The length of a string value for a property such as the title of a book may have to be constrained, typically rather by a maximum length, but possibly also by a minimum length. In an SQL table definition, a maximum string length can be specified in parenthesis appended to the SQL datatype CHAR or VARCHAR, as in VARCHAR(50).

UML does not define any special way of expressing string length constraints in class diagrams. Of course, we always have the option to use an invariant for expressing any kind of constraint, but it seems preferable to use a simpler form of expressing these property constraints. One option is to append a maximum length, or both a minimum and a maximum length, in parenthesis to the datatype name, like so

Another option is to use min/max constraint keywords in the property modifier list:

A mandatory value constraint requires that a property must have a value. This can be expressed in a UML class diagram with the help of a multiplicity constraint expression where the lower multiplicity is 1. For a single-valued property, this would result in the multiplicity expression 1..1, or the simplified expression 1, appended to the property name in brackets. For example, the following class diagram defines a mandatory value constraint for the property name:

Whenever a class rectangle does not show a multiplicity expression for a property, the property is mandatory (and single-valued), that is, the multiplicity expression 1 is the default for properties.

In an SQL table creation statement, a mandatory value constraint is expressed in a table column definition by appending the key phrase NOT NULL to the column definition as in the following example:

CREATE TABLE persons(
  name  VARCHAR(30) NOT NULL,
  age   INTEGER
)

According to this table definition, any row of the persons table must have a value in the column name, but not necessarily in the column age.

In JavaScript, we can code a mandatory value constraint by a class-level check function that tests if the provided argument evaluates to a value, as illustrated in the following example:

Person.checkName = function (n) {
  if (n === undefined) {
    return "A name must be provided!"; // constraint violation error message
  } else return "";  // no constraint violation
};

With Java Bean Validation, a mandatory property like name is annotated with NotNull in the following way:

@Entity
public class Person {
  @NotNull
  private String name;
  private int age;
}

The equivalent ASP.NET Data Annotation is Required as shown in

public class Person{
  [Required]
  public string name { get; set; }
  public int age { get; set; }
}

A range constraint requires that a property must have a value from the value space of the type that has been defined as its range. This is implicitly expressed by defining a type for a property as its range. For instance, the attribute age defined for the object type Person in the class diagram above has the range Integer, so it must not have a value like "aaa", which does not denote an integer. However, it may have values like -13 or 321, which also do not make sense as the age of a person. In a similar way, since its range is String, the attribute name may have the value "" (the empty string), which is a valid string that does not make sense as a name.

We can avoid allowing negative integers like -13 as age values, and the empty string as a name, by assigning more specific datatypes as range to these attributes, such as NonNegativeInteger to age, and NonEmptyString to name. Notice that such more specific datatypes are neither predefined in SQL nor in common programming languages, so we have to implement them either in the form of user-defined types, as supported in SQL-99 database management systems such as PostgreSQL, or by using suitable additional constraints such as interval constraints, which are discussed in the next section. In a UML class diagram, we can simply define NonNegativeInteger and NonEmptyString as custom datatypes and then use them in the definition of a property, as illustrated in the following diagram:

In JavaScript, we can code a range constraint by a check function, as illustrated in the following example:

Person.checkName = function (n) {
  if (typeof(n) !== "string" || n.trim() === "") {
    return "Name must be a non-empty string!";
  } else return "";
};

This check function detects and reports a constraint violation if the given value for the name property is not of type "string" or is an empty string.

In a Java EE web app, for declaring empty strings as non-admissible user input we must set the context parameter

javax.faces.INTERPRET_EMPTY_STRING_SUBMITTED_VALUES_AS_NULL 

to true in the web deployment descriptor file web.xml.

In ASP.NET, empty strings are non-admissible by default.

An interval constraint requires that an attribute's value must be in a specific interval, which is specified by a minimum value or a maximum value, or both. Such a constraint can be defined for any attribute having an ordered type, but normally we define them only for numeric datatypes or calendar datatypes. For instance, we may want to define an interval constraint requiring that the age attribute value must be in the interval [25,70]. In a class diagram, we can define such a constraint by using the property modifiers min and max, as shown for the age attribute of the Driver class in the following diagram.

