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This is the complete code for the simple car model. It allows you to model the car as a composite of subsystems and components in any number of different system levels. You could, if you wanted to, take this down to the level of nuts, bolts, O-rings, grommets, and gaskets, with thousands of records for these subsystems and components in the table.

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The removing process of the LRU cache can be fine-tuned by adjusting the size property up or down to allow for more or fewer categories to be cached. Remember that the goal of the LRU is to cache only items that are accessed constantly. Don t set the size too high. If you do, you effectively make your LRU cache into a STRONG memory cache which would defeat the whole purpose of the LRU cache. Now that we ve discussed an effective means of caching long-term, read-only data, let s consider another situation that you will often face when asking the to cache or not to cache question: caching read-write data.

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Another well-known data structure is the heap, a kind of priority queue. A priority queue lets you add objects in an arbitrary order, and at any time (possibly in between the adding), find (and possibly remove) the smallest element. It does so much more efficiently than, say, using min on a list. In fact, there is no separate heap type in Python only a module with some heapmanipulating functions. The module is called heapq (the q stands for queue), and it contains six functions (see Table 10-5), the first four of which are directly related to heap manipulation. You must use a list as the heap object itself. Table 10-5. Some Important Functions in the fileinput Module

Suppose you want to cache objects that are of a changing nature. You must do this with caution; if you have a high-transaction environment, you may find that your

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cache is overburdened and effectively useless. The reason for this is that an attempt to keep high-transaction data cached would require that you flush the cache often. Flushing the cache too often could create a twofold burden. First, your application would constantly be in a state of checking the cache, clearing the cache, and repopulating the cache on every request to the database. Consequently, if the cache is constantly being cleared, this means that the database is also being hit to retrieve fresh results. You ll come to a point where it is simply better for performance if you use database techniques such as indexing and table pinning and avoid application-based caching. Even though you need to be careful when caching data that may change, it also makes sense to do so when the data is of a less volatile state. In the JGameStore application, we find a good example of this with caching products. With many storefronts, there is a need for administrators to enter new products, update existing ones, mark others as sale items, and similar tasks. These kinds of activities will produce a mild level of volatility. Since this is not a high-transaction environment, caching can play a role in improving overall application performance. As long as the cache has time to build up and provide users with improved performance over a span of time, you will avoid the sinkhole described previously. When examining the nature of the data that you want to cache, consider several factors:

To save this code as an M file, click File Save File As in the Quadrant menu bar (see Figure 4-5), and save the file as CarModel.m.

heappush(heap, x) heappop(heap) heapify(heap) heapreplace(heap, x) nlargest(n, iter) nsmallest(n, iter)

The number of products will likely be significant. The products data is of a changing nature. The products that are most often accessed will change throughout the day depending on consumer habits.

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I used to know the HP font select for UPCA because I had to quickly gene4rate barcodes to test a scanner system I was building. Typing an ...

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c# upc-a reader

c# upc-a reader

c# upc-a reader

c# upc-a reader

c# upc-a reader

c# upc-a reader

c# upc-a reader

c# upc-a reader

c# upc-a reader

c# upc-a reader

c# upc-a reader