Columnstore Indexes in SQL Server: What They Are and When to Use Them

SQL Server columnstore indexes are one of the best, yet often overlooked, ways to enhance your database performance.

Here's why.

If you've ever struggled with slow queries, reports, or faced long-running analytics, columnstore indexes are what you need to solve this. Unlike traditional rowstore indexes that store data row by row, columnstore indexes store data column by column. This fundamental change in data storage architecture improves compression, memory efficiency, and query speed, particularly when working with millions (or billions) of rows in Online Analytical Processing (OLAP) systems or reporting workloads.

This guide explains what SQL Server columnstore indexes are, when to use them, and how to implement them. Additionally, you can use the robust features in tools like dbForge Studio for SQL Server to see how your indexes are performing and analyze their structures.

Let's get started.

How columnstore indexes work

To fully understand how SQL Server columnstore indexes work, let's break down what happens behind the scenes.

In SQL Server, databases store data row by row. This means all the column values for a single row are stored next to each other in the same block. This technique is perfect when you're inserting one record at a time, for instance, in an e-commerce transaction or banking system. But when you're querying only a few columns across millions of rows, this method becomes painfully inefficient.

Columnstore indexes change the game by introducing a better method of storing data - columnar data format, called a columnstore. Instead of storing data in rows, you store it in columns. Meaning, column values are stored together, separately from other columns. This makes it easy to scan large numbers of rows in seconds, handle analytics, and gain insight faster.

For example, in rowstore indexes, data is stored like this.

Row 1: 1, Alice, 30, New York
Row 2: 2, Bob, 25, San Francisco
Row 3: 3, Charlie, 40, Chicago

While in SQL Server columnstore indexes, data is stored like this.

Column: ID → [1, 2, 3]
Column: Name → [Alice, Bob, Charlie]
Column: Age → [30, 25, 40]
Column: City → [New York, San Francisco, Chicago]

Use case scenario

Imagine you have a Sales table with 100 million rows and 20 columns. You want to find the average TotalAmount for the last year.

• With rowstore, SQL Server pulls all 20 columns for every row, even when you only need one.
• With columnstore, SQL Server skips 95% of the data and scans only the TotalAmount column, leading to faster results, less memory usage, and lower CPU.

Column-oriented vs. row-oriented storage

Rowstore is ideal for transactions, but columnstore is your ideal solution to gain insights.

The table below shows a detailed comparison of the two.

Rowgroups and column segments

SQL Server provides two features known as rowgroups and column segments to cleverly structure your columnstore data for scalability and speed.

• Rowgroups: When creating columnstore indexes, SQL Server divides your database table into logical batches of approximately 1 million rows. This means that the columnstore index is divided into multiple rowgroups to facilitate easy data processing. Also, each rowgroup is processed independently. As such, parallelism and batch processing are built in from the start.
• Column segment: Within each rowgroup, each column is stored independently in a column segment. These segments are compressed individually, using algorithms tailored to each column's data type.

For example, if you have a table with 5 million rows and 10 columns, SQL Server creates 5 rowgroups (1M rows each). Each rowgroup contains 10 column segments (1 per column). That's 50 compressed segments, all independently readable, cacheable, and optimizable.

This structure helps you achieve the following benefits:

• Faster filtering and scanning
• Less memory and disk space used
• Parallel query execution (think multiple threads scanning at once)

The delta store and tuple mover

SQL Server columnstore indexes are optimized for reads, not frequent writes. So, what happens when you insert or update data? This is where the delta store and tuple mover come in.

• Delta store: A rowstore B-tree structure that temporarily holds new or modified rows that can't be immediately compressed into the columnstore format.
• Tuple mover: An internal automatic background process that monitors the delta store and, once enough data accumulates (typically 1 million rows), compresses and moves it into the main columnstore structure. Then, the delta store is free to receive new data.

This hybrid approach seamlessly manages real-time data changes, allowing you to continue enjoying the compression and performance of columnstore storage.