In an SQL table creation statement, an interval constraint is expressed in a table column definition by appending a suitable CHECK clause to the column definition as in the following example:

CREATE TABLE drivers(
  name  VARCHAR NOT NULL,
  age   INTEGER CHECK (age >= 25 AND age <= 70)
)

In JavaScript, we can code an interval constraint in the following way:

Driver.checkAge = function (a) {
  if (a < 25 || a > 70) {
    return "Age must be between 25 and 70!";
  } else return "";
};

In Java Bean Validation, we express this interval constraint by adding the annotations Min(0) and Max(120) to the property age in the following way:

@Entity
public class Driver {
  @NotNull
  private String name;
  @Min(25) @Max(70)
  private int age;
} 

The equivalent ASP.NET Data Annotation is Range(25,70) as shown in

public class Driver{
  [Required]
  public string name { get; set; }
  [Range(25,70)]
  public int age { get; set; }
} 

A pattern constraint requires that a string attribute's value must match a certain pattern, typically defined by a regular expression. For instance, for the object type Book we define an isbn attribute with the datatype String as its range and add a pattern constraint requiring that the isbn attribute value must be a 10-digit string or a 9-digit string followed by "X" to the Book class rectangle shown in the following diagram.

In an SQL table creation statement, a pattern constraint is expressed in a table column definition by appending a suitable CHECK clause to the column definition as in the following example:

CREATE TABLE books(
  isbn   VARCHAR(10) NOT NULL CHECK (isbn ~ '^\d{9}(\d|X)$'),
  title  VARCHAR(50) NOT NULL
)

The ~ (tilde) symbol denotes the regular expression matching predicate and the regular expression ^\d{9}(\d|X)$ follows the syntax of the POSIX standard (see, e.g. the PostgreSQL documentation).

In JavaScript, we can code a pattern constraint by using the built-in regular expression function test, as illustrated in the following example:

Person.checkIsbn = function (id) {
  if (!/\b\d{9}(\d|X)\b/.test( id)) {
    return "The ISBN must be a 10-digit string or a 9-digit string followed by 'X'!";
  } else return "";
};

In Java EE Bean Validation, this pattern constraint for isbn is expressed with the annotation Pattern in the following way:

@Entity
public class Book {
  @NotNull
  @Pattern(regexp="^\\(\d{9}(\d|X))$")
  private String isbn;
  @NotNull
  private String title;
} 

The equivalent ASP.NET Data Annotation is RegularExpression as shown in

public class Book{
  [Required]
  [RegularExpression(@"^(\d{9}(\d|X))$")]
  public string isbn { get; set; }
  public string title { get; set; }
}

A cardinality constraint requires that the cardinality of a multi-valued property's value set is not less than a given minimum cardinality or not greater than a given maximum cardinality. In UML, cardinality constraints are called multiplicity constraints, and minimum and maximum cardinalities are expressed with the lower bound and the upper bound of the multiplicity expression, as shown in the following diagram, which contains two examples of properties with cardinality constraints.

The attribute definition nickNames[0..3] in the class Person specifies a minimum cardinality of 0 and a maximum cardinality of 3, with the meaning that a person may have no nickname or at most 3 nicknames. The reference property definition members[3..5] in the class Team specifies a minimum cardinality of 3 and a maximum cardinality of 5, with the meaning that a team must have at least 3 and at most 5 members.

It's not obvious how cardinality constraints could be checked in an SQL database, as there is no explicit concept of cardinality constraints in SQL, and the generic form of constraint expressions in SQL, assertions, are not supported by available DBMSs. However, it seems that the best way to implement a minimum (or maximum) cardinality constraint is an on-delete (or on-insert) trigger that tests the number of rows with the same reference as the deleted (or inserted) row.

In JavaScript, we can code a cardinality constraint validation for a multi-valued property by testing the size of the property's value set, as illustrated in the following example:

Person.checkNickNames = function (nickNames) {
  if (nickNames.length > 3) {
    return "There must be no more than 3 nicknames!";
  } else return "";
};

With Java Bean Validation annotations, we can specify

@Size( max=3) 
List<String> nickNames
@Size( min=3, max=5) 
List<Person> members

A uniqueness constraint (or key constraint) requires that a property's value (or the value list of a list of properties in the case of a composite key constraint) is unique among all instances of the given object type. For instance, in a UML class diagram with the object type Book we can define the isbn attribute to be unique, or, in other words, a key, by appending the (user-defined) property modifier keyword key in curly braces to the attribute's definition in the Book class rectangle shown in the following diagram.

