How To Boost SQL Server Performance Using Query Store And Intelligent Query Processing

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Performance tuning in SQL Server has evolved significantly over the years, especially with the introduction of features like Query Store and Intelligent Query Processing (IQP). These tools empower database administrators and developers to diagnose performance issues, stabilize query execution, and optimize workloads with minimal manual intervention. Understanding how to effectively leverage these features can dramatically improve database responsiveness, reduce resource consumption, and enhance overall system reliability.

This article explores how to boost SQL Server performance using Query Store and Intelligent Query Processing, complete with practical coding examples and a deep dive into their capabilities.

Understanding Query Store

Query Store is a built-in feature in SQL Server that captures a history of queries, execution plans, and runtime statistics. It acts as a flight recorder for your database, enabling you to analyze performance trends over time.

Key benefits include:

  • Tracking query performance history
  • Identifying plan regressions
  • Forcing stable execution plans
  • Simplifying troubleshooting

Enabling Query Store

Before using Query Store, you need to enable it on your database.

ALTER DATABASE YourDatabase
SET QUERY_STORE = ON;
GO

ALTER DATABASE YourDatabase
SET QUERY_STORE (
    OPERATION_MODE = READ_WRITE,
    CLEANUP_POLICY = (STALE_QUERY_THRESHOLD_DAYS = 30),
    DATA_FLUSH_INTERVAL_SECONDS = 900
);
GO

This configuration ensures Query Store collects and retains data efficiently.

Analyzing Query Performance

Once enabled, Query Store begins collecting data automatically. You can query its internal views to analyze performance.

Example: Finding top resource-consuming queries

SELECT TOP 10
    qsqt.query_sql_text,
    qsp.plan_id,
    rs.avg_duration,
    rs.avg_cpu_time,
    rs.avg_logical_io_reads
FROM sys.query_store_query_text qsqt
JOIN sys.query_store_query qsq
    ON qsqt.query_text_id = qsq.query_text_id
JOIN sys.query_store_plan qsp
    ON qsq.query_id = qsp.query_id
JOIN sys.query_store_runtime_stats rs
    ON qsp.plan_id = rs.plan_id
ORDER BY rs.avg_duration DESC;

This helps identify problematic queries that need optimization.

Detecting Query Plan Regressions

A query plan regression occurs when SQL Server chooses a less efficient execution plan. Query Store allows you to detect such regressions.

SELECT
    qsq.query_id,
    qsp.plan_id,
    rs.avg_duration,
    rs.last_execution_time
FROM sys.query_store_runtime_stats rs
JOIN sys.query_store_plan qsp
    ON rs.plan_id = qsp.plan_id
JOIN sys.query_store_query qsq
    ON qsp.query_id = qsq.query_id
WHERE rs.avg_duration > 1000
ORDER BY rs.avg_duration DESC;

Forcing a Stable Execution Plan

If a query performs well with a specific plan, you can force SQL Server to reuse that plan.

EXEC sp_query_store_force_plan
    @query_id = 5,
    @plan_id = 12;

This prevents performance degradation caused by suboptimal plan changes.

To remove the forced plan:

EXEC sp_query_store_unforce_plan
    @query_id = 5,
    @plan_id = 12;

Introduction to Intelligent Query Processing

Intelligent Query Processing (IQP) is a suite of features designed to improve query performance automatically. It adapts execution strategies based on runtime conditions and workload patterns.

Key components include:

  • Adaptive Query Processing
  • Memory Grant Feedback
  • Table Variable Deferred Compilation
  • Scalar UDF Inlining
  • Batch Mode on Rowstore

Adaptive Query Processing

Adaptive Query Processing allows SQL Server to adjust execution strategies during runtime.

Example scenario:

SELECT *
FROM Orders o
JOIN Customers c
    ON o.CustomerID = c.CustomerID
WHERE o.OrderDate > '2023-01-01';

SQL Server may dynamically switch join types (e.g., Nested Loop to Hash Join) depending on row counts.

Memory Grant Feedback

Memory Grant Feedback optimizes memory allocation for queries by learning from previous executions.

Example:

SELECT *
FROM LargeTable
ORDER BY SomeColumn;

If the query initially requests too much or too little memory, SQL Server adjusts future executions automatically.

You can observe memory grants:

SELECT
    request_memory_kb,
    granted_memory_kb,
    ideal_memory_kb
FROM sys.dm_exec_query_memory_grants;

Table Variable Deferred Compilation

Historically, table variables caused poor performance due to incorrect cardinality estimates. IQP improves this by delaying compilation until actual row counts are known.