Types of columnstore indexes in SQL Server

The two major types of columnstore indexes in SQL Server are clustered columnstore index (CCI) and nonclustered columnstore index (NCCI). Although both are designed to store data by column, they serve different purposes.

Let's break them down and see how they work.

Clustered columnstore index (CCI)

CCI is a columnstore index that replaces the traditional clustered index. In other words, it becomes the primary method for storing data in the table. No row-based storage, no additional indexes, just pure columnar data storage.

When should you use it?

CCI is highly effective for large, read-intensive workloads, especially when you're working with fact tables in data warehouses or analytical databases. For example, if you have a table with hundreds of millions of sales transactions. You're not updating those rows often; instead, you're aggregating, filtering, and scanning. This is a perfect scenario where you can use CCI.

However, CCI is not ideal for frequent small updates or inserts.

Nonclustered columnstore index (NCCI)

NCCI is the more flexible option. It allows you to add a columnstore index to a table that still uses rowstore for its base storage. This means you keep your Online Transaction Processing (OLTP) rowstore layout for transactions and updates while also running fast analytical queries using the columnstore index.

When should you use it?

NCCI is best used in hybrid environments, where you have a mix of frequent inserts/updates and reporting queries. For example, CRM systems, e-commerce platforms, or any system where you both write data often and need to run analytics on it.

NCCI enables real-time analytics on live transactional data and works side-by-side with row-based indexes, with little to no need to refactor your schema. It also allows you to run batch-mode queries on OLTP systems, improving speed without sacrificing write performance.

With NCCIs, you can perform analytics without sacrificing transactional performance, but CCIs are built for analytics at scale. Choosing the one to use depends on the shape of your workload. When in doubt, you can start small with NCCI and scale up to CCI as your analytics grow.

Below is a comparison table of these two columnstore index methods.

When to use columnstore indexes

Although SQL columnstore indexes are great to scale up your database performance, they're not always the right tool for every job. In the right setting, they can deliver massive performance gains. In the wrong one, they might slow down your database.

Below is a list of perfect scenarios to use columnstore indexes.

1. You're working with large volumes of data

According to Microsoft, columnstore indexes are the standard for storing and querying large data warehousing fact tables. As such, if your table has millions or billions of rows, a columnstore index can significantly improve query speed by reducing I/O and compressing data more effectively than rowstore formats.

For example, a SalesFact table with 500 million rows tracking 5 years of transactions is a perfect scenario for a CCI.

2. You need to support analytics and reporting

Columnstore indexes thrive in OLAP scenarios, including dashboards, business intelligence tools, complex aggregates, and filtering across large datasets. They enable batch-mode processing, which means SQL Server can process multiple rows at once, leading to a significant improvement in performance on analytical queries.

For instance, you can use columnstore indexes for queries that GROUP BY region, calculate sales trends, or filter on date ranges.

3. You're building a data warehouse or star schema

Fact tables with many rows and relatively static data are ideal for clustered columnstore indexes. They compress well, scan faster, and make your warehouse run smoother under pressure.

For instance, if you have a star schema with a large SalesFact table and smaller dimension tables (Product, Customer, etc.), CCI works well.

4. You're running hybrid workloads

NCCI is your best bet for reporting queries on OLTP systems without disrupting transactional performance. You get fast analytics on live data, which is great for dashboards or live KPIs. A perfect scenario for NCCI is a CRM system where users input customer interactions while managers run real-time reports on engagement trends.

When traditional indexes are a better option

Here is a list of scenarios in which you should not use the SQL Server columnstore indexes.

1. Your workload is OLTP-heavy (lots of small reads/writes)

SQL columnstore indexes aren't optimized for high-frequency INSERT, UPDATE, or DELETE operations. If your application is constantly writing to the database, you'll likely experience performance bottlenecks with columnstore indexes.

For example, rowstore wins in point-of-sale systems writing thousands of transactions per minute with minimal read queries.

2. You frequently update small numbers of rows

Columnstore indexes store data in compressed segments and handle changes through a delta store. If you're constantly updating a few rows here and there, this can lead to fragmentation and increased overhead. Therefore, if you have an application like an order-tracking system where rows are updated every few seconds by many users, traditional indexes are more efficient.

