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Generation of expressions is only part of the query-optimization process, since each operation in the expression can be implemented with different algorithms An evaluation plan is therefore needed to de ne exactly what algorithm should be used for each operation, and how the execution of the operations should be coordinated Figure 144 illustrates one possible evaluation plan for the expression from Figure 143 As we have seen, several different algorithms can be used for each relational operation, giving rise to alternative evaluation plans Further, decisions about pipelining have to be made In the gure, the edges from the selection operations to the merge join operation are marked as pipelined; pipelining is feasible if the selection operations generate their output sorted on the join attributes They would do so if the indices on branch and account store records with equal values for the index attributes sorted by branch-name

Fig 6-4 Circuit analyzed in Example 6-2

Once the video track is locked, click the recorded audio track to select it separately from the video track FIGURE 14-5

One way to choose an evaluation plan for a query expression is simply to choose for each operation the cheapest algorithm for evaluating it We can choose any ordering of the operations that ensures that operations lower in the tree are executed before operations higher in the tree However, choosing the cheapest algorithm for each operation independently is not necessarily a good idea Although a merge join at a given level may be costlier than a hash join, it may provide a sorted output that makes evaluating a later operation (such as duplicate elimination, intersection, or another merge join) cheaper Similarly, a nested-loop join with indexing may provide opportunities for pipelining the results to the next operation, and thus may be useful even if it is not the cheapest way of

customer-name (sort to remove duplicates) (hash join)

The McGraw Hill Companies, 2001

performing the join To choose the best overall algorithm, we must consider even nonoptimal algorithms for individual operations Thus, in addition to considering alternative expressions for a query, we must also consider alternative algorithms for each operation in an expression We can use rules much like the equivalence rules to de ne what algorithms can be used for each operation, and whether its result can be pipelined or must be materialized We can use these rules to generate all the query-evaluation plans for a given expression Given an evaluation plan, we can estimate its cost using statistics estimated by the techniques in Section 142 coupled with cost estimates for various algorithms and evaluation methods described in 13 That still leaves the problem of choosing the best evaluation plan for a query There are two broad approaches: The rst searches all the plans, and chooses the best plan in a cost-based fashion The second uses heuristics to choose a plan We discuss these approaches next Practical query optimizers incorporate elements of both approaches

C = 2 + 3 = 5 F Now we have the 8 F capacitor in series with C Hence the total or equivalent capacitance seen by the voltage source is CT = 1 40 = = 31 F 1/8 + 1/5 13

If you aren t sure that you want to permanently remove the recorded audio track, you can also use the audio toolbox to reduce its volume level to the lowest possible level This may not always completely remove any especially loud sounds on the track, however

A cost-based optimizer generates a range of query-evaluation plans from the given query by using the equivalence rules, and chooses the one with the least cost For a complex query, the number of different query plans that are equivalent to a given plan can be large As an illustration, consider the expression r1

where the joins are expressed without any ordering With n = 3, there are 12 different join orderings: r1 r2 r3

1 (r2 1 r3 ) 1 (r1 1 r3 ) 1 (r1 1 r2 )