{"id":526,"date":"2011-10-05T08:43:00","date_gmt":"2011-10-05T08:43:00","guid":{"rendered":"\/blogs\/joe\/post\/Under-the-covers-with-sp_server_diagnostics-data-(Part-II).aspx"},"modified":"2013-01-02T20:32:36","modified_gmt":"2013-01-03T04:32:36","slug":"under-the-covers-with-sp_server_diagnostics-data-part-ii","status":"publish","type":"post","link":"https:\/\/www.sqlskills.com\/blogs\/joe\/under-the-covers-with-sp_server_diagnostics-data-part-ii\/","title":{"rendered":"Under the covers with sp_server_diagnostics data (Part II)"},"content":{"rendered":"

<\/font><\/p>\n

\nThis post covers examples from Denali CTP3, version 11.0.1440<\/em>.<\/font><\/font>\n<\/p>\n

<\/font><\/p>\n

\nIn my last post, “<\/font>Under The Covers With Sp_server_diagnostics Data (Part I)<\/font><\/a>” I started an exploration of the output surfaced from the sp_server_diagnostics stored procedure.  <\/span>In this post, I’d like to continue this exploration, having a look specifically at the “query_processing”, “io_subsystem” and “events” component data. <\/font><\/font>\n<\/p>\n

<\/font><\/p>\n

\nJust a reminder of why we would be looking at this in the first place?  <\/span>Quite simply, I see this output as a means of gathering out-of-the-box SQL Server health information in absence of (or in compliment of) existing scripts or processes running on the SQL Server instance.<\/font><\/font>\n<\/p>\n

<\/font><\/p>\n

\nSo continuing on the exploration, the query_processing component data can actually be broken down into five useful groupings of statistics:<\/font><\/font>\n<\/p>\n

<\/font><\/p>\n

\n·<\/font>        <\/span><\/span><\/span>Query processing statistics<\/font><\/font>\n<\/p>\n

<\/font><\/p>\n

\n·<\/font>        <\/span><\/span><\/span>Top non-preemptive wait statistics by count<\/font><\/font>\n<\/p>\n

<\/font><\/p>\n

\n·<\/font>        <\/span><\/span><\/span>Top non-preemptive wait statistics by duration<\/font><\/font>\n<\/p>\n

<\/font><\/p>\n

\n·<\/font>        <\/span><\/span><\/span>Top preemptive waits by count<\/font><\/font>\n<\/p>\n

<\/font><\/p>\n

\n·<\/font>        <\/span><\/span><\/span>Top preemptive waits by duration<\/font><\/font>\n<\/p>\n

<\/font><\/p>\n

\nThe following is an example of the query processing statistics data:<\/font><\/font>\n<\/p>\n

<\/font><\/p>\n

\n<queryProcessing maxWorkers="512" workersCreated="46" workersIdle="17" tasksCompletedWithinInterval="12" pendingTasks="0" oldestPendingTaskWaitingTime="0" hasUnresolvableDeadlockOccurred="0" hasDeadlockedSchedulersOccurred="0" trackingNonYieldingScheduler="0x0"><\/font><\/font>\n<\/p>\n

\n \n<\/p>\n

<\/font><\/p>\n

\n<\/font>\n<\/p>\n

<\/font><\/p>\n

\nThe naming conventions are intuitive – returning a point of time measurement of max workers, actual workers created, idle workers, pending tasks, oldest pending task waiting time (I’ll definitely be paying attention to this one) and scheduler deadlock and non-yielding counts.<\/font><\/font>\n<\/p>\n

\n \n<\/p>\n

<\/font><\/p>\n

\n<\/font>\n<\/p>\n

<\/font><\/p>\n

\nFor fans of evaluating wait statistics (and hopefully that’s most SQL Server folks these days) – we have a four sets of Top 10 wait stats results.  <\/span>The following is the output of the top non-preemptive waits by count (and specifically – by the “waits” attribute):<\/font><\/font>\n<\/p>\n

\n \n<\/p>\n

<\/font><\/p>\n

\n<\/font>\n<\/p>\n

<\/font><\/p>\n

\n<byCount><\/font><\/span>\n<\/p>\n

<\/font><\/p>\n

\n        <\/span><wait waitType="HADR_FILESTREAM_IOMGR_IOCOMPLETION" waits="1884" averageWaitTime="967892" maxWaitTime="800" \/><\/font><\/span>\n<\/p>\n

