The most common database benchmarks are standardized tests used to evaluate performance, scalability, and reliability of database systems. These benchmarks simulate real-world workloads to help developers compare systems objectively. Widely recognized examples include TPC-C for transactional workloads, TPC-H for analytical queries, YCSB for NoSQL/key-value stores, and the Star Schema Benchmark for data warehousing. Each focuses on specific use cases, ensuring databases can handle tasks like high-volume transactions, complex joins, or large-scale data analytics.
TPC-C, developed by the Transaction Processing Performance Council (TPC), emulates an order-processing system with concurrent transactions like order creation and payment processing. It measures throughput (transactions per minute) and response times under load. TPC-H, another TPC benchmark, tests decision-support systems using complex queries over large datasets, emphasizing join performance and query optimization. For NoSQL databases like Cassandra or MongoDB, Yahoo’s YCSB (Yahoo! Cloud Serving Benchmark) is popular. It measures latency and throughput across workloads with varying read/write ratios. The Star Schema Benchmark, designed for star schema data models, evaluates data warehousing systems by simulating multi-table joins and aggregations typical in business intelligence scenarios.
When choosing a benchmark, developers should align it with their workload type. For example, TPC-C suits OLTP systems requiring high concurrency, while TPC-H fits analytics platforms. YCSB’s flexibility makes it ideal for tuning NoSQL systems, and HammerDB (an open-source tool implementing TPC-C) offers accessible testing for relational databases. Benchmarks also highlight trade-offs: a database optimized for TPC-H might struggle with TPC-C’s write-heavy load. Real-world testing often combines benchmarks with custom workloads to validate performance under specific conditions, ensuring the database meets both general and application-specific demands.