Monitoring Microsoft Azure SQL Database performance using dynamic management views
Applies to: Azure SQL Database Azure SQL Managed Instance
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 article provides information on how to detect common performance problems by using dynamic management views.
This article is about Azure SQL Database, see also Monitoring Microsoft Azure SQL Managed Instance performance using dynamic management views.
In Azure SQL Database, depending on the compute size and deployment option, querying a DMV may require either VIEW DATABASE STATE or VIEW SERVER STATE permission. The latter permission may be granted via membership in the
##MS_ServerStateReader## server role.
To grant the VIEW DATABASE STATE permission to a specific database user, run the following query as an example:
GRANT VIEW DATABASE STATE TO database_user;
To grant membership to the
##MS_ServerStateReader## server role to a login for the logical server in Azure, connect to the
master database then run the following query as an example:
ALTER SERVER ROLE [##MS_ServerStateReader##] ADD MEMBER [login];
In an instance of SQL Server and in Azure SQL Managed Instance, dynamic management views return server state information. In Azure SQL Database, they return information regarding your current logical database only.
Identify CPU performance issues
If CPU consumption is above 80% for extended periods of time, consider the following troubleshooting steps:
The CPU issue is occurring now
If issue is occurring right now, there are two possible scenarios:
Many individual queries that cumulatively consume high CPU
Use the following query to identify top query hashes:
PRINT '-- top 10 Active CPU Consuming Queries (aggregated)--'; SELECT TOP 10 GETDATE() runtime, * FROM (SELECT query_stats.query_hash, SUM(query_stats.cpu_time) 'Total_Request_Cpu_Time_Ms', SUM(logical_reads) 'Total_Request_Logical_Reads', MIN(start_time) 'Earliest_Request_start_Time', COUNT(*) 'Number_Of_Requests', SUBSTRING(REPLACE(REPLACE(MIN(query_stats.statement_text), CHAR(10), ' '), CHAR(13), ' '), 1, 256) AS "Statement_Text" FROM (SELECT req.*, SUBSTRING(ST.text, (req.statement_start_offset / 2)+1, ((CASE statement_end_offset WHEN -1 THEN DATALENGTH(ST.text)ELSE req.statement_end_offset END-req.statement_start_offset)/ 2)+1) AS statement_text FROM sys.dm_exec_requests AS req CROSS APPLY sys.dm_exec_sql_text(req.sql_handle) AS ST ) AS query_stats GROUP BY query_hash) AS t ORDER BY Total_Request_Cpu_Time_Ms DESC;
Long running queries that consume CPU are still running
Use the following query to identify these queries:
PRINT '--top 10 Active CPU Consuming Queries by sessions--'; SELECT TOP 10 req.session_id, req.start_time, cpu_time 'cpu_time_ms', OBJECT_NAME(ST.objectid, ST.dbid) 'ObjectName', SUBSTRING(REPLACE(REPLACE(SUBSTRING(ST.text, (req.statement_start_offset / 2)+1, ((CASE statement_end_offset WHEN -1 THEN DATALENGTH(ST.text)ELSE req.statement_end_offset END-req.statement_start_offset)/ 2)+1), CHAR(10), ' '), CHAR(13), ' '), 1, 512) AS statement_text FROM sys.dm_exec_requests AS req CROSS APPLY sys.dm_exec_sql_text(req.sql_handle) AS ST ORDER BY cpu_time DESC; GO
The CPU issue occurred in the past
If the issue occurred in the past and you want to do root cause analysis, use Query Store. Users with database access can use T-SQL to query Query Store data. Query Store default configurations use a granularity of 1 hour. Use the following query to look at activity for high CPU consuming queries. This query returns the top 15 CPU consuming queries. Remember to change
rsi.start_time >= DATEADD(hour, -2, GETUTCDATE():
