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The index-locking protocol takes advantage of the availability of indices on a relation, by turning instances of the phantom phenomenon into con icts on locks on index leaf nodes The protocol operates as follows: Every relation must have at least one index A transaction Ti can access tuples of a relation only after rst nding them through one or more of the indices on the relation A transaction Ti that performs a lookup (whether a range lookup or a point lookup) must acquire a shared lock on all the index leaf nodes that it accesses A transaction Ti may not insert, delete, or update a tuple ti in a relation r without updating all indices on r The transaction must obtain exclusive locks on all index leaf nodes that are affected by the insertion, deletion, or update For insertion and deletion, the leaf nodes affected are those that contain (after insertion) or contained (before deletion) the search-key value of the tuple For updates, the leaf nodes affected are those that (before the modi cation) contained the old value of the search-key, and nodes that (after the modi cation) contain the new value of the search-key The rules of the two-phase locking protocol must be observed Variants of the index-locking technique exist for eliminating the phantom phenomenon under the other concurrency-control protocols presented in this chapter

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This table has insertion anomalies A n insertion anomaly occurs w h e n extra data beyond the desired data must be added to the database For example, to insert a course, it is necessary to know a student and an offering because the combination o f StdSSN and OfferNo is the primary key Remember that a row cannot exist with null values for part o f its primary key

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Serializability is a useful concept because it allows programmers to ignore issues related to concurrency when they code transactions If every transaction has the property that it maintains database consistency if executed alone, then serializability ensures that concurrent executions maintain consistency However, the protocols required to ensure serializability may allow too little concurrency for certain applications In these cases, weaker levels of consistency are used The use of weaker levels of consistency places additional burdens on programmers for ensuring database correctness

Data Rate (Baud)

The purpose of degree-two consistency is to avoid cascading aborts without necessarily ensuring serializability The locking protocol for degree-two consistency uses the same two lock modes that we used for the two-phase locking protocol: shared (S) and exclusive (X) A transaction must hold the appropriate lock mode when it accesses a data item In contrast to the situation in two-phase locking, S-locks may be released at any time, and locks may be acquired at any time Exclusive locks cannot be released until the transaction either commits or aborts Serializability is not ensured by this protocol Indeed, a transaction may read the same data item twice and obtain different

The McGraw Hill Companies, 2001

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This table has update anomalies A n update anomaly occurs w h e n it is necessary to change multiple rows to modify only a single fact For example, if w e change the StdClass o f student S I , two rows must be changed If SI was enrolled in 10 classes, 10 rows must be changed

2,400 4,800 9,600 19,200 38,400 56,000

16

T3 lock-S(Q) read(Q) unlock(Q)

This table has deletion anomalies A deletion anomaly occurs whenever deleting a row inadvertently causes other data to be deleted For example, if we delete the enrollment o f S2 in 0 3 (third row), w e lose the information about offering 0 3 and course C3 To deal with these anomalies, users may circumvent them (such as using a default

lock-X(Q) read(Q) write(Q) unlock(Q)

1,220 625 312 156 78 31

results In Figure 1620, T3 reads the value of Q before and after that value is written by T4 The potential for inconsistency due to nonserializable schedules under degree-two consistency makes this approach undesirable for many applications

Cursor stability is a form of degree-two consistency designed for programs written in host languages, which iterate over tuples of a relation by using cursors Instead of locking the entire relation, cursor stability ensures that The tuple that is currently being processed by the iteration is locked in shared mode Any modi ed tuples are locked in exclusive mode until the transaction commits These rules ensure that degree-two consistency is obtained Two-phase locking is not required Serializability is not guaranteed Cursor stability is used in practice on heavily accessed relations as a means of increasing concurrency and improving system performance Applications that use cursor stability must be coded in a way that ensures database consistency despite the possibility of nonserializable schedules Thus, the use of cursor stability is limited to specialized situations with simple consistency constraints

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