The SQL Server Query Optimizer is a cost-based Query Optimizer. This means that it selects query plans that have the lowest estimated processing cost to execute. The Query Optimizer determines the cost of executing a query plan based on two main factors:
- The total number of rows processed at each level of a query plan, referred to as the cardinality of the plan.
- The cost model of the algorithm dictated by the operators used in the query.
The first factor, cardinality, is used as an input parameter of the second factor, the cost model. Therefore, improved cardinality leads to better estimated costs and, in turn, faster execution plans.
Cardinality estimation (CE) in SQL Server is derived primarily from histograms that are created when indexes or statistics are created, either manually or automatically. Sometimes, SQL Server also uses constraint information and logical rewrites of queries to determine cardinality.
In the following cases, SQL Server cannot accurately calculate cardinalities. This causes inaccurate cost calculations that may cause suboptimal query plans. Avoiding these constructs in queries may improve query performance. Sometimes, alternative query formulations or other measures are possible and these are pointed out:
- Queries with predicates that use comparison operators between different columns of the same table.
- Queries with predicates that use operators, and any one of the following are true:
- There are no statistics on the columns involved on either side of the operators.
- The distribution of values in the statistics is not uniform, but the query seeks a highly selective value set. This situation can be especially true if the operator is anything other than the equality (=) operator.
- The predicate uses the not equal to (!=) comparison operator or the NOT logical operator.
- Queries that use any of the SQL Server built-in functions or a scalar-valued, user-defined function whose argument is not a constant value.
- Queries that involve joining columns through arithmetic or string concatenation operators.
- Queries that compare variables whose values are not known when the query is compiled and optimized.
This article illustrates how you can assess and choose the best CE configuration for your system. Most systems benefit from the latest CE because it is the most accurate. The CE predicts how many rows your query will likely return. The cardinality prediction is used by the Query Optimizer to generate the optimal query plan. With more accurate estimations, the Query Optimizer can usually do a better job of producing a more optimal query plan.
Your application system could possibly have an important query whose plan is changed to a slower plan due to changes in the CE throughout versions. You have techniques and tools for identifying a query that performs slower due to CE issues. And you have options for how to address the ensuing performance issues.
In 1998, a major update of the CE was part of SQL Server 7.0, for which the compatibility level was 70. This version of the CE model is set on four basic assumptions:
- Independence: Data distributions on different columns are assumed to be independent of each other, unless correlation information is available and usable.
- Uniformity: Distinct values are evenly spaced and that they all have the same frequency. More precisely, within each histogram step, distinct values are evenly spread and each value has same frequency.
- Containment (Simple): Users query for data that exists. For example, for an equality join between two tables, factor in the predicates selectivity1 in each input histogram, before joining histograms to estimate the join selectivity.
- Inclusion: For filter predicates where Column = Constant, the constant is assumed to actually exist for the associated column. If a corresponding histogram step is non-empty, one of the step’s distinct values is assumed to match the value from the predicate.
Row count that satisfies the predicate.
Subsequent updates started with SQL Server 2014 (12.x), meaning compatibility levels 120 and above. The CE updates for levels 120 and above incorporate updated assumptions and algorithms that work well on modern data warehousing and on OLTP workloads. From the CE 70 assumptions, the following model assumptions were changed starting with CE 120:
- Independence becomes Correlation: The combination of the different column values are not necessarily independent. This may resemble more real-life data querying.
- Simple Containment becomes Base Containment: Users might query for data that does not exist. For example, for an equality join between two tables, we use the base tables histograms to estimate the join selectivity, and then factor in the predicates selectivity.
Compatibility level: You can ensure your database is at a particular level by using the following Transact-SQL code for COMPATIBILITY_LEVEL.
ALTER DATABASE <yourDatabase>
SET COMPATIBILITY_LEVEL = 130;
SELECT d.name, d.compatibility_level
FROM sys.databases AS d
WHERE d.name = ‘yourDatabase’;
For a SQL Server database set at compatibility level 120 or above, activation of the trace flag 9481 forces the system to use the CE version 70.
