I borrowed the original code from a Microsoft engineer who had posted it on the Microsoft website. I would credit him but I forgot who he was and I can't find the post now. At any rate I have added my touches.
This query helps you identify the code that is giving you the biggest problems. It pulls out execution information for every plan in the procedure cache. It aggregates totals and averages for key performance metrics, logical i/o, cpu, etc. It comes with multiple ORDER BY clauses so you can highlight the biggest users of any particular resource. Uncomment the clause you want to use.
The one I use most is logical i/o. I think that is the truest measure of code efficiency. Execution time will vary depending on a lot of factors not directly related to the efficiency of the code and indexing. If the data is in cache, the query will run a lot faster than if it has to page data off the disk. Blocking or a heavy load on the server will also change the execution time of a statement.
Physical i/o is an accident involving how much of the data happens to be in cache when the statement runs. It fluctuates randomly and is not a reliable metric.
But, given the same code, same data, same indexing and the same query plan, logical page reads will always be the same. That gives you a baseline to start from when you are optimizing. You can try indexing tricks, code modification, or forcing different query plans. If logical i/o is reduced, you know you are making progress.
Here is the code. I have throttled it down to the top 100 because a big system can have a huge number of plans in cache and you are only interested in the heavy-hitters anyway. I'll bet you already know that the procdure cache is flushed at each restart of sql server or execution of 'DBCC FREEPROCCACHE' so I won't mention it.
SELECT TOP 100 t.[text] AS [Batch],
SUBSTRING(t.[text], (qs.[statement_start_offset]/2) + 1,
WHEN -1 THEN DATALENGTH(t.[text]) ELSE qs.[statement_end_offset] END
- qs.[statement_start_offset])/2) + 1) AS [Statement]
, qs.[execution_count] AS [Count]
, qs.[total_worker_time] AS [Tot_CPU], (qs.[total_worker_time] /
qs.[execution_count]) AS [Avg_CPU]
, qs.[total_physical_reads] AS [Tot_Phys_Reads],
(qs.[total_physical_reads] / qs.[execution_count]) AS [Avg_Phys_Reads]
, qs.[total_logical_writes] AS [Tot_Logic_Writes],
(qs.[total_logical_writes] / qs.[execution_count]) AS [Avg_Logic_Writes]
, qs.[total_logical_reads] AS [Tot_Logic_Reads],
(qs.[total_logical_reads] / qs.[execution_count]) AS [Avg_Logic_Reads]
, qs.[total_clr_time] AS [Tot_CLR_Time], (qs.[total_clr_time] /
qs.[execution_count]) AS [Avg_CLR_Time]
, qs.[total_elapsed_time] AS [Tot_Elapsed_Time], (qs.[total_elapsed_time]
/ qs.[execution_count]) AS [Avg_Elapsed_Time]
, qs.[last_execution_time] AS [Last_Exec], qs.[creation_time] AS [Creation Time]
FROM sys.dm_exec_query_stats AS qs
CROSS APPLY sys.dm_exec_sql_text(qs.sql_handle) AS t
-- ORDER BY [Tot_CPU] DESC
-- ORDER BY [Tot_Phys_Reads] DESC
-- ORDER BY [Tot_Logic_Writes] DESC
ORDER BY [Tot_Logic_Reads] DESC
-- ORDER BY [Avg_Logic_Reads] DESC
-- ORDER BY [Tot_CLR_Time] DESC
-- ORDER BY [Tot_Elapsed_Time] DESC
-- ORDER BY [Count] DESC