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to the left child of the root It then accesses that node, and obtains a pointer to the left child This left-child node originally contained the search-key values Brighton and Downtown Since this node is currently locked by the insertion operation in exclusive mode, the lookup operation must wait Note that, at this point, the lookup operation holds no locks at all! The insertion operation now unlocks the leaf node and relocks its parent, this time in exclusive mode It completes the insertion, leaving the B+ -tree as in Figure 1622 The lookup operation proceeds However, it is holding a pointer to an incorrect leaf node It therefore follows the right-sibling pointer to locate the next node If this node, too, turns out to be incorrect, the lookup follows that node s right-sibling pointer It can be shown that, if a lookup holds a pointer to an incorrect node, then, by following right-sibling pointers, the lookup must eventually reach the correct node Lookup and insertion operations cannot lead to deadlock Coalescing of nodes during deletion can cause inconsistencies, since a lookup may have read a pointer to a deleted node from its parent, before the parent node was updated, and may then try to access the deleted node The lookup would then have to restart from the root Leaving nodes uncoalesced avoids such inconsistencies This solution results in nodes that contain too few search-key values and that violate some properties of B+ -trees In most databases, however, insertions are more frequent than deletions, so it is likely that nodes that have too few search-key values will gain additional values relatively quickly Instead of locking index leaf nodes in a two-phase manner, some index concurrency control schemes use key-value locking on individual key values, allowing other key values to be inserted or deleted from the same leaf Key-value locking thus provides increased concurrency Using key-value locking naively, however, would allow the phantom phenomenon to occur; to prevent the phantom phenomenon, the next-key locking technique is used In this technique, every index lookup must lock not only the keys found within the range (or the single key, in case of a point lookup) but also the next key value that is, the key value just greater than the last key value that was within the range Also, every insert must lock not only the value that is inserted, but also the next key value Thus, if a transaction attempts to insert a value that was within the range of the index lookup of another transaction, the two transactions would con ict on the key value next to the inserted key value Similarly, deletes must also lock the next key value to the value being deleted, to ensure that con icts with subsequent range lookups of other queries are detected.

Unshielded Twisted Pair (UTP) Table 4-11 shows the pinouts for Token Ring UTP ports Table 4-11: Token Ring DB-9 Pinout for UTP Port

The McGraw Hill Companies, 2001

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Besides understanding the functional dependency definition and notation, database design ers must be able to identify functional dependencies when collecting database requirements In problem narratives, s o m e functional dependencies can be identified by statements about uniqueness For example, a user m a y state that each course offering has a unique offering number along with the year and term o f the offering From this statement, the designer should assert that OfferNo > OffYear and OffTerm You can also identify functional depen dencies in a table design resulting from the conversion o f an ERD Functional dependencies would be asserted for each unique column (primary key or other candidate key) with the unique column as the L H S and other columns in the table o n the right-hand side (RHS)

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1610 Summary

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