Monitoring Azure SQL Database using dynamic management views
Microsoft Azure SQL Database enables a subset of dynamic management views to diagnose performance problems, which might be caused by blocked or long-running queries, resource bottlenecks, poor query plans, and so on. This topic provides information on how to detect common performance problems by using dynamic management views.
SQL Database partially supports three categories of dynamic management views:
- Database-related dynamic management views.
- Execution-related dynamic management views.
- Transaction-related dynamic management views.
For detailed information on dynamic management views, see Dynamic Management Views and Functions (Transact-SQL) in SQL Server Books Online.
In SQL Database, querying a dynamic management view requires VIEW DATABASE STATE permissions. The VIEW DATABASE STATE permission returns information about all objects within the current database. To grant the VIEW DATABASE STATE permission to a specific database user, run the following query:
GRANT VIEW DATABASE STATE TO database_user;
In an instance of on-premises SQL Server, dynamic management views return server state information. In SQL Database, they return information regarding your current logical database only.
Calculating database size
The following query returns the size of your database (in megabytes):
-- Calculates the size of the database. SELECT SUM(CAST(FILEPROPERTY(name, 'SpaceUsed') AS bigint) * 8192.) / 1024 / 1024 AS DatabaseSizeInMB FROM sys.database_files WHERE type_desc = 'ROWS'; GO
The following query returns the size of individual objects (in megabytes) in your database:
-- Calculates the size of individual database objects. SELECT sys.objects.name, SUM(reserved_page_count) * 8.0 / 1024 FROM sys.dm_db_partition_stats, sys.objects WHERE sys.dm_db_partition_stats.object_id = sys.objects.object_id GROUP BY sys.objects.name; GO
You can use the sys.dm_exec_connections view to retrieve information about the connections established to a specific Azure SQL Database server and the details of each connection. In addition, the sys.dm_exec_sessions view is helpful when retrieving information about all active user connections and internal tasks. The following query retrieves information on the current connection:
SELECT c.session_id, c.net_transport, c.encrypt_option, c.auth_scheme, s.host_name, s.program_name, s.client_interface_name, s.login_name, s.nt_domain, s.nt_user_name, s.original_login_name, c.connect_time, s.login_time FROM sys.dm_exec_connections AS c JOIN sys.dm_exec_sessions AS s ON c.session_id = s.session_id WHERE c.session_id = @@SPID;
When executing the sys.dm_exec_requests and sys.dm_exec_sessions views, if you have VIEW DATABASE STATE permission on the database, you see all executing sessions on the database; otherwise, you see only the current session.
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 IO, 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 IO in percent', MAX(avg_data_io_percent) AS 'Maximum data IO 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 compute size. The data is collected every 5 minutes and is maintained for approximately 14 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 compute size 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 compute size (midday on Tuesday). If CPU is the dominant factor in the application’s resource profile, then you might decide that P2 is the right compute size 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 compute size, 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 compute size. The P1 compute size has 100 DTUs compared to 200 DTUs at the P2 compute size. The P1 compute size provides half the performance of the P2 compute size. 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 compute size. 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 compute size 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 compute size, 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 IO use in percent', max(avg_data_io_percent) AS 'Maximum physical data IO 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 compute size 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 compute size. The average use percentages for CPU and IO 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 compute size. It's easy to see whether your database fits in the worker and session limits. To see whether a database fits into a lower compute size with regards to CPU, reads, and writes, divide the DTU number of the lower compute size by the DTU number of your current compute size, 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 compute sizes in percentage. If your resource use doesn't exceed this amount, your workload might fit into the lower compute size. 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 compute size. 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 tier, you can decide whether your workload fits into the lower compute size. If your database workload objective 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 compute size.
Looking at the fit percentage also gives you insight into whether you should move to the next higher compute size to meet your objective. 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 compute size, which would fit well into the compute size of the database. But, the maximum value shows that the database reaches the limit of the compute size. Do you need to move to the next higher compute size? Look at how many times your workload reaches 100 percent, and then compare it to your database workload objective.
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 IO 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 compute size or use application-tuning techniques to reduce the load on the SQL database.
- This exercise also considers your projected workload increase in the future.
For elastic pools, you can monitor individual databases in the pool with the techniques described in this section. But you can also monitor the pool as a whole. For information, see Monitor and manage an elastic pool.
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.
Monitoring query performance
Slow or long running queries can consume significant system resources. This section demonstrates how to use dynamic management views to detect a few common query performance problems. An older but still helpful reference for troubleshooting, is the Troubleshooting Performance Problems in SQL Server 2008 article on Microsoft TechNet.
Finding top N queries
The following example returns information about the top five queries ranked by average CPU time. This example aggregates the queries according to their query hash, so that logically equivalent queries are grouped by their cumulative resource consumption.
SELECT TOP 5 query_stats.query_hash AS "Query Hash", SUM(query_stats.total_worker_time) / SUM(query_stats.execution_count) AS "Avg CPU Time", MIN(query_stats.statement_text) AS "Statement Text" FROM (SELECT QS.*, SUBSTRING(ST.text, (QS.statement_start_offset/2) + 1, ((CASE statement_end_offset WHEN -1 THEN DATALENGTH(ST.text) ELSE QS.statement_end_offset END - QS.statement_start_offset)/2) + 1) AS statement_text FROM sys.dm_exec_query_stats AS QS CROSS APPLY sys.dm_exec_sql_text(QS.sql_handle) as ST) as query_stats GROUP BY query_stats.query_hash ORDER BY 2 DESC;
Monitoring blocked queries
Slow or long-running queries can contribute to excessive resource consumption and be the consequence of blocked queries. The cause of the blocking can be poor application design, bad query plans, the lack of useful indexes, and so on. You can use the sys.dm_tran_locks view to get information about the current locking activity in your Azure SQL Database. For example code, see sys.dm_tran_locks (Transact-SQL) in SQL Server Books Online.
Monitoring query plans
An inefficient query plan also may increase CPU consumption. The following example uses the sys.dm_exec_query_stats view to determine which query uses the most cumulative CPU.
SELECT highest_cpu_queries.plan_handle, highest_cpu_queries.total_worker_time, q.dbid, q.objectid, q.number, q.encrypted, q.[text] FROM (SELECT TOP 50 qs.plan_handle, qs.total_worker_time FROM sys.dm_exec_query_stats qs ORDER BY qs.total_worker_time desc) AS highest_cpu_queries CROSS APPLY sys.dm_exec_sql_text(plan_handle) AS q ORDER BY highest_cpu_queries.total_worker_time DESC;