Relational databases handle large-scale transactions through various techniques that ensure data integrity, concurrency, and performance. One of the fundamental concepts is the use of ACID properties: Atomicity, Consistency, Isolation, and Durability. Atomicity ensures that all parts of a transaction are completed successfully or none at all, which prevents partial updates. For instance, when transferring money between accounts, both withdrawal and deposit must happen together. If either fails, the transaction will rollback, ensuring account states are consistent.
To manage concurrent transactions, relational databases implement isolation levels, allowing multiple transactions to occur simultaneously without interfering with each other. There are several isolation levels, such as Read Committed, Repeatable Read, and Serializable. These levels determine how transaction visibility is handled, impacting performance and consistency. For example, in an e-commerce application, when two users attempt to buy the last item in stock, a proper isolation level can prevent overselling by ensuring that one transaction either completes fully before the other starts or is blocked until it can safely proceed.
Finally, relational databases often utilize indexing and partitioning methods to optimize performance with large-scale data. Indexes allow quick data retrieval, drastically reducing the time taken for read operations in massive datasets. Partitioning involves dividing tables into smaller, more manageable pieces, improving query performance and making maintenance tasks easier. As an illustration, a large retail database might partition sales records by region or time, thus speeding up queries that target specific subsets of data. By combining these techniques, relational databases can efficiently support large-scale transactions while maintaining reliability and performance.