Tune applications and databases for performance in Azure SQL Database
Applies to: 
Azure SQL Database
SQL database in Fabric
Once you have identified a performance issue that you're facing with Azure SQL Database or Fabric SQL database, this article is designed to help you:
- Tune your application and apply some best practices that can improve performance.
- Tune the database by changing indexes and queries to more efficiently work with data.
This article assumes that you have already worked through the database advisor recommendations and automatic tuning recommendations, if applicable. It also assumes that you have reviewed the overview of monitoring and tuning, Monitor performance by using the Query Store, and related articles related to troubleshooting performance issues. Additionally, this article assumes that you do not have a performance issue related to CPU resource utilization that can be resolved by increasing the compute size or service tier to provide more resources to your database.
Note
For similar guidance in Azure SQL Managed Instance, see Tune applications and databases for performance in Azure SQL Managed Instance.
Tune your application
In traditional on-premises SQL Server, the process of initial capacity planning often is separated from the process of running an application in production. Hardware and product licenses are purchased first, and performance tuning is done afterward. When you use Azure SQL, it's a good idea to interweave the process of running an application and tuning it. With the model of paying for capacity on demand, you can tune your application to use the minimum resources needed now, instead of over-provisioning on hardware based on guesses of future growth plans for an application, which often are incorrect.
Some customers might choose not to tune an application, and instead choose to over-provision hardware resources. This approach might be a good idea if you don't want to change a key application during a busy period. But, tuning an application can minimize resource requirements and lower monthly bills.
Best practices and antipatterns in application design for Azure SQL Database
Although Azure SQL Database service tiers are designed to improve performance stability and predictability for an application, some best practices can help you tune your application to better take advantage of the resources at a compute size. Although many applications have significant performance gains simply by switching to a higher compute size or service tier, some applications need additional tuning to benefit from a higher level of service. For increased performance, consider additional application tuning for applications that have these characteristics:
Applications that have slow performance because of "chatty" behavior
Chatty applications make excessive data access operations that are sensitive to network latency. You might need to modify these kinds of applications to reduce the number of data access operations to the database. For example, you might improve application performance by using techniques like batching ad hoc queries or moving the queries to stored procedures. For more information, see Batch queries.
Databases with an intensive workload that can't be supported by an entire single machine
Databases that exceed the resources of the highest Premium compute size might benefit from scaling out the workload. For more information, see Cross-database sharding and Functional partitioning.
Applications that have suboptimal queries
Applications that have poorly tuned queries might not benefit from a higher compute size. This includes queries that lack a WHERE clause, have missing indexes, or have outdated statistics. These applications benefit from standard query performance-tuning techniques. For more information, see Missing indexes and Query tuning and hinting.
Applications that have suboptimal data access design
Applications that have inherent data access concurrency issues, for example deadlocking, might not benefit from a higher compute size. Consider reducing round trips against the database by caching data on the client side with the Azure Caching service or another caching technology. See Application tier caching.
To prevent deadlocks from reoccurring in Azure SQL Database, see Analyze and prevent deadlocks in Azure SQL Database and Fabric SQL database.
Tune your database
In this section, we look at some techniques that you can use to tune database to gain the best performance for your application and run it at the lowest possible compute size. Some of these techniques match traditional SQL Server tuning best practices, but others are specific to Azure SQL Database. In some cases, you can examine the consumed resources for a database to find areas to further tune and extend traditional SQL Server techniques to work in Azure SQL Database.
Identify and add missing indexes
A common problem in OLTP database performance relates to the physical database design. Often, database schemas are designed and shipped without testing at scale (either in load or in data volume). Unfortunately, the performance of a query plan might be acceptable on a small scale but degrade substantially under production-level data volumes. The most common source of this issue is the lack of appropriate indexes to satisfy filters or other restrictions in a query. Often, missing indexes manifests as a table scan when an index seek could suffice.