<\/font><\/p>\n

\n        <\/span><wait waitType="PAGEIOLATCH_SH" waits="534" averageWaitTime="1329" maxWaitTime="37" \/><\/font><\/span>\n<\/p>\n

<\/font><\/p>\n

\n        <\/span><wait waitType="IO_COMPLETION" waits="71" averageWaitTime="128" maxWaitTime="4" \/><\/font><\/span>\n<\/p>\n

<\/font><\/p>\n

\n        <\/span><wait waitType="THREADPOOL" waits="53" averageWaitTime="209" maxWaitTime="25" \/><\/font><\/span>\n<\/p>\n

<\/font><\/p>\n

\n        <\/span><wait waitType="WRITELOG" waits="35" averageWaitTime="58" maxWaitTime="17" \/><\/font><\/span>\n<\/p>\n

<\/font><\/p>\n

\n        <\/span><wait waitType="PERFORMANCE_COUNTERS_RWLOCK" waits="18" averageWaitTime="3" maxWaitTime="1" \/><\/font><\/span>\n<\/p>\n

<\/font><\/p>\n

\n        <\/span><wait waitType="ASYNC_NETWORK_IO" waits="10" averageWaitTime="125" maxWaitTime="118" \/><\/font><\/span>\n<\/p>\n

<\/font><\/p>\n

\n        <\/span><wait waitType="PAGEIOLATCH_EX" waits="8" averageWaitTime="14" maxWaitTime="6" \/><\/font><\/span>\n<\/p>\n

<\/font><\/p>\n

\n        <\/span><wait waitType="SLEEP_DBSTARTUP" waits="8" averageWaitTime="779" maxWaitTime="175" \/><\/font><\/span>\n<\/p>\n

<\/font><\/p>\n

\n        <\/span><wait waitType="CLR_AUTO_EVENT" waits="8" averageWaitTime="8527" maxWaitTime="4085" \/><\/font><\/span>\n<\/p>\n

<\/font><\/p>\n

\n      <\/span><\/byCount><\/font><\/span>\n<\/p>\n

\n \n<\/p>\n

<\/font><\/p>\n

\n<\/font>\n<\/p>\n

<\/font><\/p>\n

\nWe also have the non-preemptive waits by duration (and specifically – by the average wait time):<\/font><\/font>\n<\/p>\n

\n \n<\/p>\n

<\/font><\/p>\n

\n<\/font>\n<\/p>\n

<\/font><\/p>\n

\n    <\/span><byDuration><\/font><\/span>\n<\/p>\n

<\/font><\/p>\n

\n        <\/span><wait waitType="HADR_FILESTREAM_IOMGR_IOCOMPLETION" waits="1884" averageWaitTime="967892" maxWaitTime="800" \/><\/font><\/span>\n<\/p>\n

<\/font><\/p>\n

\n        <\/span><wait waitType="CLR_AUTO_EVENT" waits="8" averageWaitTime="8527" maxWaitTime="4085" \/><\/font><\/span>\n<\/p>\n

<\/font><\/p>\n

\n        <\/span><wait waitType="FT_IFTSHC_MUTEX" waits="6" averageWaitTime="4381" maxWaitTime="2977" \/><\/font><\/span>\n<\/p>\n

<\/font><\/p>\n

\n        <\/span><wait waitType="SLEEP_MASTERDBREADY" waits="1" averageWaitTime="1836" maxWaitTime="1836" \/><\/font><\/span>\n<\/p>\n

<\/font><\/p>\n

\n        <\/span><wait waitType="PWAIT_ALL_COMPONENTS_INITIALIZED" waits="3" averageWaitTime="1558" maxWaitTime="541" \/><\/font><\/span>\n<\/p>\n

<\/font><\/p>\n

\n        <\/span><wait waitType="PAGEIOLATCH_SH" waits="534" averageWaitTime="1329" maxWaitTime="37" \/><\/font><\/span>\n<\/p>\n

<\/font><\/p>\n

\n        <\/span><wait waitType="LCK_M_S" waits="5" averageWaitTime="1146" maxWaitTime="336" \/><\/font><\/span>\n<\/p>\n