Legacy CE: For a SQL Server database set at compatibility level 120 and above, the CE version 70 can be can be activated at the database level by using the ALTER DATABASE SCOPED CONFIGURATION.
ALTER DATABASE SCOPED CONFIGURATION
SET LEGACY_CARDINALITY_ESTIMATION = ON;
SELECT name, value
WHERE name = ‘LEGACY_CARDINALITY_ESTIMATION’;
Or starting with SQL Server 2016 (13.x) SP1, the Query Hint USE HINT (‘FORCE_LEGACY_CARDINALITY_ESTIMATION’).
SELECT CustomerId, OrderAddedDate
WHERE OrderAddedDate >= ‘2016-05-01’
OPTION (USE HINT (‘FORCE_LEGACY_CARDINALITY_ESTIMATION’));
Query store: Starting with SQL Server 2016 (13.x), the query store is a handy tool for examining the performance of your queries. In Management Studio, in the Object Explorer under your database node, a Query Store node is displayed when the query store is enabled.
ALTER DATABASE <yourDatabase>
SET QUERY_STORE = ON;
SELECT q.actual_state_desc AS [actual_state_desc_of_QueryStore],
FROM sys.database_query_store_options AS q;
ALTER DATABASE <yourDatabase>
SET QUERY_STORE CLEAR;
Another option for tracking the cardinality estimation process is to use the extended event named query_optimizer_estimate_cardinality. The following Transact-SQL code sample runs on SQL Server. It writes a .xel file to C:\Temp\ (although you can change the path). When you open the .xel file in Management Studio, its detailed information is displayed in a user friendly manner.
DROP EVENT SESSION Test_the_CE_qoec_1 ON SERVER;
CREATE EVENT SESSION Test_the_CE_qoec_1
ADD EVENT sqlserver.query_optimizer_estimate_cardinality
sql_text LIKE ‘%yourTable%’
and sql_text LIKE ‘%SUM(%’
ADD TARGET package0.asynchronous_file_target
filename = ‘c:\temp\xe_qoec_1.xel’,
metadatafile = ‘c:\temp\xe_qoec_1.xem’
ALTER EVENT SESSION Test_the_CE_qoec_1
STATE = START; –STOP;
Next are steps you can use to assess whether any of your most important queries perform less well under the latest CE. Some of the steps are performed by running a code sample presented in a preceding section.
- Open Management Studio. Ensure your SQL Serverdatabase is set to the highest available compatibility level.
- Perform the following preliminary steps:
- Open Management Studio.
- Run the T-SQL to ensure that your SQL Server database is set to the highest available compatibility level.
- Ensure that your database has its LEGACY_CARDINALITY_ESTIMATION configuration turned OFF.
- Clear your Query Store. Ensure your Query Store is ON.
- Run the statement: SET NOCOUNT OFF;
- Run the statement: SET STATISTICS XML ON;
- Run your important query.
- In the results pane, on the Messages tab, note the actual number of rows affected.
- In the results pane on the Results tab, double-click the cell that contains the statistics in XML format. A graphic query plan is displayed.
- Right-click the first box in the graphic query plan, and then click Properties.
- For later comparison with a different configuration, note the values for the following properties:
- Estimated Number of Rows.
- Estimated I/O Cost, and several similar Estimated properties that involve actual performance rather than row count predictions.
- Logical Operation and Physical Operation. Parallelism is a good value.
- Actual Execution Mode. Batch is a good value, better than Row.
- Compare the estimated number of rows to the actual number of rows. Is the CE inaccurate by 1% (high or low), or by 10%?
- Run: SET STATISTICS XML OFF;
- Run the T-SQL to decrease the compatibility level of your database by one level (such as from 130 down to 120).
- Rerun all the non-preliminary steps.
- Compare the CE property values from the two runs.