-- Top 15 CPU consuming queries by query hash -- note that a query hash can have many query id if not parameterized or not parameterized properly -- it grabs a sample query text by min WITH AggregatedCPU AS (SELECT q.query_hash, SUM(count_executions * avg_cpu_time / 1000.0) AS total_cpu_millisec, SUM(count_executions * avg_cpu_time / 1000.0)/ SUM(count_executions) AS avg_cpu_millisec, MAX(rs.max_cpu_time / 1000.00) AS max_cpu_millisec, MAX(max_logical_io_reads) max_logical_reads, COUNT(DISTINCT p.plan_id) AS number_of_distinct_plans, COUNT(DISTINCT p.query_id) AS number_of_distinct_query_ids, SUM(CASE WHEN rs.execution_type_desc='Aborted' THEN count_executions ELSE 0 END) AS Aborted_Execution_Count, SUM(CASE WHEN rs.execution_type_desc='Regular' THEN count_executions ELSE 0 END) AS Regular_Execution_Count, SUM(CASE WHEN rs.execution_type_desc='Exception' THEN count_executions ELSE 0 END) AS Exception_Execution_Count, SUM(count_executions) AS total_executions, MIN(qt.query_sql_text) AS sampled_query_text FROM sys.query_store_query_text AS qt JOIN sys.query_store_query AS q ON qt.query_text_id=q.query_text_id JOIN sys.query_store_plan AS p ON q.query_id=p.query_id JOIN sys.query_store_runtime_stats AS rs ON rs.plan_id=p.plan_id JOIN sys.query_store_runtime_stats_interval AS rsi ON rsi.runtime_stats_interval_id=rs.runtime_stats_interval_id WHERE rs.execution_type_desc IN ('Regular', 'Aborted', 'Exception')AND rsi.start_time>=DATEADD(HOUR, -2, GETUTCDATE()) GROUP BY q.query_hash), OrderedCPU AS (SELECT query_hash, total_cpu_millisec, avg_cpu_millisec, max_cpu_millisec, max_logical_reads, number_of_distinct_plans, number_of_distinct_query_ids, total_executions, Aborted_Execution_Count, Regular_Execution_Count, Exception_Execution_Count, sampled_query_text, ROW_NUMBER() OVER (ORDER BY total_cpu_millisec DESC, query_hash ASC) AS RN FROM AggregatedCPU) SELECT OD.query_hash, OD.total_cpu_millisec, OD.avg_cpu_millisec, OD.max_cpu_millisec, OD.max_logical_reads, OD.number_of_distinct_plans, OD.number_of_distinct_query_ids, OD.total_executions, OD.Aborted_Execution_Count, OD.Regular_Execution_Count, OD.Exception_Execution_Count, OD.sampled_query_text, OD.RN FROM OrderedCPU AS OD WHERE OD.RN<=15 ORDER BY total_cpu_millisec DESC;
Once you identify the problematic queries, it's time to tune those queries to reduce CPU utilization. If you don't have time to tune the queries, you may also choose to upgrade the SLO of the database to work around the issue.
For more information about handling CPU performance problems in Azure SQL Database, see Diagnose and troubleshoot high CPU on Azure SQL Database.
Identify IO performance issues
When identifying IO performance issues, the top wait types associated with IO issues are:
For data file IO issues (including
PAGEIOLATCH_UP). If the wait type name has IO in it, it points to an IO issue. If there is no IO in the page latch wait name, it points to a different type of problem (for example,
For transaction log IO issues.
If the IO issue is occurring right now
Use the sys.dm_exec_requests or sys.dm_os_waiting_tasks to see the
Identify data and log IO usage
Use the following query to identify data and log IO usage. If the data or log IO is above 80%, it means users have used the available IO for the Azure SQL Database service tier.
SELECT end_time, avg_data_io_percent, avg_log_write_percent FROM sys.dm_db_resource_stats ORDER BY end_time DESC;
If the IO limit has been reached, you have two options:
- Option 1: Upgrade the compute size or service tier
- Option 2: Identify and tune the queries consuming the most IO.
