SQL Server’s Automated Plan Correction: Ensuring Stable Query Performance
In the evolving world of database management, stability and performance are evergreen priorities for database administrators and developers. With the advent of automated plan correction features in SQL Server, professionals have at their disposal a robust tool to counteract query plan regression and maintain the most efficient performance without continuous manual oversight. In this comprehensive analysis, we will dive deep into the functionality, benefits, and usage of SQL Server’s Automated Plan Correction to understand how it ensures stable query performance.
Introduction to Query Execution Plans
Before exploring Automated Plan Correction, it’s important to understand the concept of query execution plans in SQL Server. A query execution plan is a roadmap that the SQL Server database engine follows to retrieve data requested by a query. Complex algorithms determine the most efficient way to execute a query based on the current state of the database, the structure of the tables, indexes, and the query itself, among many other factors.
What Causes Plan Regression?
Plan regression happens when a previously efficient execution plan becomes less optimal, leading to decreased query performance. It can be caused by various factors, such as changes in database schema, updated statistics, data growth, or different query parameters. Plan regression can lead to erratic performance and suboptimal use of resources, making it a constant concern for database professionals.
Introducing Automated Plan Correction
The Automated Plan Correction feature aims to address the issue of plan regression. Introduced in SQL Server 2017 as part of the Autonomous Database capabilities, it continuously monitors query performance, identifies queries suffering from plan regression, and automatically switches them back to their previously known good execution plans. This process of reverting to a last known good plan is known as Automatic Plan Correction.
Key Components of Automated Plan Correction
Query Store
The backbone of Automated Plan Correction is the Query Store, introduced in SQL Server 2016. The Query Store collects detailed historical performance data about all queries executed over time. It retains a history of query execution plans and their performance, making it an invaluable asset for automated plan correction.
Automatic Tuning
Automatic Tuning is a feature enabled by default when using the Query Store. It allows SQL Server to automatically identify and fix performance issues. Automatic Plan Correction is part of Automatic Tuning, specifically targeting query plan regression.
How Automated Plan Correction Works
When a query’s performance degrades, SQL Server can detect this shift by comparing the current execution plan with historical data stored in the Query Store. If the Automatic Tuning feature is on, and it detects a regressed plan, the system can revert the execution plan of the problematic query back to the last known good plan. This feature is referred to as Automatic Plan Correction and it operates with minimal intervention required from the user.
Force Last Good Plan
Under the hood, the Automated Plan Correction utilizes a mechanism known as Force Last Good Plan (FLGP). This mechanism practically ‘forces’ SQL Server to use the historically most efficient plan for the same or similar queries. This is only done when a significant drop in performance is detected. The benefit is a more reliable and stable performance, with the avoidance of potential query plan instability.
Advantages of Automated Plan Correction
Utilizing Automated Plan Correction comes packed with a plethora of benefits:
- Improved Query Performance: By automatically switching to the optimal execution plan, SQL Server can maintain high performance for its queries.
- Reduced Need for Manual Tuning: The system’s ability to self-correct reduces the time and effort required by database administrators for manual query tuning.
- Better Resource Utilization: With stable query performance, the use of computing resources is optimized, leading to cost savings and better system efficiency.
- Increased System Reliability: The reduction of sudden performance drops contributes to a more reliable and predictable SQL Server environment.
- Faster Recovery from Regression: Automatic Plan Correction can quickly respond to performance issues, ensuring a swifter return to optimal performance levels without human intervention.
Best Practices and Considerations
While Automated Plan Correction can phenomenally aid performance stability, there are best practices and considerations to be aware of:
- Maintaining Up-to-Date Statistics: Keeping statistics up-to-date ensures that Automatic Tuning makes decisions based on the most current data distribution.
- Regular Monitoring: Even with automation, it’s important to monitor the system regularly to understand its behavior and preempt any potential issues that automation might not address.
- Comprehensive Indexing Strategy: A well-thought-out indexing strategy compliments Automated Plan Correction, enhancing the overall query performance.
- Understanding of Workloads: Knowing your workload patterns can help you fine-tune the settings for Automated Plan Correction to work in your system’s best interest.
- Education on Automation: A deep understanding of how Automated Plan Correction and the Query Store work is crucial for effectively using these features.
- Use Caution with Sensitive Workloads: For extremely critical or sensitive workloads, manual oversight might still be preferable to ensure precise control over performance.
Getting Started with Automated Plan Correction
To begin using Automated Plan Correction in SQL Server, you must:
- Ensure that your SQL Server version supports the feature (SQL Server 2017 and later versions).
- Have the Query Store enabled on the databases of interest.
- Turn on Automatic Tuning at the database level, setting it to ON or using the ALTER DATABASE command.
Conclusion
Automated Plan Correction in SQL Server represents a significant advancement in database performance tuning. By leveraging historical data and modern algorithms, SQL Server can provide a self-tuning database environment that minimizes plan regression issues with minimal human intervention, thus aligning with the goal of modern autonomous systems. Administrators and developers who understand and utilize this powerful feature can ensure their database systems deliver consistent and efficient performance, truly making it a game-changer in the landscape of database management.
