Overcoming SQL Server’s Suboptimal Plan Generation with Adaptive Query Plans
SQL Server performance tuning is often considered an art as much as it is a science. One of the fundamental challenges Database Administrators (DBAs) and developers face is dealing with suboptimal query plan generation. Query plans, as we know, are the roadmaps the SQL Server Query Optimizer creates to retrieve the data requested by an SQL query most efficiently. However, not all query plans are created equal, and sometimes, the plans generated by the optimizer can be less than ideal, leading to poor performance and sluggish systems. In this article, we’ll delve into the intricacies of Adaptive Query Plans in SQL Server – an advanced solution built to tackle the vexing problem of suboptimal plan generation.
Understanding The Basics of Query Plans in SQL Server
The SQL Server Query Optimizer is a component that tries to determine the most efficient way to execute a given query by considering various execution strategies. These strategies are based on statistical information about the distribution of data within your database’s tables and indexes. A query plan is produced after the optimizer assesses the cost associated with different approaches, and it details the steps SQL Server will undertake to retrieve or modify data.
When SQL Server executes a stored procedure or dynamic SQL statement for the first time, it compiles a query plan, which is then stored in the plan cache. The goal is to reuse these plans to save on the cost of compilation in future executions. However, for various reasons such as parameter sniffing, changing data distributions (also known as data skew), or complex queries with multiple joins and search arguments, the chosen query plan may not always be optimal.
What Causes Suboptimal Plan Generation?
Several factors can lead to the Query Optimizer selecting a suboptimal plan for query execution. Parameter sniffing is when initially compiled plan is used for future executions, regardless of the suitability of the plan for those subsequent executions’ parameters; this is often a primary culprit. Data skew, whereby uneven data distributions exist that the statistics fail to accurately represent, is another factor. Moreover, plan caching and reusability can sometimes work against performance if overly generic plans are used across varying scenarios. All these, coupled with the complexity of query design, indexed data access methods, and other database configurations, can culminate in a substandard query performance.
Enter Adaptive Query Processing
First introduced in SQL Server 2017, Adaptive Query Processing provides a set of capabilities designed to improve the performance of query execution through real-time adjustment. This is particularly significant in removing some of the roadblocks presented by traditional batch processing. Under Adaptive Query Processing, SQL Server can alter execution plans on the fly, considering the real-time row counts and execution characteristics, thereby adapting to current operational realities.
This suite of features includes Batch Mode Memory Grant Feedback, Batch Mode Adaptive Joins, and Interleaved Execution. Let’s explore each of these features to understand how they help in producing optimal query plans.
Batch Mode Memory Grant Feedback
Memory grant feedback targets scenarios where queries are provided with either too much or too little memory. If a query is given more memory than necessary, it can lead to wasted resources, impacting the overall system performance; however, if a query is allotted too little memory, it can lead to spills to disk, significantly degrading its performance. Batch Mode Memory Grant Feedback tries to correct the memory grants for future executions by observing the memory usage of its previous executions. With enough executions and memory grant adjustments, the system narrows down to a near-optimal memory grant size, improving performance.
Batch Mode Adaptive Joins
Choosing the right join type (Hash vs. Nested Loops) can have a significant material effect on the performance of a query. Batch Mode Adaptive Joins feature allows the Query Optimizer to defer the choice of the join strategy until the actual execution time. Consequently, based on the actual row counts flowing through the join’s build phase, SQL Server can switch between a Hash Join or a Nested Loop Join dynamically. This flexibility empowers the query plan to select the most efficient join type based on the live execution statistics, rather than purely relying on pre-execution estimates.
Interleaved Execution
Interleaved Execution addresses situations where multi-statement table-valued functions (MSTVFs) may cause inaccurate cardinality estimates, impacting the plan quality. Under interleaved execution, the Query Optimizer pauses the optimization process after compilation, executes the MSTVF, gathers the accurate cardinality, and then resumes the optimization process. This feature helps to generate more refined query plans when MSTVFs are involved in the query.
Implementing Adaptive Query Processing
Before embarking on the implementation of Adaptive Query Processing, it is important to understand that this functionality requires the database to be in compatibility level 140 or higher. The query must also be running in batch mode, which is mostly applicable for columnstore indexes.
After meeting the prerequisites, you can engage Adaptive Query Processing by following standard performance tuning protocols. This includes monitoring your system’s performance, analyzing individual query execution plans, and then allowing the system to exploit the above mechanisms. It is critical to note that Adaptive Query Processing isn’t a silver bullet and may not yield improvements for all types of queries. Careful analysis and monitoring are therefore requisite in identifying the suitable candidates for adaptive query processing features.
Best Practices and Considerations
While adopting Adaptive Query Processing can lead to superior performance in many cases, DBAs should be mindful of some best practices and considerations.
- Monitor Before and After: Always monitor the system’s performance before and after enabling Adaptive Query Processing features. This is key in measuring the actual benefits and adjustments it brings to your workload.
- Compatibility Level: Ensure that your databases are at the compatible level (140 or above) to utilize the features of Adaptive Query Processing.
- Update Statistics: Maintain up-to-date statistics to help the optimizer make the best initial plan decisions.
- Comprehensive Testing: Conduct comprehensive testing across a broad set of queries to ensure the Adaptive Query Processing features positively affect your workload.
- Gradual Implementation: Implement Adaptive Query Processing in a gradual manner. Enable the features for a subset of queries and expand as you notice improvements.
In conclusion, SQL Server’s Adaptive Query Processing is a promising set of technologies that aim to modernize the ever-evolving landscape of database management by automatically optimizing execution plans in real-time. For organizations looking to mitigate the issues of suboptimal plan generation, Adaptive Query Processing could be the key to unlocking performance enhancements and achieving scalable and efficient systems. By combining the understanding of these features with diligent monitoring and testing, DBAs and developers can significantly alleviate the challenge of plan-related performance woes and streamline their SQL Server performance.
