How does database storage type impact benchmarks?

Database storage type directly impacts benchmark results by influencing how data is stored, accessed, and processed. Row-based storage (e.g., MySQL, PostgreSQL) stores entire rows together, making it efficient for transactional workloads that read or write entire records. Columnar storage (e.g., Cassandra, Redshift) groups data by columns, optimizing analytical queries that aggregate specific fields. In-memory databases (e.g., Redis, MemSQL) store data in RAM, reducing disk I/O latency for high-speed operations. Benchmarks measuring read/write throughput, query latency, or concurrency will vary significantly based on these storage models. For example, a transactional benchmark like TPC-C favors row-based systems, while analytical benchmarks like TPC-H perform better with columnar storage.

Specific use cases highlight these differences. Consider a benchmark testing bulk data inserts: a row-based database might struggle with high write volumes due to row-level locking, whereas a columnar system could handle batched writes more efficiently by compressing columns. Similarly, an in-memory database will outperform disk-based systems in low-latency read benchmarks but may falter in durability tests if power loss occurs. Storage engines within the same database (e.g., InnoDB vs. MyISAM in MySQL) also show variance. For instance, InnoDB’s ACID compliance and row-level locking yield different concurrency results compared to MyISAM’s table-level locking in multi-threaded benchmarks. These examples illustrate why benchmarks must align with the storage type’s strengths.

Trade-offs and configuration choices further complicate benchmark comparisons. Columnar storage’s compression reduces disk usage but increases CPU load during queries. In-memory systems sacrifice persistence for speed unless combined with snapshotting or replication. Hardware dependencies also matter: SSDs mitigate some drawbacks of disk-based systems, narrowing performance gaps. Benchmarks should account for real-world scenarios, such as mixed read/write workloads or index-heavy operations, where storage type optimizations (e.g., indexing strategies) play a critical role. Ultimately, no single storage type dominates all benchmarks—developers must prioritize use-case requirements (speed, scalability, consistency) when interpreting results.