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

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

Hyper-V Architecture: Intercepts, interrupts and Hypercalls

Intercepts, interrupts and Hypercalls Three interfaces exist that allow for interaction and communication between the hypervisor, the Root partition and the guest partitions: intercepts, interrupts, and the direct Hypercall interface. These interfaces are necessary for the virtualization scheme to function properly, and their usage accounts for much of the overhead virtualization adds to the system. Hyper-V measures and reports on the rate these different interfaces are used, which is, of course, workload dependent. Frankly, the measurements that show the rate that the hypervisor processes interrupts and Hypercalls is seldom of interest outside the Microsoft developers working on Hyper-V performance itself. But these measurements do provide insight into the Hyper-V architecture and can help us understand how the performance of the applications running on guest machines is impacted due to virtualization. Figure 3 is a graph showing these three major sources of virtualization overhead

Performance By Design

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