Azure SQL Database and performance for single databases
Azure SQL Database offers three service tiers: Basic, Standard, and Premium. Each service tier strictly isolates the resources that your SQL database can use, and guarantees predictable performance for that service level. In this article, we offer guidance that can help you choose the service tier for your application. We also discuss ways that you can tune your application to get the most from Azure SQL Database.
This article focuses on performance guidance for single databases in Azure SQL Database. For performance guidance related to elastic pools, see Price and performance considerations for elastic pools. Note, though, that you can apply many of the tuning recommendations in this article to databases in an elastic pool, and get similar performance benefits.
There are the three Azure SQL Database service tiers that you can choose from (performance is measured in database throughput units, or DTUs:
- Basic. The Basic service tier offers good performance predictability for each database, hour over hour. In a Basic database, sufficient resources support good performance in a small database that doesn't have multiple concurrent requests.
- Standard. The Standard service tier offers improved performance predictability and provides good performance for databases that have multiple concurrent requests, like workgroup and web applications. When you choose a Standard service tier database, you can size your database application based on predictable performance, minute over minute.
- Premium. The Premium service tier provides predictable performance, second over second, for each Premium database. When you choose the Premium service tier, you can size your database application based on the peak load for that database. The plan removes cases in which performance variance can cause small queries to take longer than expected in latency-sensitive operations. This model can greatly simplify the development and product validation cycles for applications that need to make strong statements about peak resource needs, performance variance, or query latency.
At each service tier, you set the performance level, so you have the flexibility to pay only for the capacity you need. You can adjust capacity, up or down, as workload changes. For example, if your database workload is high during the back-to-school shopping season, you might increase the performance level for the database for a set time, July through September. You can reduce it when your peak season ends. You can minimize what you pay by optimizing your cloud environment to the seasonality of your business. This model also works well for software product release cycles. A test team might allocate capacity while it does test runs, and then release that capacity when they finish testing. In a capacity request model, you pay for capacity as you need it, and avoid spending on dedicated resources that you might rarely use.
Why service tiers?
Although each database workload can differ, the purpose of service tiers is to provide performance predictability at various performance levels. Customers with large-scale database resource requirements can work in a more dedicated computing environment.
Common service tier use cases
- You're just getting started with Azure SQL Database. Applications that are in development often don't need high-performance levels. Basic databases are an ideal environment for database development, at a low price point.
- You have a database with a single user. Applications that associate a single user with a database typically don’t have high concurrency and performance requirements. These applications are candidates for the Basic service tier.
- Your database has multiple concurrent requests. Applications that service more than one user at a time usually need higher performance levels. For example, websites that get moderate traffic or departmental applications that require more resources are good candidates for the Standard service tier.
Most Premium service tier use cases have one or more of these characteristics:
- High peak load. An application that requires substantial CPU, memory, or input/output (I/O) to complete its operations requires a dedicated, high-performance level. For example, a database operation known to consume several CPU cores for an extended time is a candidate for the Premium service tier.
- Many concurrent requests. Some database applications service many concurrent requests, for example, when serving a website that has a high traffic volume. Basic and Standard service tiers limit the number of concurrent requests per database. Applications that require more connections would need to choose an appropriate reservation size to handle the maximum number of needed requests.
- Low latency. Some applications need to guarantee a response from the database in minimal time. If a specific stored procedure is called as part of a broader customer operation, you might have a requirement to have a return from that call in no more than 20 milliseconds, 99 percent of the time. This type of application benefits from the Premium service tier, to make sure that the required computing power is available.
The service level that you need for your SQL database depends on the peak load requirements for each resource dimension. Some applications use a trivial amount of a single resource, but have significant requirements for other resources.
Service tier capabilities and limits
Each service tier and performance level is associated with different limits and performance characteristics. This table describes these characteristics for a single database.
Basic service tier
|Max database size*||2 GB|
|Max in-memory OLTP storage||N/A|
|Max concurrent workers||30|
|Max concurrent logins||30|
|Max concurrent sessions||300|
Standard service tier
|Max database size*||250 GB||250 GB||250 GB||250 GB|
|Max in-memory OLTP storage||N/A||N/A||N/A||N/A|
|Max concurrent workers||60||90||120||200|
|Max concurrent logins||60||90||120||200|
|Max concurrent sessions||600||900||1200||2400|
Premium service tier
|Max database size*||500 GB||500 GB||500 GB||500 GB||1 TB||1 TB|
|Max in-memory OLTP storage||1 GB||2 GB||4 GB||8 GB||14 GB||32 GB|
|Max concurrent workers||200||400||800||1600||2400||6400|
|Max concurrent logins||200||400||800||1600||2400||6400|
|Max concurrent sessions||30000||30000||30000||30000||30000||30000|
* Max database size refers to the maximum size of the data files and does not include the space used by log files.
