Blogging live again!

I am returning to Google's blogger after a long hiatus. Initially, I plan to discuss a new project I have been working on -- a collection agent to gather data from several key SQL Server Dynamic Management views (DMVs) and transform that data into Windows performance counters. In a series of blog posts, I will try and make the case that generating Windows performance counters from several of these DMVs is a useful thing to do. I believe the project has the potential to help DBAs address issues in physical database design that play a pivotal role in application performance, issues that are too often ignored or, unfortunately, treated in a less than rigorous manner. I am looking to engage with other database performance professionals who have experience in this area.

Over the past several years, I have not been posting here, but instead to a WordPress web site named http://computerperformancebydesign.com/ that I own. There is a lot to like about WordPress, including superior composition tools. Unfortunately, Google's web crawler seldom gets around to that web site, so I don't see enough Readers there. As I am writing this series of posts on SQL Server performance, I will also pull some of the older material I have published on the other site.

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Monitoring SQL Server: the OS Wait stats DMV

This is the 2nd post in a series on SQL Server performance monitoring, emphasizing the use of key Dynamic Management View. The series starts here : OS Waits The consensus among SQL Server performance experts is that the best place to start looking for performance problems is the OS Wait stats from the sys.dm_os_wait_stats DMV. Whenever it is running, the SQL Server database Engine dispatches worker threads from a queue of ready tasks that it services in a round-robin fashion. (There is evidently some ordering of the queue based on priority –background tasks with lower priority that defer to foreground tasks with higher priority.) The engine records the specific wait reason for each task waiting for service in the queue and also accumulates the Wait Time (in milliseconds) for each Wait reason. These Waits and Wait Time statistics accumulate at the database level and reported via the sys.dm_os_wait_stats DMV. Issuing a Query like the following on one of my SQL Server test mac

High Resolution Clocks and Timers for Performance Measurement in Windows.

Within the discipline of software performance engineering (SPE), application response time monitoring refers to the capability of instrumenting application requests, transactions and other vital interaction scenarios in order to measure their response times. There is no single, more important performance measurement than application response time, especially in the degree which the consistency and length of application response time events reflect the user experience and relate to customer satisfaction. All the esoteric measurements of hardware utilization that Perfmon revels in pale by comparison. Of course, performance engineers usually still want to be able to break down application response time into its component parts, one of which is CPU usage. Other than the Concurrency Visualizer that is packaged with the Visual Studio Profiler that was discussed in the previous post , there are few professional-grade, application response time monitoring and profiling tools that exploit

Memory Ballooning in Hyper-V

The previous post in this series discussed the various Hyper-V Dynamic Memory configuration options. Ballooning Removing memory from a guest machine while it is running is a bit more complicated than adding memory to it, which makes use of a hardware interface that the Windows OS supports. One factor that makes removing memory from a guest machine difficult is that the Hyper-V hypervisor does not gather the kind of memory usage data that would enable it to select guest machine pages that are good candidates for removal. The hypervisor’s virtual memory capabilities are limited to maintaining the second level page tables needed to translate Guest Virtual addresses to valid machine memory addresses. Because the hypervisor does not maintain any memory usage information that could be used, for example, to identify which of a guest machine’s physical memory pages have been accessed recently, when Guest Physical memory needs to be removed from a partition, it uses ballooning, which transfe

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