Indexing in relational databases is a technique used to improve the speed of data retrieval operations. An index is essentially a data structure, often a balanced tree or hash table, that stores a small portion of the data from a database table in a way that allows for quick searching. When you create an index on one or more columns of a table, the database builds this structure using the values from those columns. The index serves as a look-up table, enabling the database to find data without scanning every row in the table, which can be very inefficient, especially when dealing with large datasets.
For example, if you have a table containing millions of records about customers, and you frequently query this table based on the customer’s last name, creating an index on the “last_name” column can significantly speed up these queries. Without the index, the database would have to go through each record to find matches, resulting in slow performance. With the index, the database can quickly locate the positions of the matching records by scanning the index structure instead, reducing the time complexity of the search.
However, while indexes greatly enhance read performance, they come with trade-offs. Specifically, indexing consumes additional disk space and can slow down write operations like inserts, updates, or deletes. This is because the database needs to maintain the index and update it every time the underlying data changes. Therefore, it is essential to carefully consider which columns to index. For example, columns that are searched frequently or are involved in join operations are good candidates for indexing, while columns that are seldom used for these purposes may not benefit as much.