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Additional Components of the Access Methods Manager
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The row operations manager and the index manager can be considered components of the access methods manager, because they carry out the actual method of access. Each is responsible for manipulating and maintaining its respective on-disk data structures, namely rows of data or B-tree indexes. They understand and manipulate information on data and index pages.
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The Row Operations Manager
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This component retrieves, modifies, and performs operations on individual rows. It performs an operation within a row, such as retrieve column 2 or write this value to column 3. As a result of the work performed by the access methods manager, lock manager, and transaction manager, the row will have been found and will be appropriately locked and part of a transaction. After formatting or modifying a row in memory, the row operations manager inserts or deletes a row. The row operations manager also handles updates. SQL Server 2000 offers three methods for handling updates. All three are direct, which means that there is no need for two passes through the transaction log, as was the case with deferred updates in earlier versions of SQL Server. SQL Server 2000 has no concept of a deferred data modification operation.
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The three update modes in SQL Server 2000 are as follows:
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In-Place Mode This mode is used to update a heap or clustered index when none of the clustering keys change. The update can be done in place, and the new data is written to the same slot on the data page. Split Mode This mode is used to update nonunique indexes when the index keys change. The update is split into two operations a delete followed by an insert and these operations are performed independently of each other. Split with Collapse Mode This mode is used to update a unique index when the index keys change. After the update is rewritten into delete and insert operations, if the same index key is both deleted and then reinserted with a new value, it is collapsed into a single update operation.
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If you want to reorganize a table for example, to reestablish a FILLFACTOR value or to make data more contiguous after a lot of data modification has occurred you can use a clustered index, which makes the reorganization easy. You simply rebuild the clustered index, which rebuilds the entire table. In the case of a delete, if the row deleted is the last row on a data page, that page is deallocated. (The only exception occurs if that page is the only one remaining in the table. A table always contains at least one page, even if it is empty.)
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The Index Manager
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While the row operations manager works with individual rows, the index manager maintains and supports searches on B-trees, which are used for SQL Server indexes. An index is structured as a tree, with both a root page and intermediate- and lower-level pages (or branches). A B-tree groups records that have similar index keys, thereby allowing fast access to data by searching on a key value. The B-tree s core feature is its ability to balance the index tree. ( B stands for balanced.) Branches of the index tree are spliced together or split apart as necessary so that the search for any given record always traverses the same number of levels and thus requires the same number of page accesses.
SQL Server 2000 Administration
The traverse begins at the root page, progresses to intermediate index levels, and finally moves to bottom-level pages, called leaf pages. The index is used to find the correct leaf page. On a qualified retrieval or delete, the correct leaf page is the lowest page of the tree at which one or more rows with the specified key or keys reside. SQL Server supports both clustered and nonclustered indexes. In a nonclustered index, shown in Figure 2-3, the leaf level of the tree (the leaf page of the index) contains every key value in the index along with a row locator for each key value. The row locator is also called a bookmark and indicates where to find the referenced data. A row locator can have one of two forms. If the base table has no clustered index, the table is referred to as a heap. The row locators in nonclustered index leaf pages for a heap are pointers to the actual rows in which the data can be found, and these pointers consist of a Row ID (RID), which is a file number, a page number, and a row number on the page. If the base table has a clustered index, the row locators in any nonclustered index leaf pages contain the clustered index key value for the row. After reaching the leaf level in a nonclustered index, you can find the exact location of the data, though the page on which that data resides must still be separately retrieved. Because you can access the data directly, you do not need to scan all the data pages for a qualifying row. Better yet, in a clustered index, shown in Figure 2-4, the leaf level actually contains the data row, not simply the index key. A clustered index keeps the data in a table physically ordered around the key of the clustered index, while the leaf page of a clustered index is in fact the data page itself. Because data can be physically ordered in only one way, only one clustered index can exist per table. This makes the selection of the appropriate key value on which to cluster data an important performance consideration. You can also use indexes to ensure the uniqueness of a particular key value. In fact, the PRIMARY KEY and UNIQUE constraints on a column work by creating a unique index on the column s values. The optimizer can use the knowledge that an index is unique in formulating an effective query plan.
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