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3.1 Transactions

A transaction is an event or sequence of events that takes a database from one consistent state to the next. If any of the events fails or the sequence fails to complete, the system is returned to its initial state. Once it completes, it is guaranteed to be in the new consistent state until it is acted upon to completion by another transaction.

3.1.1 ACID Requirements

Formally, a transaction is a group of database operations that together have a shared set of guaranteed properties—commonly known as ACID properties. ACID is an acronym that stands for Atomicity, Consistency, Isolation, and Durability.


The atomicity of a transaction means that it represents an indivisible unit of work. Any attempt to break the transaction into smaller parts breaks the transaction. Atomicity is what guarantees that all of the operations that make up a transaction succeed or none of them succeeds.


The consistency of a transaction means that all of the operations that make up a transaction operate on a consistent set of data and leave the database in a consistent state once it has completed. In other words, the transaction is ignorant of any changes made to the database by other transactions. Data is considered consistent when all of its constraints such as unique indexes and foreign indexes are intact.


A transaction should be ignorant of the existence of any other transactions. In other words, from the point of view of a given transaction, it is the only thing operating on the database.


When a transaction commits, its changes are permanent—even in the event of a system failure.

In other words, if you have an account transfer, it meets ACID requirements in the following ways:

  • The transfer is atomic if and only if the sum of the two bank accounts is the same before and after the transfer. The debit to the savings account cannot occur without the credit to the checking account following.

  • The transfer is consistent as long as nothing else happens to either account while the transfer is in process. For example, the checking account cannot be deleted from the database while the debit to the savings is occurring.

  • The transfer is isolated as long as another transaction is incapable of seeing the debited savings account before the transfer has completed.

  • The transfer is durable as long as the transfer can survive a catastrophic event such as a server crash or other kind of system failure.

Your database engine is largely responsible for guaranteeing the ACIDity of your transactions. You, the software architect, are nevertheless responsible for identifying what events make up a transaction and how the application should package those transactions for the database.

BEST PRACTICE: Choose technologies that will guarantee the ACIDity of your application's transactions.

3.1.2 Transaction Design

The key to identifying transactions is determining what stages in your use cases represent consistent database states. If we examine the transfer example, we see two distinct events that make up the transfer:

  1. Debit the savings account.

  2. Credit the checking account.

After the debit of the savings account, the database is said to be in an inconsistent state because important information about the system would be lost should the transfer fail to complete. In this case, the important information is someone's money!

Inconsistency can also mean having orphaned data in the database. Consider, for example, the deletion of a person from a database with the following steps:

  1. Delete the person from the Person table.

  2. Delete all addresses for the person from the Address table.

  3. Delete all phone numbers for the person from the Phone table.

If something goes wrong after the first step, you could end up with a database full of unreferenced addresses and phone numbers unless the transaction returns the system to its initial state. The database is therefore in an inconsistent state until all addresses and phone numbers are deleted.

Though you want your transactions to be large enough to prevent the database from entering inconsistent states, you also want them as small as possible. As you will see later in this chapter, transactions make huge demands on a database. The larger the transaction, the more resources it eats and the worse the performance of your application.

BEST PRACTICE: Define your transactions to have the minimum number of steps necessary while leaving the data store in a consistent state.

To illustrate this problem in action, consider an e-commerce application that needs to create a customer before the customer can place an order. To make the application as easy to use for the customer as possible, you may let her enter her customer information along with her first order. The use case thus looks something like this:

  1. Add the customer information to the Customer table.

  2. Add address information to the Address table.

  3. Add phone information to the Phone table.

  4. Create an order in the Order table.

  5. Add a line item in the LineItem table.

  6. Decrease the inventory count in the Product table.

It is the combined job of the business analyst and information architect to craft this use case as the best way for a new customer to buy something. It is your job as the software architect to figure out its implications on the database. In many situations, there will be a one-to-one correspondence between use cases and transactions. In this case, however, we have two separate transactions because the database can be in a consistent state with the customer information and no order information. In other words, steps 1-3 make up the first transaction and steps 4-6 make up the second. Figure 3-1 contrasts the use case from the client's perspective with the sequence from the architect's perspective.

Figure 3-1. A multitransaction use case from the perspectives of a client application and an architect

By breaking this one use case into two transactions, you will not sacrifice database consistency but you will gain performance. The division enhances performance because you enable other transactions that may be waiting on resources blocked only by the first three steps to execute prior to the completion of the entire use case. If you encapsulated the use case in a single transaction, then those other transactions would have to wait until the completion of the use case. Figure 3-2 shows how breaking the use case into two transactions can reduce blocking.

Figure 3-2. Reduced blocking by dividing work between two transactions
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