Transactions are critical to maintaining data integrity. They are used to group operations into units of work that act in an all-or-nothing fashion. Transactions have the following qualities:
Atomicity. Atomicity refers to the all-or-nothing property of transactions. Either every data update in the transaction completes successfully, or they all fail, leaving the datastore in its original state. A transaction cannot be only partially successful.
Consistency. Each transaction takes the datastore from one consistent state to another consistent state.
Isolation. Transactions are isolated from each other. When you are reading persistent data in one transaction, you cannot "see" the changes that are being made to that data in other uncompleted transactions. Similarly, the updates you make in one transaction cannot conflict with updates made in other concurrent transactions. The form of conflict resolution employed depends on whether you are using pessimistic or optimistic transactions. Both types are described later in this chapter.
Durability. The effects of successful transactions are durable; the updates made to persistent data last for the lifetime of the datastore.
Together, these qualities are called the ACID properties of transactions. To understand why these properties are so important to maintaining data integrity, consider the following example:
Suppose you create an application to manage bank accounts. The application includes a method to transfer funds from one user to another, and it looks something like this:
public void transferFunds (User from, User to, double amnt)
{
from.decrementAccount (amnt);
to.incrementAccount (amnt);
}
Now suppose that user Alice wants to transfer 100 dollars to user Bob.
No problem: you invoke your
transferFunds method, supplying Alice in the
from parameter, Bob in the to
parameter, and 100.00 as the amnt.
The first line of the method is executed, and 100 dollars is subtracted
from Alice's account. But then, something goes wrong. An unexpected
exception occurs, or the network fails, and your method never
completes.
You are left with a situation in which the 100 dollars has simply disappeared. Thanks to the first line of your method, it is no longer in Alice's account, and yet it was never transferred to Bob's account either. The datastore is in an inconsistent state.
The importance of transactions should now be clear. If the two lines
of the transferFunds method had been placed
together in a transaction, it would be impossible for only the
first line to succeed - either the funds would be transferred
properly or they would not be transferred at all and an exception
would be thrown. Money could never vanish into thin air, and the data
store could never get into an inconsistent state.
There are two major types of transactions: pessimistic transactions and optimistic transactions. Each type has both advantages and disadvantages.
Pessimistic transactions generally lock the datastore records they act on, preventing other concurrent transactions from using the same data. This avoids conflicts between transactions, but consumes a lot of database resources. Additionally, locking records can result in deadlock, a situation in which two transactions are both waiting for the other to release its locks before completing. The results of a deadlock are datastore-dependent; usually one transaction is forcefully rolled back after some specified timeout interval, and an exception is thrown.
JDO does not have pessimistic transactions per se. Instead, JDO has datastore transactions. This is JDO's way of admitting that some datastores do not support pessimistic semantics, and that the exact meaning of a non-optimistic JDO transaction is dependent on the datastore. In most JDO implementations, a datastore transaction is equivalent to a pessimistic transaction.
Optimistic transactions consume less resources than pessimistic/datastore transactions, but only at the expense of reliability. Because optimistic transactions do not lock datastore records, two transactions might change the same persistent information at the same time, and the conflict will not be detected until the second transaction attempts to flush or commit. At this time, the second transaction will realize that another transaction has concurrently modified the same records (usually through a timestamp or versioning system), and will throw an appropriate exception. Note that optimistic transactions still maintain data integrity; they are simply more likely to fail in heavily concurrent situations.
Despite their drawbacks, optimistic transactions are the best choice for most applications. They offer better performance, better scalability, and lower risk of hanging due to deadlock.
![[Note]](img/note.gif) | Note |
|---|---|
Kodo supports both optimistic and datastore transactions. Kodo defaults JDO datastore transactions to pessimistic semantics; however, Kodo also offers advanced locking and versioning APIs for fine-grained control over database resource allocation and object versioning. See Section 9.4, “Object Locking” and Section 5.8, “Lock Groups” of the Reference Guide for details on locking. Section 15.10, “Version” of this document covers object versioning strategies. |
