Lesson 5: Correlating Performance and Monitoring Data in .NET framework

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Lesson 5: Correlating Performance and Monitoring Data
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Correlating DMVs/DMFs with System Monitor Data
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Via System Monitor, you have noticed that certain CPUs are running at 100 percent utilization, whereas others are sitting nearly idle. The busy CPUs suddenly drop to very low utilization while others are nearly idle. At the same time, users start complaining about performance issues on the order entry database, which is used for purely online transaction processing (OLTP) operations. You launch Profiler, but it does not show any queries that would exhibit the behavior that you are observing through System Monitor. By using the sys.dm_os_schedulers DMV, you could determine that processing is nearly evenly distributed on each UMS and that no single UMS has been overloaded with executing requests to create a bottleneck. However, the sys.dm_os_wait_stats DMV shows that there is currently an extremely high wait time value for the CXPACKET wait type. This condition corresponds to thread synchronization for parallel queries, which would explain the behavior of the processors along with the query performance degradation. Where a SQL Trace would not provide any solutions to this type of performance problem, by using the information from the DMVs, you could determine that you need to change the max degree of parallelism value to 1, which eliminates the possibility of having parallel query plans generated. As a result of this change, query performance would almost immediately improve in an OLTP environment because more queries could be executed at any given time. You would still need to investigate why parallel query plans were being selected in the first place. But in the meantime, users wouldn t be calling to complain about performance issues.
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Correlating Multiple DMVs/DMFs
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Consider a situation in which you have severe blocking. You have analyzed all the queries that are constantly blocking each other. Although some blocking is expected to ensure data integrity as inserts, updates, and deletes are performed against the database, the blocking should not be as severe as what you are seeing in production. By using the sys.dm_exec_requests DMV and the sys.dm_os_waiting_tasks DMV, you might find that queries exhibiting the severe blocking are also appearing in this combined list far too frequently to be a coincidence. And the wait type of these problem queries is almost always WRITELOG.
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Monitoring and Troubleshooting SQL Server Performance
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By moving the transaction log to dedicated drives that can provide better performance, you can reduce the bottleneck to the transaction log. This would cause a significant decline in the severity of the blocking issues, getting you back to a level typical for any multiuser system.
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Quick Check
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What is required to correlate information between SQL Server Profiler, System Monitor, and DMVs/DMFs You need to capture the Start Time data column in the SQL Server Profiler trace definition to correlate information. Profiler can display a System Monitor counter log alongside a trace as long as the trace has captured the Start Time data column. DMVs and DMFs can be used in conjunction with this data as well if the information is also stamped by a time.
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Quick Check Answer
PRACTICE
Creating a Consolidated Performance View
With the capability to correlate data from multiple tools to fix issues in near real-time, the big question becomes how to create a longer-term solution. You can use SQL Server Profiler to create a script that will allow a trace to be executed programmatically through SQL Server Agent. You can have the trace data output to a file and loaded into a table by using fn_trace_gettable. In addition, the DTA has a command-line interface that allows analysis to be performed programmatically. And counter logs in System Monitor can be run on a scheduled basis by using the Windows scheduler. You can even log data from DMVs and DMFs to tracking tables to provide point-in-time snapshots of your system. By now, you will have worked through a variety of exercises and practices in the chapters within this book. Each of those exercises provided a step-by-step procedure to create a very specific solution. This exercise takes a different approach. 1. Combine all the information and best practices from this lesson into an automated (or at least semiautomated) process to gather and analyze monitoring data for your SQL Server 2005 databases.
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