Calculating DTU in Azure SQL Database

A few months ago, I posted a question over on  In a nutshell, it was how do you measure DTU? How could you pull the data shown in the Azure Portal graphs with T-SQL?  No one answered, so this motivated me get off my butt and answer my question.  In doing so, I wanted to share the results with all my readers as well.

I started thinking like a detective.  If I was DTU how would I want to be found with T-SQL?

It was the Azure Portal in the Query Store

It was the Azure Portal in the Query Store

Knowing, that both “sys.dm_db_resource_stats” and “sys.resource_stats” holds data that is used to calculate DTU. I decided to leverage Query Store on an Azure SQL Database to see if I could quickly see how DTU is calculated. Behold, I was right.


Query Store

How DTU is calculated in Azure Portal

The Secret Sauce

The whole query is below. Right now, let’s just focus on the secret sauce. The secret sauce is how DTU percentage gets calculated.  In a nutshell, the maximum of CPU, Data IO, Log Write Percent determine your DTU percentage.  What does this mean to you? Your max consumer limits you. So, you can be using 1% of your IO but still be slowed down because CPU could be your max consumer resource.

(SELECT MAX(v) FROM (VALUES (avg_cpu_percent), (avg_data_io_percent), (avg_log_write_percent)) AS value(v)), 0) as dtu_consumption_percent

DTU Calculating Query

SELECT ((CONVERT(BIGINT, DATEDIFF(day, 0, [end_time])) * 24 * 3600 + DATEDIFF(second, DATEADD(day, DATEDIFF(day, 0, [end_time]), 0), [end_time])) / @timeGrain) * @timeGrain as start_time_interval
                , MAX(cpu_percent) as cpu_percent
                , MAX(physical_data_read_percent) as physical_data_read_percent
                , MAX(log_write_percent) as log_write_percent
                , MAX(memory_usage_percent) as memory_usage_percent
                , MAX(xtp_storage_percent) as xtp_storage_percent
                , MAX(dtu_consumption_percent) as dtu_consumption_percent
                , MAX(workers_percent) as workers_percent
                , MAX(sessions_percent) as sessions_percent
                , MAX(dtu_limit) as dtu_limit
                , MAX(dtu_used) as dtu_used
                    , ISNULL(avg_cpu_percent, 0) as cpu_percent
                    , ISNULL(avg_data_io_percent, 0) as physical_data_read_percent
                    , ISNULL(avg_log_write_percent, 0) as log_write_percent
                    , ISNULL(avg_memory_usage_percent, 0) as [memory_usage_percent]
                    , ISNULL(xtp_storage_percent, 0) as xtp_storage_percent
                    , ISNULL((SELECT MAX(v) FROM (VALUES (avg_cpu_percent), (avg_data_io_percent), (avg_log_write_percent)) AS value(v)), 0) as dtu_consumption_percent
                    , ISNULL(max_worker_percent, 0) as workers_percent
                    , ISNULL(max_session_percent, 0) as sessions_percent
                    , ISNULL(dtu_limit, 0) as dtu_limit
                    , ISNULL(dtu_limit, 0) * ISNULL((SELECT MAX(v) FROM (VALUES (avg_cpu_percent), (avg_data_io_percent), (avg_log_write_percent)) AS value(v)), 0) / 100.0 as dtu_used
                 FROM sys.dm_db_resource_stats 
                 WHERE [end_time] >= @startTime AND [end_time] <= @endTime
                 ) t
             GROUP BY ((CONVERT(BIGINT, DATEDIFF(day, 0, [end_time])) * 24 * 3600 + DATEDIFF(second, DATEADD(day, DATEDIFF(day, 0, [end_time]), 0), [end_time])) / @timeGrain) * @timeGrain

A Future Blog Post

Now that we can calculate DTU we could trend this data and automatically make changes like moving up and down from the current performance tier.  I would love to wrap this into an Azure SQL Database Alert but a process in PowerShell might be a great starting point.

John Sterrett is a Microsoft Data Platform MVP and a Group Principal for Procure SQL. If you need any help with your on-premise or cloud SQL Server databases, he would love to chat with you. You can contact him directly at john AT ProcureSQL dot com or here.

  • itellu3times

    Terrific, thanks for posting.

  • John

    Glad to see that you enjoyed the blog post.