qr code scaner java app Understanding object/relational persistence in Java

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Understanding object/relational persistence
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The problem of data navigation
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There is a fundamental difference in the way you access data in Java and in a relational database. In Java, when you access a user s billing information, you call aUser.getBillingDetails().getAccountNumber() or something similar. This is the most natural way to access object-oriented data, and it s often described as walking the object network. You navigate from one object to another, following pointers between instances. Unfortunately, this isn t an efficient way to retrieve data from an SQL database. The single most important thing you can do to improve the performance of data access code is to minimize the number of requests to the database. The most obvious way to do this is to minimize the number of SQL queries. (Of course, there are other more sophisticated ways that follow as a second step.) Therefore, efficient access to relational data with SQL usually requires joins between the tables of interest. The number of tables included in the join when retrieving data determines the depth of the object network you can navigate in memory. For example, if you need to retrieve a User and aren t interested in the user s billing information, you can write this simple query:
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select * from USERS u where u.USER_ID = 123
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On the other hand, if you need to retrieve a User and then subsequently visit each of the associated BillingDetails instances (let s say, to list all the user s credit cards), you write a different query:
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select * from USERS u left outer join BILLING_DETAILS bd on bd.USER_ID = u.USER_ID where u.USER_ID = 123
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As you can see, to efficiently use joins you need to know what portion of the object network you plan to access when you retrieve the initial User this is before you start navigating the object network! On the other hand, any object persistence solution provides functionality for fetching the data of associated objects only when the object is first accessed. However, this piecemeal style of data access is fundamentally inefficient in the context of a relational database, because it requires executing one statement for each node or collection of the object network that is accessed. This is the dreaded n+1 selects problem. This mismatch in the way you access objects in Java and in a relational database is perhaps the single most common source of performance problems in Java applications. There is a natural tension between too many selects and too big
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The paradigm mismatch
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selects, which retrieve unnecessary information into memory. Yet, although we ve been blessed with innumerable books and magazine articles advising us to use StringBuffer for string concatenation, it seems impossible to find any advice about strategies for avoiding the n+1 selects problem. Fortunately, Hibernate provides sophisticated features for efficiently and transparently fetching networks of objects from the database to the application accessing them. We discuss these features in chapters 13, 14, and 15.
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The cost of the mismatch
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We now have quite a list of object/relational mismatch problems, and it will be costly (in time and effort) to find solutions, as you may know from experience. This cost is often underestimated, and we think this is a major reason for many failed software projects. In our experience (regularly confirmed by developers we talk to), the main purpose of up to 30 percent of the Java application code written is to handle the tedious SQL/JDBC and manual bridging of the object/relational paradigm mismatch. Despite all this effort, the end result still doesn t feel quite right. We ve seen projects nearly sink due to the complexity and inflexibility of their database abstraction layers. We also see Java developers (and DBAs) quickly lose their confidence when design decisions about the persistence strategy for a project have to be made. One of the major costs is in the area of modeling. The relational and domain models must both encompass the same business entities, but an object-oriented purist will model these entities in a different way than an experienced relational data modeler would. The usual solution to this problem is to bend and twist the domain model and the implemented classes until they match the SQL database schema. (Which, following the principle of data independence, is certainly a safe long-term choice.) This can be done successfully, but only at the cost of losing some of the advantages of object orientation. Keep in mind that relational modeling is underpinned by relational theory. Object orientation has no such rigorous mathematical definition or body of theoretical work, so we can t look to mathematics to explain how we should bridge the gap between the two paradigms there is no elegant transformation waiting to be discovered. (Doing away with Java and SQL, and starting from scratch isn t considered elegant.) The domain modeling mismatch isn t the only source of the inflexibility and the lost productivity that lead to higher costs. A further cause is the JDBC API itself. JDBC and SQL provide a statement-oriented (that is, command-oriented) approach to moving data to and from an SQL database. If you want to query or
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