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Recall the outer-join operations described in Section 333 For example, the natural left outer join customer 1 depositor contains the join of customer and depositor, and, in addition, for each customer tuple t that has no matching tuple in depositor (that is, where customer-name is not in depositor), the following tuple t1 is added to the result For all attributes in the schema of customer, tuple t1 has the same values as tuple t The remaining attributes (from the schema of depositor) of tuple t1 contain the value null We can implement the outer-join operations by using one of two strategies: 1 Compute the corresponding join, and then add further tuples to the join result to get the outer-join result Consider the left outer-join operation and two relations: r(R) and s(S) To evaluate r 1 s, we rst compute r 1 s, and save that result as temporary relation q1 Next, we compute r R (q1 ), which gives tuples in r that did not participate in the join We can use any of the algorithms for computing the joins, projection, and set difference described earlier to compute the outer joins We pad each of these tuples with null values for attributes from s, and add it to q1 to get the result of the outer join The right outer-join operation r 1 s is equivalent to s 1 r, and can therefore be implemented in a symmetric fashion to the left outer join We can implement the full outer-join operation r 1 s by computing the join r 1 s, and then adding the extra tuples of both the left and right outer-join operations, as before 2 Modify the join algorithms It is easy to extend the nested-loop join algorithms to compute the left outer join: Tuples in the outer relation that do not match any tuple in the inner relation are written to the output after being padded with null values However, it is hard to extend the nested-loop join to compute the full outer join. excel data matrix font data matrix excel free download - SourceForge
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Complete Data Matrix generation function for Microsoft Office Excel spreadsheet. Natural outer joins and outer joins with an equi-join condition can be computed by extensions of the merge join and hash join algorithms Merge join can be extended to compute the full outer join as follows: When the merge of the two relations is being done, tuples in either relation that did not match any tuple in the other relation can be padded with nulls and written to the output Similarly, we can extend merge join to compute the left and right outer joins by writing out nonmatching tuples (padded with nulls) from only one of the relations Since the relations are sorted, it is easy to detect whether or not a tuple matches any tuples from the other relation For example, when a merge join of customer and depositor is done, the tuples are read in sorted order of customer-name, and it is easy to check, for each tuple, whether there is a matching tuple in the other The cost estimates for implementing outer joins using the merge join algorithm are the same as are those for the corresponding join The only difference lies in size of the result, and therefore in the block transfers for writing it out, which we did not count in our earlier cost estimates The extension of the hash join algorithm to compute outer joins is left for you to do as an exercise (Exercise 1311) data matrix generator excel template Excel 2016/2013 Data Matrix Generator free download. No Excel ...
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