<\/font><\/p>\n

\n        <\/span><wait waitType="SLEEP_DBSTARTUP" waits="8" averageWaitTime="779" maxWaitTime="175" \/><\/font><\/span>\n<\/p>\n

<\/font><\/p>\n

\n        <\/span><wait waitType="SLEEP_DCOMSTARTUP" waits="1" averageWaitTime="377" maxWaitTime="377" \/><\/font><\/span>\n<\/p>\n

<\/font><\/p>\n

\n        <\/span><wait waitType="FFT_RECOVERY" waits="5" averageWaitTime="241" maxWaitTime="118" \/><\/font><\/span>\n<\/p>\n

<\/font><\/p>\n

\n      <\/span><\/byDuration><\/font><\/span>\n<\/p>\n

\n \n<\/p>\n

<\/font><\/p>\n

\n<\/font><\/span>\n<\/p>\n

<\/font><\/p>\n

\nWe have two more result sets for isolating out preemptive waits that are identical in format to the non-preemptive results, so I won’t repeat them here.  <\/span>And while we can pull aggregated wait statistics from other sources – it is convenient to have this data at-the-ready.<\/font><\/font>\n<\/p>\n

\n \n<\/p>\n

<\/font><\/p>\n

\n<\/font>\n<\/p>\n

<\/font><\/p>\n

\nNext we have the I\/O component data which is far less verbose in my sample output:<\/font><\/font>\n<\/p>\n

\n \n<\/p>\n

<\/font><\/p>\n

\n<\/font>\n<\/p>\n

<\/font><\/p>\n

\n<ioSubsystem ioLatchTimeouts="0" intervalLongIos="0" totalLongIos="0"><\/font><\/font>\n<\/p>\n

<\/font><\/p>\n

\n  <\/span><longestPendingRequests \/><\/font><\/font>\n<\/p>\n

<\/font><\/p>\n

\n<\/ioSubsystem><\/font><\/font>\n<\/p>\n

\n \n<\/p>\n

<\/font><\/p>\n

\n<\/font>\n<\/p>\n

<\/font><\/p>\n

\nThere we can see information on latch timeouts and long I\/Os.  <\/span>My test system didn’t have a value for the longest pending requests – and that is something I’ll look for later – but if you have a sample on your own Denali system, please post to the comments.   <\/span>I’d also be curious to compare the value of this data against what gets exposed in sys.dm_io_pending_io_requests  <\/span>and see which has more value from a troubleshooting perspective.<\/font><\/font>\n<\/p>\n

\n \n<\/p>\n

<\/font><\/p>\n

\n<\/font>\n<\/p>\n

<\/font><\/p>\n

\nThe last component to cover from sp_server_diagnostics data is the “events” output (here is the vanilla output without events for starters):<\/font><\/font>\n<\/p>\n

\n \n<\/p>\n

<\/font><\/p>\n

\n<\/font>\n<\/p>\n

<\/font><\/p>\n

\n<events><\/font><\/font>\n<\/p>\n

<\/font><\/p>\n

\n  <\/span><session startTime="2011-10-03T14:11:52.380" droppedEvents="0" largestDroppedEvent="0"><\/font><\/font>\n<\/p>\n

<\/font><\/p>\n

\n    <\/span><RingBufferTarget truncated="0" processingTime="0" totalEventsProcessed="0" eventCount="0" droppedCount="0" memoryUsed="0" \/><\/font><\/font>\n<\/p>\n

<\/font><\/p>\n

\n  <\/span><\/session><\/font><\/font>\n<\/p>\n

<\/font><\/p>\n

\n<\/events><\/font><\/font>\n<\/p>\n

\n \n<\/p>\n

<\/font><\/p>\n

\n<\/font>\n<\/p>\n

<\/font><\/p>\n

\nBOL states that this will expose events “of interest” recorded on the SQL Server instance – similar to what you could pull from the ring buffer.  <\/span>So when testing a login failure by putting in a bad login identifier, I saw three events raised – one being “security_error_ring_buffer_recorded” and then the others being two “connectivity_ring_buffer_recorded” events.  <\/span>The first connectivity event was for an event type of “Error” and another of “ConnClose”.  <\/span><\/font><\/font>\n<\/p>\n