In this example, the selected query plan uses a scan when a seek would suffice:
DROP TABLE dbo.missingindex;
CREATE TABLE dbo.missingindex (col1 INT IDENTITY PRIMARY KEY, col2 INT);
DECLARE @a int = 0;
SET NOCOUNT ON;
BEGIN TRANSACTION
WHILE @a < 20000
BEGIN
INSERT INTO dbo.missingindex(col2) VALUES (@a);
SET @a += 1;
END
COMMIT TRANSACTION;
GO
SELECT m1.col1
FROM dbo.missingindex m1 INNER JOIN dbo.missingindex m2 ON(m1.col1=m2.col1)
WHERE m1.col2 = 4;
Azure SQL Database can help you find and fix common missing index conditions. DMVs that are built into Azure SQL Database look at query compilations in which an index would significantly reduce the estimated cost to run a query. During query execution, the database engine tracks how often each query plan is executed, and tracks the estimated gap between the executing query plan and the imagined one where that index existed. You can use these DMVs to quickly guess which changes to your physical database design might improve overall workload cost for a database and its real workload.
You can use this query to evaluate potential missing indexes:
SELECT
CONVERT (varchar, getdate(), 126) AS runtime
, mig.index_group_handle
, mid.index_handle
, CONVERT (decimal (28,1), migs.avg_total_user_cost * migs.avg_user_impact *
(migs.user_seeks + migs.user_scans)) AS improvement_measure
, 'CREATE INDEX missing_index_' + CONVERT (varchar, mig.index_group_handle) + '_' +
CONVERT (varchar, mid.index_handle) + ' ON ' + mid.statement + '
(' + ISNULL (mid.equality_columns,'')
+ CASE WHEN mid.equality_columns IS NOT NULL
AND mid.inequality_columns IS NOT NULL
THEN ',' ELSE '' END + ISNULL (mid.inequality_columns, '') + ')'
+ ISNULL (' INCLUDE (' + mid.included_columns + ')', '') AS create_index_statement
, migs.*
, mid.database_id
, mid.[object_id]
FROM sys.dm_db_missing_index_groups AS mig
INNER JOIN sys.dm_db_missing_index_group_stats AS migs
ON migs.group_handle = mig.index_group_handle
INNER JOIN sys.dm_db_missing_index_details AS mid
ON mig.index_handle = mid.index_handle
ORDER BY migs.avg_total_user_cost * migs.avg_user_impact * (migs.user_seeks + migs.user_scans) DESC
In this example, the query resulted in this suggestion:
CREATE INDEX missing_index_5006_5005 ON [dbo].[missingindex] ([col2])
After it's created, that same SELECT statement picks a different plan, which uses a seek instead of a scan, and then executes the plan more efficiently:
The key insight is that the IO capacity of a shared, commodity system is more limited than that of a dedicated server machine. There's a premium on minimizing unnecessary IO to take maximum advantage of the system in the resources of each compute size of the service tiers. Appropriate physical database design choices can significantly improve the latency for individual queries, improve the throughput of concurrent requests handled per scale unit, and minimize the costs required to satisfy the query.
For more information about tuning indexes using missing index requests, see Tune nonclustered indexes with missing index suggestions.
Query tuning and hinting
The query optimizer in Azure SQL Database is similar to the traditional SQL Server query optimizer. Most of the best practices for tuning queries and understanding the reasoning model limitations for the query optimizer also apply to Azure SQL Database. If you tune queries in Azure SQL Database, you might get the additional benefit of reducing aggregate resource demands. Your application might be able to run at a lower cost than an untuned equivalent because it can run at a lower compute size.
