How do relational databases manage indexes?

Relational databases manage indexes by using specialized data structures to quickly locate and access data without scanning entire tables. Indexes work like a book’s index, mapping column values to their physical storage locations. The most common structure is the B-tree (balanced tree), which organizes data hierarchically for efficient lookups, inserts, and deletions. For example, if a users table has an index on the email column, the database stores sorted email addresses in a B-tree, allowing it to find a specific user’s row in logarithmic time. Other index types, like hash indexes (for exact matches) or bitmap indexes (for low-cardinality columns), are used in specific scenarios, but B-trees are the default for most general-purpose queries.

When a query is executed, the database’s query optimizer determines whether to use an index based on factors like filter conditions, table size, and data distribution. For instance, a SELECT * FROM orders WHERE customer_id = 123 query would leverage an index on customer_id to skip scanning the entire orders table. The optimizer estimates the cost of using an index versus a full table scan and chooses the faster path. Composite indexes (indexes on multiple columns) further refine this process. For example, an index on (department, salary) can efficiently filter rows by department first and then sort or filter by salary, avoiding the need for separate lookups. However, the order of columns in composite indexes matters—queries filtering only on salary won’t benefit from this index.

Indexes require ongoing maintenance to stay efficient. When rows are inserted, updated, or deleted, the database must update associated indexes, which adds overhead. For example, inserting a new row into a table with five indexes requires five additional write operations. Over time, frequent updates can fragment indexes, degrading performance. Many databases automatically rebuild or reorganize indexes during maintenance windows. Developers must balance the number of indexes: too few slow down reads, while too many degrade write speeds. Tools like execution plans (e.g., EXPLAIN in PostgreSQL) help identify missing or unused indexes. For example, a query scanning a large table without using an index might signal the need to create one, while unused indexes can be safely dropped to reduce write costs. Proper index management ensures efficient data access without compromising overall system performance.