Indexing Structures for Files
In this chapter we assume that a file already exists with some primary organization such as the unordered, ordered, or hashed organizations that were described in Chapter 17. We will describe additional auxiliary access structures called indexes, which are used to speed up the retrieval of records in response to certain search conditions. The index structures are additional files on disk that provide secondary access paths, which provide alternative ways to access the records without affecting the physical place-ment of records in the primary data file on disk. They enable efficient access to records based on the indexing fields that are used to construct the index. Basically, any field of the file can be used to create an index, and multiple indexes on different fields—as well as indexes on multiple fields—can be constructed on the same file. A variety of indexes are possible; each of them uses a particular data structure to speed up the search. To find a record or records in the data file based on a search condition on an indexing field, the index is searched, which leads to pointers to one or more disk blocks in the data file where the required records are located. The most prevalent types of indexes are based on ordered files (single-level indexes) and tree data structures (multilevel indexes, B+-trees). Indexes can also be constructed based on hashing or other search data structures. We also discuss indexes that are vectors of bits called bitmap indexes.
We describe different types of single-level ordered indexes—primary, secondary, and clustering—in Section 18.1. By viewing a single-level index as an ordered file, one can develop additional indexes for it, giving rise to the concept of multilevel indexes. A popular indexing scheme called ISAM (Indexed Sequential Access Method) is based on this idea. We discuss multilevel tree-structured indexes in Section 18.2. In Section 18.3 we describe B-trees and B+-trees, which are data structures that are commonly used in DBMSs to implement dynamically changing mul-tilevel indexes. B+-trees have become a commonly accepted default structure for generating indexes on demand in most relational DBMSs. Section 18.4 is devoted to alternative ways to access data based on a combination of multiple keys. In Section 18.5 we discuss hash indexes and introduce the concept of logical indexes, which give an additional level of indirection from physical indexes, allowing for the physical index to be flexible and extensible in its organization. In Section 18.6 we discuss multikey indexing and bitmap indexes used for searching on one or more keys. Section 18.7 summarizes the chapter.
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