- Is the inaccuracy percentage under the newest CE less than under the older CE?
- Finally, compare the various performance property values from the two runs.
- Did your query use a different plan under the two differing CE estimations?
- Did your query run slower under the latest CE?
- Unless your query runs better and with a different plan under the older CE, you almost certainly want the latest CE.
- However, if your query runs with a faster plan under the older CE, consider forcing the system to use the faster plan and to ignore the CE. This way you can have the latest CE on for everything, while keeping the faster plan in the one odd case.
Suppose that with CE 120 or above, a less efficient query plan is generated for your query. Here are some options you have to activate the better plan:
- You could set the compatibility level to a value lower than the latest available, for your whole database.
- For example, setting the compatibility level 110 or lower activates CE 70, but it makes all queries subject to the previous CE model.
- Further, setting a lower compatibility level also misses a number of improvements in the query optimizer for latest versions.
- You could use LEGACY_CARDINALITY_ESTIMATION database option, to have the whole database use the older CE, while retaining other improvements in the query optimizer.
- You could use LEGACY_CARDINALITY_ESTIMATION query hint, to have a single query use the older CE, while retaining other improvements in the query optimizer.
For the finest control, you could force the system to use the plan that was generated with CE 70 during your testing. After you pin your preferred plan, you can set your whole database to use the latest compatibility level and CE. The option is elaborated next.
How to force a particular query plan
The query store gives you different ways that you can force the system to use a particular query plan:
- Execute sp_query_store_force_plan.
- In Management Studio, expand your Query Store node, right-click Top Resource Consuming Nodes, and then click View Top Resource Consuming Nodes. The display shows buttons labeled Force Plan and Unforce Plan.
This section describes example queries that benefit from the enhancements implemented in the CE in recent releases. This is background information that does not call for specific action on your part.
Example A. CE understands maximum value might be higher than when statistics were last gathered
Suppose statistics were last gathered for OrderTable on 2016-04-30, when the maximum OrderAddedDate was 2016-04-30. The CE 120 (and above version) understands that columns in OrderTable which have ascending data might have values larger than the maximum recorded by the statistics. This understanding improves the query plan for Transact-SQL SELECT statements such as the following.
SELECT CustomerId, OrderAddedDate
WHERE OrderAddedDate >= ‘2016-05-01’;
Example B. CE understands that filtered predicates on the same table are often correlated
In the following SELECT we see filtered predicates on Model and ModelVariant. We intuitively understand that when Model is ‘Xbox’ there is a chance the ModelVariant is ‘One’, given that Xbox has a variant called One.
Starting with CE 120, SQL Server understands there might be a correlation between the two columns on the same table, Model and ModelVariant. The CE makes a more accurate estimation of how many rows will be returned by the query, and the query optimizer generates a more optimal plan.
SELECT Model, Purchase_Price
WHERE Model = ‘Xbox’ AND
ModelVariant = ‘One’;
Example C. CE no longer assumes any correlation between filtered predicates from different tables
With extense new research on modern workloads and actual business data reveal that predicate filters from different tables usually do not correlate with each other. In the following query, the CE assumes there is no correlation between s.type and r.date. Therefore the CE makes a lower estimate of the number of rows returned.
SELECT s.ticket, s.customer, r.store
FROM dbo.Sales AS s
CROSS JOIN dbo.Returns AS r
WHERE s.ticket = r.ticket AND
s.type = ‘toy’ AND
r.date = ‘2016-05-11’;
In-Memory OLTP is the premier technology available in SQL Server and SQL Database for optimizing performance of transaction processing, data ingestion, data load, and transient data scenarios. This article includes an overview of the technology and outlines usage scenarios for In-Memory OLTP. Use this information to determine whether In-Memory OLTP is right for your application. The article concludes with an example that shows In-Memory OLTP objects, reference to a perf demo, and references to resources you can use for next steps.
This article covers the In-Memory OLTP technology in both SQL Server and SQL Database.