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Castor JDO - Advanced features

Documentation Author(s):
Werner Guttmann

Reference: The Java Data Objects API


Introduction
Caching
    Caching and clustered environments
    Custom cache provider
Dependent and related relationships
Different cardinalities of relationship
Lazy Loading
    1:1 relations
    1:M and M:N relations
Multiple columns primary keys


Introduction

As explained at the introduction to Castor JDO, Castor has support for many advanced features such as caching, depend relations, inheritance, polymorphism, etc. The below sections detail these features, as their understanding is required to use Castor JDO in a performant and secure way.

Caching

Castor maintains and properly locks one copy of the dirty checking cache for each object it loads from or creates into persistence storage. Until all transactions involving the object are completed (See Design: Persistence), the cache is released. Starting from Castor 0.8.6, performance cache is implemented and developer can specify the mechanism and capacity of performance cache for each persistence capable type. Options includes 'none', 'count-limited', 'time-limited' and 'unlimited'. Starting from Castor 0.9.9, the cache types 'coherence', 'jcs', 'jcache' and 'fkcache' have been added as explaind below. Performance cache is write-through, because all changes to objects of a transaction should be persisted at commit time without delay.

Performance caches enhance the application performance by reducing the number of read operations against the persistence storage, by storing and reusing the last read or committed values of the object. Performance caches do not affect the behavior of short transactions or locking. It only affects persistence objects that have been released from any transactional context.

With the current release, performance caches also serve a dual purpose as dirty checking caches for long-transactions. This limitation implies that the object's duration in the performance cache determines the allowed time span of a long transaction. This might become an issue when performance caches of type 'count-limited' or 'time-limited' are being used, as objects will eventually be disposed. If an application tries to update an object that has been disposed from the dirty checking cache, an ObjectModifedException will be thrown.

The DTD declaration is as the following:

<!ATTLIST cache-type
    type ( none | count-limited
          | time-limited | unlimited | coherence | fkcache | jcache | jcs ) "count-limited"
    capacity NMTOKEN #IMPLIED>

For example,

<mapping>
    <class name="com.xyz.MyObject">
        <cache-type type="time-limited" capacity="30"/>
        <field name= ... >
               ...
        </field>
    </class>
</mapping>

defines the time-limited least-recently-used algorithm as the caching algorithm for com.xyz.MyObject class and the cache will be disposed of if it is not used within 30 seconds. (Note that the time-limit ticks after all transaction involved with the objects complete, but not start after it is loaded.)

NOTE: The default cache-type is count-limited with a capacity of 100. This will be used when no cache-type is specified in the mapping for a class.

NOTE: As of release 0.9.6, Castor allows for the addition of user-defined cache implementations. Instructions can be found here

NOTE: As of release 0.9.9, four new cache types have been added to Castor, three of them open source and one commercial:

name Vendor Version
coherence Tangosol Coherence 2.5
jcs JCS 1.2.5
fkcache FKCache 1.0-beta6

As some of these cache providers allow for allow you to use it in a distributed mode, this allows Castor JDO to be used in a clustered (multi-JVM) environment. Please see the section below for short summayr fo this feature.

For problems related to the use of performance caches, please consult with the relevant entries in the JDO F.A.Q..

Caching and clustered environments

All of the cache providers added with release 0.9.9 are distributed caches per se or can be configured to operate in such a mode. This effectively allows Castor JDO to be used in a clustered J2EE (multi-JVM) environment, where Castor JDO runs on each of the cluster instances, and where cache state is automatically snychronized between these instances.

In such an environment, Castor JDO wil make use of the underlying cache provider to replicate/distribute the content of a specific cache between the various JDOManager instances. Through the distribution mechanism of the cache provider, a client of a Castor JDO instance on one JVM will see any updates made to domain objects performed against any other JVM/JDO instance.

The following class mapping, for example, ...

<mapping>
    <class name="com.xyz.MyOtherObject">
        <cache-type type="coherence" />
        <field name= ... >
               ...
        </field>
    </class>
</mapping>

defines that for all objects of type com.xyz.MyOtherObject Tangosol's Coherence cache provider should be used.

Custom cache provider

Castor provides a set of pre-built cache providers, offering a variety of different cache algorithms. Nevertheless, special needs might require the application developer to implement a custom cache algorithm. Castor facilitates such need by making available standardized interfaces and an easy to understand recipe for integrating a custom cache provider with Castor.

The main component of Castor's cache system is the interface Cache, which declares required functionality of a performance cache provider. Existing (and future) cache implementations have to implement this interface, which is closely modelled after java.util.Map.

Below are defined the steps to build a custom cache provider and register it with Castor JDO.

  1. Create a class that implements Cache (we have provided an abstract base class that you can use at your convenience).
  2. Create a class that implements CacheFactory (we have provided an abstract base class that you can use at your convenience).
  3. Register your custom cache implementation with Castor JDO in the castor.properties file.

Detailed instructions can be found in one of the Castor JDO HOW-TO documents.

Dependent and related relationships

Castor distinguishes the relationship of two objects as dependent or related, and maintains the life cycle independently for the two types of relationships. Starting from Castor 0.9, the developer can explicitly define a dependent relationship in the mapping file.

When using independent relations, related objects? life cycle is independent of each other, meaning that they have to be created, removed and updated (for long transaction) independently.

When using dependent relations, one data object class must be declared as depends on one other data object class in the mapping file, and such an object is called a dependent data object class. A data object class without depends declared in the mapping is called a master object. A master object can be depended upon by zero or more dependent data object class.

As of Castor 0.9, a dependent object class can be related to other master data object classes including extended classes, but cannot depend on more than one master class.