Example:

DECLARE @Temp TABLE (ID INT);

INSERT INTO @Temp
SELECT ID FROM LargeTable;

SELECT *
FROM @Temp t
JOIN AnotherTable a
    ON t.ID = a.ID;

With deferred compilation, SQL Server generates a better execution plan based on actual data.

Scalar UDF Inlining

Scalar user-defined functions (UDFs) often degrade performance. IQP transforms them into inline expressions.

Before optimization:

CREATE FUNCTION dbo.GetDiscount(@Price DECIMAL(10,2))
RETURNS DECIMAL(10,2)
AS
BEGIN
    RETURN @Price * 0.1;
END;

Usage:

SELECT dbo.GetDiscount(Price)
FROM Products;

With inlining, SQL Server integrates the function logic directly into the query, reducing overhead.

Batch Mode on Rowstore

Batch mode processing significantly speeds up analytical queries, even on traditional rowstore tables.

Example:

SELECT SUM(SalesAmount)
FROM Sales
WHERE SalesDate > '2024-01-01';

SQL Server may automatically use batch mode, improving performance for large datasets.

Combining Query Store and IQP

The real power lies in combining Query Store insights with IQP capabilities.

Workflow:

  1. Use Query Store to identify slow queries
  2. Analyze execution plans
  3. Apply IQP features (automatic or manual tuning)
  4. Monitor improvements via Query Store

Example:

-- Identify slow query
SELECT TOP 1 query_id
FROM sys.query_store_runtime_stats
ORDER BY avg_duration DESC;

-- Force better plan if needed
EXEC sp_query_store_force_plan @query_id = 10, @plan_id = 3;

Practical Optimization Scenario

Let’s walk through a real-world example.

Problem: A reporting query is running slowly.

SELECT c.CustomerName, SUM(o.TotalAmount)
FROM Orders o
JOIN Customers c
    ON o.CustomerID = c.CustomerID
GROUP BY c.CustomerName;

Steps:

  1. Use Query Store to analyze performance
  2. Identify inefficient plan
  3. Check if IQP features are active
  4. Force a better plan if necessary

Optimization:

CREATE INDEX IX_Orders_CustomerID
ON Orders(CustomerID);

CREATE INDEX IX_Customers_CustomerID
ON Customers(CustomerID);

Then monitor improvements:

SELECT *
FROM sys.query_store_runtime_stats
WHERE query_id = 15;

Monitoring and Maintenance

To keep performance optimal:

  • Regularly review Query Store data
  • Clean up stale data
  • Monitor forced plans
  • Ensure IQP features are enabled

Example cleanup:

ALTER DATABASE YourDatabase
SET QUERY_STORE CLEAR;

Best Practices

  • Enable Query Store on all production databases
  • Avoid forcing plans unless necessary
  • Let IQP handle most optimizations automatically
  • Monitor changes after upgrades
  • Test performance in staging environments

Conclusion

Boosting SQL Server performance is no longer solely dependent on manual tuning and deep query rewriting. With the introduction of Query Store and Intelligent Query Processing, Microsoft has fundamentally changed the way performance optimization is approached. These tools provide both visibility and automation, allowing database professionals to make informed decisions while also benefiting from built-in intelligence.

Query Store serves as the backbone of performance diagnostics. It gives you historical insight into query behavior, making it possible to detect regressions, compare execution plans, and enforce stability when needed. This historical perspective is invaluable, especially in complex systems where performance issues may not be immediately apparent.

On the other hand, Intelligent Query Processing represents a shift toward self-optimizing databases. By incorporating adaptive mechanisms such as memory grant feedback, deferred compilation, and UDF inlining, SQL Server can dynamically improve execution strategies without constant human intervention. This reduces the burden on administrators and ensures more consistent performance across varying workloads.

When used together, Query Store and IQP create a powerful synergy. Query Store identifies problems, while IQP often resolves them automatically. In cases where manual intervention is required, Query Store provides the data needed to make precise and effective adjustments.

However, it is important to approach these tools thoughtfully. Overusing forced plans can lead to rigidity, and blindly relying on automation without monitoring can mask underlying issues. The best results come from a balanced strategy—leveraging automation while maintaining visibility and control.

Ultimately, mastering Query Store and Intelligent Query Processing equips you with a modern, efficient, and scalable approach to SQL Server performance tuning. Whether you are managing a small database or a large enterprise system, these features can significantly enhance responsiveness, reduce resource consumption, and ensure a smoother experience for end users.

By continuously monitoring, analyzing, and adapting, you can ensure that your SQL Server environment remains optimized and resilient in the face of evolving data demands.