An example that is common in SQL Server and which also applies to Azure SQL Database is how the query optimizer "sniffs" parameters. During compilation, the query optimizer evaluates the current value of a parameter to determine whether it can generate a more optimal query plan. Although this strategy often can lead to a query plan that is significantly faster than a plan compiled without known parameter values, currently it works imperfectly both in Azure SQL Database. (A new Intelligent Query Performance feature introduced with SQL Server 2022 named Parameter Sensitivity Plan Optimization addresses the scenario where a single cached plan for a parameterized query is not optimal for all possible incoming parameter values. Currently, Parameter Sensitivity Plan Optimization is not available in Azure SQL Database.)
The database engine supports query hints (directives) so that you can specify intent more deliberately and override the default behavior of parameter sniffing. You might choose to use hints when the default behavior is imperfect for a specific workload.
The next example demonstrates how the query processor can generate a plan that is suboptimal both for performance and resource requirements. This example also shows that if you use a query hint, you can reduce query run time and resource requirements for your database:
DROP TABLE psptest1;
CREATE TABLE psptest1(col1 int primary key identity, col2 int, col3 binary(200));
DECLARE @a int = 0;
SET NOCOUNT ON;
BEGIN TRANSACTION
WHILE @a < 20000
BEGIN
INSERT INTO psptest1(col2) values (1);
INSERT INTO psptest1(col2) values (@a);
SET @a += 1;
END
COMMIT TRANSACTION
CREATE INDEX i1 on psptest1(col2);
GO
CREATE PROCEDURE psp1 (@param1 int)
AS
BEGIN
INSERT INTO t1 SELECT * FROM psptest1
WHERE col2 = @param1
ORDER BY col2;
END
GO
CREATE PROCEDURE psp2 (@param2 int)
AS
BEGIN
INSERT INTO t1 SELECT * FROM psptest1 WHERE col2 = @param2
ORDER BY col2
OPTION (OPTIMIZE FOR (@param2 UNKNOWN))
END
GO
CREATE TABLE t1 (col1 int primary key, col2 int, col3 binary(200));
GO
The setup code creates skewed (or irregularly distributed) data in the t1 table. The optimal query plan differs based on which parameter is selected. Unfortunately, the plan caching behavior doesn't always recompile the query based on the most common parameter value. So, it's possible for a suboptimal plan to be cached and used for many values, even when a different plan might be a better plan choice on average. Then the query plan creates two stored procedures that are identical, except that one has a special query hint.
-- Prime Procedure Cache with scan plan
EXEC psp1 @param1=1;
TRUNCATE TABLE t1;
-- Iterate multiple times to show the performance difference
DECLARE @i int = 0;
WHILE @i < 1000
BEGIN
EXEC psp1 @param1=2;
TRUNCATE TABLE t1;
SET @i += 1;
END
We recommend that you wait at least 10 minutes before you begin part 2 of the example, so that the results are distinct in the resulting telemetry data.
EXEC psp2 @param2=1;
TRUNCATE TABLE t1;
DECLARE @i int = 0;
WHILE @i < 1000
BEGIN
EXEC psp2 @param2=2;
TRUNCATE TABLE t1;
SET @i += 1;
END
Each part of this example attempts to run a parameterized insert statement 1,000 times (to generate a sufficient load to use as a test data set). When it executes stored procedures, the query processor examines the parameter value that is passed to the procedure during its first compilation (parameter "sniffing"). The processor caches the resulting plan and uses it for later invocations, even if the parameter value is different. The optimal plan might not be used in all cases. Sometimes you need to guide the optimizer to pick a plan that is better for the average case rather than the specific case from when the query was first compiled. In this example, the initial plan generates a "scan" plan that reads all rows to find each value that matches the parameter:
Because we executed the procedure by using the value 1, the resulting plan was optimal for the value 1 but was suboptimal for all other values in the table. The result likely isn't what you would want if you were to pick each plan randomly, because the plan performs more slowly and uses more resources.
