Simple Object Persistence with the db4o Object Database

Simple Object Persistence with the db4o Object Database
Pages: 1, 2

Storing the data

A Team object can be stored with a single line of code:

db.set(t1);

where db is a reference to an ObjectContainer object, which was created by opening a new database file, like this:

ObjectContainer db = Db4o.openFile(filename);

A db4o database is a single file with a .yap extension, and its set method is used to store objects.

Note that this line stores the Team object and its collection of Player objects. We can test this by retrieving one of those Player objects. The simplest way to do this is by using QBE.

Simple querying: QBE

The following code lists all Player objects that match an example object; there should only be one here. Results are retrieved as an ObjectSet by calling the get method of the ObjectContainer.


Player examplePlayer = new Player("Barry Bonds",0,0f);
ObjectSet result=db.get(examplePlayer);
        
System.out.println(result.size());
while(result.hasNext()) {
    System.out.println(result.next());
}

We can get all the Player objects that have been stored by creating a dummy example object (all fields are null or 0), as follows:


Player examplePlayer = new Player(null,0,0f);
ObjectSet result=db.get(examplePlayer);

System.out.println(result.size());
while(result.hasNext()) {
    System.out.println(result.next());
}

The output looks like this:


8
Kazuhisa Ishii:0.127, 13
Shawn Green:0.266
Cesar Izturis:0.288
Adrian Beltre:0.334
Kirk Rueter:0.131, 9
Ray Durham:0.282
Marquis Grissom:0.279
Barry Bonds:0.362

Note that we can retrieve all objects of the Player class and its subclasses (just Pitcher in this example) without any extra effort. The Pitcher objects show up in the output as they have the extra wins attribute listed. With a relational database we would have had to decide how to map the inheritance tree to tables and possibly have had to join tables to retrieve all the attributes of all the objects.

Updating and deleting

Updating objects can be achieved using a combination of the above techniques. The following code assumes that only one match is found, and the matching object is cast to Player so that its attributes can be modified.


Player examplePlayer = new Player("Shawn Green",0,0f);
ObjectSet result = db.get(examplePlayer);
Player p = (Player) result.next();                  
p.setBattingAverage(0.299f);
db.set(p);

Objects can be deleted from the database in a similar way:


Player examplePlayer = new Player("Ray Durham",0,0f);
ObjectSet result = db.get(examplePlayer);
Player p = (Player) result.next();    
db.delete(p);

More powerful query support

One of the major drawbacks of early versions of db4o was that QBE provides fairly limited querying capability. For example, you couldn't run a query like "all players with batting average greater than .300". db4o now includes the S.O.D.A. API to provide querying that comes much closer to the power of SQL. An instance of the Query class represents a node in a query criteria graph to which constraints can be applied. A node can represent a class, multiple classes, or a class attribute.

The following code demonstrates how to do the query described in the previous paragraph. We define a query graph node and constrain it to the Player class. This means that the query will only return Player objects. We then descend the graph to find a node representing an attribute named “battingAverage” and constrain this to be greater than 0.3. Finally, the query is executed to return all objects in the database that match the constraints.


Query q = db.query();
q.constrain(Player.class);
q.descend("battingAverage").constrain(new Float(0.3f)).greater();
ObjectSet result = q.execute();

At first glance, this performs a similar query to an SQL query, like this:

SELECT * FROM players WHERE battingAverage > 0.3

However, the design of the Player class allows inverse relationships to be created between Team and Player objects, as shown in the test data. A Team has a reference to a list of Player objects, while each Player has a reference to a Team. This means that the result of this query contains Player and Team objects. The code below demonstrates this:


System.out.println(result.size());
while(result.hasNext()) {
    // Print Player
    Player p = (Player) result.next();
    System.out.println(p);
    // Getting Player also gets Team - print Team
    Team t = p.getTeam();
    System.out.println(t);
}

Output:


2
Adrian Beltre:0.334
Dodgers
Barry Bonds:0.362
Giants

The query is now similar to an SQL query, like this:


SELECT teams.name, players.name, players.battingAverage FROM teams, players 
WHERE teams.teamID = players.playerID
AND battingAverage > 0.3

This worked because the inverse relationship was designed into the object model. Object databases are navigational: you can only retrieve data following the direction of predefined relationships. Relational databases, on the other hand, have no directionality in their table joins and therefore allow more flexibility for ad-hoc queries. However, given the right object relationships, related objects can be retrieved from the object database with very little programming effort. The database model and the application object model are identical, so there is no need for the programmer to think differently about the data. If you can get the Team for a given Player when the objects are in memory, you can do the same from the database.

What else can S.O.D.A. do?

SQL allows results to be sorted in order; S.O.D.A. does too. This example shows how the stored Player objects can be retrieved in order of battingAverage. (It's pretty obvious which ones are the pitchers now!)


Query q = db.query();
q.constrain(Player.class);
q.descend("battingAverage").orderAscending();
ObjectSet result = q.execute();

Output:


7
Kazuhisa Ishii:0.127, 13
Kirk Rueter:0.131, 9
Marquis Grissom:0.279
Cesar Izturis:0.288
Shawn Green:0.299
Adrian Beltre:0.334
Barry Bonds:0.362

S.O.D.A. allows more complex queries to be defined using code that is quite simple once you get past the temptation to think the relational way. To set constraints you just navigate around the query graph to find the classes or attributes you want to put conditions on. The query graph is closely related to the domain object model, which should (hopefully) be well understood by the developer. On the other hand, to achieve a similar result with SQL you need to take account of how the domain objects have been mapped to relational tables.

This example shows how to set conditions on two attributes of the Player class to find players with batting average above .130 who are also pitchers with more than 5 wins. Again, we define a query graph node and constrain it to the Player class. We then descend the graph to find a node representing the attribute named “battingAverage” and constrain this to be greater than 0.13. The result of this is a Constraint object. To set the next constraint, we descend to find the node representing the attribute "wins"; this in itself means that the query will only find Pitcher objects. This node is constrained to be greater than 5, and this is combined using a logical "AND" with the first Constraint object.


Query q = db.query();
q.constrain(Player.class);
Constraint constr =
  q.descend("battingAverage").constrain(
      new Float(0.13f)).greater();
q.descend("wins").constrain(
    new Integer(5)).greater().and(constr);
result = q.execute();

Output:


1
Kirk Rueter:0.131, 9
Giants

The last example shows how to combine conditions on attributes of different classes to find players with batting average above .300 who are in teams with more than 92 wins. The easiest way to do this is to start with Player, and then navigate to Team. We descend to find the "battingAverage" node as before and set a Constraint. We then descend to find the "team" attribute. As this attribute is of type Team, the node represents the Team class, so we can descend again to the node representing the "won" attribute of Team and set a constraint on that. Finally, we combine this with the first Constraint.


Query q = db.query();
q.constrain(Player.class);
Constraint constr =
    q.descend("battingAverage").constrain(
        new Float(0.3f)).greater();
q.descend("team").descend("won").constrain(
    new Integer(92)).smaller().and(constr);
result = q.execute();

Output:


1
Barry Bonds:0.362
Giants

Conclusion

A small footprint, embeddable object database offers a very simple, compact route to object persistence. db4o is now an open source object database that offers a range of attractive features and supports both Java and .NET. The simplicity of installation and use as well as the lack of an impedance mismatch between object and data models make db4o very useful in a range of business and educational applications.