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The sys.dm_exec_query_stats DMV

Antonios Chatzipavlis
Friday 12 April 2019

Ένα από τα πράγματα που κάνω στην δουλειά μου είναι πηγαίνω σε πελάτες και να τους βοηθάω να λύσουν τα προβλήματα τους.

Αυτό σημαίνει ότι έρχομαι αντιμέτωπος με instances που δεν γνωρίζω και πρέπει να μάθω για αυτά.Έτσι εκτελώ αρκετά scripts που έχω γράψει για να συλλέξω τις πληροφορίες που θέλω.

Μέσα σε αυτά κάνω αρκετά μεγάλη χρήση των DMV και ένα από αυτά που αγαπώ καθώς μου δίνουν αρκετές πληροφορίες είναι η sys.dm_exec_query_stats.

Το συγκεκριμένο DMV όπως ξεκάθαρα αναφέρεται στο documentation “Returns aggregate performance statistics for cached query plans in SQL Server. The view contains one row per query statement within the cached plan, and the lifetime of the rows are tied to the plan itself. When a plan is removed from the cache, the corresponding rows are eliminated from this view.”

Παρόλο που μπορεί να υπάρχουν περιπτώσεις που αυτή να μην επιστρέφει δεδομένα ή να επιστρέφει λιγότερα και αρκετοί συνάδελφοι να μιλάνε για bug, εντούτοις ακόμα και αυτό είναι μια ένδειξη για memory pressure με συνέπεια να έχουμε αυτό που λέμε plan cache eviction και θα πρέπει σε αυτή την περίπτωση να δούμε τα δεδομένα που επιστρέφουν οι sys.dm_os_memory_cache_clocks_hands και την sys.dm_os_ring_buffers.

Σε κάθε άλλη περίπτωση μπορώ να βγάλω αρκετά στοιχεία από την χρήση της σε συνδυασμό με άλλεs DMVs. Ας δούμε μερικά ενδεικτικά παραδείγματα αλλά φυσικά μπορείτε να φτιάξετε πολλά περισσότερα.

Find the most expensive database

    select  db_name(t.dbid) as database_name,
            sum(s.execution_count)  as execution_count, 
            sum(s.total_worker_time ) as total_worker_time ,
            sum(s.total_physical_reads) as total_physical_reads ,
            sum(s.total_logical_writes) as total_logical_writes ,
            sum(s.total_logical_reads) as total_logical_reads ,
            sum(s.total_clr_time) as total_clr_time ,
            sum(s.total_elapsed_time) as total_elapsed_time ,
            sum(s.total_rows) as total_rows ,
            sum(s.total_dop) as total_dop ,
            sum(s.total_grant_kb) as total_grant_kb,
            sum(s.total_used_grant_kb) as total_used_grant_kb , 
            sum(s.total_ideal_grant_kb)  as total_ideal_grant_kb ,
            sum(s.total_reserved_threads) as total_reserved_threads ,
            sum(s.total_used_threads) as total_used_threads ,
            sum(s.total_columnstore_segment_reads) as total_columnstore_segment_reads ,
            sum(s.total_columnstore_segment_skips) as total_columnstore_segment_skips ,
            sum(s.total_spills) as total_spills 
    from    sys.dm_exec_query_stats as s
    cross apply sys.dm_exec_sql_text(s.sql_handle) as t
    group by t.dbid
    order by 2 desc

Finding the TOP 10 queries

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; 
    select  top 10
            t.text as 'sql text', 
            st.execution_count , 
            isnull( st.total_elapsed_time / st.execution_count, 0 ) as 'avg excecution time',
            st.total_worker_time / st.execution_count as 'avg worker time',
            st.total_worker_time,
            st.max_logical_reads, 
            st.max_logical_writes, 
            st.creation_time,
            isnull( st.execution_count / datediff( second, st.creation_time, getdate()), 0 ) as 'calls per second'
    from    sys.dm_exec_query_stats st
    cross apply sys.dm_exec_sql_text( st.sql_handle )  t
    order by st.total_elapsed_time desc

Returning row count aggregates for a query

select qs.execution_count,  
    substring(qt.text,qs.statement_start_offset/2 +1,   
                 (case when qs.statement_end_offset = -1   
                       then len(convert(nvarchar(max), qt.text)) * 2   
                       else qs.statement_end_offset end -  
                            qs.statement_start_offset  
                 )/2  
             ) as query_text,   
     qt.dbid, dbname= db_name (qt.dbid), qt.objectid,   
     qs.total_rows, qs.last_rows, qs.min_rows, qs.max_rows  
from sys.dm_exec_query_stats as qs   
cross apply sys.dm_exec_sql_text(qs.sql_handle) as qt   
where qt.text like '%select%'   
order by qs.execution_count desc; 

Monitor Query Plans

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

//antonch

Antonios Chatzipavlis

Antonios Chatzipavlis

Antonios Chatzipavlis is a highly experienced Data Solutions Consultant and Trainer. He has been working in the IT industry since 1988, holding various roles such as senior developer, IT Manager, Data & AI Solutions Architect and Consultant.

Since 1995, Antonios has focused on modern technologies and software development tools, primarily by Microsoft. He has specialized in Data & AI since 2000, with expertise in Microsoft Data Platform (SQL Server, Azure SQL Databases, Azure Synapse Analytics, Microsoft Fabric, Power BI, AI) and Databricks.

Antonios is also a Microsoft Certified Trainer (MCT) for over 25 years, has been recognized as a Microsoft Most Valuable Professional (MVP) in Data Platform since 2010 and he is in the Data Expert 40 Powerlist 2024 by Boussias. He is the co-founder and visionary behind XLYTiCA, a company dedicated to Data & AI solutions.

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