Maximum In-Memory OLTP storage
You can use the sys.dm_db_resource_stats view to monitor your Azure In-Memory storage use. For more information about monitoring, see Monitor In-Memory OLTP storage.
Maximum concurrent requests
To see the number of concurrent requests, run this Transact-SQL query on your SQL database:
SELECT COUNT(*) AS [Concurrent_Requests] FROM sys.dm_exec_requests R
To analyze the workload of an on-premises SQL Server database, modify this query to filter on the specific database you want to analyze. For example, if you have an on-premises database named MyDatabase, this Transact-SQL query returns the count of concurrent requests in that database:
SELECT COUNT(*) AS [Concurrent_Requests] FROM sys.dm_exec_requests R INNER JOIN sys.databases D ON D.database_id = R.database_id AND D.name = 'MyDatabase'
This is just a snapshot at a single point in time. To get a better understanding of your workload and concurrent request requirements, you'll need to collect many samples over time.
Maximum concurrent logins
You can analyze your user and application patterns to get an idea of the frequency of logins. You also can run real-world loads in a test environment to make sure that you're not hitting this or other limits we discuss in this article. There isn’t a single query or dynamic management view (DMV) that can show you concurrent login counts or history.
If multiple clients use the same connection string, the service authenticates each login. If 10 users simultaneously connect to a database by using the same username and password, there would be 10 concurrent logins. This limit applies only to the duration of the login and authentication. If the same 10 users connect to the database sequentially, the number of concurrent logins would never be greater than 1.
Currently, this limit does not apply to databases in elastic pools.
To see the number of current active sessions, run this Transact-SQL query on your SQL database:
SELECT COUNT(*) AS [Sessions] FROM sys.dm_exec_connections
If you're analyzing an on-premises SQL Server workload, modify the query to focus on a specific database. This query helps you determine possible session needs for the database if you are considering moving it to Azure SQL Database.
SELECT COUNT(*) AS [Sessions] FROM sys.dm_exec_connections C INNER JOIN sys.dm_exec_sessions S ON (S.session_id = C.session_id) INNER JOIN sys.databases D ON (D.database_id = S.database_id) WHERE D.name = 'MyDatabase'
Again, these queries return a point-in-time count. If you collect multiple samples over time, you’ll have the best understanding of your session use.
For SQL Database analysis, you can get historical statistics on sessions by querying the sys.resource_stats view and reviewing the active_session_count column.
Monitor resource use
You can also monitor usage using these two views:
You can use the sys.dm_db_resource_stats view in every SQL database. The sys.dm_db_resource_stats view shows recent resource use data relative to the service tier. Average percentages for CPU, data I/O, log writes, and memory are recorded every 15 seconds and are maintained for 1 hour.
Because this view provides a more granular look at resource use, use sys.dm_db_resource_stats first for any current-state analysis or troubleshooting. For example, this query shows the average and maximum resource use for the current database over the past hour:
SELECT AVG(avg_cpu_percent) AS 'Average CPU use in percent', MAX(avg_cpu_percent) AS 'Maximum CPU use in percent', AVG(avg_data_io_percent) AS 'Average data I/O in percent', MAX(avg_data_io_percent) AS 'Maximum data I/O in percent', AVG(avg_log_write_percent) AS 'Average log write use in percent', MAX(avg_log_write_percent) AS 'Maximum log write use in percent', AVG(avg_memory_usage_percent) AS 'Average memory use in percent', MAX(avg_memory_usage_percent) AS 'Maximum memory use in percent' FROM sys.dm_db_resource_stats;
For other queries, see the examples in sys.dm_db_resource_stats.
The sys.resource_stats view in the master database has additional information that can help you monitor the performance of your SQL database at its specific service tier and performance level. The data is collected every 5 minutes and is maintained for approximately 35 days. This view is useful for a longer-term historical analysis of how your SQL database uses resources.
The following graph shows the CPU resource use for a Premium database with the P2 performance level for each hour in a week. This graph starts on a Monday, shows 5 work days, and then shows a weekend, when much less happens on the application.
From the data, this database currently has a peak CPU load of just over 50 percent CPU use relative to the P2 performance level (midday on Tuesday). If CPU is the dominant factor in the application’s resource profile, then you might decide that P2 is the right performance level to guarantee that the workload always fits. If you expect an application to grow over time, it's a good idea to have an extra resource buffer so that the application doesn't ever reach the performance-level limit. If you increase the performance level, you can help avoid customer-visible errors that might occur when a database doesn't have enough power to process requests effectively, especially in latency-sensitive environments. An example is a database that supports an application that paints webpages based on the results of database calls.
