Understanding the Performance Variance of a T-SQL Function Across Different Database Instances

Introduction

As a database administrator or developer, it’s common to create User-Defined Functions (UDFs) that perform complex operations on data. However, when running these functions across different database instances, unexpected performance variations can occur. In this article, we’ll explore the reasons behind these differences and provide guidance on how to achieve consistent performance.

The Mysterious Case of DBFTN1

We’re presented with a scenario where a T-SQL function DBFTN1 performs differently when executed from different database instances. The function is defined in one instance (DB1) and used across three other instances (DB2, DB3). Despite using the same command to execute the function, performance varies significantly between the instances.

Let’s examine the commands used to execute the function on each instance:

• Instance DB1: SELECT * FROM DBFTN1('20220801');
• Instance DB2: SELECT * FROM DB1.DBFTN1('20220801'); (note the database alias)
• Instance DB3: SELECT * FROM DB1.DBFTN1('20220801');

The command used in all instances is identical, suggesting that the issue lies elsewhere. To gain a deeper understanding of the problem, it’s essential to review the database configuration and execution plans.

Database Configuration Parameters

One crucial aspect to consider when running UDFs across different database instances is the level of configuration parameters. There are two primary levels:

• Database parameters: These settings apply to the entire database and can influence the behavior of individual functions.
• Scoped database parameters: These settings are specific to each database instance and can override or complement global configuration parameters.

To identify potential differences in these parameters, we’ll need to inspect the database settings for each instance.

Querying Database Configuration Parameters

Using the following query, you can retrieve information about the database configuration parameters:

{< highlight sql >}
SELECT 
    "DB1" AS DBAME, 
    * FROM 
    sys.databases;

SELECT "DB1" AS DBNAME, 
    "DB2" AS DBNAME, 
    * FROM 
    DB1.sys.database_scoped_configurations
UNION ALL
SELECT "DB3" AS DBNAME, 
    * FROM 
    DB1.sys.database_scoped_configurations;
{< /highlight >}

This query provides a comprehensive overview of the database parameters for each instance. By comparing these values, you can identify potential differences that may impact the performance of your UDF.

Execution Plans

Another essential aspect to investigate is the execution plan generated by SQL Server for each instance. This plan outlines the optimal sequence of steps required to execute the function, including any optimizations or rewrites made during query optimization.

To view the execution plans, follow these steps:

1. Open SQL Server Management Studio (SSMS) and connect to the instance where you want to analyze the execution plan.
2. Open the Query Store by clicking on Query Store in the Object Explorer.
3. Locate the specific query that’s causing performance issues.
4. Click on the Analyze Query Plan button to generate a new plan.

Compare the execution plans for each instance using the following steps:

1. Right-click on the plan and select Save as…
2. Save the plan with a unique name (e.g., DBFTN1_DB1_Plan)
3. Repeat steps 1-2 for each instance (DB2, DB3)
4. Open the saved plans in SSMS
5. Compare the execution plans using the Plan Comparison feature

By analyzing these two aspects – database configuration parameters and execution plans – we can gain a deeper understanding of the performance differences between instances.

Performance Variance Explained

Now that we’ve explored the potential causes, let’s consider how they might affect the performance of your UDF:

• Database parameters: Configuration settings like MaxDegreeOfParallelism or SortRowCost can influence query performance. However, these values are typically set at a global level and don’t directly impact the execution plan for a specific function.
• Scoped database parameters: These settings can override or complement global configuration parameters. For example, if you have a scoped parameter that sets MaxDegreeOfParallelism to 8, it may affect the performance of your UDF even if the global setting is set to 4.

In addition to these factors, other considerations come into play when executing functions across different database instances:

• Database connections: The quality and efficiency of database connections can impact performance. Poor connection quality or excessive wait times on previous operations might affect query execution.
• Server load: High server load due to concurrent queries or resource-intensive processes can degrade performance.

Optimizing Performance

To achieve consistent performance for your UDF, consider the following best practices:

1. Test thoroughly: Perform thorough testing across multiple instances to identify potential performance bottlenecks.
2. Analyze execution plans: Use query optimization tools and techniques to optimize your function’s plan.
3. Tune configuration parameters: Adjust database configuration parameters as needed to balance performance and resource utilization.
4. Monitor server load: Regularly monitor server load and adjust resources or partitioning strategies if necessary.

Conclusion

In this exploration, we’ve examined the factors that contribute to performance differences when executing a T-SQL function across different database instances. By understanding the role of configuration parameters, execution plans, and other considerations, you can take steps to optimize your UDF’s performance and ensure consistent results across multiple databases.

Last modified on 2023-05-27