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Correlating SQL Profiler with PerfMon
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I ve always enjoyed my grandparents use of old-timey words and phrases. When my great-uncle wanted to know what was troubling me, he d always ask What vexes you When it comes to database problems, Gartner Research has already answered that question for us. Their research shows that about 80 percent of database performance problems comes from poorly or improperly coded SQL statements, 15 percent from database design, and 5 percent from poor or inadequate hardware configurations. Contrary to Gartner s wisdom, most attempted remedies to performance problems with database applications are exactly the opposite of what research shows us is the source of the problem. We ve all heard product managers who, when encountering a performance problem with their applications, declare, Let s put in more RAM or another CPU! But if you ve been around for a while, you know that a poorly coded SQL Server application can suck up all the hardware you give it, and still want more. When adding hardware doesn t work, the product manager usually says, Let s try to throw a few more indexes in there. That ought to fix it! But more indexes can t help a query or transaction that doesn t utilize the indexes. By implementing a few simple processes to check your code using PerfMon and Profiler, you can help ensure that all the SQL and T-SQL code used in your application performs optimally.
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To get useful information about your SQL Server s behavior, you need to combine the use of both PerfMon and Profiler (server-side traces are a good alternative to Profiler). That s because of the fundamental nature of each tool. PerfMon is great at showing the overall resource consumption of most every area of Windows and of SQL Server. Profiler, on the other hand, shows you specific events as they occur (or fire) inside the SQL Server. By combining the output of both tools, we get a great view of overall resource consumption and the individual SQL Server events that are causing that resource consumption. When resource consumption, say CPU or Disk I/O, gets unacceptably high, we can tell exactly what statement, batch, or job caused it. That way, we know exactly where to focus our attention when rewriting or tuning a particular SQL Server activity.
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Methods for invoking and using PerfMon are well documented in the Microsoft help system, so I won t cover all of the basics of PerfMon here. For example, http:/ /support.microsoft.com/kb/175658/EN-US/ tells you how to create a new PerfMon log of a given SQL Server s activity. However, keep some best practices in mind. Just in case you ve never seen or used PerfMon before, figure 1 shows you what it looks like in action. When using PerfMon, remember that PerfMon helps you identify the bottleneck on resources, not the exact problem. Because of that behavior, we need to get PerfMon to give us a good overview of what s happening across our SQL Server as well as
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insight into where, specifically, SQL Server is spending its time. Here is a short list of best practices: Always start with the Big Four PerfMon counters Memory, CPU, I/O, and Network. Don t wing it when figuring out which PerfMon counters to use with SQL Server. Use the accumulated decades of experience of other SQL Server experts (like myself, Jimmy May, Brent Ozar, and the SQL Server CSS team) to guide you in choosing from the literally thousands of PerfMon counters that apply to SQL Server. Resources are identified at the end of the chapter. To reduce overhead, monitor only the counters that you need. The more you monitor, the more the overhead. (When in doubt, monitor more counters and then cut back one at a time rather than monitoring too few counters and not getting enough actionable information.) You can further reduce PerfMon overhead by reducing the polling frequency of your logging activity from 15 seconds to 30 seconds (or more) when the system is under a great deal of strain. The longer the polling interval, the smaller the log file. You can also cap the size of the log file, where disk space is scarce, by setting the file type to binary circular file and giving it a specific file size. When the log file reaches its maximum size, it continues to record PerfMon information, overwriting data at the start of the circular file.
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