Plan regression analysis is a valuable tool offered by the SQL Query Store in Microsoft SQL Server 2016 and above. It helps database administrators and developers identify performance regressions in SQL query plans, allowing them to analyze and optimize query performance. In this blog post, we will explore how to interpret and understand plan regression analysis in the SQL Query Store.

Table of Contents

1. Introduction to Plan Regression Analysis
2. Enabling the SQL Query Store
3. Analyzing Plan Regressions
4. Interpreting Plan Regression Analysis Results
5. Optimizing Query Performance
6. Conclusion

Introduction to Plan Regression Analysis

Plan regression occurs when the execution plan for a SQL query suddenly becomes slower or less efficient. This can happen due to changes in data distribution, statistics, or configuration settings. The SQL Query Store in SQL Server stores historical information about query plans, allowing for the comparison of present and past query plans to detect regressions.

Enabling the SQL Query Store

Before analyzing plan regressions, you need to enable the SQL Server Query Store for your database. To enable the Query Store, execute the following SQL command:

ALTER DATABASE YourDatabase SET QUERY_STORE = ON;

Once enabled, the Query Store starts capturing execution statistics and plan information for your queries.

Analyzing Plan Regressions

To analyze plan regressions, you can use the built-in reports and functions provided by the SQL Query Store. The Query Store provides information about the execution plans, runtime statistics, and performance metrics for your queries.

To identify plan regressions, you can utilize the following Query Store reports and functions:

1. Top Resource Consuming Queries: This report displays the top queries consuming the most resources, allowing you to identify queries with potential regressions.

2. Top Plan Stability Queries: This report provides a list of queries with the most plan changes, indicating possible plan regressions.

3. Plan Regression Function: The sys.dm_exec_query_plan_stats dynamic management view can be used to identify queries with plan regressions based on metrics like average elapsed time, average CPU time, and more.

Interpreting Plan Regression Analysis Results

When analyzing the plan regression analysis results, keep the following points in mind:

• Plan Stability: A stable query plan means that the plan remains consistent over time, resulting in consistent performance. When plan regressions occur, it indicates a deviation from the expected performance.

• Plan Versioning: The SQL Query Store provides multiple versions of a query plan, allowing you to compare different plan versions. This helps identify the specific changes that may have caused the regression.

• Performance Metrics: Examine the performance metrics such as elapsed time, CPU time, and I/O activity for each plan version to understand the impact of plan regressions on query performance.

Optimizing Query Performance

Once you have identified plan regressions, you can take measures to optimize query performance. Here are a few steps you can follow:

1. Review Query Plans: Compare the performance of the current query plan with previous versions to identify the specific changes causing the regression.

2. Update Statistics: Out-of-date statistics can lead to inefficient query plans. Update statistics on relevant tables and indexes to ensure accurate cardinality estimates.

3. Query Tuning: Analyze the query itself for potential optimization opportunities. Consider rewriting the query, adding indexes, or restructuring the schema as needed.

4. Index Maintenance: Evaluate index fragmentation and perform index maintenance tasks like rebuilding or reorganizing fragmented indexes.

Conclusion

Plan regression analysis in the SQL Query Store is a powerful tool for detecting and optimizing performance regressions in query plans. By enabling the Query Store, analyzing plan regressions, and interpreting the results, you can take proactive measures to optimize query performance and ensure a smooth database experience.

#querystore #performancetuning