DAO Generator: Data Access Object Design Pattern
A DAO generator solves a basic business application development problem. Business applications almost always need access to data from relational or object databases and the Java platform offers many techniques for accessing this data. The oldest and most mature technique is to use the Java Database Connectivity API, which provides the capability to execute SQL queries against a database and then fetch the results, one column at a time. Although this API provides everything a developer needs to access data and to persist application state, it is a cumbersome API to develop against - which makes a code generator particularly useful. That's where a DAO generator is very useful.
Java 2 Enterprise Edition (J2EE) offers a newer persistence framework in the form of Entity Beans, a subset of the Enterprise JavaBean (EJB) framework. Although there have been many improvements in the more recent EJB 2.0 specification, many developers are now looking to alternative persistence frameworks, such as Spring DAO and Hibernate DAO.
The Benefits of a DAO Generator
The Data Access Object (DAO) design pattern provides a technique for separating object persistence and data access logic from any particular persistence mechanism or API. There are clear benefits to this approach from an architectural perspective. The DAO generator approach provides flexibility to change an application's persistence mechanism over time without the need to re-engineer application logic that interacts with the DAO tier. For example, there may be performance benefits in changing an application's performance mechanism from using Entity Beans to using direct JDBC calls from a session bean, or even a move to an alternative persistence framework, such as Spring or Hibernate. Without a DAO tier in place, this sort of transition would require extensive re-engineering of existing code.
The DAO design pattern also provides a simple, consistent API for data access that does not require knowledge of JDBC, EJB, Hibernate, or Spring interfaces. A typical DAO interface, produced by a DAO generator, is shown below.
public interface CustomerDAO
public void insert(Customer customer)
public void update(CustomerPK pk, Customer customer)
public void delete(CustomerPK pk)
public Customer findAll()
public Customer findByPrimaryKey(String email)
public Customer findByCompany(int companyId)
It is important to note that a DAO generator does not just apply to simple mappings of one object to one relational table, but also allows complex queries to be performed and allows for stored procedures and database views to be mapped into Java data structures. This makes a DAO generator useful for real-world applications.
Minimizing the Impact of Moving to A DAO Generator
CodeFutures' objective is to minimize the importance of the main argument against using DAO: the fact that it requires a significant amount of repetitive source code to be produced for no immediate advantage over using JDBC, EJB, or JDO directly. For many developers, this disadvantage is good enough reason to ignore the long-term benefits of using a framework-neutral API, especially where there are strict project deadlines. Without the code generation advantages of FireStorm/DAO, it is not easy to justify to a project manager or project sponsor the time and cost of manually writing DAO code, regardless of any future benefits they may see.
CodeFutures' solution to the manual coding problem is to automate the production of a DAO tier with a DAO generator, as well as automating the actual implementation logic for whichever persistence framework is deemed appropriate for an application - all provided by the DAO generator. This approach is easy to adopt because almost all databases use a standard language for defining their structure (SQL).
Click on a product edition of the DAO generator for more details or view an overview of the product range.
CodeFutures provides a free program to analyze the performance of your MySQL database.
Read about how Database Sharding helps many major companies to linearly scale their database applications.
dbShards economically scales large, high transaction volume databases using Database Sharding.