View buffer-related IO using the Query Store
For option 2, you can use the following query against Query Store for buffer-related IO to view the last two hours of tracked activity:
-- top queries that waited on buffer -- note these are finished queries WITH Aggregated AS (SELECT q.query_hash, SUM(total_query_wait_time_ms) total_wait_time_ms, SUM(total_query_wait_time_ms / avg_query_wait_time_ms) AS total_executions, MIN(qt.query_sql_text) AS sampled_query_text, MIN(wait_category_desc) AS wait_category_desc FROM sys.query_store_query_text AS qt JOIN sys.query_store_query AS q ON qt.query_text_id=q.query_text_id JOIN sys.query_store_plan AS p ON q.query_id=p.query_id JOIN sys.query_store_wait_stats AS waits ON waits.plan_id=p.plan_id JOIN sys.query_store_runtime_stats_interval AS rsi ON rsi.runtime_stats_interval_id=waits.runtime_stats_interval_id WHERE wait_category_desc='Buffer IO' AND rsi.start_time>=DATEADD(HOUR, -2, GETUTCDATE()) GROUP BY q.query_hash), Ordered AS (SELECT query_hash, total_executions, total_wait_time_ms, sampled_query_text, wait_category_desc, ROW_NUMBER() OVER (ORDER BY total_wait_time_ms DESC, query_hash ASC) AS RN FROM Aggregated) SELECT OD.query_hash, OD.total_executions, OD.total_wait_time_ms, OD.sampled_query_text, OD.wait_category_desc, OD.RN FROM Ordered AS OD WHERE OD.RN<=15 ORDER BY total_wait_time_ms DESC; GO
View total log IO for WRITELOG waits
If the wait type is
WRITELOG, use the following query to view total log IO by statement:
-- Top transaction log consumers -- Adjust the time window by changing -- rsi.start_time >= DATEADD(hour, -2, GETUTCDATE()) WITH AggregatedLogUsed AS (SELECT q.query_hash, SUM(count_executions * avg_cpu_time / 1000.0) AS total_cpu_millisec, SUM(count_executions * avg_cpu_time / 1000.0) / SUM(count_executions) AS avg_cpu_millisec, SUM(count_executions * avg_log_bytes_used) AS total_log_bytes_used, MAX(rs.max_cpu_time / 1000.00) AS max_cpu_millisec, MAX(max_logical_io_reads) max_logical_reads, COUNT(DISTINCT p.plan_id) AS number_of_distinct_plans, COUNT(DISTINCT p.query_id) AS number_of_distinct_query_ids, SUM( CASE WHEN rs.execution_type_desc = 'Aborted' THEN count_executions ELSE 0 END ) AS Aborted_Execution_Count, SUM( CASE WHEN rs.execution_type_desc = 'Regular' THEN count_executions ELSE 0 END ) AS Regular_Execution_Count, SUM( CASE WHEN rs.execution_type_desc = 'Exception' THEN count_executions ELSE 0 END ) AS Exception_Execution_Count, SUM(count_executions) AS total_executions, MIN(qt.query_sql_text) AS sampled_query_text FROM sys.query_store_query_text AS qt JOIN sys.query_store_query AS q ON qt.query_text_id = q.query_text_id JOIN sys.query_store_plan AS p ON q.query_id = p.query_id JOIN sys.query_store_runtime_stats AS rs ON rs.plan_id = p.plan_id JOIN sys.query_store_runtime_stats_interval AS rsi ON rsi.runtime_stats_interval_id = rs.runtime_stats_interval_id WHERE rs.execution_type_desc IN ( 'Regular', 'Aborted', 'Exception' ) AND rsi.start_time >= DATEADD(HOUR, -2, GETUTCDATE()) GROUP BY q.query_hash), OrderedLogUsed AS (SELECT query_hash, total_log_bytes_used, number_of_distinct_plans, number_of_distinct_query_ids, total_executions, Aborted_Execution_Count, Regular_Execution_Count, Exception_Execution_Count, sampled_query_text, ROW_NUMBER() OVER (ORDER BY total_log_bytes_used DESC, query_hash ASC) AS RN FROM AggregatedLogUsed) SELECT OD.total_log_bytes_used, OD.number_of_distinct_plans, OD.number_of_distinct_query_ids, OD.total_executions, OD.Aborted_Execution_Count, OD.Regular_Execution_Count, OD.Exception_Execution_Count, OD.sampled_query_text, OD.RN FROM OrderedLogUsed AS OD WHERE OD.RN <= 15 ORDER BY total_log_bytes_used DESC; GO
tempdb performance issues
When identifying IO performance issues, the top wait types associated with
tempdb issues is
PAGELATCH_* waits do not always mean you have
tempdb contention. This wait may also mean that you have user-object data page contention due to concurrent requests targeting the same data page. To further confirm
tempdb contention, use sys.dm_exec_requests to confirm that the wait_resource value begins with
2:x:y where 2 is
tempdb is the database ID,
x is the file ID, and
y is the page ID.
tempdb contention, a common method is to reduce or rewrite application code that relies on
tempdb usage areas include:
- Temp tables
- Table variables
- Table-valued parameters
- Version store usage (associated with long running transactions)
- Queries that have query plans that use sorts, hash joins, and spools
Top queries that use table variables and temporary tables
Use the following query to identify top queries that use table variables and temporary tables:
SELECT plan_handle, execution_count, query_plan INTO #tmpPlan FROM sys.dm_exec_query_stats CROSS APPLY sys.dm_exec_query_plan(plan_handle); GO WITH XMLNAMESPACES('http://schemas.microsoft.com/sqlserver/2004/07/showplan' AS sp) SELECT plan_handle, stmt.stmt_details.value('@Database', 'varchar(max)') 'Database', stmt.stmt_details.value('@Schema', 'varchar(max)') 'Schema', stmt.stmt_details.value('@Table', 'varchar(max)') 'table' INTO #tmp2 FROM(SELECT CAST(query_plan AS XML) sqlplan, plan_handle FROM #tmpPlan) AS p CROSS APPLY sqlplan.nodes('//sp:Object') AS stmt(stmt_details); GO SELECT t.plan_handle, [Database], [Schema], [table], execution_count FROM(SELECT DISTINCT plan_handle, [Database], [Schema], [table] FROM #tmp2 WHERE [table] LIKE '%@%' OR [table] LIKE '%#%') AS t JOIN #tmpPlan AS t2 ON t.plan_handle=t2.plan_handle;
Identify long running transactions
Use the following query to identify long running transactions. Long running transactions prevent version store cleanup.