Other application types might interpret the same graph differently. For example, if an application tries to process payroll data each day and has the same chart, this kind of "batch job" model might do fine at a P1 performance level. The P1 performance level has 100 DTUs compared to 200 DTUs at the P2 performance level. The P1 performance level provides half the performance of the P2 performance level. So, 50 percent of CPU use in P2 equals 100 percent CPU use in P1. If the application does not have timeouts, it might not matter if a job takes 2 hours or 2.5 hours to finish, if it gets done today. An application in this category probably can use a P1 performance level. You can take advantage of the fact that there are periods of time during the day when resource use is lower, so that any "big peak" might spill over into one of the troughs later in the day. The P1 performance level might be good for that kind of application (and save money), as long as the jobs can finish on time each day.
Azure SQL Database exposes consumed resource information for each active database in the sys.resource_stats view of the master database in each server. The data in the table is aggregated for 5-minute intervals. With the Basic, Standard, and Premium service tiers, the data can take more than 5 minutes to appear in the table, so this data is more useful for historical analysis rather than near-real-time analysis. Query the sys.resource_stats view to see the recent history of a database and to validate whether the reservation you chose delivered the performance you want when needed.
You must be connected to the master database of your logical SQL database server to query sys.resource_stats in the following examples.
This example shows you how the data in this view is exposed:
SELECT TOP 10 * FROM sys.resource_stats WHERE database_name = 'resource1' ORDER BY start_time DESC
The next example shows you different ways that you can use the sys.resource_stats catalog view to get information about how your SQL database uses resources:
To look at the past week’s resource use for the database userdb1, you can run this query:
SELECT * FROM sys.resource_stats WHERE database_name = 'userdb1' AND start_time > DATEADD(day, -7, GETDATE()) ORDER BY start_time DESC;
To evaluate how well your workload fits the performance level, you need to drill down into each aspect of the resource metrics: CPU, reads, writes, number of workers, and number of sessions. Here's a revised query using sys.resource_stats to report the average and maximum values of these resource metrics:
SELECT avg(avg_cpu_percent) AS 'Average CPU use in percent', max(avg_cpu_percent) AS 'Maximum CPU use in percent', avg(avg_data_io_percent) AS 'Average physical data I/O use in percent', max(avg_data_io_percent) AS 'Maximum physical data I/O use in percent', avg(avg_log_write_percent) AS 'Average log write use in percent', max(avg_log_write_percent) AS 'Maximum log write use in percent', avg(max_session_percent) AS 'Average % of sessions', max(max_session_percent) AS 'Maximum % of sessions', avg(max_worker_percent) AS 'Average % of workers', max(max_worker_percent) AS 'Maximum % of workers' FROM sys.resource_stats WHERE database_name = 'userdb1' AND start_time > DATEADD(day, -7, GETDATE());
With this information about the average and maximum values of each resource metric, you can assess how well your workload fits into the performance level you chose. Usually, average values from sys.resource_stats give you a good baseline to use against the target size. It should be your primary measurement stick. For an example, you might be using the Standard service tier with S2 performance level. The average use percentages for CPU and I/O reads and writes are below 40 percent, the average number of workers is below 50, and the average number of sessions is below 200. Your workload might fit into the S1 performance level. It's easy to see whether your database fits in the worker and session limits. To see whether a database fits into a lower performance level with regards to CPU, reads, and writes, divide the DTU number of the lower performance level by the DTU number of your current performance level, and then multiply the result by 100:
S1 DTU / S2 DTU * 100 = 20 / 50 * 100 = 40
The result is the relative performance difference between the two performance levels in percentage. If your resource use doesn't exceed this amount, your workload might fit into the lower performance level. However, you need to look at all ranges of resource use values, and determine, by percentage, how often your database workload would fit into the lower performance level. The following query outputs the fit percentage per resource dimension, based on the threshold of 40 percent that we calculated in this example:
SELECT (COUNT(database_name) - SUM(CASE WHEN avg_cpu_percent >= 40 THEN 1 ELSE 0 END) * 1.0) / COUNT(database_name) AS 'CPU Fit Percent' ,(COUNT(database_name) - SUM(CASE WHEN avg_log_write_percent >= 40 THEN 1 ELSE 0 END) * 1.0) / COUNT(database_name) AS 'Log Write Fit Percent' ,(COUNT(database_name) - SUM(CASE WHEN avg_data_io_percent >= 40 THEN 1 ELSE 0 END) * 1.0) / COUNT(database_name) AS 'Physical Data IO Fit Percent' FROM sys.resource_stats WHERE database_name = 'userdb1' AND start_time > DATEADD(day, -7, GETDATE());
Based on your database service level objective (SLO), you can decide whether your workload fits into the lower performance level. If your database workload SLO is 99.9 percent and the preceding query returns values greater than 99.9 percent for all three resource dimensions, your workload likely fits into the lower performance level.
