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Transactional replication in a bidirectional configuration Transaction replication in a peer-to-peer configuration

Each of these configurations uses transactional replication moving data in a single direction. The configuration is such that each side publishes the same data. Although the data succeeds in flowing in two directions, this configuration is different from twoway data movement. Transactional replication does not have any conflict detection or resolution, so any data conflicts introduced will create errors. The worst of these conflicts is an update conflict. If the same row of data were updated at multiple locations between cycles of the replication engine, each change would be distributed to all other databases partic ipating in these configurations, but the change would be prevented from looping back to the originator. Although it is desirable to prevent endlessly looping transactions, a very serious situation is created. Consider the following example. DatabaseA and DatabaseB are participating with each other in a bidirectional transac tional replication configuration. A user on DatabaseA changes the ABC Company to XYZ Company. A user on DatabaseB changes the credit limit from $5,000 to $75,000. The replication engine then runs a cycle to move the changes. Because the default con figuration for transactional replication is to move the entire row, you would wind up with XYZ Company having a credit line of $5,000 on DatabaseB, and ABC Company having a credit line of $75,000 on DatabaseA. The replication engine would prevent the transactions from looping back and, because each transaction would succeed, no errors would be generated. The databases would be permanently out of sync with each other, and the only way to fix the problem would be through user intervention.

As discussed earlier, the most basic unit of storage in SQL Server is a data page. Data pages are organized into extents, and extents are referenced by tables. In SQL Server 2005, tables are organized into one or more partitions, and each partition is stored in either a heap, which is simply a non-grouped data page, or clustered index structure. (See 7, Optimizing SQL Server 2005 Performance, Lesson 2, Optimizing Index Strategies, for more information on indexes.) The pages within the heap or clustered index structure are organized into allocation units, depending on the type of data contained within the row of data. (For example, TEXT and IMAGE data are stored in different allocation units than VARCHAR data.)

11

RAID is an acronym for redundant array of inexpensive disks. Configuring RAID is one way of protecting the computer that hosts a SQL Server 2005 instance from the failure of a hard disk drive. Software RAID levels 0, 1, 5, and 10 are commonly used with Microsoft SQL Server 2005. RAID 10 is supported only in hardware and not by the operating system. Each type of software RAID has benefits and drawbacks in terms of performance and redundancy. A good rule of thumb is that the better the redundancy, the smaller the performance benefit.

SQL Server 2005 supports a single Clustered Index per table and up to 249 NonClustered indexes per table. Indexes in SQL Server 2005 utilize a structure known as a Balanced Tree (B-Tree). A B-Tree structure is a structure where navigating from the root of the structure to any point should take exactly the same number of intermediate steps no matter what end point is chosen. Each page in an index B-tree is called an index node. The top node of the B-tree is called the root node. The bottom level of nodes in the index is called the leaf nodes. Any index levels between the root and the leaf nodes are collectively known as intermediate levels. In a clustered index, the leaf nodes contain the data pages of the underlying table. The root and intermediate level nodes contain index pages holding index rows. Each index row contains a key value and a pointer to either an intermediate-level page in the B-tree, or a data row in the leaf level of the index. The pages in each level of the index are linked in a doubly linked list. Data is either organized into a Clustered Index or a Heap.