If you run the test with SET STATISTICS IO set to ON, the logical scan work in this example is done behind the scenes. You can see that there are 1,148 reads done by the plan (which is inefficient, if the average case is to return just one row):
The second part of the example uses a query hint to tell the optimizer to use a specific value during the compilation process. In this case, it forces the query processor to ignore the value that is passed as the parameter, and instead to assume UNKNOWN. This refers to a value that has the average frequency in the table (ignoring skew). The resulting plan is a seek-based plan that is faster and uses fewer resources, on average, than the plan in part 1 of this example:
You can see the effect in the sys.resource_stats system view, which is specific to Azure SQL Database. There's a delay from the time that you execute the test and when the data populates the table. For this example, part 1 executed during the 22:25:00 time window, and part 2 executed at 22:35:00. The earlier time window used more resources in that time window than the later one (because of plan efficiency improvements).
SELECT TOP 1000 *
FROM sys.resource_stats
WHERE database_name = 'resource1'
ORDER BY start_time DESC
Note
Although the volume in this example is intentionally small, the effect of suboptimal parameters can be substantial, especially on larger databases. The difference, in extreme cases, can be between seconds for fast cases and hours for slow cases.
You can examine sys.resource_stats to determine whether the resource for a test uses more or fewer resources than another test. When you compare data, separate the timing of tests so that they are not in the same 5-minute window in the sys.resource_stats view. The goal of the exercise is to minimize the total amount of resources used, and not to minimize the peak resources. Generally, optimizing a piece of code for latency also reduces resource consumption. Make sure that the changes you make to an application are necessary, and that the changes don't negatively affect the customer experience for someone who might be using query hints in the application.
If a workload has a set of repeating queries, often it makes sense to capture and validate the optimality of your plan choices because it drives the minimum resource size unit required to host the database. After you validate it, occasionally reexamine the plans to help you make sure that they haven't degraded. You can learn more about query hints (Transact-SQL).
Optimize connectivity and connection pooling
To reduce the overhead of creating frequent application connections in Azure SQL Database, connection pooling is available in data providers. Connection pooling is enabled in ADO.NET by default, for example. Connection pooling allows an application to reuse connections and minimize the overhead of establishing new ones.
Connection pooling can improve throughput, reduce latency, and enhance the overall performance of your database workloads. Keep in mind these best practices:
Configure connection pool settings, such as maximum connections, connection timeouts, or connection lifetime, based on your workload's concurrency and latency requirements. For more information, refer to data provider documentation.
Cloud applications should implement retry logic to handle transient connectivity failures gracefully. Learn more about how to design retry logic for transient errors.
Monitor Azure SQL Database connection performance and resource usage to identify bottlenecks, such as excessive idle connections or insufficient pool limits, and adjust configurations accordingly. Consider using database watcher or Azure Monitor.
Best practices for very large database architectures in Azure SQL Database
Before the release of the Hyperscale service tier for single databases in Azure SQL Database, customers could run into capacity limits for individual databases. While Hyperscale elastic pools offer significantly higher storage limits, elastic pools and pooled databases in other service tiers might still be constrained by those storage capacity limits in the non-Hyperscale service tiers.
The following two sections discuss two options for solving problems with very large databases in Azure SQL Database when you can't use the Hyperscale service tier.
Note
Elastic pools are not available for Azure SQL Managed Instance, SQL Server instances on-premises, SQL Server on Azure VMs, or Azure Synapse Analytics.
Cross-database sharding
Because Azure SQL Database runs on commodity hardware, the capacity limits for an individual database are lower than for a traditional on-premises SQL Server installation. Some customers use sharding techniques to spread database operations over multiple databases when the operations don't fit inside the limits of an individual database in Azure SQL Database. Most customers who use sharding techniques in Azure SQL Database split their data on a single dimension across multiple databases. For this approach, you need to understand that OLTP applications often perform transactions that apply to only one row or to a small group of rows in the schema.
Note
Azure SQL Database now provides a library to assist with sharding. For more information, see Elastic Database client library overview.