Looking at the fit percentage also gives you insight into whether you should move to the next higher performance level to meet your SLO. For example, userdb1 shows the following CPU use for the past week:
Average CPU percent Maximum CPU percent 24.5 100.00
The average CPU is about a quarter of the limit of the performance level, which would fit well into the performance level of the database. But, the maximum value shows that the database reaches the limit of the performance level. Do you need to move to the next higher performance level? Look at how many times your workload reaches 100 percent, and then compare it to your database workload SLO.
SELECT (COUNT(database_name) - SUM(CASE WHEN avg_cpu_percent >= 100 THEN 1 ELSE 0 END) * 1.0) / COUNT(database_name) AS 'CPU fit percent' ,(COUNT(database_name) - SUM(CASE WHEN avg_log_write_percent >= 100 THEN 1 ELSE 0 END) * 1.0) / COUNT(database_name) AS 'Log write fit percent' ,(COUNT(database_name) - SUM(CASE WHEN avg_data_io_percent >= 100 THEN 1 ELSE 0 END) * 1.0) / COUNT(database_name) AS 'Physical data I/O fit percent' FROM sys.resource_stats WHERE database_name = 'userdb1' AND start_time > DATEADD(day, -7, GETDATE());
If this query returns a value less than 99.9 percent for any of the three resource dimensions, consider either moving to the next higher performance level or use application-tuning techniques to reduce the load on the SQL database.
- This exercise also considers your projected workload increase in the future.
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 Database, 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 overprovisioning 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 overprovision 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 when you use the service tiers in 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 performance level. Although many applications have significant performance gains simply by switching to a higher performance level 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 SQL 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 performance level might benefit from scaling out the workload. For more information, see Cross-database sharding and Functional partitioning.
- Applications that have suboptimal queries. Applications, especially those in the data access layer, that have poorly tuned queries might not benefit from a higher performance level. 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 performance level. Consider reducing round trips against the Azure SQL Database by caching data on the client side with the Azure Caching service or another caching technology. See Application tier caching.
In this section, we look at some techniques that you can use to tune Azure SQL Database to gain the best performance for your application and run it at the lowest possible performance level. 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.
Azure portal tools
The following tools in the Azure portal can help you analyze and fix performance issues with your SQL database:
The Azure portal has more information about both of these tools and how to use them. To efficiently diagnose and correct problems, we recommend that you first try the tools in the Azure portal. We recommend that you use the manual tuning approaches that we discuss next, for missing indexes and query tuning, in special cases.
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, SQL Database 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 I/O capacity of a shared, commodity system is more limited than that of a dedicated server machine. There's a premium on minimizing unnecessary I/O to take maximum advantage of the system in the DTU of each performance level of the Azure SQL Database 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 the missing index DMVs, see sys.dm_db_missing_index_details.
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 performance level.
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 SQL Server and in Azure SQL Database. Sometimes the parameter is not sniffed, and sometimes the parameter is sniffed but the generated plan is suboptimal for the full set of parameter values in a workload. Microsoft includes query hints (directives) so that you can specify intent more deliberately and override the default behavior of parameter sniffing. Often, if you use hints, you can fix cases in which the default SQL Server or Azure SQL Database behavior is imperfect for a specific customer 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 SQL 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 a table that has skewed data distribution. 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.
Example, part 1
-- 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
Example, part 2
(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 table (there is 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
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 have not degraded. You can learn more about query hints (Transact-SQL).
Because Azure SQL Database runs on commodity hardware, the capacity limits for a single 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 a single 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.
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 the traditional example Northwind database that ships with SQL Server), 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 a single 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 performance levels, as long as each individual database fits into its DTU.
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 performance level to support very large, "effective" databases with high resource requirements.
SQL Server users often combine many functions in a single 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. By using this technique, each application scales independently. As an application becomes busier (and the load on the database increases), the administrator can choose independent performance levels 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.
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 Azure SQL Database tier. Even when both the application and Azure SQL 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 Azure SQL 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 Azure SQL Database so you can use the stored procedure again.
Some applications are write-intensive. Sometimes you can reduce the total I/O load on a database by considering how to batch writes together. Often, this is as simple as using explicit transactions instead of auto-commit transactions in stored procedures and ad hoc batches. For an evaluation of different techniques you can use, see Batching techniques for SQL 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.
Some database applications have read-heavy workloads. Caching layers might reduce the load on the database and might potentially reduce the performance level required to support a database by using Azure SQL Database. With Azure Redis Cache, if you have a read-heavy workload, you can read the data once (or perhaps once per application-tier machine, depending on how it is configured), and then store that data outside your SQL database. This is a way to reduce database load (CPU and read I/O), but there is 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.