I'm using the below T-SQL to check for root level estimate skews in execution plans by pulling the estimated rows from the execution plans and comparing to the last rows from the sys.dm_exec_query_stats DMV. Obviously I can't do this calculation due to the data type mismatch, converting from varchar to bigint won't work in this scenario. Is there any way around this?

SELECT  DB_NAME(qt.dbid) AS [Database], cp.objtype AS [Cached_Plan_Type],
    cp.usecounts AS [Use_Counts],
    qp.query_plan.value('(//@CardinalityEstimationModelVersion)[1]','INT') AS [CE_Version],
    qp.query_plan.value('(//@EstimateRows)[1]', 'varchar(128)') AS [Estimated_Rows],
    qs.last_rows AS [Last Rows],
    --(qp.query_plan.value('(//@EstimateRows)[1]', 'varchar(128)') - qs.last_rows) AS [Estimate_Skew], 
    qs.total_logical_reads / qs.execution_count AS [Avg_Logical_Reads],
    CAST((qs.total_elapsed_time ) / 1000000.0/ qs.execution_count AS DECIMAL(28, 2)) AS [Average Execution time(s)],
    CAST((qs.total_worker_time) / 1000000.0/ qs.execution_count AS DECIMAL(28, 2)) AS [CPU Time Average (s)],
    qt.text AS [SQL_Statement],
    qs.query_hash AS [QueryHash],
    qp.query_plan AS [QueryPlan]
FROM sys.dm_exec_cached_plans cp WITH (NOLOCK)
    CROSS APPLY sys.dm_exec_query_plan (cp.plan_handle) qp
    CROSS APPLY sys.dm_exec_sql_text (cp.plan_handle) qt
    INNER JOIN sys.dm_exec_query_stats qs ON qs.plan_handle = cp.plan_handle
WHERE   qt.text NOT LIKE '%sys.%'
  • 1
    Use float instead. Commented Nov 18, 2016 at 16:43
  • Thanks for pointing me in the right direction @sp_BlitzErik , got it now. May your front squats forever be clean and strong.
    – Fza
    Commented Nov 18, 2016 at 16:56
  • 1
    No problem. There's actually a bit more involved version of your query as a feature request for BlitzCache over here: github.com/BrentOzarULTD/SQL-Server-First-Responder-Kit/issues/… -- As for the front squats: don't tell anyone, I just do leg presses instead :) Commented Nov 18, 2016 at 16:59

1 Answer 1


If you can't do it in one query, then put the results from the first query into a #temporary table. Now do a second query to do the math.

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