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Query performance troubleshooting using wait statistics in the SQL Query Store

In today’s blog post, we will explore how to use wait statistics in the SQL Query Store to troubleshoot query performance issues. Wait statistics provide valuable insights into the resources a query is waiting on, helping us identify bottlenecks and optimize our queries for better performance.

Table of Contents

Introduction to Query Store

The SQL Query Store is a powerful feature introduced in SQL Server 2016 that collects and retains historical data of query plans and their performance characteristics. It enables us to analyze query performance over time, identify regressions, and make informed decisions for query optimization.

Understanding Wait Statistics

Wait statistics provide information about the resources a query is waiting on, such as locks, IO operations, or CPU time. By analyzing wait statistics, we can understand where queries are spending the most time waiting and address those areas to improve performance.

Enabling Query Store for Wait Statistics

To enable query store for capturing wait statistics, we need to set the QUERY_STORE_CAPTURE_MODE to ALL. This ensures that not only query plans but also wait statistics are collected and stored in the Query Store.

ALTER DATABASE [YourDatabase]
SET QUERY_STORE (
    OPERATION_MODE = READ_WRITE,
    QUERY_CAPTURE_MODE = ALL
);

Analyzing Wait Statistics in Query Store

Once wait statistics are enabled in the Query Store, we can analyze them using the sys.query_store_wait_stats view. This view provides information about the wait statistics for each query execution within a given time frame.

Here is an example query to retrieve wait statistics for a specific query within a time range:

SELECT *
FROM sys.query_store_wait_stats
WHERE query_id = <query_id>
    AND start_time > '2022-01-01'
    AND end_time < '2022-02-01';

Troubleshooting Query Performance

To troubleshoot query performance using wait statistics, we can focus on queries with high wait times or a large number of waits. By identifying the most significant wait types and the corresponding queries, we can take necessary actions to address the performance bottlenecks.

Some common wait types to look for include:

Based on the identified wait types, we can take various measures such as optimizing indexes, adjusting query hints, or tuning parallelism settings to improve query performance.

Conclusion

Wait statistics in the SQL Query Store provide deep insights into the resources queries are waiting on. By enabling and analyzing wait statistics, we can identify and resolve performance issues, leading to better query performance. Use the techniques mentioned in this blog post to troubleshoot query performance and optimize your database applications.

Let’s #improvesqlperformance and make our queries faster and more efficient!

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