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PerfMon is a small but powerful tool in any DBA s toolbox. Used alone, it provides a comprehensive overview of the server s status both Windows and SQL Server. Used in conjunction with Pro ler (covered in the next chapter), it opens up a visual microscope into performance. The key to PerfMon is understanding the overwhelming number of counters. If you know which counters to focus on, you ll nd that PerfMon can be ef cient. If not, you ll waste time and fail to come to any useful conclusion. The next chapter looks at Pro ler, by far the most popular SQL Server monitoring tool, and for good reason. Pro ler is indeed an excellent tool. With PerfMon, it s even better.

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359 12.1 Introduction 359 12.2 Goals, questions and paradigms 360 12.2.1 What and when to observe 361 12.2.2 Approaches to observation 363 1 2.3 How to observe 364 12.3.1 In controlled environments 365 12.3.2 In the field 368 12.3.3 Participant observation and ethnography 370 12.4 Data collection 373 12.4.1 Notes plus still camera 374 12.4.2 Audio recording plus still camera 374 12.4.3 Video 374 12.5 Indirect observation: tracking users' activities 377 12.5.1 Diaries 377 12.5.2 Interaction logging 377 12.6 Analyzing, interpreting and presenting data 379 12.6.1 Qualitative analysis to tell a story 380 1 2.6.2 Qualitative analysis for categorization 381 12.6.3 Quantitative data analysis 384 12.6.4 Feeding the findings back into design 384 Interview with Sara B 387 b

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The number and type of equilibrium points of the system depends on the values of the model parameters and can be determined conveniently by a bifurcation analysis. Figure 3.3 shows the solution dependency on the model parameters and tot. In region I , the only existing equilibrium is a saddle point and hence no closed trajectories exist. Closed trajectories and pattern solutions exist in the shaded area in Fig. 3.3. The boundary of the shaded area is given by the locus of saddle-node bifurcations, where a saddle point and a focus coincide. The shaded region can be sub-divided into four areas of qualitatively different behavior. In regions II , III , and V , two saddle points and one focus exist. In region IV , three saddle points and two focuses are found. It can be seen from Fig. 3.3 that the interval of cell voltages, for which pattern solution are possible, becomes larger, if the value of increases, i.e., if there is stronger temperature dependence of the electrolyte s electrical conductivity.

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C header files are filled with preprocessor directives and not much C. These directives are typically handled before the C program compiles. Preprocessor directives modify the C in your native application, preparing it for compilation. Many C books (and classes) ignore preprocessor directives, but no one writes useful C programs without a large number of directives. All the lines that look like Java comments are exactly that. They are C comments. Even if the compiler complains a bit, most C compilers support both // and /* */ style comments. C does not have an equivalent to Javadoc comments. Sorry. This is the first line that influences the program:

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advantage that they are not affected by water; they may therefore be employed to study protein solutions or suspensions. It was used to investigate bacteriorhodopsin as well as the activity of bacterial reaction centers. As example a BR-K-FT-Ratnan difference spectrum is given. A procedure similar to FTIR is employed to monitor the BR to K transition by FT-Raman spectroscopy. In the dark, the sample is cooled to 77 K. The resulting FT-Raman spectra agree very well with the corresponding RR spectra (Fig 6.6-20). This indicates that a preresonant Raman state is excited and only the retinal vibrations of the chromophore protein complex are detected. After substracting the spectrum of BR, a BR-K difference spectrum (Fig. 6.622) is obtained. The chromophore vibrations between 1300 cm and 1 I00 cm in this spectrum agree nicely with those in the FTIR BR-K difference spectrum (compare with Fig. 6.6-10). The 1640/1650 cm-' and 1550/1540 cm-' difference bands do not appear in the FT-Raman difference spectrum, which points to the fact that these bands are caused by protein backbone vibrations. The spectrum of bacteriorhodopsin only shows chromophore vibrations of the protein. In contrast, rhodopsin also seems to exhibit protein vibrations (Sawatzki et al., 1990).

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