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pressure on the primary OLTP system while ensuring that the reporting needs are being met It allows you to customize the tables being used by the reporting system to combine the data sets, thereby satisfying the queries being run in the most efficient way possible Again, this means incurring overhead to store data that is already being stored, but often the trade-off is worthwhile in terms of performance both on the OLTP system and the reporting system Now let s look at OLAP systems, which are used primarily for decision support and reporting These types of systems are based on the concept of providing a cube of data, whereby the dimensions of the cube are based on fact tables provided by an OLTP system These fact tables are derived from the OLTP versions of data being stored in the relational database These tables are often denormalized versions, however, and they are optimized for the OLAP system to retrieve the data that eventually is loaded into the cube Because OLAP is outside the scope of this book, it s enough for now to know that if you re working on a system in which OLAP will be used, you will probably go through the exercise of building fact tables that are, in some respects, denormalized versions of your normalized tables When identifying entities that should be denormalized, you should rely heavily on the actual queries that are being used to retrieve data from these entities You should evaluate all the existing join conditions and search arguments, and you should look closely at the data retrieval needs of the end users Only after performing adequate analysis on these queries will you be able to correctly identify the entities that need to be denormalized, as well as the attributes that will be combined into the new entities You ll also want to be very aware of the overhead the system will incur when you denormalize these objects Remember that you will have to store not only the rows of data but also (potentially) index data, and keep in mind that the size of the data being backed up will increase Overall, denormalization could be considered the final step of the normalization process Some OLTP systems have denormalized entities to improve the performance of very specific queries, but more than likely you will be responsible for developing an additional data model outside the actual application, which may be used for reporting, or even OLAP Either way, understanding the normal forms, denormalization, and their implications for data storage and manipulation will help you design an efficient, logical, and scalable data model.

d1 d2 d3 d4 d5 = = = = = dict({"id": 1948, "name": "Washer", "size": 3}) dict(id=1948, name="Washer", size=3) dict([("id", 1948), ("name", "Washer"), ("size", 3)]) dict(zip(("id", "name", "size"), (1948, "Washer", 3))) {"id": 1948, "name": "Washer", "size": 3}

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The most computationally intensive part of the problem is organized around the manipulation of a large data structure Similar operations are being applied t different parts of the data structure, in such a way that o the different parts can be operated on relatively independently

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Dictionary d1 is created using a dictionary literal Dictionary d2 is created using keyword arguments Dictionaries d3 and d4 are created from sequences, and dictionary d5 is created from a dictionary literal The built-in zip() function that is used to create dictionary d4 returns a list of tuples, the rst of which has the rst items of each of the zip() function s iterable arguments, the second of which has the second items, and so on The keyword argument syntax (used to create dictionary d2) is usually the most compact and convenient, providing the keys are valid identi ers Figure 35 illustrates the dictionary created by the following code snippet:

For example, many linear algebra problems update large matrices, applying a similar set of operations to each element of the matrix In these cases, it is straightforward to drive the parallel algorithm design by looking at how the matrix can be broken up into blocks that are updated concurrently The task definitions then follow from how the blocks are defined and mapped onto the processing elements of the parallel computer Forces The main forces influencing the design at this point are flexibility, efficiency, and simplicity

d = {"root": 18, "blue": [75, "R", 2], 21: "venus", -14: None, "mars": "rover", (4, 11): 18, 0: 45}

zip()

Flexibility Flexibility will allow the design to be adapted to different implementation requirements For example, it is usually not a good idea to narrow the options to a single computer system or style of programming at this stage of the design Efficiency A parallel program is only useful if it scales efficiently with the size of the parallel computer (in terms of reduced runtime and/or memory utilization) Simplicity The decomposition needs to be complex enough to get the job done, but simple enough to let the program be debugged and maintained with reasonable effort

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