Relational databases store data in structured tables composed of rows and columns. Each table represents a specific entity (e.g., “Users” or “Orders”), with columns defining the data types (e.g., integers, strings) and rows representing individual records. For example, a “Users” table might have columns like user_id, name, and email, with each row containing data for a specific user. Relationships between tables are established using keys: a primary key (like user_id) uniquely identifies a row in a table, while foreign keys (e.g., user_id in an “Orders” table) link to primary keys in related tables. This structure enforces data integrity and avoids duplication.
At a physical level, relational databases use storage engines to manage data on disk. Data is typically stored in fixed-size blocks or pages, which are optimized for efficient read/write operations. For example, a database might store all rows of a table sequentially, with indexes (like B-trees) enabling fast lookups. When a query is executed, the storage engine retrieves relevant pages from disk, loads them into memory, and processes the data. Transactions are managed using write-ahead logs (WAL) to ensure durability—changes are logged before being applied to the actual data files, allowing recovery in case of failures. PostgreSQL, for instance, uses a combination of heap tables and indexes to balance read/write efficiency.
Developers interact with relational databases using SQL (Structured Query Language). SQL allows querying data (e.g., SELECT * FROM Users WHERE user_id = 123), inserting or updating records, and defining schemas. The query optimizer translates SQL statements into execution plans, choosing the most efficient way to access data (e.g., using an index scan vs. a full table scan). For example, an index on user_id would allow the database to quickly locate a specific user’s data without scanning the entire table. While relational databases excel at handling structured data and complex queries, they require careful schema design (normalization) to minimize redundancy and ensure consistency. Tools like EXPLAIN in PostgreSQL help developers analyze query performance and optimize indexes.