A database index is a data structure that improves the speed of data retrieval operations on a database table at the cost of slower writes and increased storage space. Indexes can be created using one or more columns of a database table, providing the basis for both rapid random lookups and efficient access of ordered records. The disk space required to store the index is typically less than that required by the table (since indices usually contain only the key-fields according to which the table is to be arranged, and exclude all the other details in the table), yielding the possibility to store indices 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 indices 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” indices , 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.
Indices 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.
Clustering alters the data block into a certain distinct order to match the index, resulting in the row data being stored in order. Therefore, only one clustered index can be created on a given database table. Clustered indices 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 is 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). Create an object where the physical order of rows is same as the index order of the rows and the bottom(leaf) level of clustered index contains the actual data rows.
The data is present in random order, but the logical ordering is specified by the index. The data rows may be randomly spread throughout the table. The non-clustered index tree contains the index keys in sorted order, with the leaf level of the index containing the pointer to the page and the row number in the data page. In non-clustered index:
- The physical order of the rows is not the same as the index order.
- Typically created on column used in JOIN, WHERE, and ORDER BY clauses.
- Good for tables whose values may be modified frequently.
With a clustered index the rows are stored physically on the disk in the same order as the index. There can therefore be only one clustered index.
With a non clustered index there is a second list that has pointers to the physical rows. You can have many non clustered indexes, although each new index will increase the time it takes to write new records.
It is generally faster to read from a clustered index if you want to get back all the columns. You do not have to go first to the index and then to the table.
Writing to a table with a clustered index can be slower, if there is a need to rearrange the data.
Clustered indexes physically order the data on the disk. This means no extra data is needed for the index, but there can be only one clustered index (obviously). Accessing data using a clustered index is fastest.
All other indexes must be non-clustered. A non-clustered index has a duplicate of the data from the indexed columns kept ordered together with pointers to the actual data rows (pointers to the clustered index if there is one). This means that accessing data through a non-clustered index has to go through an extra layer of indirection. However if you select only the data that’s available in the indexed columns you can get the data back directly from the duplicated index data (that’s why it’s a good idea to SELECT only the columns that you need and not use *)