SELECT WHERE foreign_key_column = 70 in Java

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SELECT WHERE foreign_key_column = 70
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has a good chance of picking up all the matching rows with one page read Primarykey/foreign-key joins will be faster for a similar reason Notice that this advantage exists precisely because foreign keys, unlike primary keys, are not unique
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Base the cluster key on the column with which you most often will do range searches or sequential passes For example, if you will frequently do this type of search:
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SELECT WHERE column1 BETWEEN 70 AND 150 ORDER BY column1
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then column1 is a good choice for the cluster key
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Beware, though, if the possible cluster key is a monotonically sequential, or serial key for example, an integer whose values always go up and always go up by the same amount, when you INSERT The problem with monotonics (which are sometimes hidden behind names like "identity" or "timestamp" columns see 7, "Columns") is that the nth row and the (n + 1)th row will both fit in the same page so there might be contention for the same resource Usually, in multiuser systems, you want rows to be dispersed This above all The benefits of clustered indexing depend on a monster assumption; namely, that you want to retrieve and sort by the cluster key far, far more often than with any other key Secondary indexes to a strong-clustered index Consider this scenario You've defined Table1 with a strong-clustered index the cluster key is column1 and inserted the following data: Table1column1 1 10 100 Table1column2 Willy Margaret Fiona
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Now you want to add a secondary index a nonclustered index on column2 Here's the problem Because each row of the table is in an index leaf page, it can move within the page whenever another row is inserted or deleted above it, and it can even move to a different page if there's an index split If that happens, the column2 pointers are useless because the pointer part of an index key (aka row locator or bookmark) is normally a row identifier (aka ROWID or RID) Recall from 5, "Joins," that a row identifier is a physical address, such as {File number, Page number, Row number within page} But the physical address of the row can change so a ROWID is unreliable One solution to this problem the old Oracle8 solution is to disallow the creation of any nonclustered indexes to a strong-clustered table[4] This has the unfortunate effect of limiting the
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usefulness of strong-clustered indexes, and in fact they're rarer in Oracle shops than in Microsoft shops You can write your own schemes for doing "manual joins," and so on, but that's no fun
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The latest Oracle incarnation Oracle9i now allows secondary indexes to clustered tables Oracle uses the term "index-organized tables" where we use "strong-clustered index" and insists that the cluster key be the table's primary key but the differences are at a detailed level, and we won't go into them here
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Another solution the Microsoft solution is to make the row locator be the cluster key That way, when the DBMS looks up column2, it ends up with the cluster key value that uniquely identifies the row: the value of column1 For example, suppose you ask for information about Fiona:
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SELECT * FROM Table1 WHERE column2 = 'Fiona'
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Now the DBMS can look up the data row in the clustered index, by searching for the column1 value an elegant solution If the clustered index and the secondary index both have three levels, the DBMS needs (3 + 3) six I/Os to reach the data row, given the column2 value The search would be faster if there was a ROWID In fact, a heap is "as good" (same I/O) if you're always looking for a unique value So let's suppose this You have a table with only two unique or nearly unique indexes Access is equally likely via either index, so you're better off with a heap This may sound counter-intuitive, so let's examine the situation more closely Here, once again, are our assumptions:
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Both indexes are unique, or nearly so Both indexes have three levels Both indexes are equally likely to be chosen
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Now, if you're looking up in a heap and the number of index levels is always three, then the average I/O count for the lookup is four three I/Os to access via either index and one I/O to access the heap page, given the ROWID On the other hand, if you're looking up in a cluster, the time to look up with the cluster key is three I/Os, and the time to look up via the secondary index is six as shown earlier The average when either index is equally likely is thus 45 I/Os ((3 I/Os + 6 I/Os)/2) This is more than the four I/Os needed for a heap, and so a heap is better if access is equally likely with either index Still, Microsoft recommends clustered indexes "always" While we're on the subject of strong-clustered indexes, here are some tips on their use:
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Remember that a nonclustered index on a table points to the leaf page of the table's clustered index (because the clustered index's leaf page is the same as the data page) It is therefore a duplication if you have a column in the nonclustered index that's also in the clustered index For example, suppose you have a strong-clustered index on (emp_id, sex) and are considering adding another index on (surname, sex) Don't Since sex is already "covered" in the clustered index, make the nonclustered index on (surname) alone A covering index is even more important because you're not just saving one I/O, you're saving three or four If you want quick access to a table's cluster key columns alone, then make a nonclustered index of the same columns The index must be a covering index, because you don't want to access the actual data rows The index must also have a short key, because the advantage lies in the fact that the nonclustered index should have fewer layers than the clustered index This
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tip will work, but if the clustered index is so big that it has too many levels, you should solve that problem with normalizing or partitioning, which we discussed in 8, "Tables"
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