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Year:  <select name= YearNumber size= 1 > <option value= 2001 <cfif FORM.YearNumber EQ 2001 >SELECTED</cfif>>2001</option> <option value= 2002 <cfif FORM.YearNumber EQ 2002 >SELECTED</cfif>>2002</option> <option value= 2003 <cfif FORM.YearNumber EQ 2003 >SELECTED</cfif>>2003</option> </select>   <input type= submit > </form> <cfchart format= flash chartheight= 400 chartwidth= 600 scalefrom= 0 seriesplacement= default foregroundcolor= Black font= Arial fontsize= 8 showborder= yes labelformat= number show3d= yes rotated= no tipstyle= mouseOver pieslicestyle= sliced > <cfchartseries type= bar query= QoQ itemcolumn= MonthName valuecolumn= MetricValue seriescolor= ##FF0000 paintstyle= plain > </cfchartseries> </cfchart>

The principle of ESTM is very simple: it is based on electron tunnelling between the studied electrode surface and a tip of sharply pointed wire, which is placed in close vicinity above the studied surface The tunnelling current decreases exponentially with the distance of the tip from the surface (roughly by an order of magnitude for every distance increase by 01 nm) A significant tunnelling current flows therefore only between several atoms at the apex of the tip and the nearest atoms in the examined surface The exponential decay of the tunnelling current with distance enables one to measure the vertical position of the tip with a precision in the order of 001 nm By sweeping the tip across the studied surface, an image of its topography with a comparable vertical resolution can be obtained (the lateral resolution is about 02 nm).

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The reasons why multi-dimensional charting is such a great application of the QoQ feature are as follows: No matter which dimensions are chosen, they all originate from the same small set of rows retrieved from the database, so QoQ eliminates the need to requery the database. The result set that QoQ operates over is very small, so the lack of indexes or query optimization is of little hindrance to performance. In cases where the result set must be resorted according to the data dimensions plotted, the QoQ feature makes this task practical. If fact, these are good criteria to use in determining whether your application should use QoQ versus requerying the database.

QoQ and query caching work together beautifully Combining the capability to cache a query with the capability to requery that same query can make for a nicely scalable solution, as long as the result set you re requerying isn t extremely large You may remember from 9 that the CACHEDWITHIN attribute of the CFQUERY tag is what prevents ColdFusion from returning to the database every time that it needs the base query from which QoQ makes its detailed queries Whenever you build an action page that enables users to requery existing ColdFusion queries, you should cache the base query from which QoQ works or you overburden your database server with a redundant base query each time that the action page is run This technique works best if the base query results in a relatively small selection of rows from the database tables queried.

The experimental set-up usually utilizes a piezoelectric tripod as a support of the tip (Fig 536) This is movable vertically and laterally over the examined surface; the vertical distance is fixed by a feedback loop to a constant tunnelling current at each point of the scan The contours of the surface are thus visualized by voltage changes needed to move the piezoelectric tripod to a desired position The in-situ ESTM was utilized for studying adsorbed or underpotential-.

If you are caching a 10,000-row database query and then querying those 10,000 rows by using QoQ, stop and think whether you would be better off requerying the database each time so that you can take full advantage of all the query optimization features that your database server offers Better yet, formally load-test both methods and have the metrics decide for you Although you can use QoQ in many more ways than those described in this chapter, overtaxing such an underpowered feature is most likely not a good idea If you are in doubt as to whether to use QoQ in a specific part of your application if other querying techniques are available to you, perform a formal load test by using both techniques and pay special attention to the RAM usage meter as the test runs.

If 100 virtual users simultaneously slamming QoQ s pegs your RAM usage, while alternative query techniques don t, implementing QoQ in that part of your application is probably asking for trouble On the other hand, if QoQ is your only possible solution, the only thing that you can do is to try to limit the total amount of data manipulated in a QoQ call, which in turn limits the amount of RAM needed to handle that data..

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