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This chapter covers
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Database transactions and locking Long-running application transactions The Hibernate first- and second-level caches The caching system in practice with CaveatEmptor
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Now that you understand the basics of object/relational mapping with Hibernate, let s take a closer look at one of the core issues in database application design: transaction management. In this chapter, we examine how you use Hibernate to manage transactions, how concurrency is handled, and how caching is related to both aspects. Let s look at our example application. Some application functionality requires that several different things be done together. For example, when an auction finishes, our CaveatEmptor application has to perform four different tasks:
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Mark the winning (highest amount) bid. Charge the seller the cost of the auction. Charge the successful bidder the price of the winning bid. Notify the seller and the successful bidder.
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What happens if we can t bill the auction costs because of a failure in the external credit card system Our business requirements might state that either all listed actions must succeed or none must succeed. If so, we call these steps collectively a transaction or unit of work. If only one step fails, the whole unit of work must fail. We say that the transaction is atomic: Several operations are grouped together as a single indivisible unit. Furthermore, transactions allow multiple users to work concurrently with the same data without compromising the integrity and correctness of the data; a particular transaction shouldn t be visible to and shouldn t influence other concurrently running transactions. Several different strategies are used to implement this behavior, which is called isolation. We ll explore them in this chapter. Transactions are also said to exhibit consistency and durability. Consistency means that any transaction works with a consistent set of data and leaves the data in a consistent state when the transaction completes. Durability guarantees that once a transaction completes, all changes made during that transaction become persistent and aren t lost even if the system subsequently fails. Atomicity, consistency, isolation, and durability are together known as the ACID criteria. We begin this chapter with a discussion of system-level database transactions, where the database guarantees ACID behavior. We ll look at the JDBC and JTA APIs and see how Hibernate, working as a client of these APIs, is used to control database transactions. In an online application, database transactions must have extremely short lifespans. A database transaction should span a single batch of database operations, interleaved with business logic. It should certainly not span interaction with the
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user. We ll augment your understanding of transactions with the notion of a longrunning application transaction, where database operations occur in several batches, alternating with user interaction. There are several ways to implement application transactions in Hibernate applications, all of which are discussed in this chapter. Finally, the subject of caching is much more closely related to transactions than it might appear at first sight. In the second half of this chapter, armed with an understanding of transactions, we explore Hibernate s sophisticated cache architecture. You ll learn which data is a good candidate for caching and how to handle concurrency of the cache. We ll then enable caching in the CaveatEmptor application. Let s begin with the basics and see how transactions work at the lowest level, the database.
5.1 Understanding database transactions
Databases implement the notion of a unit of work as a database transaction (sometimes called a system transaction). A database transaction groups data-access operations. A transaction is guaranteed to end in one of two ways: it s either committed or rolled back. Hence, database transactions are always truly atomic. In figure 5.1, you can see this graphically. If several database operations should be executed inside a transaction, you must mark the boundaries of the unit of work. You must start the transaction and, at some point, commit the changes. If an error occurs (either while executing operations or when committing the changes), you have to roll back the transaction to leave the data in a consistent state. This is known as transaction demarcation, and (depending on the API you use) it involves more or less manual intervention.
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