\n<\/span><\/font><\/font>\n<\/p>\n

<\/font><\/p>\n

\n<\/font>\n<\/p>\n

<\/font><\/p>\n

\nThe data exposed is pretty chatty – so you might wonder (as compared to the wait statistics) – whether it is worth sifting through.  <\/span>One example is for security error ring buffer data – which in my testing did show a few bread crumbs of interesting troubleshooting data.  <\/span>For example, useful data on the error number, state and remote host:<\/font><\/font>\n<\/p>\n

\n \n<\/p>\n

<\/font><\/p>\n

\n<\/font>\n<\/p>\n

<\/font><\/p>\n

\n<data name="sni_consumer_error"><\/font><\/font>\n<\/p>\n

<\/font><\/p>\n

\n          <\/span><type name="uint32" package="package0" \/><\/font><\/font>\n<\/p>\n

<\/font><\/p>\n

\n          <\/span><value>18456<\/value><\/font><\/font>\n<\/p>\n

<\/font><\/p>\n

\n        <\/span><\/data><\/font><\/font>\n<\/p>\n

<\/font><\/p>\n

\n<data name="state"><\/font><\/font>\n<\/p>\n

<\/font><\/p>\n

\n          <\/span><type name="uint32" package="package0" \/><\/font><\/font>\n<\/p>\n

<\/font><\/p>\n

\n          <\/span><value>5<\/value><\/font><\/font>\n<\/p>\n

<\/font><\/p>\n

\n        <\/span><\/data><\/font><\/font>\n<\/p>\n

<\/font><\/p>\n

\n<data name="remote_host"><\/font><\/font>\n<\/p>\n

<\/font><\/p>\n

\n          <\/span><type name="ansi_string" package="package0" \/><\/font><\/font>\n<\/p>\n

<\/font><\/p>\n

\n          <\/span><value>&lt;local machine&gt;<\/value><\/font><\/font>\n<\/p>\n

<\/font><\/p>\n

\n        <\/span><\/data><\/font><\/font>\n<\/p>\n

\n \n<\/p>\n

<\/font><\/p>\n

\n<\/font>\n<\/p>\n

<\/font><\/p>\n

\nSo from this data we know that an error 18456 and state 5 occurred.  <\/span>We also know that state 5 represents an invalid user id.  <\/span>Testing a bad password, I saw a state of 8, which does indeed represent a password mismatch (see the post <\/font>Understanding "login failed" (Error 18456) error messages in SQL Server 2005<\/font><\/a> for more on this tangent).<\/font><\/font>\n<\/p>\n

<\/font><\/p>\n

\n<\/font>\n<\/p>\n

<\/font><\/p>\n

\nSo that’s an initial tour of the data exposed by sp_server_diagnostics.  <\/span>As Denali starts rolling out more broadly, I anticipate several parsing scripts being generated by the SQL community.  <\/span>There are several use-cases that I can imagine from this data, so I’ll keep watching this space and will report back the more valuable uses as I find them.<\/font><\/font>\n<\/p>\n

\n \n<\/p>\n

\n** Update – Aaron Bertrand wrote a very comprehensive 18456 reference in the following post, "Troubleshooting Error 18456<\/a>". <\/font>\n<\/p>\n

<\/font><\/p>\n","protected":false},"excerpt":{"rendered":"

