Row-Level Security (RLS) is a powerful feature introduced in SQL Server 2016 that allows you to control access to specific rows in a table based on user permissions. It provides a convenient way to implement security without the need to write complex filters. However, it’s important to consider the performance implications of using RLS in your database.

Let’s take a look at a simple example to understand how RLS can impact performance. Imagine we have an orders table with three different salespeople responsible for different orders. We want to restrict each salesperson to only see their own orders.

In the past, we would have relied on techniques like using the USER_NAME() function or passing a variable from the application to filter the rows based on the current user. While this approach works, it can have performance drawbacks.

With RLS, we can define a function that filters out rows that a given salesperson should not have access to. We can then associate this function with our orders table using a security policy. This allows SQL Server to automatically apply the necessary filters when querying the table.

However, there is a performance penalty associated with using RLS. When executing queries with RLS enabled, SQL Server needs to evaluate the security predicate for each row, which can impact query performance, especially for large tables.

To illustrate this, let’s compare the performance of queries with and without RLS using a sample dataset. We’ll insert 2,000 orders for Salesperson 1, 20 orders for Salesperson 2, and 2 orders for Salesperson 3.

When executing a query without RLS, we would manually filter the rows based on the current user using a WHERE clause. This approach results in different execution plans for each user, with some using clustered index scans and others using index seeks.

On the other hand, when executing a query with RLS, the same execution plan is used for all users, resulting in clustered index scans for all queries. This is because the security predicate is applied to the entire table, filtering out rows that the user should not have access to.

It’s important to note that the performance impact of RLS can vary depending on the complexity of the security predicate and the size of the table. In some cases, the use of RLS functions can lead to suboptimal execution plans and increased resource consumption.

Before implementing RLS in your database, it’s recommended to thoroughly test its performance impact with a realistic dataset and workload. Consider alternative approaches, such as using SESSION_CONTEXT() to avoid complex conditional statements in the security predicate, and design optimizations to minimize the number of trips to the base table.

While Row-Level Security provides a convenient way to enforce data access controls, it’s crucial to carefully evaluate its performance implications to ensure optimal query performance in your SQL Server environment.