3. Your dataset is small or fits in memory

Columnstore indexes shine at scale. For smaller tables, especially those under a few hundred thousand rows, you likely won't see meaningful performance gains and might even introduce unnecessary complexity.

For example, a reference table like Countries or Currencies doesn't need columnstore optimization; use rowstore instead.

The table below explains more about scenarios where you can use either columnstore indexes or rowstore indexes.

Columnstore vs. rowstore

How to create and manage columnstore indexes in SQL Server

Creating and maintaining clustered and nonclustered columnstore indexes in SQL Server is quite straightforward. You only need to write the right commands, do some smart planning, and have a bit of performance insight.

The best part is that columnstore indexing is built into SQL Server, and you don't need to be an expert to learn how to use it. Here is a quick guide on how to create each columnstore index.

Creating a clustered columnstore index

CREATE CLUSTERED COLUMNSTORE INDEX CCI_SalesFact
ON dbo.SalesFact;

Creating a nonclustered columnstore index

CREATE NONCLUSTERED COLUMNSTORE INDEX NCCI_Orders
ON dbo.Orders (OrderID, ProductID, Quantity);

Managing and maintaining columnstore indexes

When using SQL Server columnstore indexes, there are a few important areas to monitor for maximum benefits. Below are these key areas.

Fragmentation & maintenance

• Use ALTER INDEX ... REORGANIZE to defragment rowgroups and compress data.
• Monitor the delta store size, because frequent small inserts can leave data uncompressed until the Tuple Mover kicks in.

Statistics & query plans

• Keep index statistics up-to-date to help the optimizer select the best execution plan.
• Use the Execution Plan Viewer in tools like dbForge Studio for SQL Server to visually explore how your columnstore index is being used.

Monitoring usage

• Use Dynamic Management Views (DMVs) like sys.dm_db_column_store_row_group_physical_stats to track rowgroup health and compression state.
• Track how often queries hit the columnstore using sys.dm_exec_query_stats and sys.dm_exec_query_plan.

Columnstore index performance benefits

Beyond scalability, here are other benefits of using columnstore indexes.

Scan only what you need

Unlike rowstore indexes, SQL Server columnstore indexes read only the columns required by a query. This approach drastically reduces disk I/O and enhances database performance.

Compression and storage savings

Column-level compression in columnstore indexes can reduce storage by up to 90%, depending on data repetition and types. Numeric and repeating values compress extremely well, resulting in fewer reads from disk, faster performance, and lower memory usage.

Faster aggregations, filtering, and JOINs

Batch-mode processing allows SQL Server to process thousands of rows at once, instead of row by row. This helps you achieve the following:

• Better performance on GROUP BY, SUM, COUNT, and other aggregate queries
• Fast filtering across millions of rows
• Smarter join execution, especially when paired with parallelism

Segment elimination using metadata

In columnstore indexes, SQL Server uses metadata about each rowgroup (like min/max values per column) to skip entire chunks of data that don't match query filters. For example, if you are querying only data from the last month, SQL Server uses metadata to skip rowgroups that only contain older dates, and no scans are required.

Conclusion

SQL Server columnstore indexes are a powerful feature designed for modern data workloads. When used correctly, they improve query performance, reduce storage needs, and streamline large-scale analytics. Clustered columnstore indexes are ideal for large, analytical workloads like data warehouses and fact tables, while nonclustered columnstore indexes are perfect for hybrid OLTP-OLAP environments where fast reporting on live data is needed. However, when dealing with small tables or high-volume transactional systems, it is advisable to stick with rowstore indexes.

To further improve your query performance and streamline large-scale analytics, use SQL Server tools like dbForge Studio for SQL Server.

dbForge Studio for SQL Server is designed with features like execution plan analysis, query profiling, and index insights to help you visually create, manage, and analyze your columnstore indexes. With these features, you can make smarter optimization decisions without diving deep into the command line.

Download dbForge Studio for a free trial to see how it works.

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