Beginning Oracle Database 11g Administration From Novice to Professional

CHAPTER 7 PHYSICAL DATABASE DESIGN 137
Advanced Topics
Two advanced indexing methods are worth knowing about but are beyond the scope of
this book: bitmap indexing and reverse key indexing. Information on those methods can
be found in Oracle Database 11g Concepts Manual or the other references listed at the
end of this chapter.
bitmap index uses a single bit (0 or 1) to
represent each row; a “bitmap” is generated for each distinct value contained in
the indexed column.
increasing values, Oracle will attempt to
insert the index information into the same leaf block each time. This dramatically
increases the frequency of splitting and balancing operations. The solution is to
create a reverse key index.
Partitions
Because Oracle tables are stored as heaps, with no discernible internal organization, new
rows of data are simply appended to the end of the table or anywhere else in the table
there happens to be room. The strategy of using indexes to find the required rows of data
quickly works well up to a point. But it begins to reveal its limitations as the amounts
of data and the numbers of users approach the levels that we see in modern data warehouses
and in online stores such as Amazon.com.
To understand the problem with heaps, imagine a history book containing facts like
the following: “The Declaration of Independence was adopted by the Second Continental
Congress on July 4, 1776.” Suppose that these facts are not naturally organized into separate
chapters for, say, individual countries but are randomly scattered throughout the book. We
can still use the alphabetical index at the back of the book to discover facts about the United
States relatively easily but, each time we retrieve a fact, we might have to visit a different
page; this would obviously not be an efficient process.
A page of a book is analogous to a data block on a storage disk, and a chapter of related
information in a book is analogous to an Oracle partition. Partitions divide data tables into
separate storage pools; rows of data can be allocated to each storage pool using some criterion.
Suppose we have a very large table of credit- card transactions, and they are randomly
stored throughout the table without regard to date. To process the transactions completed
in the last month, Oracle will have to retrieve more blocks of data from the storage disks
than if transactions were stored with regard to date. The solution is to create a separate
partition for each month. If a query spans partitions, Oracle need only visit the appropriate
partitions; this is called partition pruning.