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Every relationship in a database needs to be defined as either optional or mandatory It helps to think of mandatory relationships as must have relationships, and optional relationships as may have relationships For example, if you have an Employee entity and an Office entity, an employee must have a home office The relationship between these two entities defines the home office for an employee In this case, we have a nonidentifying relationship, and because we can t have a null value for the foreign key reference to the Office entity in the Employee entity, this relationship is also described as being mandatory The relationship defines that every employee has a single home office, and although an employee may work in other offices, only one office is considered his or her home office Now consider a business that assigns vehicles to some employees That business practice is reflected in the data model as an Employee entity and a Vehicle entity, with a relationship between them You can see that an employee may have a vehicle, thus fitting our definition of an optional relationship

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As a shortcut, OpenMP defines a combined construct:

exists = Keyword("exists") implies = Literal("->") or_ = Literal("|") and_ = Literal("&") not_ = Literal("~") equals = Literal("=") boolean = Keyword("false") | Keyword("true") symbol = Word(alphas, alphanums)

All the parser elements created here are straightforward, although we had to add underscores to the end of a few names to avoid con icts with Python keywords If we wanted to give users the choice of using ASCII or the proper Unicode symbols, we could change some of the de nitions For example:

This is identical to the case where the parallel and for constructs are placed within separate pragmas

forall = Keyword("forall") | Literal(" ")

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In every relationship we ve discussed, we ve specified only the general type of relationship one-to-one, one-to-many, and many-to-many In each case, the description of the relationship is a specification of the number of records in a parent entity in relation to the number of records in a child entity To more clearly model the actual relation of the data, you can be more specific when defining these relationships What you are specifying is the cardinality of the relationship With a one-to-one relationship, the cardinality is implied You are clearly stating that for every one record in the parent entity, there might be one record in the child entity It would be more specific to say that there is zero or one record in the child entity for every one record in the parent entity But if you mean to say that there absolutely must be a record in each entity, then the relationship s cardinality would be one record in the child entity for every one record in the parent entity The cardinality of a one-to-one relationship is notated as [1:1] In a one-to-many relationship, notated as [1:M], the cardinality implied is one or more records in the child entity for every one record in the parent entity But if the intent is that there doesn t need to be a record in the child entity, then the alternative definition is zero or more records in

If we are using a non-Unicode editor we could use the appropriate escaped Unicode code point, such as Literal("\u2200"), instead of the symbol

term = Forward() term << (Group(symbol + Group(left_parenthesis + delimitedList(term) + right_parenthesis)) | symbol)

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A term is de ned in terms of itself, which is why we begin by creating it as a Forward() And rather than using a straight translation of the BNF we use one of PyParsing s coding patterns Recall that the delimitedList() function returns a parser element that can match a list of one or more occurrences of the given parser element, separated by commas (or by something else if we explicitly specify the separator) So here we have de ned the term parser element as being either a symbol followed by a comma-separated list of terms or a symbol and since both start with the same parser element we must put the one with the longest potential match rst

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formula = Forward() forall_expression = Group(forall + symbol + colon + formula) exists_expression = Group(exists + symbol + colon + formula) operand = forall_expression | exists_expression | boolean | term formula << operatorPrecedence(operand, [ (equals, 2, opAssocLEFT), (not_, 1, opAssocRIGHT), (and_, 2, opAssocLEFT), (or_, 2, opAssocLEFT), (implies, 2, opAssocRIGHT)])

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