To identify the queries that are responsible for high-CPU activity currently, run the following statement:
SELECT TOP 10 s.session_id, r.status, r.cpu_time, r.logical_reads, r.reads, r.writes, r.total_elapsed_time / (1000 * 60) 'Elaps M', SUBSTRING(st.TEXT, (r.statement_start_offset / 2) + 1, ((CASE r.statement_end_offset WHEN -1 THEN DATALENGTH(st.TEXT) ELSE r.statement_end_offset END - r.statement_start_offset) / 2) + 1) AS statement_text, COALESCE(QUOTENAME(DB_NAME(st.dbid)) + N'.' + QUOTENAME(OBJECT_SCHEMA_NAME(st.objectid, st.dbid)) + N'.' + QUOTENAME(OBJECT_NAME(st.objectid, st.dbid)), '') AS command_text, r.command, s.login_name, s.host_name, s.program_name, s.last_request_end_time, s.login_time, r.open_transaction_count FROM sys.dm_exec_sessions AS s JOIN sys.dm_exec_requests AS r ON r.session_id = s.session_id CROSS APPLY sys.Dm_exec_sql_text(r.sql_handle) AS st WHERE r.session_id != @@SPID ORDER BY r.cpu_time DESC
If queries aren’t driving the CPU at this moment, you can run the following statement to look for historical CPU-bound queries:
SELECT TOP 10 st.text AS batch_text, SUBSTRING(st.TEXT, (qs.statement_start_offset / 2) + 1, ((CASE qs.statement_end_offset WHEN - 1 THEN DATALENGTH(st.TEXT) ELSE qs.statement_end_offset END - qs.statement_start_offset) / 2) + 1) AS statement_text, (qs.total_worker_time / 1000) / qs.execution_count AS avg_cpu_time_ms, (qs.total_elapsed_time / 1000) / qs.execution_count AS avg_elapsed_time_ms, qs.total_logical_reads / qs.execution_count AS avg_logical_reads, (qs.total_worker_time / 1000) AS cumulative_cpu_time_all_executions_ms, (qs.total_elapsed_time / 1000) AS cumulative_elapsed_time_all_executions_ms FROM sys.dm_exec_query_stats qs CROSS APPLY sys.dm_exec_sql_text(sql_handle) st ORDER BY(qs.total_worker_time / qs.execution_count) DESC