Parameter Sensitive Plan Optimization in SQL Server 2022. What is it and is it any good?

What is it?

Parameter Sensitive Plan (PSP) optimization is a new feature in SQL Server 2022 that aims to improve the performance of parameterized queries. It is part of the Intelligent Query Processing family of features and addresses the scenario where a single cached plan for a parameterized query isn’t optimal for all possible incoming parameter values. This is the case with non-uniform data distributions.

PSP optimization automatically enables multiple, active cached plans for a single parameterized statement. Cached execution plans will accommodate different data sizes based on the actual provided runtime parameter value(s).

During the initial compilation, column statistics histograms identify non-uniform distributions and evaluate the most at-risk parameterized predicates, up to three out of all available predicates. In other words, if multiple predicates within the same query meet the criteria, PSP optimization chooses the top three.

For eligible plans, the initial compilation produces a dispatcher plan that contains the PSP optimization logic called a dispatcher expression. A dispatcher plan maps to query variants based on the cardinality range boundary values predicates.

Here’s an example to illustrate how PSP optimization works:

Let’s say we have a table Orders with columns OrderID, CustomerID, OrderDate, and TotalAmount. The table has a non-uniform distribution of data, with some customers having many orders while others have only a few.

We have a stored procedure that retrieves all orders for a given customer:

CREATE PROCEDURE dbo.GetCustomerOrders (@CustomerID int)
   SELECT * FROM dbo.Orders WHERE CustomerID = @CustomerID;

Without PSP optimization, SQL Server would generate a single execution plan for this query, based on the parameters supplied the first time it is called, and cache it for reuse. However, this plan may not be optimal for all possible values of @CustomerID.

With PSP optimization enabled, SQL Server will generate multiple execution plans for this query – one for low cardinality ranges, one for medium cardinality ranges, and one for high cardinality ranges. At runtime, SQL Server will choose the optimal execution plan based on the value of @CustomerID.

For example, if @CustomerID corresponds to a customer with many orders (high cardinality), SQL Server will choose the high cardinality range execution plan. If @CustomerID corresponds to a customer with few orders (low cardinality), SQL Server will choose the low cardinality range execution plan.

This allows SQL Server to determine the optimal execution plan based on the parameter value(s), improving query performance.

PSP aims to reduce parameter sniffing, a situation where SQL Server generates an execution plan based on the first parameter value it encounters and then reuses that plan for all subsequent parameter values. This can lead to suboptimal performance for some parameter values.

In SQL Server 2022, PSP optimization is automatically enabled by default. However, it can be disabled at the database or query level using the following T-SQL statement:


Is it any good?

The biggest problem in terms of fixing performance is that cardinality estimation is based on statistics objects which are effectively a histogram (with up to 200 steps) of data distribution across a certain column. In the example above, if we have statistics on CustomerID then we may have accurate figures if we have less than 200 customers, but if we have a lot more then they will be grouped in ranges and we may have a lot of data skew within a range, e.g. CustomerID 1 may have 10 orders where CustomerID 2 has 10,000. That means PSP optimization may still not always choose the correct plan.

Also, there are lots of parameter sniffing scenarios which can depend on a lot more than 3 parameters – such as “kitchen-sink” search procedures where you can search data based on one or more of many different parameters.

That said, there will be many scenarios where it does help – it’s just not a magic bullet to fix parameter sniffing.

Brent Ozar talks about another issue in his post How SQL Server 2022 Tries to Fix Parameter Sniffing. That is the issue around monitoring and performance troubleshooting. When you try to view the execution plan for a stored procedure where the optimizer has decided to use PSP optimization, you only see the dispatcher plan, not the actual plans that will be used in practice. Also, should your monitoring or performance troubleshooting tools flag up a query that is causing problems, where that query is using PSP optimization, it is difficult to link that query back to the stored procedure it belongs to. Not impossible, but it is a pain.

I attended a talk by Conor Cunningham, Principal Software Architect at Microsoft on SQL Server, a few years back at SQL Bits, where he was presenting the original set of features collectively known as Intelligent Query Processing. He pointed out that a lot of people running SQL Server, especially on Azure with Azure SQL Database and Azure SQL Managed Instance, don’t have SQL Server performance tuning expertise in house and these sorts of features were targeted at these people most of all. Features that will not solve all performance issues of a given type, but will certainly help in a number of circumstances.

From that point of view PSP optimization is a good addition to the set of features, even if it doesn’t do as good a job as someone with real expertise might, and even if it does come with it’s own problems.

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