SELECT DB_NAME(dtr.database_id) 'database_name', sess.session_id, atr.name AS 'tran_name', atr.transaction_id, transaction_type, transaction_begin_time, database_transaction_begin_time, transaction_state, is_user_transaction, sess.open_transaction_count, TRIM(REPLACE( REPLACE( SUBSTRING( SUBSTRING( txt.text, (req.statement_start_offset / 2) + 1, ((CASE req.statement_end_offset WHEN -1 THEN DATALENGTH(txt.text) ELSE req.statement_end_offset END - req.statement_start_offset ) / 2 ) + 1 ), 1, 1000 ), CHAR(10), ' ' ), CHAR(13), ' ' ) ) Running_stmt_text, recenttxt.text 'MostRecentSQLText' FROM sys.dm_tran_active_transactions AS atr INNER JOIN sys.dm_tran_database_transactions AS dtr ON dtr.transaction_id = atr.transaction_id LEFT JOIN sys.dm_tran_session_transactions AS sess ON sess.transaction_id = atr.transaction_id LEFT JOIN sys.dm_exec_requests AS req ON req.session_id = sess.session_id AND req.transaction_id = sess.transaction_id LEFT JOIN sys.dm_exec_connections AS conn ON sess.session_id = conn.session_id OUTER APPLY sys.dm_exec_sql_text(req.sql_handle) AS txt OUTER APPLY sys.dm_exec_sql_text(conn.most_recent_sql_handle) AS recenttxt WHERE atr.transaction_type != 2 AND sess.session_id != @@spid ORDER BY start_time ASC;
Identify memory grant wait performance issues
If your top wait type is
RESOURCE_SEMAPHORE and you don't have a high CPU usage issue, you may have a memory grant waiting issue.
Determine if a
RESOURCE_SEMAPHORE wait is a top wait
Use the following query to determine if a
RESOURCE_SEMAPHORE wait is a top wait
SELECT wait_type, SUM(wait_time) AS total_wait_time_ms FROM sys.dm_exec_requests AS req JOIN sys.dm_exec_sessions AS sess ON req.session_id = sess.session_id WHERE is_user_process = 1 GROUP BY wait_type ORDER BY SUM(wait_time) DESC;
Identify high memory-consuming statements
If you encounter out of memory errors in Azure SQL Database, review sys.dm_os_out_of_memory_events.
Use the following query to identify high memory-consuming statements:
SELECT IDENTITY(INT, 1, 1) rowId, CAST(query_plan AS XML) query_plan, p.query_id INTO #tmp FROM sys.query_store_plan AS p JOIN sys.query_store_runtime_stats AS r ON p.plan_id = r.plan_id JOIN sys.query_store_runtime_stats_interval AS i ON r.runtime_stats_interval_id = i.runtime_stats_interval_id WHERE start_time > '2018-10-11 14:00:00.0000000' AND end_time < '2018-10-17 20:00:00.0000000'; GO ;WITH cte AS (SELECT query_id, query_plan, m.c.value('@SerialDesiredMemory', 'INT') AS SerialDesiredMemory FROM #tmp AS t CROSS APPLY t.query_plan.nodes('//*:MemoryGrantInfo[@SerialDesiredMemory[. > 0]]') AS m(c) ) SELECT TOP 50 cte.query_id, t.query_sql_text, cte.query_plan, CAST(SerialDesiredMemory / 1024. AS DECIMAL(10, 2)) SerialDesiredMemory_MB FROM cte JOIN sys.query_store_query AS q ON cte.query_id = q.query_id JOIN sys.query_store_query_text AS t ON q.query_text_id = t.query_text_id ORDER BY SerialDesiredMemory DESC;
Identify the top 10 active memory grants
Use the following query to identify the top 10 active memory grants:
SELECT TOP 10 CONVERT(VARCHAR(30), GETDATE(), 121) AS runtime, r.session_id, r.blocking_session_id, r.cpu_time, r.total_elapsed_time, r.reads, r.writes, r.logical_reads, r.row_count, wait_time, wait_type, r.command, OBJECT_NAME(txt.objectid, txt.dbid) 'Object_Name', TRIM(REPLACE( REPLACE( SUBSTRING( SUBSTRING( text, (r.statement_start_offset / 2) + 1, ((CASE r.statement_end_offset WHEN -1 THEN DATALENGTH(text) ELSE r.statement_end_offset END - r.statement_start_offset ) / 2 ) + 1 ), 1, 1000 ), CHAR(10), ' ' ), CHAR(13), ' ' ) ) stmt_text, mg.dop, --Degree of parallelism mg.request_time, --Date and time when this query requested the memory grant. mg.grant_time, --NULL means memory has not been granted mg.requested_memory_kb / 1024.0 requested_memory_mb, --Total requested amount of memory in megabytes mg.granted_memory_kb / 1024.0 AS granted_memory_mb, --Total amount of memory actually granted in megabytes. NULL if not granted mg.required_memory_kb / 1024.0 AS required_memory_mb, --Minimum memory required to run this query in megabytes. max_used_memory_kb / 1024.0 AS max_used_memory_mb, mg.query_cost, --Estimated query cost. mg.timeout_sec, --Time-out in seconds before this query gives up the memory grant request. mg.resource_semaphore_id, --Non-unique