How does indexing work in relational databases?

Indexing in relational databases is a mechanism that speeds up data retrieval by creating a structured pathway to locate records efficiently. When you create an index on a database column, the database builds a separate data structure—typically a B-tree (balanced tree)—that stores sorted values of the indexed column along with pointers to the corresponding rows in the table. This structure allows the database to bypass scanning the entire table when querying data. For example, if you have an index on a users.email column, searching for a specific email address involves traversing the B-tree to find the matching entry, which directly points to the row in the table. This reduces the search time from linear (O(n)) to logarithmic (O(log n)) complexity.

Indexes come in different types, each suited for specific scenarios. The most common are clustered and non-clustered indexes. A clustered index determines the physical order of data in the table, meaning the rows are stored on disk in the same order as the index. A table can have only one clustered index, often used for the primary key. Non-clustered indexes, on the other hand, are separate structures that store sorted values and pointers to the actual data rows. For instance, if you create a non-clustered index on an orders.customer_id column, queries filtering by customer_id can quickly locate relevant orders without scanning the entire table. Composite indexes, which include multiple columns, are useful for queries that filter or sort on several fields, like searching for orders by both customer_id and order_date.

While indexes improve read performance, they introduce trade-offs. Every time data is inserted, updated, or deleted, the database must update the associated indexes, which can slow down write operations. For example, adding a new row to a table with five indexes requires updating all five index structures. Over-indexing can lead to increased storage usage and maintenance overhead. Developers must balance the need for query speed with these costs by analyzing query patterns. A good practice is to index columns frequently used in WHERE, JOIN, or ORDER BY clauses. Tools like execution plans can help identify missing or underused indexes. Properly managed indexes significantly enhance performance, but they require careful planning and monitoring to avoid unintended bottlenecks.