In an SQL table creation statement, a uniqueness constraint is expressed by appending the keyword UNIQUE to the column definition as in the following example:

CREATE TABLE books(
  isbn   VARCHAR(10) NOT NULL UNIQUE,
  title  VARCHAR(50) NOT NULL
)

In JavaScript, we can code this uniqueness constraint by a check function that tests if there is already a book with the given isbn value in the books table of the app's database.

A unique attribute (or a composite key) can be declared to be the standard identifier for objects of a given type, if it is mandatory (or if all attributes of the composite key are mandatory). We can indicate this in a UML class diagram with the help of the property modifier id appended to the declaration of the attribute isbn as shown in the following diagram.

Notice that such a standard ID declaration implies both a mandatory value and a uniqueness constraint on the attribute concerned.

Often, practitioners do not recommended using a composite key as a standard ID, since composite identifiers are more difficult to handle and not always supported by tools. Whenever an object type does not have a key attribute, but only a composite key, it may therefore be preferable to add an artificial standard ID attribute (also called surrogate ID) to the object type. However, each additional surrogate ID has a price: it creates some cognitive and computational overhead. Consequently, in the case of a simple composite key, it may be preferable not to add a surrogate ID, but use the composite key as the standard ID.

There is also an argument against using any real attribute, such as the isbn attribute, for a standard ID. The argument points to the risk that the values even of natural ID attributes like isbn may have to be changed during the life time of a business object, and any such change would require an unmanageable effort to change also all corresponding ID references. However, the business semantics of natural ID attributes implies that they are frozen. Thus, the need of a value change can only occur in the case of a data input error. But such a case is normally detected early in the life time of the object concerned, and at this stage the change of all corresponding ID references is still manageable.

Standard IDs are called primary keys in relational databases. We can declare an attribute to be the primary key in an SQL table creation statement by appending the phrase PRIMARY KEY to the column definition as in the following example:

CREATE TABLE books(
  isbn   VARCHAR(10) PRIMARY KEY,
  title  VARCHAR(50) NOT NULL
)

In object-oriented programming languages, like JavaScript and Java, we cannot code a standard ID declaration, because this would have to be part of the metadata of a class definition, and there is no support for such metadata. However, we should still check the implied mandatory value and uniqueness constraints.

A referential integrity constraint requires that the values of a reference property refer to an object that exists in the population of the property's range class. Since we do not deal with reference properties in this chapter, we postpone the discussion of referential integrity constraints to the chapter on "Reference Properties and Unidirectional Associations".

A frozen value constraint defined for a property requires that the value of this property must not be changed after it has been assigned. This includes the special case of read-only value constraints on mandatory properties that are initialized at object creation time.

Typical examples of properties with a frozen value constraint are standard identifier attributes and event properties. In the case of events, the semantic principle that the past cannot be changed prohibits that the property values of events can be changed. In the case of a standard identifier attribute we may want to prevent users from changing the ID of an object since this requires that all references to this object using the old ID value are changed as well, which may be difficult to achieve (even though SQL provides special support for such ID changes by means of its ON UPDATE CASCADE clause for the change management of foreign keys).

The following diagram shows how to define a frozen value constraint for the isbn attribute:

In Java, a read-only value constraint can be enforced by declaring the property to be final. In JavaScript, a read-only property slot can be implemented as in the following example:

Object.defineProperty( obj, "teamSize", {value: 5, writable: false, enumerable: true})

where the property slot obj.teamSize is made unwritable. An entire object obj can be frozen with Object.freeze( obj).

We can implement a frozen value constraint for a property in the property's setter method like so:

Book.prototype.setIsbn = function (i) {
  if (this.isbn === undefined) this.isbn = i;
  else console.log("Attempt to re-assign a frozen property!");
}

So far, we have only discussed how to define and check property constraints. However, in certain cases there may be also integrity constraints that do not just depend on the value of a particular property, but rather on

In a class model, property constraints can be expressed within the property declaration line in a class rectangle (typically with keywords, such as id, max, etc.). For expressing more complex constraints, such as object-level or type-level constraints, we can attach an invariant declaration box to the class rectangle(s) concerned and express the constraint in unambiguous plain English or in the Object Constraint Language (OCL). A simple example of an object-level constraint expressed as an invariant is shown in Figure 4.1.

Figure 4.1. An example of an object-level constraint

A general approach for implementing object-level constraint validation consists of taking the following steps:

Constraints affecting two or more model classes could be defined in the form of static methods (in a model layer method library) that are invoked from the validate methods of the affected model classes.