OLTP (Online Transaction Processing) and OLAP (Online Analytical Processing) benchmarks differ in their goals, workload types, and performance metrics. OLTP benchmarks focus on measuring a system’s ability to handle short, frequent transactions typical of operational systems, like processing orders or updating inventory. OLAP benchmarks, on the other hand, test how well a system can execute complex analytical queries over large datasets, such as aggregating sales trends or analyzing customer behavior. These differences stem from the distinct requirements of transactional versus analytical workloads.
OLTP benchmarks prioritize low-latency read/write operations, high concurrency, and data integrity. For example, the TPC-C benchmark simulates a retail order-processing system, measuring transactions per minute (tpmC) and response times for operations like adding orders or checking stock. The workload involves many small, atomic transactions with strict ACID (Atomicity, Consistency, Isolation, Durability) guarantees. In contrast, OLAP benchmarks like TPC-H or SSB (Star Schema Benchmark) emphasize query throughput and scalability. TPC-H uses complex multi-table joins and aggregations on large datasets (e.g., calculating total revenue across regions), measuring execution time and throughput in queries per hour (QphH). OLAP systems often use columnar storage and denormalized schemas to optimize for bulk data scans, which is irrelevant in OLTP.
The metrics and infrastructure requirements also differ. OLTP benchmarks stress transactional consistency, lock contention handling, and disk I/O for random access patterns. Systems are tuned for index-heavy designs and row-based storage. OLAP benchmarks focus on parallel query execution, memory bandwidth, and efficient handling of sequential scans. For example, OLAP systems might prioritize cache efficiency for large analytical queries, while OLTP systems optimize for index lookups and write-ahead logging. Real-world examples include credit card processing (OLTP) versus quarterly financial reporting (OLAP). These distinctions ensure benchmarks reflect the specific trade-offs developers face when designing systems for either transactional speed or analytical depth.