For example, if a database has customer name, order, and order details (like in the AdventureWorks database), you could split this data into multiple databases by grouping a customer with the related order and order detail information. You can guarantee that the customer's data stays in an individual database. The application would split different customers across databases, effectively spreading the load across multiple databases. With sharding, customers not only can avoid the maximum database size limit, but Azure SQL Database also can process workloads that are significantly larger than the limits of the different compute sizes, as long as each individual database fits into its service tier limits.
Although database sharding doesn't reduce the aggregate resource capacity for a solution, it's highly effective at supporting very large solutions that are spread over multiple databases. Each database can run at a different compute size to support very large, "effective" databases with high resource requirements.
Functional partitioning
Users often combine many functions in an individual database. For example, if an application has logic to manage inventory for a store, that database might have logic associated with inventory, tracking purchase orders, stored procedures, and indexed or materialized views that manage end-of-month reporting. This technique makes it easier to administer the database for operations like backup, but it also requires you to size the hardware to handle the peak load across all functions of an application.
If you use a scale-out architecture in Azure SQL Database, it's a good idea to split different functions of an application into different databases. If you use this technique, each application scales independently. As an application becomes busier (and the load on the database increases), the administrator can choose independent compute sizes for each function in the application. At the limit, with this architecture, an application can be larger than a single commodity machine can handle because the load is spread across multiple machines.
Batch queries
For applications that access data by using high-volume, frequent, ad hoc querying, a substantial amount of response time is spent on network communication between the application tier and the database tier. Even when both the application and the database are in the same data center, the network latency between the two might be magnified by a large number of data access operations. To reduce the network round trips for the data access operations, consider using the option to either batch the ad hoc queries, or to compile them as stored procedures. If you batch the ad hoc queries, you can send multiple queries as one large batch in a single trip to the database. If you compile ad hoc queries in a stored procedure, you could achieve the same result as if you batch them. Using a stored procedure also gives you the benefit of increasing the chances of caching the query plans in the database so you can use the stored procedure again.
Some applications are write-intensive. Sometimes you can reduce the total IO load on a database by considering how to batch writes together. Often, this is as simple as using explicit transactions instead of autocommit transactions in stored procedures and ad hoc batches. For an evaluation of different techniques you can use, see Batching techniques for database applications in Azure. Experiment with your own workload to find the right model for batching. Be sure to understand that a model might have slightly different transactional consistency guarantees. Finding the right workload that minimizes resource use requires finding the right combination of consistency and performance trade-offs.
Application-tier caching
Some database applications have read-heavy workloads. Caching layers might reduce the load on the database and might potentially reduce the compute size required to support a database by using Azure SQL Database. With Azure Cache for Redis, if you have a read-heavy workload, you can read the data once (or perhaps once per application-tier machine, depending on how it's configured), and then store that data outside of your database. This is a way to reduce database load (CPU and read IO), but there's an effect on transactional consistency because the data being read from the cache might be out of sync with the data in the database. Although in many applications some level of inconsistency is acceptable, that's not true for all workloads. You should fully understand any application requirements before you implement an application-tier caching strategy.
Get configuration and design tips
If you use Azure SQL Database, you can execute an open-source T-SQL script for improving database configuration and design in Azure SQL Database. The script analyzes your database on demand and provide tips to improve database performance and health. Some tips suggest configuration and operational changes based on best practices, while other tips recommend design changes suitable for your workload, such as enabling advanced database engine features.
To learn more about the script and get started, visit the Azure SQL Tips wiki page.
To keep up to date with the latest features and updates to Azure SQL Database, see What's new in Azure SQL Database?
Related content
- Monitor Azure SQL Database
- Query Performance Insight for Azure SQL Database
- Monitoring performance using dynamic management views
- Database watcher
- Diagnose and troubleshoot high CPU on Azure SQL Database
- Tune nonclustered indexes with missing index suggestions
- Video: Data Loading Best Practices on Azure SQL Database