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To ensure data consistency, each user must see a consistent set of data that includes all changes made by that user s transactions as well as all the other users transactions. In a single-user database, it s a trivial matter to achieve data consistency. However, real-life databases need to allow simultaneous operations by numerous users, a requirement that s known as data concurrency. Improper interactions among transactions can cause data to become inconsistent. Transaction concurrency is achieved by managing various users simultaneous transactions without permitting any interference among them. If you re the only user of the database, you don t need to worry about concurrency control of transactions. However, in most cases, databases enable thousands of users to perform simultaneous select, update, insert, and delete transactions against the same table. One solution to concurrency control is to lock the entire table for the duration of each operation, so one user s transactions do not impact another s. Thus, each user would be operating in isolation, thereby sacrificing data concurrency. However, this would mean that access to the table would be severely reduced. As you ll see, Oracle does use locking mechanisms to keep the data consistent, but the locking is done in the least restrictive fashion, with the goal of maintaining the maximum amount of concurrency. Concurrency no doubt increases the throughput of an RDBMS, but it brings along its own special set of problems, which we ll look at next.

Concurrent access to the database by multiple users introduces several problems. Some of the most important problems potentially encountered in concurrent transaction processing are dirty reads, phantom reads, lost updates, and nonrepeatable reads.

As you ve seen in the Coordinators section, coordinators and workers can be organized into teams, and teams can be wired together to implement a complete system. You can break some systems down into a hierarchy of teams organized into layers, as shown in Figure 10-51.

A dirty read occurs when a transaction reads data that has been updated by an ongoing transaction but has not been committed permanently to the database. For example, say transaction A has just updated the value of a column, and it is now read by transaction B. What if transaction A rolls back its changes, whether intentionally or because it aborts for some reason The value of the updated column will also be rolled back as a result. Unfortunately, transaction B has already read the new value of the column, which is now incorrect because of the rolling back of transaction A.

The problem described in this section could be avoided by imposing a simple rule: Don t let any transaction read the intermediate results of another transaction before the other transaction is either committed or rolled back. This way, the reads are guaranteed to be consistent.

Figure 10-51. A system design organized by layer While in theory any team can talk to any other team, in a layered design you ll often impose constraints on how communication between layers is handled. The following are common constraints: A team on one layer can only talk to one team on adjacent layers. A team on one layer can talk to any team on adjacent layers. Each layer has a dedicated component that handles interlayer communication. In the first case, there are multiple communication paths between layers, as shown in the diagram in Figure 10-52.

Say you re reading data from a table (using a SELECT statement). You re-execute your query after some time elapses, and in the meantime, some other user has inserted new data into the table. Because your second query will come up with extra rows that weren t in the first read, they re referred to as phantom reads, and the problem is termed a phantom read. Phantom-read problems are caused by the appearance of new data in between two database operations in a transaction.

The lost-update problem is caused by transactions trying to read data while it is being updated by other transactions. Say transaction A is reading a table s data while it is being updated by transaction