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modified to reflect the new order for the laptop Finally, the Product table must be modified to show that there is now one less laptop in the store inventory In an advanced DBMS package (such as PostgreSQL), all of these steps can be combined into a single database transaction, which represents the activity of a customer purchasing a laptop Of course, with a multistep transaction there are more opportunities for things to go wrong The trick for any DBMS is to know how to properly handle transactions This is where the database ACID test comes in
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Over the years, database experts have devised rules for how databases should handle transactions The benchmark of all professional database systems is the ACID test The ACID test is actually an acronym for a set of database features defining how a professionalquality database should support transactions These features are as follows: Atomicity Consistency Isolation Durability The ACID tests define a set of standards for ensuring that data is protected in all circumstances It is crucial for databases to protect data at all cost Invalid or lost data can render a database useless The following sections describe each of the features of the ACID test
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The atomicity feature states that for a transaction to be considered successful, all steps within the transaction must complete successfully For a single command transaction, this is no big deal The trick comes when handling transactions that contain multiple commands In atomicity, either all of the database modification commands within the transaction should be applied to the database, or none of them should A transaction should not be allowed to complete part-way In our store example, it would be a huge problem if the Order table is updated to reflect a purchase without the Product table inventory field being updated to reflect the number of items purchased The store would have one less laptop in inventory than what the database thought was there To support atomicity, PostgreSQL uses a system called commit and rollback Database modifications are only temporarily performed during a transaction When it appears that all of the modifications in a transaction would complete successfully, the transaction is committed (all of the data in the affected tables is modified per the transaction commands) If it appears that any of the modifications in the transaction would fail (such as an item not being in the Product table), the transaction is rolled back (any previous steps that were successful are reversed) This ensures that the transaction is completed as a whole
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PostgreSQL uses the two-phase commit approach to committing transactions The two-phase commit performs the transaction using two steps (or phases): 1 A prepare phase where a transaction is analyzed to determine if the database is able to commit the entire transaction 2 A commit phase, where the transaction is physically committed to the database The two-phase commit approach allows PostgreSQL to test all transaction commands during the prepare phase without having to modify any data in the actual tables Table data is not changed until the commit phase is complete
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The concept of consistency is a little more difficult than atomicity The consistency feature states that every transaction should leave the database in a valid state The tricky part here is what is considered a valid state For most simple databases, this is not an issue Transactions that update or modify simple tables are usually not a problem Often this feature is used when advanced rules or triggers are present in a database for defining how data is stored (we will talk more about these in the Rules and Triggers sections later in this chapter) For now, it is sufficient to know that rules and triggers are internal database functions that occur based on a specific activity on data in a table Developers create triggers to ensure that data is entered into the database correctly, such as ensuring that each record in the Customer table contains a valid phone number If a customer record is added to the Customer table without a phone number entry, a trigger can cause the record to be rejected by the DBMS Consistency states that all rules and triggers are applied properly to a transaction If any rule or trigger fails, the transaction is not committed to the database For our example, if a store clerk attempts to add a new customer record without a phone number, the trigger would prevent the record from being added, causing the transaction to fail, thus preserving the integrity of the customer record Consistency can also be applied to multiple tables For example, a developer can create a rule for the Order table that automatically updates a Billing table with the cost of a customer s order What would happen if an order was inserted into the Order table, but the database system crashed before the rule could update the Billing table Free products are good for customers, but a bad way to do business for the store To meet the ACID consistency test, an entry into the Order table should not be made until it is certain that the database rule creating an entry in the Billing table was completed This ensures that the data in the two tables remains consistent
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