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Another type of fragmentation that you typically care about is what I referred to as average page density. Some database professionals refer to this type of fragmentation as internal fragmentation , but to avoid confusion I consciously didn't use this term earlier. While logical scan fragmentation is never a good thing, average scan fragmentation has two facets. A low percentage (low level of page population) has a negative impact on queries that read data, as they end up reading more pages than they could potentially if the pages were better populated. The positive impact of having some free space in index pages is that insertions of rows to such pages would not cause page splitswhich are very expensive. As you can guess, free space in index pages is bad in systems that involve mostly reads (for example, data warehouses) and good for systems that involve many inserts (for example, OLTP systems). You might even want to introduce some free space in index pages by specifying a fillfactor value when you rebuild your indexes. To determine whether you need to rebuild or reorganize your indexes, you need information about both types of fragmentation. In SQL Server 2005, you can get this information by querying the DMF dm_db_index_physical_stats . For example, the following query will return fragmentation information about the indexes in the Performance database: SELECT * FROM sys.dm_db_index_physical_stats( DB_ID('Performance'), NULL, NULL, NULL, NULL);

This chapter covers treatment of specialized data structures called graphs, trees, and hierarchies in Microsoft SQL Server using T-SQL. Of the three, probably the most commonly used among T-SQL programmers is the hierarchy, and this term is sometimes used even when the data structure involved is not really a hierarchy. I ll start with a terminology section describing each data structure to clear the confusion. Treatment (representation, maintenance, and manipulation) of graphs, trees, and hierarchies in an RDBMS is far from trivial. I ll discuss two main approaches, one based on iterative/recursive logic and another based on materializing extra information in the database that describes the data structure. This chapter also covers the HIERARCHYID data type introduced in SQL Server 2008, which is designed to help in maintaining and querying graphs.

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Note The explanations in this section are based on de nitions from the National Institute of Standards and Technology (NIST). I made some revisions and added some narrative to the original de nitions to make them less formal and keep them relevant to the subject area (T-SQL).

For more complete and formal de nitions of graphs, trees, and related terms, please refer to http://www.nist.gov/dads/.

Microsoft SQL Server 2008 introduced a number of new technologies related to tracking changes to data . Although alternatives have been available for this in earlier versions of the product, tracking access to data has been more difficult in the past . SQL Server 2008 helps address this need .

The fragmentation types I mentioned will show up in the attributes avg_fragmentation_in_percent and avg_page_space_used_in_percent , and as you can see, the attribute names are self explanatory. In SQL Server 2000, you use the DBCC SHOWCONTIG command to get similar information: DBCC SHOWCONTIG WITH ALL_INDEXES, TABLERESULTS, NO_INFOMSGS;

A graph is a set of items connected by edges. Each item is called a vertex or node. An edge is a connection between two vertices of a graph. A graph is a catchall term for a data structure, and many scenarios can be represented as graphs for example, employee organizational charts, bills of materials (BOMs), road systems, and so on. To narrow down the type of graph to a more speci c case, you need to identify its properties: Directed/Undirected In a directed graph (also known as a digraph), the two vertices of an edge have a direction or order. For example, in a BOM graph for coffee shop products,

We need to track access and changes to data for several reasons:

Latte contains Milk and not the other way around. The graph has an edge (containment relationship) for the pair of vertices/items (Latte, Milk) but has no edge for the pair (Milk, Latte). In an undirected graph, each edge simply connects two vertices, with no particular order. For example, a road system graph could have a road between Los Angeles and San Francisco. The edge (road) between the vertices (cities) Los Angeles and San Francisco can be expressed as either of the following: {Los Angeles, San Francisco} or {San Francisco, Los Angeles}. Acyclic An acyclic graph is a graph with no cycle that is, no path that starts and ends at the same vertex for example, employee organizational charts and BOMs. A directed acyclic graph is also known as a DAG. If the graph has paths that start and end at the same vertex as there usually are in road systems the graph is not acyclic. Connected A connected graph is a graph where there s a path between every pair of vertices for example, employee organizational charts.

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