The following practices will help you build indexes more safely and efficiently:

• Use online index creation: In Enterprise, Developer, or higher editions, you can avoid locking by adding the
  WITH (ONLINE = ON) option to your index statement. This allows concurrent reads and writes during index creation.

  CREATE NONCLUSTERED INDEX IX_LargeTable_Column  
  ON LargeTable (ColumnName)  
  WITH (ONLINE = ON);

• Schedule during off-peak hours: For environments without online indexing support, run index operations during low-traffic windows to minimize disruptions.
• Temporarily scale up resources: Increase memory or CPU during index creation, especially on multi-million-row tables.
• Split the workload: If you're rebuilding or creating many indexes, stagger them to avoid overloading tempdb or blocking transactions.
• Update statistics beforehand: Ensure SQL Server has the current distribution data before creating the index.

  UPDATE STATISTICS dbo.TableName;  
  -- Or run system-wide:
  EXEC sp_updatestats;

• Monitor progress and blocking: Use dynamic management views like sys.dm_exec_requests and sys.dm_tran_locks to track execution progress and detect blocking or waits.

With those issues handled, let’s move on to smarter strategies that keep indexing efficient from the start.

Best practices for indexing in SQL Server

Effective indexing is about creating and maintaining the proper indexes over time. The following best practices will help ensure your indexing strategy improves performance without introducing overhead or complexity.

Create indexes on primary and foreign keys

Always index primary keys; they are unique by design and frequently used in joins. Likewise, foreign key columns should be indexed to improve join performance and maintain referential integrity efficiently. This improves performance, reduces resource usage, and minimizes locking in busy systems. It also helps your relational structure remain efficient as data volumes grow.

Index large tables strategically

Large tables benefit the most from indexing, but the key is to focus on high-value columns—those frequently used in filters, joins, or sorting operations. Avoid indexing every possible column; each index adds write overhead and consumes storage.

Tip: Use sys.dm_db_index_usage_stats to identify underused or unused indexes that can be dropped.

Avoid over-indexing small tables

Full table scans are often faster than indexed lookups for small tables with fewer than a few hundred rows. Adding indexes in these cases offers little performance benefit but increases maintenance overhead. Always test query performance before applying indexes to ensure they’re worth the cost.

Cover queries with included columns

Use the INCLUDE clause to add non-key columns that allow SQL Server to satisfy queries directly from the index, without accessing the base table. This reduces logical reads and eliminates key lookups, which is especially useful for queries with selective filters but exhaustive SELECT lists in OLTP workloads.

Regularly rebuild or reorganize indexes

Indexes fragment over time due to INSERT, UPDATE, and DELETE operations. Regular maintenance—either rebuilding or reorganizing—keeps indexes efficient.

• Rebuild if fragmentation > 30%
• Reorganize if fragmentation is between 5% and 30%

Use sys.dm_db_index_physical_stats to monitor fragmentation levels.

Update statistics before and after significant changes

Accurate statistics help SQL Server choose the right index. Constantly update statistics after loading large volumes of data or rebuilding indexes:

  UPDATE STATISTICS TableName;

Use filtered indexes for sparse data

A filtered index can dramatically improve performance while reducing storage if a column is frequently queried for a specific value (e.g., Status = 'Active').

  CREATE NONCLUSTERED INDEX IX_Users_ActiveOnly
  ON Users (LastLoginDate)
  WHERE Status = 'Active';

Benefit: Smaller index size and faster lookups for targeted queries.

How dbForge Studio helps you take control of indexing

dbForge Studio for SQL Server is a professional-grade SQL Studio that brings visibility and structure to your indexing strategy. With its integrated visual tools, it's more than just a development environment—it’s a centralized platform for performance tuning and schema management.

Built as a professional IDE for SQL Server, it gives developers and DBAs complete control over index creation, analysis, and maintenance, without switching between tools or relying on manual scripts.

Here’s how it brings precision to your indexing workflow:

• Visual index designer: Enables teams to create, modify, and drop clustered or non-clustered indexes using a point-and-click interface that auto-generates optimized SQL syntax.
• Index manager: The SQL index manager detects duplicate, unused, or fragmented indexes in your databases and offers data-backed suggestions for cleanup or optimization.
• Execution plan viewer: Visualizes how indexes influence query performance, highlighting bottlenecks and inefficiencies before changes.
• Fragmentation analysis: Surfaces page-level fragmentation across indexes and recommends rebuilding or reorganizing actions based on custom thresholds.
• Script generation and CI/CD integration: This function generates version-controlled SQL scripts for index changes, enabling teams to push updates through automated pipelines confidently.

dbForge Studio turns indexing into a proactive, repeatable process—guided by insight, not guesswork. Download the free trial and manage indexes with clarity and control.

Conclusion

Indexing is an essential tool for improving SQL Server performance. It helps queries run faster, reduces system load, and allows databases to scale smoothly. But getting real results requires more than just creating indexes—it takes strategy.

You need to know which queries matter, which columns to index, and how to maintain those indexes as your data grows. Following best practices—like indexing primary and foreign keys, avoiding extra indexes on small tables, and using included columns to cover queries—lays the foundation for a high-performing system.

However, managing all of this manually can be time-consuming and risky. dbForge Studio simplifies the process. It combines visual tools, performance insights, and automation to help you confidently design and manage indexes.

Download the free trial and bring clarity and control to your indexing strategy.

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