Understanding dm_exec_query_stats in SQL Server for Performance Monitoring

In SQL Server database management, monitoring query performance is crucial for maintaining optimal system efficiency. SQL Server provides a wealth of Dynamic Management Views (DMVs) to assist in this task, and among them, sys.dm_exec_query_stats stands out as a valuable tool for analyzing the performance statistics of cached query plans. This article delves into the dm_exec_query_stats DMV, explaining its purpose, the key performance metrics it provides, and how you can leverage it to enhance your SQL Server performance tuning efforts.

What is sys.dm_exec_query_stats?

sys.dm_exec_query_stats is a Dynamic Management View in SQL Server that returns aggregated performance statistics for query plans that are currently cached. It provides a row for each query statement within these cached plans, and the lifespan of these rows is directly tied to the plan’s presence in the cache. When a query plan is evicted from the cache due to memory pressure or other reasons, the corresponding rows in sys.dm_exec_query_stats are also removed.

This DMV is essential for database administrators and performance tuning experts because it offers insights into the execution history and resource consumption of queries. By examining the data provided by sys.dm_exec_query_stats, you can identify resource-intensive queries, understand query execution patterns, and pinpoint areas for optimization.

It’s important to note that the data in sys.dm_exec_query_stats is dynamic and reflects only the queries that have completed execution. Queries that are currently running or still in-flight are not included in the results until they finish. For users working with specific Azure Synapse Analytics or Analytics Platform System (PDW) environments, it is important to use sys.dm_pdw_nodes_exec_query_stats, while serverless SQL pools should utilize sys.dm_exec_query_stats.

Key Performance Metrics in dm_exec_query_stats

The sys.dm_exec_query_stats DMV exposes a wide range of columns, each providing valuable information about query performance. Understanding these metrics is key to effectively using this DMV for performance analysis. Here are some of the most significant columns:

Metric Category	Column Name	Description	Importance for Performance Tuning
Query Identification	`sql_handle`	A unique token that identifies the batch or stored procedure containing the query. Combined with `statement_start_offset` and `statement_end_offset`, it allows retrieval of the SQL text using `sys.dm_exec_sql_text`.	Essential for linking performance statistics back to the actual SQL query text. Helps identify the specific query that needs attention.
	`statement_start_offset`	Starting byte position of the query within the batch or object text.	Used with `sql_handle` to pinpoint the exact query within a larger script or stored procedure.
	`statement_end_offset`	Ending byte position of the query within the batch or object text.	Completes the range defined by `statement_start_offset`, allowing precise extraction of the query text.
	`plan_handle`	A unique token identifying the query execution plan in the plan cache. Can be used with `sys.dm_exec_query_plan` to retrieve the query plan XML.	Allows examination of the query execution plan, which is critical for understanding how SQL Server is executing the query and identifying potential plan optimization opportunities.
Execution Statistics	`execution_count`	The number of times the query plan has been executed since it was compiled.	Indicates query frequency. High execution counts for resource-intensive queries signal potential performance bottlenecks.
	`last_execution_time`	Timestamp of the last time the query plan started execution.	Helps understand recent query activity and identify queries that are frequently executed or have been recently run.
CPU Usage	`total_worker_time`	Total CPU time (in microseconds) consumed by all executions of the plan since compilation.	A primary indicator of CPU-bound queries. High `total_worker_time` suggests the query is CPU-intensive and might benefit from optimization.
	`last_worker_time`	CPU time (in microseconds) consumed during the last execution of the plan.	Shows the CPU cost of the most recent execution, useful for tracking performance changes over time.
	`min_worker_time`	Minimum CPU time (in microseconds) consumed during a single execution of the plan.	Provides a baseline for CPU usage, useful for identifying performance regressions.
	`max_worker_time`	Maximum CPU time (in microseconds) consumed during a single execution of the plan.	Highlights peak CPU usage, useful for understanding worst-case performance scenarios.
I/O Statistics	`total_physical_reads`	Total number of physical reads performed by all executions of the plan.	Indicates queries that are I/O-bound due to disk reads. High physical reads can point to missing indexes or inefficient data access patterns.
	`total_logical_reads`	Total number of logical reads performed by all executions.	Represents reads from the buffer pool. High logical reads can also indicate inefficient queries, even if data is in memory.
	`total_logical_writes`	Total number of logical writes performed by all executions.	Indicates write operations. High logical writes, especially in read-heavy workloads, might be unexpected and require investigation.
Elapsed Time	`total_elapsed_time`	Total elapsed time (in microseconds) for all completed executions of the plan.	Measures the total duration of query execution. High elapsed time can indicate various performance issues, including blocking, resource contention, or inefficient query design.
	`last_elapsed_time`	Elapsed time (in microseconds) for the most recent execution.	Shows the execution duration of the last run, useful for monitoring real-time performance.
	`min_elapsed_time`	Minimum elapsed time (in microseconds) for any completed execution.	Baseline for execution duration, helpful for detecting performance degradation.
	`max_elapsed_time`	Maximum elapsed time (in microseconds) for any completed execution.	Highlights the longest execution time, useful for understanding potential timeout issues or extreme performance variations.
Memory & Parallelism	`total_grant_kb`	Total memory grant (in KB) reserved for the plan since compilation.	Indicates memory usage. Excessive memory grants can contribute to memory pressure on the server.
	`total_dop`	Total degree of parallelism used by the plan since compilation.	Reveals parallelism usage. Inappropriate parallelism settings can sometimes hinder performance.
Columnstore Metrics	`total_columnstore_segment_reads`	Total columnstore segments read by the query.	Relevant for columnstore indexes. Indicates the efficiency of columnstore usage.
	`total_columnstore_segment_skips`	Total columnstore segments skipped by the query.	High segment skips are generally good for columnstore performance, indicating effective segment elimination.

