Register

# 17.1. Indexing Chapter Introduction¶

Many large-scale computing applications are centered around data sets that are too large to fit into main memory. The classic example is a large database of records with multiple search keys, requiring the ability to insert, delete, and search for records. Hashing provides outstanding performance for such situations, but only in the limited case in which all searches are of the form “find the record with key value $K$”. Many applications require more general search capabilities. One example is a range query search for all records whose key lies within some range. Other queries might involve visiting all records in order of their key value, or finding the record with the greatest key value. Hash tables are not organized to support any of these queries efficiently.

This chapter introduces file structures used to organize a large collection of records stored on disk. Such file structures support efficient insertion, deletion, and search operations, for exact-match queries, range queries, and largest/smallest key value searches.

Before discussing such file structures, we must become familiar with some basic file-processing terminology. An entry-sequenced file stores records in the order that they were added to the file. Entry-sequenced files are the disk-based equivalent to an unsorted list and so do not support efficient search. The natural solution is to sort the records by order of the search key. However, a typical database, such as a collection of employee or customer records maintained by a business, might contain multiple search keys. To answer a question about a particular customer might require a search on the name of the customer. Businesses often wish to sort and output the records by zip code order for a bulk mailing. Government paperwork might require the ability to search by Social Security number. Thus, there might not be a single “correct” order in which to store the records.

Indexing is the process of associating a key with the location of a corresponding data record. An external sort typically uses the concept of a key sort, in which an index file is created whose records consist of key/pointer pairs. Here, each key is associated with a pointer to a complete record in the main database file. The index file could be sorted or organized using a tree structure, thereby imposing a logical order on the records without physically rearranging them. One database might have several associated index files, each supporting efficient access through a different key field.

Each record of a database normally has a unique identifier, called the primary key. For example, the primary key for a set of personnel records might be the Social Security number or ID number for the individual. Unfortunately, the ID number is generally an inconvenient value on which to perform a search because the searcher is unlikely to know it. Instead, the searcher might know the desired employee’s name. Alternatively, the searcher might be interested in finding all employees whose salary is in a certain range. If these are typical search requests to the database, then the name and salary fields deserve separate indices. However, key values in the name and salary indices are not likely to be unique.

A key field such as salary, where a particular key value might be duplicated in multiple records, is called a secondary key. Most searches are performed using a secondary key. The secondary key index (or more simply, secondary index) will associate a secondary key value with the primary key of each record having that secondary key value. At this point, the full database might be searched directly for the record with that primary key, or there might be a primary key index (or primary index) that relates each primary key value with a pointer to the actual record on disk. In the latter case, only the primary index provides the location of the actual record on disk, while the secondary indices refer to the primary index.

Indexing is an important technique for organizing large databases, and many indexing methods have been developed. Direct access through hashing is discussed in Chapter Hashing. A simple list sorted by key value can also serve as an index to the record file. Indexing disk files by sorted lists are discussed in the following section. Unfortunately, a sorted list does not perform well for insert and delete operations.

A third approach to indexing is the tree index. Trees are typically used to organize large databases that must support record insertion, deletion, and key range searches. ISAM was a a tentative step toward solving the problem of storing a large database that must support insertion and deletion of records. Its shortcomings help to illustrate the value of tree indexing techniques. Module TreeIndexing introduces the basic issues related to tree indexing. Module 2-3 tree introduces the 2-3 tree, a balanced tree structure that is a simple form of the B-tree. B-trees are the most widely used indexing method for large disk-based databases, and for implementing file systems.