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The rst part of the de nition means that in any legal instance, the values in the key elds uniquely identify a tuple in the instance When specifying a key constraint, the DBA or user must be sure that this constraint will not prevent them from storing a correct set of tuples (A similar comment applies to the speci cation of other kinds of ICs as well) The notion of correctness here depends upon the nature of the data being stored For example, several students may have the same name, although each student has a unique student id If the name eld is declared to be a key, the DBMS will not allow the Students relation to contain two tuples describing di erent students with the same name! The second part of the de nition means, for example, that the set of elds {sid, name} is not a key for Students, because this set properly contains the key {sid} The set {sid, name} is an example of a superkey, which is a set of elds that contains a key Look again at the instance of the Students relation in Figure 31 Observe that two di erent rows always have di erent sid values; sid is a key and uniquely identi es a tuple However, this does not hold for nonkey elds For example, the relation contains two rows with Smith in the name eld Note that every relation is guaranteed to have a key Since a relation is a set of tuples, the set of all elds is always a superkey If other constraints hold, some subset of the elds may form a key, but if not, the set of all elds is a key A relation may have several candidate keys For example, the login and age elds of the Students relation may, taken together, also identify students uniquely That is, {login, age} is also a key It may seem that login is a key, since no two rows in the example instance have the same login value However, the key must identify tuples uniquely in all possible legal instances of the relation By stating that {login, age} is a key, the user is declaring that two students may have the same login or age, but not both Out of all the available candidate keys, a database designer can identify a primary key Intuitively, a tuple can be referred to from elsewhere in the database by storing the values of its primary key elds For example, we can refer to a Students tuple by storing its sid value As a consequence of referring to student tuples in this manner, tuples are frequently accessed by specifying their sid value In principle, we can use any key, not just the primary key, to refer to a tuple However, using the primary key is preferable because it is what the DBMS expects this is the signi cance of designating a particular candidate key as a primary key and optimizes for For example, the DBMS may create an index with the primary key elds as the search key, to make the retrieval of a tuple given its primary key value e cient The idea of referring to a tuple is developed further in the next section.

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The value of W for various types of blocking is provided by Eqs 2-9 to 2-11 E ect of Seeks on the Transfer Rate In reading even larger quantities of data sequentially, we will have to cross over cylinder boundaries One minimal seek is required once per cylinder Even though seeks may be infrequent, the required correction may still be far from negligible because of the relatively long seek times on many units On disks with few surfaces, seeks will be needed frequently In order to account for the e ect of seeks, we will consider the seek frequency and seek time, and use the combination to predict an e ective seek time per record s The assessment requires some understanding of the operating environment In a multiprogrammed environment, competition may exist for the seek mechanism, so that a full seek time delay s may be incurred frequently, up to once per block The e ective delay per record is then s = s/Bfr But when interference is modest or can be controlled during a transaction, the expected delay beyween blocks is much less In the case where there is no interference at all, the seek delay will occur only at the end of a cylinder We use the number of surfaces

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namespace Example_5_20_ _ _ _A_Forever_Loop { class Program { public static void Main( ) { int counterVariable = 0; // initialization for (; ; ) // forever { Console.WriteLine("counter: {0} ", counterVariable++); // increment if (counterVariable > 10) // test { break; } } } } }

Sec 2-2

The output looks like this:

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k to determine the size of a cylinder in terms of tracks and then apply the seek delay s1 To evaluate seeks per record, the number of records per track nrt is also needed; the entire cylinder contains k nrt records In general a seek to the next track (s1 ) requires less than the time of one revolution (2r) The desire to keep s1 < 2r caused in fact the mechanical complexity which led to the complex seek-time curve shown in Fig 2-6 Since we continue the reading of records at the beginning of the next track of the new cylinder, we have, if s1 < 2r, a seek delay per cylinder of 2r One revolution can again be expressed in terms of the time to transfer all records from a track, so that 2r = nrt(R + W )/t Combining these terms we nd the minimal e ect, from a track-switch delay occurring only at cylinder boundaries, to be s = s1 /(k nrt) 2r/(k nrt) = ((R + W )/t)/k per record The seek overhead per record for continuous reading s is now bound by s 1R+W s k t Bfr bounds 2-15

counter: counter: counter: counter: counter: counter: counter: counter: counter: counter: counter: 0 1 2 3 4 5 6 7 8 9 10

The range of these bounds is great, easily a factor 100 As a practical estimate the seek time overhead per record, while reading sequentially, may be taken to be equal to the minimum value (2r = nrt(R + W )/t), but occurring at every track boundary, so that s (R + W ) 2r = rt n t estimate 2-16

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