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Naming Guidelines 189 General Naming Guidelines 191 Naming Tables 193 Naming Columns 195 Naming Views 195 Naming Stored Procedures 196 Naming User-Defined Functions 196 Naming Triggers 196 Naming Indexes 196 Naming User-Defined Data Types 197 Naming Primary Keys and Foreign Keys 197 Naming Constraints 197 Deriving the Physical Model 198 Using Entities to Model Tables 198 Using Relationships to Model Keys 209 Using Attributes to Model Columns 210 Implementing Business Rules in the Physical Model 211 Using Constraints to Implement Business Rules 211 Using Triggers to Implement Business Rules 213 Implementing Advanced Cardinality 217 Summary 219

These regular expressions ( regexes from now on) match an HTML href s URL and the text contained in <h1> and <h2> header tags (Regular expressions are covered in 13; understanding them is not essential to understanding this example)

receiver = reporter() matchers = (regex_matcher(receiver, URL_RE), regex_matcher(receiver, H1_RE), regex_matcher(receiver, H2_RE))

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Consider a simple mesh computation that solves the 1D heat diffusion equation The details of this problem and its solution using OpenMP are presented in the Geometric Decomposition pattern We reproduce this solution in Fig 527 This program would work well on most shared memory computers A careful analysis of the program performance, however, would expose two performance problems First, the single directive required to protect the swapping of the shared pointers adds an extra barrier, thereby greatly increasing the synchronization overhead Second, on NUMA computers, memory access overhead is likely to be high because we've made no effort to keep the arrays near the PEs that will be manipulating them We address both of these problems in Fig 528 To eliminate the need for the single directive, we modify the program so each thread has its own copy of the pointers uk and ukp1 This can be done with a private clause, but to be useful, we need the new, private copies of uk and ukp1 to point to the shared arrays comprising the mesh of values We do this with the firstprivate clause applied to the parallel directive that creates the team of threads The other performance issue we address, minimizing memory access overhead, is more subtle As discussed earlier, to reduce memory traffic in the system, it is important to keep the data close to the PEs that will work with the data On NUMA computers, this corresponds to making sure the pages of memory are allocated and "owned" by the PEs that will be working with the data contained in the page The most common NUMA page placement algorithm is the "first touch" algorithm, in which

Since coroutines always have a yield expression, they are generators So although here we create a tuple of matcher coroutines, in effect we are creating a tuple of generators Each regex_matcher() is a coroutine that takes a receiver function (itself a coroutine) and a regex to match Whenever the matcher matches it sends the match to the receiver

@coroutine def regex_matcher(receiver, regex): while True: text = (yield) for match in regexfinditer(text): receiversend(match)

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the PE first referencing a region of memory will have the page holding that memory assigned to it So a very common technique in OpenMP programs is to initialize data in parallel using the same loop schedule as will be used later in the computations

The matcher starts by entering an in nite loop and immediately suspends execution waiting for the yield expression to return a text to apply the regex to Once the text is received, the matcher iterates over every match it makes, sending each one to the receiver Once the matching has nished the coroutine loops back to the yield and again suspends waiting for more text There is one tiny problem with the (undecorated) matcher when it is rst created it should commence execution so that it advances to the yield ready to receive its rst text We could do this by calling the built-in next() function on each coroutine we create before sending it any data But for convenience we have created the @coroutine decorator to do this for us

def coroutine(function): @functoolswraps(function) def wrapper(*args, **kwargs): generator = function(*args, **kwargs) next(generator) return generator return wrapper

The @coroutine decorator takes a coroutine function, and calls the built-in next() function on it this causes the function to be executed up to the rst yield expression, ready to receive data

Figure 527 Parallel heat-diffusion program using OpenMP This program is described in the Examples section of the Geometric Decomposition pattern

Indexing Overview 221 What Are Indexes 222 Types 224 Database Usage Requirements 230 Reads versus Writes 230 Transaction Data 232 Determining the Appropriate Indexes 233 Reviewing Data Access Patterns 233 Balancing Indexes 233 Covering Indexes 234

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