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A database index is a data structure that improves the speed of operations on a database table. Indexes can be created using one or more columns of a database table, providing the basis for both rapid random look ups and efficient access of ordered records. The disk space required to store the index is typically less than that required by the table (since indexes usually contain only the key-fields according to which the table is to be arranged, and excludes all the other details in the table), yielding the possibility to store indexes in memory for a table whose data is too large to store in memory.
In a relational database, an index is a copy of one part of a table. Some databases extend the power of indexing by allowing indexes to be created on functions or expressions. For example, an index could be created on
upper(last_name), which would only store the upper case versions of the last_name field in the index. Another option sometimes supported is the use of "filtered" indexes, where index entries are created only for those records that satisfy some conditional expression. A further aspect of flexibility is to permit indexing on user-defined functions, as well as expressions formed from an assortment of built-in functions.
Indexes may be defined as unique or non-unique. A unique index acts as a constraint on the table by preventing duplicate entries in the index and thus, the backing table.
Index architectures can be classified as clustered or unclustered.
An Unclustered index is structured in a way that doesn't correspond to the order of the actual data records. It resembles the words index at the back of a book. For this reason, it will perform worse than clustered indexes on ranged searches where the result set is large, since each result could cost an additional I/O-operation to get the actual data record. One can have any number of these kinds of indexes—all that is required is the space to store the index itself; one does not copy the actual data to create a new index.
Clustering alters the data block into a certain distinct order to match the index, hence it is also an operation on the data storage blocks as well as on the index. An address book ordered by first name resembles a clustered index in its structure and purpose. The exact operation of database systems vary, but because storing data is very redundant the row data can only be stored in one order. Therefore, only one clustered index can be created on a given database table. Clustered indexes can greatly increase overall speed of retrieval, but usually only where the data is accessed sequentially in the same or reverse order of the clustered index, or when a range of items are selected.
Since the physical records are in this sort order on disk, the next row item in the sequence is immediately before or after the last one, and so fewer data block reads are required. The primary feature of a clustered index is therefore the ordering of the physical data rows in accordance with the index blocks that point to them. Some databases separate the data and index blocks into separate files, others put two completely different data blocks within the same physical file(s).
Indexes can be implemented using a variety of data structures. Popular indexes include balanced trees, B+ trees and hashes.
In Microsoft SQL Server, the leaf node of the clustered index corresponds to the actual data, not simply a pointer to data that resides elsewhere, as is the case with a non-clustered index. Each relation can have a single clustered index and many unclustered indexes.
The order in which columns are listed in the index definition is important. It is possible to retrieve a set of row identifiers using only the first indexed column. However, it is not possible or efficient (on most databases) to retrieve the set of row identifiers using only the second or greater indexed column.
For example, imagine a phone book that is organized by city first, then by last name, and then by first name. If you are given the city, you can easily extract the list of all phone numbers for that city. However, in this phone book it would be very tedious to find all the phone numbers for a given last name. You would have to look within each city's section for the entries with that last name. Some databases can do this, others just won’t use the index.
Applications and limitations
Indexes are useful for many applications but come with some limitations. Consider the following SQL statement:
SELECT first_name FROM people WHERE last_name = 'Smith';. To process this statement without an index the database software must look at the last_name column on every row in the table (this is known as a full table scan). With an index the database simply follows the b-tree data structure until the Smith entry has been found; this is much less computationally expensive than a full table scan.
Consider this SQL statement:
SELECT email_address FROM customers WHERE email_address LIKE '%@yahoo.com';. This query would yield an email address for every customer whose email address ends with "@yahoo.com", but even if the email_address column has been indexed the database still must perform a full table scan. This is because the index is built with the assumption that words go from left to right. With a wildcard at the beginning of the search-term, the database software is unable to use the underlying b-tree data structure. This problem can be solved through the addition of another index created on
reverse(email_address) and a SQL query like this:
SELECT email_address FROM customers WHERE reverse(email_address) LIKE reverse('%@yahoo.com');. This puts the wild-card at the right-most part of the query (now moc.oohay@%) which the index on reverse(email_address) can satisfy.
A bitmap index is a special kind of index that stores the bulk of its data as bit arrays (bitmaps) and answers most queries by performing bitwise logical operations on these bitmaps. The most commonly used index, such as B+trees, are most efficient if the values it indexes do not repeat or repeat a smaller number of times. In contrast, the bitmap index is designed for cases where the values of a variable repeat very frequently. For example, the gender field in a customer database usually contains two distinct values: male or female. For such variables, the bitmap index can have a significant performance advantage over the commonly used trees.
A dense index in databases is a file with pairs of keys and pointers for every record in the data file. Every key in this file is associated with a particular pointer to a record in the sorted data file. In clustered indices with duplicate keys, the dense index points to the first record with that key.
A sparse index in databases is a file with pairs of keys and pointers for every block in the data file. Every key in this file is associated with a particular pointer to the block in the sorted data file. In clustered indexes with duplicate keys, the sparse index points to the lowest search key in each block.
In most cases, an index is used to quickly locate the data record(s) from which the required data is read. In other words, the index is only used to locate data records in the table and not to return data.
A covering index is a special case where the index itself contains the required data field(s) and can return the data.
Consider the following table (other fields omitted):
To find the Name for ID 13, an index on (ID) will be useful, but the record must still be read to get the Name. However, an index on (ID, Name) contains the required data field and eliminates the need to look up the record.
A covering index can dramatically speed up data retrieval but may itself be large due to the additional keys, which slows down data insertion & update. To reduce such index size, some systems allow non-key fields to be included in the index. Non-key fields are not themselves part of the index ordering but only included at the leaf level, allowing for a covering index with less overall index size.
- ↑ Gavin Powell (2005-12). "Chapter 12: Building Fast-Performing Data Models". Beginning Database Design ISBN 978-0-7645-7490-0. Wrox Publishing. http://searchsecurity.techtarget.com/generic/0,295582,sid87_gci1184450,00.html.
- ↑ "Clustered Index Structures". SQL Server 2005 Books Online (September 2007). http://msdn2.microsoft.com/en-us/library/ms177443.aspx.
- ↑ Daren Bieniek, Randy Dess, Mike Hotek, Javier Loria, Adam Machanic, Antonio Soto, Adolfo Wiernik (2006-01). "Chapter 4: Creating Indices". SQL Server 2005 Implementation and Management. Microsoft Press. http://www.microsoft.com/mspress/books/9364.aspx.
- ↑ Database Systems: The Complete Book. Hector Garcia-Molina, Jeffrey D. Ullman, Jennifer D. Widom
- An Introduction to Database Indexes
- B-Tree Versus Bitmap Indexes
- How B-Tree Database Indexes Work
- Structure of B-Tree Indexes