This post covers examples from Denali CTP3, version 11.0.1440. In my last post, “Under The Covers With Sp_server_diagnostics Data (Part I)” I started an exploration of the output surfaced from the sp_server_diagnostics stored procedure.  In this post, I’d like to continue this exploration, having a look specifically at the “query_processing”, “io_subsystem” and “events” component data. 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In my last post, “Under The Covers With Sp_server_diagnostics Data (Part I)” I started an exploration of the output surfaced from the sp_server_diagnostics stored procedure.  In this post, I’d like to continue this exploration, having a look specifically at the “query_processing”, “io_subsystem” and “events” component data. 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He was given the SQL Server MVP award in 2013.\",\"sameAs\":[\"http:\/\/3.209.169.194\/blogs\/joe\",\"https:\/\/twitter.com\/https:\/\/twitter.com\/josephsack\"],\"url\":\"https:\/\/www.sqlskills.com\/blogs\/joe\/author\/joe\/\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Under the covers with sp_server_diagnostics data (Part II) - Joe Sack","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.sqlskills.com\/blogs\/joe\/under-the-covers-with-sp_server_diagnostics-data-part-ii\/","og_locale":"en_US","og_type":"article","og_title":"Under the covers with sp_server_diagnostics data (Part II) - Joe Sack","og_description":"This post covers examples from Denali CTP3, version 11.0.1440. In my last post, “Under The Covers With Sp_server_diagnostics Data (Part I)” I started an exploration of the output surfaced from the sp_server_diagnostics stored procedure.  In this post, I’d like to continue this exploration, having a look specifically at the “query_processing”, “io_subsystem” and “events” component data. […]","og_url":"https:\/\/www.sqlskills.com\/blogs\/joe\/under-the-covers-with-sp_server_diagnostics-data-part-ii\/","og_site_name":"Joe Sack","article_published_time":"2011-10-05T08:43:00+00:00","article_modified_time":"2013-01-03T04:32:36+00:00","author":"Joseph Sack","twitter_misc":{"Written by":"Joseph Sack","Est. reading time":"8 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/www.sqlskills.com\/blogs\/joe\/under-the-covers-with-sp_server_diagnostics-data-part-ii\/","url":"https:\/\/www.sqlskills.com\/blogs\/joe\/under-the-covers-with-sp_server_diagnostics-data-part-ii\/","name":"Under the covers with sp_server_diagnostics data (Part II) - Joe Sack","isPartOf":{"@id":"https:\/\/www.sqlskills.com\/blogs\/joe\/#website"},"datePublished":"2011-10-05T08:43:00+00:00","dateModified":"2013-01-03T04:32:36+00:00","author":{"@id":"https:\/\/www.sqlskills.com\/blogs\/joe\/#\/schema\/person\/533eb0113a15fb5a6e8067a49e4ae648"},"breadcrumb":{"@id":"https:\/\/www.sqlskills.com\/blogs\/joe\/under-the-covers-with-sp_server_diagnostics-data-part-ii\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.sqlskills.com\/blogs\/joe\/under-the-covers-with-sp_server_diagnostics-data-part-ii\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/www.sqlskills.com\/blogs\/joe\/under-the-covers-with-sp_server_diagnostics-data-part-ii\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.sqlskills.com\/blogs\/joe\/"},{"@type":"ListItem","position":2,"name":"Performance","item":"https:\/\/www.sqlskills.com\/blogs\/joe\/category\/performance\/"},{"@type":"ListItem","position":3,"name":"Under the covers with sp_server_diagnostics data (Part II)"}]},{"@type":"WebSite","@id":"https:\/\/www.sqlskills.com\/blogs\/joe\/#website","url":"https:\/\/www.sqlskills.com\/blogs\/joe\/","name":"Joe Sack","description":"SQL Server Performance Tuning, High Availability and Disaster Recovery Blog","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.sqlskills.com\/blogs\/joe\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"en-US"},{"@type":"Person","@id":"https:\/\/www.sqlskills.com\/blogs\/joe\/#\/schema\/person\/533eb0113a15fb5a6e8067a49e4ae648","name":"Joseph Sack","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.sqlskills.com\/blogs\/joe\/#\/schema\/person\/image\/","url":"https:\/\/secure.gravatar.com\/avatar\/a4b39a7719a6bfff1add3ec00527810734579ee114d6d983e8e68f937b77be96?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/a4b39a7719a6bfff1add3ec00527810734579ee114d6d983e8e68f937b77be96?s=96&d=mm&r=g","caption":"Joseph Sack"},"description":"Joe Sack is a Principal Consultant with SQLskills. He has worked as a SQL Server professional since 1997 and has supported and developed for SQL Server environments in financial services, IT consulting, manufacturing, retail and the real estate industry. Prior to joining SQLskills he worked at Microsoft as a Premier Field Engineer supporting very large enterprise customer environments. He was responsible for providing deep SQL Server advisory services, training, troubleshooting and ongoing solutions guidance. His areas of expertise include performance tuning, scalability, T-SQL development and high-availability. In 2006 Joe earned the \u201cMicrosoft Certified Master: SQL Server 2005\u201d certification and in 2008 he earned the \u201cMicrosoft Certified Master: SQL Server 2008\u201d certification. In 2009 he took over responsibility for the entire SQL Server Microsoft Certified Master program and held that post until 2011. 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