By analyzing these metrics, you can gain a comprehensive understanding of query performance and resource utilization.

Using sys.dm_exec_query_stats for Performance Tuning

sys.dm_exec_query_stats is not just a source of raw data; it’s a powerful tool for proactive performance tuning. Here are practical ways to use it:

Identifying Top Resource Consuming Queries:

You can easily identify the queries that consume the most resources by querying sys.dm_exec_query_stats and ordering the results by metrics like total_worker_time, total_elapsed_time, total_logical_reads, or total_physical_reads.

For example, to find the top 5 queries with the highest average CPU time:

SELECT TOP 5
    query_stats.query_hash AS Query_Hash,
    SUM(query_stats.total_worker_time) / SUM(query_stats.execution_count) AS Avg_CPU_Time,
    MIN(query_stats.statement_text) AS Sample_Statement_Text
FROM (
    SELECT QS.*,
           SUBSTRING(ST.text, (QS.statement_start_offset/2) + 1,
                     ((CASE statement_end_offset
                        WHEN -1 THEN DATALENGTH(ST.text)
                        ELSE QS.statement_end_offset
                      END - QS.statement_start_offset)/2)  + 1) AS statement_text
    FROM sys.dm_exec_query_stats AS QS
    CROSS APPLY sys.dm_exec_sql_text(QS.sql_handle) AS ST
) AS query_stats
GROUP BY query_stats.query_hash
ORDER BY Avg_CPU_Time DESC;

This query groups results by query_hash to aggregate statistics for logically equivalent queries, even if literal values differ.

Analyzing Query Execution Patterns:

By examining execution_count and last_execution_time, you can understand how frequently queries are executed and when they were last run. This helps in identifying frequently executed queries that might be good candidates for optimization or caching strategies.
Investigating I/O Bottlenecks:

High values in total_physical_reads and total_logical_reads can indicate I/O-bound queries. You can further investigate these queries to determine if missing indexes, inefficient table scans, or other I/O-related issues are contributing to the performance bottleneck.
Monitoring Query Performance Over Time:

By periodically querying sys.dm_exec_query_stats and storing the results, you can track query performance trends over time. This can help detect performance regressions introduced by code changes or database schema modifications.
Understanding Memory and Parallelism Usage:

Columns like total_grant_kb, total_dop, and related metrics provide insights into memory consumption and parallelism. Analyzing these can help identify queries that are requesting excessive memory or using inappropriate parallelism levels, which might be impacting overall server performance.

Permissions

To query sys.dm_exec_query_stats, you need specific permissions, which vary based on your SQL Server version and environment:

SQL Server 2019 and earlier, Azure SQL Managed Instance: Requires VIEW SERVER STATE permission.
SQL Server 2022 and later: Requires VIEW SERVER PERFORMANCE STATE permission on the server.
Azure SQL Database (Basic, S0, S1 service objectives, and elastic pools): Requires server admin, Microsoft Entra admin account, or membership in the ##MS_ServerStateReader## server role.
All other SQL Database service objectives: Requires VIEW DATABASE STATE permission on the database or membership in the ##MS_ServerStateReader## server role.

Ensure that the user or login you are using to query sys.dm_exec_query_stats has the necessary permissions.

Conclusion

sys.dm_exec_query_stats is an indispensable DMV for any SQL Server professional focused on performance monitoring and tuning. By understanding the wealth of performance metrics it provides and leveraging it in your daily monitoring and optimization tasks, you can proactively identify and resolve query performance bottlenecks, ensuring your SQL Server databases run efficiently and smoothly. Regularly utilizing sys.dm_exec_query_stats will empower you to maintain a healthy and high-performing SQL Server environment.