How do relational databases handle large-scale transactions?

Relational databases handle large-scale transactions through ACID properties (Atomicity, Consistency, Isolation, Durability), concurrency control mechanisms, and scalability optimizations. These systems ensure reliable processing even under high loads by balancing strict data integrity with performance. Let’s break this down into three key areas.

First, ACID properties form the foundation. Atomicity guarantees that transactions either fully complete or roll back entirely, using transaction logs to track changes. For example, if a banking transfer fails midway, the database reverts both accounts to their original states. Consistency enforces rules like foreign keys and data types, rejecting invalid entries. Isolation prevents conflicts between concurrent transactions—like ensuring two users updating the same inventory count don’t overwrite each other. Durability ensures committed data survives crashes, often through write-ahead logging (WAL), where changes are written to disk before being marked complete. PostgreSQL’s WAL is a classic example of this approach.

Second, concurrency control manages simultaneous operations. Relational databases use Multi-Version Concurrency Control (MVCC) or locking. MVCC, used by PostgreSQL and Oracle, allows read operations to access snapshots of data without blocking writes, improving throughput. For instance, a reporting query can run on a consistent snapshot while updates occur elsewhere. Isolation levels (e.g., Read Committed, Serializable) let developers trade off strictness for performance. Lower isolation reduces locking overhead but increases risks like phantom reads. SQL Server’s “snapshot isolation” is a practical middle ground, allowing high concurrency with minimal conflicts.

Third, scalability techniques address volume. Partitioning splits large tables (e.g., by date ranges for time-series data) to speed up queries and manage storage. Read replicas offload query traffic from the primary database, useful for reporting workloads. Batch processing (e.g., bulk inserts in MySQL with LOAD DATA INFILE) minimizes transaction overhead. While sharding distributes data across servers, it complicates cross-shard transactions, often requiring application-level coordination. Tools like connection pooling (e.g., pgBouncer for PostgreSQL) also help manage thousands of simultaneous connections efficiently. These strategies let relational databases scale while retaining their transactional strengths.