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Prior versions of SQL Server implemented a basic black box approach to the database engine, which made it difficult to manage and monitor. SQL Server 2005 opens up the black box by providing a large set of detailed interfaces that expose virtually every operational statistic within the database engine.
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Monitoring and Troubleshooting SQL Server Performance
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SQL Server s DMVs and DMFs are broken into four general categories, providing information about database statistics, query statistics, I/O statistics, and hardware statistics.
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The following questions are intended to reinforce key information presented in this lesson. The questions are also available on the companion CD if you prefer to review them in electronic form.
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Answers to these questions and explanations of why each answer choice is right or wrong are located in the Answers section at the end of the book.
1. You notice that performance of certain high-volume queries has suddenly degraded, and you suspect that you have contention issues within your databases. Which DMV or DMF do you use to determine whether you have a contention issue and which users are being affected A. sys.dm_os_performance_counters B. sys.dm_os_wait_stats C. sys.dm_db_index_physical_stats D. sys.dm_exec_requests
Lesson 5: Correlating Performance and Monitoring Data
Lesson 5: Correlating Performance and Monitoring Data
SQL Server Profiler, System Monitor, DTA, DMVs, and DMFs each capture a piece of monitoring data. Although you can use each individually to solve problems, their true value comes when you use all these tools in a cohesive manner to monitor systems. Because SQL Server does not operate in a vacuum, this integration enables you to evaluate data from all layers: from the disk subsystem, to the operating system, through the memory space, into the query optimizer, through the data structures, and out to the client. The sections in this lesson provide examples of correlating data from multiple sources to understand a performance issue. These examples are intended to provide a starting point to demonstrate how each of the tools fit together; they do not provide an exhaustive treatment of all the ways you can use the tools together, which would easily fill an entire book. Each of the scenarios in this lesson demonstrates how data from one tool could lead you down the incorrect path, whereas correlating multiple pieces of data enables you to pinpoint the correct bottleneck or issue in the system.
After this lesson, you will be able to:
Describe the basic processing architecture for queries. Correlate System Monitor data with a SQL Server Profiler trace. Correlate DMVs/DMFs with SQL Server Profiler traces. Correlate DMVs/DMFs with System Monitor data. Correlate several DMVs/DMFs to evaluate performance. Combine data from SQL Server Profiler, System Monitor, DMVs, and DMFs into a consolidated performance view.
Estimated lesson time: 30 minutes
Basic Query Processing Architecture
SQL Server uses a cooperative multiprocessing model instead of a symmetric multiprocessing model. The main difference between these two processing models is the way processor scheduling is handled. In a cooperative model, only a single thread is executing at one time on a processor, and the thread cedes control of the processor when it does not have work to perform. In this way, it allows multiple threads to cooperate with each other to maximize the amount of actual work being performed.
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Monitoring and Troubleshooting SQL Server Performance
Controlling this cooperative behavior is the job of the User Mode Scheduler (UMS). When SQL Server starts, it creates one UMS for each logical or physical processor that it is allowed to use on the system. Instead of handing off threads to the operating system to schedule on a processor, SQL Server performs its own scheduling via the UMS. As connections are made to SQL Server, the corresponding SPID is allocated to a UMS. This allocation process uses a basic balancing algorithm that seeks to spread the processing as evenly among the UMSs as possible. Although requests by a particular connection will generally execute on the same UMS, it is possible for a particular request to be handled by any UMS that is available. Each UMS uses three queues to process queries: runnable, running, and waiting. When a query is executed, it is assigned a thread and placed into the runnable queue. Threads are taken off this queue on a first in, first out (FIFO) basis. The thread is placed into the running queue and scheduled on the processor. At the instance the thread needs to wait for a resource such as I/O, network, or memory to be allocated, it is swapped off the processor and moved to the waiting queue. The thread lives on the waiting queue for as long as is necessary to wait for the resource to be allocated to the thread. During this time, SQL Server tracks the amount of time the thread is waiting, as indicated by the wait time, as well as the resource that it is waiting on, as indicated by the wait type. After the resource is freed up, the thread is swapped off the waiting queue and placed at the bottom of the runnable queue, where it must wait behind all other processes to reach the top of the runnable queue. The amount of time a process spends in the runnable queue before being swapped onto the processor is called the signal wait. What does all of this information about processor scheduling internals have to do with monitoring or performance When a query executes, it requires a variety of resources. The query has to be compiled, which requires memory and processor resources. The compiled plan has to be generated and stored in the query cache, which requires memory and processor. The executable plan then has to be swapped onto a processor to execute the query, which requires processor, memory, and potentially disk access. As the query reads and writes data, locks must be established, requiring yet more memory, processor, and possibly disk I/O. Finally, the results of the query have to be packaged and sent back to the client, which requires memory, processor, and network I/O.
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