If an object class declared as depends on another class, it may not be created, removed or updated separately. Attempting to create, remove or update a dependent object will result in ObjectNotPersistcapableException. Note that Castor doesn'?t allow a dependent object instance to change its master object instance during a transaction. Each dependent object can have only one master object. Both dependent and master objects must have identities, and may or may not make use of key-generators.

Here is the DTD for declaring dependent object:

<!ATTLIST class     name ID  #REQUIRED
          extends   IDREF    #IMPLIED
          depends   IDREF    #IMPLIED
          identity  CDATA   #IMPLIED
          access    ( read-only | shared | exclusive | db-locked )  "shared"
          key-generator   IDREF #IMPLIED

For example,

<mapping>
    <class name="com.xyz.MyDependentObject"
        depends="com.xyz.MyObject">
        ...
    </class>
</mapping>

declares the data object class com.xyz.MyDependentObject as a dependent upon class com.xyz.MyObject.

Different cardinalities of relationship

Castor supports different cardinalities of relationship, namely one-to-one, one-to-many, and many-to-many. Many-to-many relationship must be related rather than dependent, because each dependent object can have only one master object.

Many-to-many requires a separate table for storing the relations between two types of objects. Many-to-many introduces two attributes, namely many-key and many-table that reside in the <sql> element which is a sub-element of the <field> element. For all many-to-many relations, a many-table must be specified. If the column name of the primary key of the class is different from the foreign keys columns of the class in the relation tables, then the relation table columns can be specified using the many-key attributes. Similarly, if the column name of the primary key of the related class is different from the foreign key columns of the related class, then the relation table columns can be specified using the name attribute.

The many-table is used to store relations in a separate table

<mapping>
    <class>
        <field>
            <sql many-key="#OPTIONAL" name="#OPTIONAL"
                 many-table="#REQURIED">
        </field>
    </class>
</mapping>

So, for example, if the SQL table is the following,

employee_table
id name salary
1482 Smith, Bob $123,456
628 Lee, John $43,210
1926 Arnold, Pascal $24,680
department_table
id name comment
3 Accounting
7 Engineering The very important department. :-)
employee_department
e_id d_id
.... ....

Then, the mapping for employee data object would look like this

<mapping>
    <class name="com.xyz.Employee" identity="id">
        <map-to table="employee_table"/>
            <field name="id" type="integer">
                <sql name="id"/>
            </field>
            <field>
                <sql many-table="employee_department"
                     many-key="e_id" name="d_id"/>
            </field>
            <field name="salary">
                <sql name="salary" type="integer">
            </field>
    </class>
</mapping>

Lazy Loading

As of release 0.9.6, Castor has full support for lazy loading object instances referenced as part of all relation types currently supported:

-1:1 relations
-1:m relations
-M:N relations.

1:1 relations

As of release 0.9.6, Castor supports lazy-loading of 1:1 relations. Imagine the following class mapping:

<mapping>
    <class name="com.xzy.Department">
       ...
       <field "employee" type="com.xyz.Employee" lazy="true" />
       ...
    </class>
</mapping>

Per definition, when an instance of Department is loaded through e.g. Database.load(), Castor will not (pre-)load the Employee instance referenced (as such reducing the size pf the initial query as well as the size of the result set returned). Only when the Emplyoee instance is accessed through Department.getEmployee(), Castor will load the actual object into memory from the persistence store.

This means that if the Employee instance is not accessed at all, not only will the initial query to load the Department object have had its complexity reduced, but no performance penalty will be incurred for the additional access to the persistence store either.

1:M and M:N relations

The elements in the collection are only loaded when the application asks for the object from the collection, using, for example, iterator.next(). The iterator in Castor?s lazy collection is optimized to return a loaded object first.

In the mapping file, lazy loading is specified in the element of the collection?s <field>, for example,

<mapping>
    <class name="com.xzy.Department">
       ...
        <field name="employee" type="com.xyz.Employee" lazy="true"
               collection="collection"/>
    </class>
</mapping>

declares that the collection of type Employee in a Department is lazy loaded.

If lazy loading is specified for a field of a class, Castor will set the field with a special collection which contains only the identities of the objects. Because of that, it requires the data object to have the method setDepartment( Collection department) in the data object class which was not required in previous versions.

Note: Please note that currently only the java.util.Collection type is supported.

Multiple columns primary keys

The support of multiple column primary keys (also called compound primary keys) was another major enhancement added into Castor 0.9. Specifying multiple column primary keys is simple and straightforward, in the mapping file,

<mapping>
    <class name="com.xyz.MyObject" identity="firstName lastName">
        <field name="firstName" type="string">
           <sql name="fname"/>
        </field>
        <field name="lastName" type="string">
           <sql name="lname"/>
        </field>
           ...
    </class>
</mapping>

Multiple column primary keys work with both master and dependent objects, all cardinalities of relationship, including one-to-one, one-to-many and many-to-many, as well as lazy loading.

However, multiple column primary keys should only be used to adhere to an existing database design, not when designing a new database. In general, it is not a good idea to use an identity or identities which can be modified by the user, or which contain application-visible data. For example, if the system allows the user name to be changed, using user name as identity is highly discouraged, as this practice can require a major data migration to a new schema to update all foreign keys to adhere to a new primary key structure, should the user name no longer be adequate as a primary key. It should be noted that Castor doesn?t support identity change, as specified in the ODMG 3.0 specification. So, primary keys changes are almost certainly a large trade off between data integrity and performance. Well chosen primary keys are usually single (not multiple) column numeric or character fields for the reasons outlined above, as well as performance, as joining operations are faster for single column primary keys.

 
   
  
   
 


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