Skip to main content

NHibernate: Associations with Composite Primary Keys as Part of a Composite Primary Key

NHibernate is a pretty useful tool, but occasionally it's not entirely documented in a way that makes it's flexibility evident. Composite keys are a particularly difficult area in this regard, as evidenced by the numerous articles on the topic.

Most of the existing articles cover this simply enough, but there is one uncommon corner case I have yet to see explained anywhere: a composite primary key one of whose key properties is an association with a composite key. This is probably pretty uncommon and there are ways around it, hence the lack of examples, but as a testament to NHibernate's flexibility, it's possible! Here's the example in code listing only the primary keys:

public class MotorType
{
  public Horsepower Horsepower { get; protected set; }
  public VoltageType VoltageType { get; protected set; }
}
public class Motor
{
  public MotorType MotorType { get; protected set; }
  public Efficiency Efficiency { get; protected set; }
}

The tables look like this:

MotorType
HorsepowerIdVoltageTypeId
Motor
HorsepowerIdVoltageTypeIdEfficiencyId

Most people would probably map this with the Motor class having the three primary key properties, one for each column, and an additional many-to-one association referencing MotorType, but that shouldn't actually be necessary. Composite primary keys are possible, and the primary key can contain an association, therefore it should be possible, in principle, for that association to itself need a composite key.

And here's how it's done for the Motors table:

<?xml version="1.0" encoding="utf-8" ?>
<hibernate-mapping xmlns="urn:nhibernate-mapping-2.2">
  <class name="Motor, ExampleProject" table="Motors">
    <composite-id>
      <key-many-to-one name="MotorType">
        <column name="HorsepowerId" />
        <column name="VoltageTypeId" />
      </key-many-to-one>
      <key-property name="Efficiency" column="EfficiencyId" />
    </composite-id>
  </class>
</hibernate-mapping>

The MotorType class is a simple composite primary key:

<?xml version="1.0" encoding="utf-8" ?>
<hibernate-mapping xmlns="urn:nhibernate-mapping-2.2">
<class name="MotorType, ExampleProject" table="MotorTypes">
    <composite-id>
      <key-property name="Horsepower" column="HorsepowerId" />
      <key-property name="VoltageType" column="VoltageTypeId" />
    </composite-id>
</class>
</hibernate-mapping>
Labels:

Comments

Post a Comment

Popular posts from this blog

Blue-eyed Islander Puzzle - an analysis

Many people find themselves stumped by the so-called Blue-Eyed Islanders puzzle . There is also much controversy over its supposed solution. I'm going to analyze the problem and the solution, and in the process, explain why the solution works. To begin, let's modify the problem slightly and say that there's only 1 blue-eyed islander. When the foreigner makes his pronouncement, the blue-eyed islander looks around and sees no other blue eyes, and being logical, correctly deduces that his own eyes must be blue in order for the foreigner's statement to make sense. The lone blue-eyed islander thus commits suicide the following day at noon. Now comes the tricky part, and the source of much confusion. Let's say there are 2 blue-eyed islanders, Mort and Bob. When the foreigner makes his pronouncement, Mort and Bob look around and see only each other. Mort and Bob thus both temporarily assume that the other will commit suicide the following day at noon. Imagine their chagrin...
40 comments
Read more

Easy Reverse Mode Automatic Differentiation in C#

Continuing from my last post on implementing forward-mode automatic differentiation (AD) using C# operator overloading , this is just a quick follow-up showing how easy reverse mode is to achieve, and why it's important. Why Reverse Mode Automatic Differentiation? As explained in the last post, the vector representation of forward-mode AD can compute the derivatives of all parameter simultaneously, but it does so with considerable space cost: each operation creates a vector computing the derivative of each parameter. So N parameters with M operations would allocation O(N*M) space. It turns out, this is unnecessary! Reverse mode AD allocates only O(N+M) space to compute the derivatives of N parameters across M operations. In general, forward mode AD is best suited to differentiating functions of type: R → R N That is, functions of 1 parameter that compute multiple outputs. Reverse mode AD is suited to the dual scenario: R N → R That is, functions of many parameters t...
18 comments
Read more

Extensible, Statically Typed Pratt Parser in C#

I just completed a statically typed Pratt-style single-state extensible lexer+parser, otherwise known as a top-down operator precedence parser, for the Sasa library . The implementation is available in the Sasa.Parsing dll, under Sasa.Parsing.Pratt . Two simple arithmetic calculators are available in the unit tests . This implementation is novel in two ways: Aside from an alleged implementation in Ada , this is the only statically typed Pratt parser I'm aware of. Pratt parsers typically require a pre-tokenized input before parsing semantic tokens, but I've eliminated this step by using the symbol definitions to drive a longest-match priority-based lexer. Symbols by default match themselves, but you can optionally provide a scanning function used to match arbitrary string patterns. The symbol selected for the current position of the input is the symbol that matches the longest substring. If two symbols match equally, then the symbol with higher precedence is selected. The design...
12 comments
Read more