Understanding SQL Server’s Cardinality Estimator for Better Query Performance
When it comes to optimizing query performance in SQL Server, the Cardinality Estimator (CE) is a critical component that has a significant impact on how the Query Optimizer makes decisions for executing queries. The CE is responsible for predicting how many rows a query will return, a process known as cardinality estimation. Accurate predictions are essential for choosing the most efficient execution plans. In this comprehensive analysis, we’ll delve deeper into the mechanics of the CE and the strategies for improving its accuracy and, consequently, the performance of your SQL queries.
Introduction to Cardinality Estimation
Cardinality estimation sits at the core of SQL Server’s Query Optimizer. The primary goal of the Query Optimizer is to identify the most cost-effective way to execute a given SQL query. It achieves this by evaluating various potential execution plans and selecting the one with the lowest cost, considering factors such as I/O, CPU usage, and memory requirements. The cardinality estimates determine the intermediate result set sizes throughout the execution plan, affecting join strategies, indexing choices, and whether to use operations like sorting or hashing.
What is SQL Server’s Cardinality Estimator?
The Cardinality Estimator in SQL Server is a component of the Query Optimizer that predicts the number of rows for each operation in an execution plan. Introduced in SQL Server 2014, the new CE model brought a substantial update to the original CE model, which had been in place since SQL Server 7.0. SQL Server gives you the option of using the legacy CE model or the new model, based on which version compatibility level your database is set to.
The accuracy of the CE is critical because over- or underestimations can lead to suboptimal execution plans. An overestimate may result in unnecessary memory grants or the selection of a less ideal join strategy. An underestimate can cause excessive I/O operations due to insufficient memory allocation, resulting in slower query performance.
The Evolution of the Cardinality Estimator
The CE has undergone changes across different versions of SQL Server. Initially, the legacy CE worked well with the database systems and workloads of the time. However, as databases became more complex with more extensive sets of data and more sophisticated query patterns, the need for a more refined estimation process became critical.
In SQL Server 2014, the new CE was introduced with improved algorithms and default assumptions to better match modern workloads. These improvements included changes to the assumptions related to filter selectivity, join cardinality, and handling of ascending keys, among others. The new CE also provides better support for batch mode operations, correlated columns, and cardinality estimations for tables with multiple histograms.
How the Cardinality Estimator Works
The CE uses statistics to make its estimations. Statistics in SQL Server describe the distribution of data within a table or index and are created on a set of columns. The Query Optimizer uses these statistics to estimate the cardinality of query results. Statistics objects contain a histogram that maps the frequency of each distinct value or range of values in the data set.
When you run a query, the Query Optimizer invokes the CE, which then studies the statistics to estimate how many rows match the query predicates. The CE makes several assumptions and uses complex algorithms to calculate these estimates, which are then used to assess the cost of possible execution plans.
Understanding Statistics and Histograms
Let’s dive deeper into statistics and histograms, as they are pivotal for the CE’s accuracy. Statistics are created on columns specified in a CREATE STATISTICS statement or identified by the Query Optimizer as important for performance. You can also manually create statistics on columns that are frequently used in the WHERE clause, JOIN conditions, or aggregates.
A histogram partitions the column’s data range into buckets, with each bucket containing a range of values. The histogram records the number of rows (ROWS), the number of distinct values (DISTINCT_RANGE_ROWS), and the minimum and maximum values within this range.
When the Statistics Update is triggered manually or automatically, SQL Server updates the histogram based on the data sampling. The quality of the CE’s predictions depends on how up-to-date and comprehensive these statistics are.
Factors Affecting Cardinality Estimates
Various factors can influence the estimation process. These include data distribution skews, filter selectivity, correlations between columns, and the complexity of the query itself. Handling of these factors can affect the estimation accuracy significantly:
- Data Skew: The difference in row distribution across a data set can lead to over- or underestimation if not adequately accounted for.
- Selectivity of Predicates: The CE must effectively assess the selectivity of filter predicates to estimate the impact on the row count accurately.
- Column Correlation: The CE needs to consider the relationship between columns to adequately predict joint distributions. Traditional models often assume independence, which may not hold true.
- Parameter Sniffing: The CE uses the values of query parameters, known as parameter sniffing, to estimate cardinalities. However, this can cause issues if later executions of the same query use significantly different parameter values.
- Query Complexity: Complex queries with multiple joins and subqueries pose a challenge to the CE’s prediction capabilities.
Alterations to the physical structure of the data, such as adding or removing indexes, also affect cardinality estimates. The CE uses index statistics to determine the cost and benefits of potential index scans or seeks.
Improving Cardinality Estimation Accuracy
Having understood the cardinality estimation process and its challenges, let’s explore strategies to improve its accuracy:
- Maintain Updated Statistics: Ensure that statistics are frequently updated, especially after substantial changes to the data.
- Use Correct Database Compatibility Level: Using the proper compatibility level for your version of SQL Server can leverage improvements in the latest CE.
- Consider Database Workloads: Analyze your workload and determine if your environment benefits more from the legacy or new CE model.
- Optimize Queries: Simplify complex queries and provide hints to help the CE make better estimations.
- Use Query Store: The Query Store can identify queries that have regressed in performance due to CE changes, helping you fine-tune execution plans.
Understanding and optimizing the CE process provides significant performance gains and a more robust database environment. This blog has touched on the inner workings of the Cardinality Estimator and offered strategies for improving its accuracy. Properly managing the CE component in SQL Server can significantly enhance the efficiency of your query performance and ensure a responsive and reliable database system.