ID of the resource semaphore on which this query is waiting. mg.wait_time_ms, --Wait time in milliseconds. NULL if the memory is already granted. CASE mg.is_next_candidate --Is this process the next candidate for a memory grant WHEN 1 THEN 'Yes' WHEN 0 THEN 'No' ELSE 'Memory has been granted' END AS 'Next Candidate for Memory Grant', qp.query_plan FROM sys.dm_exec_requests AS r JOIN sys.dm_exec_query_memory_grants AS mg ON r.session_id = mg.session_id AND r.request_id = mg.request_id CROSS APPLY sys.dm_exec_sql_text(mg.sql_handle) AS txt CROSS APPLY sys.dm_exec_query_plan(r.plan_handle) AS qp ORDER BY mg.granted_memory_kb DESC;
Calculating database and objects sizes
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 database or elastic pool 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_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 monitor Azure SQL Database resource usage at the query level by using SQL Database Query Performance Insight in the Azure portal or the Query Store.
You can also monitor usage using these views:
You can use the sys.dm_db_resource_stats view in every 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 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 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 five 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.
The database engine 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.
On Azure SQL Database, you must be connected to the
master database 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 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_statsto 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_statsgive 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 regard 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 100*((COUNT(database_name) - SUM(CASE WHEN avg_cpu_percent >= 40 THEN 1 ELSE 0 END) * 1.0) / COUNT(database_name)) AS 'CPU Fit Percent', 100*((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', 100*((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 = 'sample' 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, the CPU usage for a sample database over 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 100*((COUNT(database_name) - SUM(CASE WHEN avg_cpu_percent >= 100 THEN 1 ELSE 0 END) * 1.0) / COUNT(database_name)) AS 'CPU Fit Percent', 100*((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', 100*((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 = 'sample' 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 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 current number of concurrent requests, run this Transact-SQL query on your database:
SELECT COUNT(*) AS [Concurrent_Requests] FROM sys.dm_exec_requests R;
To analyze the workload of a database, modify this query to filter on the specific database you want to analyze. For example, if you have a 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 database:
SELECT COUNT(*) AS [Sessions] FROM sys.dm_exec_connections;
If you're analyzing a 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.
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.
You can get historical statistics on sessions by querying the sys.resource_stats view and reviewing the
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.
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 database. For example code, see sys.dm_tran_locks. For more information on troubleshooting blocking, see Understand and resolve Azure SQL blocking problems.
In some cases, two or more queries may mutually block one another, resulting in a deadlock.
You can create an Extended Events trace a database in Azure SQL Database to capture deadlock events, then find related queries and their execution plans in Query Store. Learn more in Analyze and prevent deadlocks in Azure SQL Database.
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;
- Introduction to Azure SQL Database and Azure SQL Managed Instance
- Diagnose and troubleshoot high CPU on Azure SQL Database
- Tune applications and databases for performance in Azure SQL Database and Azure SQL Managed Instance
- Understand and resolve Azure SQL Database blocking problems
- Analyze and prevent deadlocks in Azure SQL Database
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