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other processes request operations by sending messages to the UE managing the data structure, which processes them serially In either environment, this approach is usually not difficult to implement, but it can be overly conservative (that is, it might disallow concurrent execution of operations that would be safe to execute simultaneously), and it can produce a bottleneck that negatively affects the performance of the program If this is the case, the remaining approaches described in this section should be reviewed to see whether one of them can reduce or eliminate this bottleneck and give better performance Noninterfering sets of operations One approach to improving performance begins by analyzing the interference between the operations We say that operation A interferes with operation B if A writes a variable that B reads Notice that an operation may interfere with itself, which would be a concern if more than one task executes the same operation (for example, more than one task executes "take" operations on a shared queue) It may be the case, for example, that the operations fall into two disjoint sets, where the operations in different sets do not interfere with each other In this case, the amount of concurrency can be increased by treating each of the sets as a different critical section That is, within each set, operations execute one a time, but operations in different sets can proceed concurrently Readers/writers If there is no obvious way to partition the operations into disjoint sets, consider the type of interference It may be the case that some of the operations modify the data, but others only read it For example, if operation A is a writer (both reading and writing the data) and operation B is a reader (reading, but not writing, the data), A interferes with itself and with B, but B does not interfere with itself Thus, if one task is performing operation A, no other task should be able to execute either A or B, but any number of tasks should be able to execute B concurrently In such cases, it may be worthwhile to implement a readers/writers protocol that will allow this potential concurrency to be exploited The overhead of managing the readers/writers protocol is greater than that of simple mutex locks, so the length of the readers' computation should be long enough to make this overhead worthwhile In addition, there should generally be a larger number of concurrent readers than writers The javautilconcurrent package provides read/write locks to support the readers/writers protocol The code in Fig 532 illustrates how these locks are typically used: First instantiate a ReadWriteLock, and then obtain its read and write locks ReentrantReadWriteLock is a class that implements the ReadWriteLock interface To perform a read operation, the read lock must be locked To perform a write operation, the write lock must be locked The semantics of the locks are that any number of UEs can simultaneously hold the read lock, but the write lock is exclusive; that is, only one UE can hold the write lock, and if the write lock is held, no UEs can hold the read lock either.

import random x = randomrandint(1, 6) y = randomchoice(["apple", "banana", "cherry", "durian"])

After these statements have been executed, x will contain an integer between 1 and 6 inclusive, and y will contain one of the strings from the list passed to the randomchoice() function

It is conventional to put all the import statements at the beginning of py les, after the shebang line, and after the module s documentation (Documenting modules is covered in 5) We recommend importing standard library modules rst, then third-party library modules, and nally your own modules

Figure 532 Typical use of read/write locks These locks are defined in the javautilconcurrentlocks package Putting the unlock in the finally block ensures that the lock will be unlocked regardless of how the try block is exited (normally or with an exception) and is a standard idiom in Java programs that use locks rather than synchronized blocks

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The sys module, some other built-in modules, and modules implemented in C don t necessarily have corresponding py les but they are used in just the same way as those that do

A well-designed data model has some level of normalization In short, normalization is the process of separating data into logical groupings Normalization is divided into levels, and each successive level builds on the preceding level First normal form, notated as 1NF, is the most basic form of normalization In essence, in 1NF the data is stored in a table and each column contains one type of data This means that any given column in the table stores the same piece of information, such as a phone number Additionally, 1NF requires that your data have a primary key A primary key is the column or columns that uniquely identify the row Normalization can go up to six levels; however, most well-built models conform to third normal form Generally, in this book we talk about topics in linear order; you must do the current one before the next one Normalization is the exception to this rule, because there is not really a specific time during modeling when you sit down and normalize the model, nor are you concerned with the level your model conforms to For the most part, normalization takes place throughout your modeling When you start defining entities that your model will have, you will have already started normalizing your model Sound transactional models are normalized, and normalization helps with many of the other areas we have discussed Normalized data is easier to retrieve, is consistent, is scalable, and so on You must understand this concept in order to build models, and we cover it in detail in 4, Normalizing a Data Model

In the preceding section we learned enough Python to write real programs In this section we will study two complete programs that use only the Python covered earlier This is both to show what is possible, and to help consolidate what has been learned so far In subsequent chapters we will increasingly cover more of Python s language and library, so that we will be able to write programs that are more concise and more robust than those shown here but rst we must have the foundations on which to build

class X {

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